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Neema Moraveji's Ph.D. Dissertation at Stanford University, spring 2012.
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AUGMENTED SELF-REGULATION
A DISSERTATION
SUBMITTED TO THE SCHOOL OF EDUCATION
AND THE COMMITTEE ON GRADUATE STUDIES
OF STANFORD UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
Neema Moraveji
June 2012
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ABSTRACT
Research in interactive technology most often enables humans to enact more,
better, or new behaviors or to gain greater or more efficient insight into our environment.
This dissertation develops the notion that technology can also help us develop greater
self-awareness. Self-awareness, made tangible and observable by physiological self-
regulatory processes, rests at the core of the human experience by supporting
comprehension, actions, and intentions in the world around us. Without it, our health and
behavior are victims of demands from our sociocultural environments or of flippant or
self-defeating patterns of thoughts.
This dissertation explores and evaluates methods of using wearable sensors and
interactive feedback to augment human self-regulation, specifically respiratory self-
regulation, primarily during information work but also in mobile contexts. This is a
potentially powerful means of not only influencing health and behavior but also
developing an inner sense of wellness independent of one’s physiological, cognitive, or
affective state.
The over-arching contribution of this dissertation is to demonstrate the view that
computers will have a more direct and pervasive impact on human psychophysiology
than is currently practiced. Rather than strictly adapting to human physiological and
affective state, future machines will explicitly induce changes in psychophysiological
state to amplify their user’s innate self-regulatory ability and skill. In this way, machines
of the future will help humans be ‘more human’ rather than simply adding or enhancing
existing human abilities.
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ACKNOWLEDGEMENTS
Maryam: for being the soul I choose to merge with. Mommy & Nick: for the
courage to open your heart when it’s easiest to close. Babai & Mamani: for creating our
collective soul and blueprint. Newsha, Jim, & Azilu: for standing up for me. Daddy &
Sorour: for teaching me the power of discipline. Roya, Hameed, Vala, & Ala: for
fearlessness. Jason: for big brotherhood. Fared: for little brotherhood. Sheida, Roxanne,
& Bijan: for leading the way West. Malek, Jaleh, Nanaz, Tannaz, Chris, Nikki, & Darius:
for going first and making fun a priority.
My academic and professional mentors: Roy: for blowing me away, with love.
BJ: for breaking all the rules. Jeff: for rigor. Jelena: for pushing me (and your baby!).
Sep: for setting a high bar, with compassion. Dan R.: for cherishing reflection. Merrie &
Dan: for perpetual playful inventiveness. Mary: for making happiness come first. Paul &
Hamet: for trusting.
My dear Stanford collaborators, who led the way in many instances: Kenneth
Jung, Charlton Soesanto, Abhishek Sharma, Jimmy Chion, Ben Olson, Mahmoud Saadat,
Mohammad Hekmat, Maryam Rasaee, Takehiro Hagiwara, Poorna Kirshnamoorthy, Truc
Nguyen, Jim Zheng, Huyen Tran, Yaser Khalighi, Jonathan Palley, Stephanie Habif,
Emily Goligoski, and Karen Everett.
Thank you to all those who worked to bring breath awareness to light.
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TABLE OF CONTENTS
ABSTRACT IV!
ACKNOWLEDGEMENTS .................................................................................. V!
TABLE OF CONTENTS .................................................................................... VI!
LIST OF TABLES ........................................................................................... XIII!
LIST OF FIGURES ......................................................................................... XIV!
CHAPTER 1!INTRODUCTION ........................................................................... 1!
1.1! The problem ................................................................................................. 2!
1.2! Proposed solution ......................................................................................... 3!
1.2.1! Thesis statement ......................................................................................... 3!
1.3! Research challenges ...................................................................................... 3!
1.4! Summary of findings .................................................................................... 5!
CHAPTER 2!RELATED WORK .......................................................................... 9!
2.1! Stress and self-regulation ............................................................................. 9!
2.1.1! Effects of stress on learning, performance, and behavior ....................... 12!
2.1.2! Self-regulation: therapeutic and developmental ...................................... 15!
2.2! Augmenting self-regulation with breath modification ............................... 19!
2.2.1! Regulating respiration to mitigate the stress response ............................ 24!
2.3! Technology-mediated respiratory self-regulation ...................................... 29!
2.3.1! Social influence on physiological behavior .............................................. 33!
2.4! Summary and implications for current research ........................................ 35!
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DESIGN AND VALIDATION OF A NON-CLINICAL AMBULATORY RESPIRATION SENSOR .............................................................................. 37!
2.4.1! Sensor design ........................................................................................... 39!
2.4.2! Industrial design evolution ....................................................................... 41!
2.4.2.1! Iteration 1: Wired USB ......................................................................... 41!
2.4.2.2! Iteration 2: Wireless USB ..................................................................... 42!
2.4.2.3! Iteration 3: Wireless Bluetooth ............................................................ 42!
2.5! Sensor validation ........................................................................................ 44!
2.6! Conclusion .................................................................................................. 48!
CHAPTER 3!PERIPHERAL PACED RESPIRATION: INFLUENCING RESPIRATORY PATTERNS DURING INFORMATION WORK ............... 49!
3.1! A peripheral paced respiration interface .................................................... 50!
3.1.1! Wizard-of-Oz prototype ........................................................................... 50!
3.1.2! User interface ............................................................................................ 52!
3.1.3! Pacing respiration peripherally ................................................................ 53!
3.2! Study ........................................................................................................... 55!
3.2.1! Procedure ................................................................................................. 55!
3.3! Results ......................................................................................................... 56!
3.4! Discussion ................................................................................................... 59!
3.5! Conclusion .................................................................................................. 60!
CHAPTER 4!BREATHCAST: A STUDY OF SOCIAL INFLUENCE ON BREATH MODIFICATION .......................................................................... 62!
4.1! Study ........................................................................................................... 64!
4.1.1! Participants ............................................................................................... 64!
4.1.2! Procedure ................................................................................................. 65!
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4.2! Results and discussion ................................................................................ 66!
4.3! Conclusion .................................................................................................. 70!
CHAPTER 5!BREATHAWARE: CONTINUOUS INFLUENCE OF SELF-REGULATION DURING INFORMATION WORK ..................................... 71!
5.1! Design principles ........................................................................................ 72!
5.1.1! Accommodate different levels of attention .............................................. 73!
5.1.2! Sustain motivation .................................................................................... 74!
5.1.3! Demonstrate desired patterns of breath .................................................. 75!
5.1.4! Personalize feedback ................................................................................ 76!
5.1.5! Reinforce the relationship between breath and body .............................. 77!
5.1.6! Avoid exasperating stress with negative feedback ................................... 77!
5.1.7! Develop awareness at different timescales ............................................... 78!
5.1.8! Encourage internal self-assessments ........................................................ 79!
5.1.9! Consider secondary components of respiration ...................................... 80!
5.1.10!Protect the privacy of breath .................................................................... 80!
5.2! Prototype design ......................................................................................... 81!
5.2.1! Client ........................................................................................................ 81!
5.2.2! Social network .......................................................................................... 82!
5.3! Interaction design ....................................................................................... 82!
5.3.1! Breath rate (immediate) – DP3, DP7 ........................................................ 83!
5.3.2! Breath rate (daily) – DP5, DP7 .................................................................. 84!
5.3.3! Breath rate (immediate but relative) – DP3, DP7 ..................................... 85!
5.3.4! Breath rate (longitudinal) – DP7 ............................................................... 86!
5.3.5! Calm points – DP2 .................................................................................... 86!
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5.3.6! Daily milestones – DP2 ............................................................................. 87!
5.3.7! Encouraging messages – DP2 ................................................................... 88!
5.3.8! Check-in – DP8 ......................................................................................... 89!
5.3.9! Cautionary feedback – DP6 ...................................................................... 90!
5.3.10!Activity stream – DP2, DP10 .................................................................... 90!
5.3.11!Buddy list – DP10 ..................................................................................... 91!
5.3.12!Re-record and modify baseline – DP3, DP4 ............................................ 92!
5.4! Test deployment ......................................................................................... 92!
5.4.1! Results and Feedback ............................................................................... 93!
5.5! Discussion and implications for research ................................................... 95!
5.6! Conclusion .................................................................................................. 97!
CHAPTER 6!BREATHTRAY: CONTINUOUS RESPIRATORY FEEDBACK AND ITS EFFECTS ON COGNITIVE PERFORMANCE ............................. 99!
6.1! BreathTray design ..................................................................................... 100!
6.2! Study design ............................................................................................. 101!
6.2.1! Participants ............................................................................................. 101!
6.2.2! Serial sevens ........................................................................................... 102!
6.2.3! Problem-solving with auditory distractors ............................................. 103!
6.2.4! Procedure ............................................................................................... 104!
6.3! Results ....................................................................................................... 106!
6.3.1! BreathTray impact on breath regulation ................................................ 107!
6.3.2! Magnify or persist? .................................................................................. 108!
6.3.3! Impact on cognitive performance .......................................................... 109!
6.3.4! Qualitative feedback ............................................................................... 109!
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6.4! Discussion ................................................................................................. 110!
6.4.1! Study limitations .................................................................................... 111!
6.5! Conclusion ................................................................................................ 112!
CHAPTER 7!BREATHWEAR: AMBULATORY INFLUENCE ON RESPIRATORY PATTERNS ...................................................................... 113!
7.1! Design opportunities and challenges ....................................................... 116!
7.1.1! Continuous state influence ..................................................................... 116!
7.1.2! Context-awareness .................................................................................. 117!
7.1.3! Sleep ....................................................................................................... 117!
7.1.4! Social ...................................................................................................... 117!
7.1.5! Annoyance .............................................................................................. 118!
7.1.6! Evolving user goals ................................................................................. 118!
7.1.7! Over-dependence ................................................................................... 118!
7.1.8! Inaccuracy ............................................................................................... 119!
7.2! Interaction design goals ............................................................................ 119!
7.2.1! Monitor ................................................................................................... 119!
7.2.2! Influence ................................................................................................. 120!
7.2.3! Customize ............................................................................................... 120!
7.3! Design iteration 1 ...................................................................................... 120!
7.5! Design iteration 2 ...................................................................................... 123!
7.5.1! Recent activity indicator ......................................................................... 124!
7.5.2! Relative breath rate ................................................................................. 125!
7.5.3! Breathbelt feedback ................................................................................ 125!
7.5.4! Additional settings .................................................................................. 125!
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7.6! study .......................................................................................................... 126!
7.6.1! Results .................................................................................................... 127!
7.6.2! User feedback ......................................................................................... 135!
7.7! Discussion ................................................................................................. 137!
7.8! Conclusion ................................................................................................ 139!
CHAPTER 8!CONCLUSIONS AND FUTURE WORK .................................... 140!
8.1! Summary of findings ................................................................................ 140!
8.2! Limitations ................................................................................................ 142!
8.2.1! Breath rate alone is not illustrative of autonomic activity ...................... 142!
8.2.2! The evaluator effect ................................................................................ 143!
8.2.3! Competition confound ........................................................................... 143!
8.2.4! Single data source .................................................................................. 144!
8.3! Supplementary contributions and implications ....................................... 144!
8.3.1! Autonomic interaction design ................................................................ 144!
8.3.2! ASR and incentivizing self-regulation ................................................... 145!
8.3.3! Techniques for incentivizing self-regulation ......................................... 146!
8.3.4! ASR, being, and doing ........................................................................... 147!
8.3.5! ASR and the purposeful evolution of human society ............................ 148!
8.4! near-term future work ............................................................................... 150!
8.5! Concluding remarks .................................................................................. 150!
BIBLIOGRAPHY .............................................................................................. 152!
APPENDIX A! PPR STUDY POST-SURVEY ................................................ 172!
APPENDIX B! BREATHCAST STUDY POST-SURVEY .............................. 175!
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APPENDIX C! BREATHTRAY STUDY MATERIALS .................................. 177!
C.1! Pre-survey ................................................................................................. 177!
C.2! Video motivating breath awareness and regulation ................................. 177!
C.3! Textual Explanation of BreathTray ........................................................... 179!
C.4! Text explanation of Serial Sevens task ..................................................... 179!
C.5! Text explanation of Problem-Solving with Audio Distractors task ......... 180!
C.6! Post-survey ................................................................................................ 180!
APPENDIX D! BREATHWEAR INSTRUCTIONS AND FEEDBACK FORM 182!
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LIST OF TABLES
Table 1.1: An overview of the studies and contributions in this dissertation. .... 6!
Table 2.1: Lay descriptions of the key concepts motivating breath regulation as a means of regulating psychophysiological state. Not meant to be comprehensive. .............................................................................. 28!
Table 3.1: Known methods of sensing respiration. ............................................ 38!
Table 6.1: 10 design principles for interactive systems aiming to influence respiratory self-regulation. ................................................................. 73!
Table 7.1: Mean (and standard deviation) breath rates across both BreathTray and NoBreathTray conditions in each task and across both tasks together. .......................................................................... 106!
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LIST OF FIGURES
Figure 2.1: An abbreviated diagram of the complementary functions of the sympathetic and parasympathetic components of the nervous system (PrecisionNutrition.com, 2012). The stress response is characterized by sympathetic activation and the relaxation response by parasympathetic. ............................................................ 12!
Figure 2.2:A diagram showing McEwen’s theory of allostatic load as emerging from interactions between environmental stressors, individual differences, behavioral responses, and physiological responses (McEwen, Gianaros, 2011). ................................................ 14!
Figure 3.1: The initial breathbelt sensor. (Top) The adjustable sensor band with the Arduino Uno board. (Bottom) Close-up of the stretch sensor held in place by two clips. ....................................................... 40!
Figure 3.2: The respiration sensor’s raw signal (red) is filtered (blue) and then peaks (black) are detected using well-studied signal processing techniques. The Y-axis refers to raw sensor values (not normalized). ........................................................................................ 41!
Figure 3.3: Wireless USB version of the original breathbelt, using paired XBee wireless communication widgets and a Lilypad Arduino (the circular PCB). The black plastic case on the top-most image holds 2 AAA batteries. ................................................................................. 42!
Figure 3.4: The strain gauge (black) has two hooks (top), which was modified to include buttons to snap in and out of the sensor (bottom). This was necessary because the strain gauge occasionally breaks and must be replaced. ............................................................................... 43!
Figure 3.5: The most recent breathbelt design includes 3 components: (a) adjustable band, (b) strain gauge, and (c) microprocessor Bluetooth 4 transceiver, allowing it to communicate continuously with a mobile phone. .......................................................................... 44!
Figure 3.6: The PASCO respiration sensor used to validate our strain gauge sensor. It uses a gas pressure sensor to measure how air pressure in the belt changes as the wearer breathes. ....................................... 45!
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Figure 3.7: X-axis is Time in seconds and the Y-axis is normalized sensor values. A 5-minute excerpt of our thoracic strain gauge breathbelt (red) compared with the PASCO sensor (blue) shows their similarity, with some noise. Each peak is the apex of an inhalation. 46!
Figure 3.8: X-axis is Time in seconds. A comparison between the sensor gauge-based breathbelt and the commercial PASCO sensor that uses highly sensitive air pressure fluctuations to measure breath rate. ..................................................................................................... 47!
Figure 4.1: The user interface of the web-based Wizard-of-Oz interface that study administrators would use to control the visualization of the desktop-based feedback of a user in real-time. The panel included elements that were not yet in use (e.g. breath regularity). ................. 51!
Figure 4.2: The user interface of the client was used by the study administrator to select the current peripheral pacing method. ......... 52!
Figure 4.3: The peripheral paced respiration feedback used an animated, semi-translucent grey bar stretching across the screen. Vertical arrows on the left indicate the full range of motion. ......................... 54!
Figure 4.4: (Top) Mean breath rate for the No Feedback and PPR conditions with standard error bars. (Bottom) Mean breath rate during the PPR condition when PPR was on and off. ......................................... 57!
Figure 4.5: Breath rate for one participant in both no feedback (top) and PPR (bottom) conditions. Bold (orange) areas indicate where PPR occurred .............................................................................................. 58!
Figure 5.1: Breathcast works by intermittently animating a semi-transparent bar across the bottom third of the user’s screen. The inset shows how profile icons of other Breathcast users are discreetly displayed on the bar. The vertical arrow on the lower right illustrates the range of bar movement. In asynchronous mode, the bar is blue to aid differentiation. ........................................................ 63!
Figure 5.2: Mean breath rates for participants in each condition. ..................... 67!
Figure 5.3: Mean breath rate for each condition. BL=baseline, WBL=working baseline, A=asynchronous, S=synchronous. ............ 68!
Figure 5.4: Breath rate of a user with a resting rate of 19.5bpm. PPR occurrences are orange. The working baseline condition (top) saw
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the breath rate climb upwards. With synchronous feedback (middle), it decreases noticeably during PPR. Asynchronous feedback (bottom) saw rates drop little and continue to climb overall. ................................................................................................ 69!
Figure 6.1: The BreathTray and its drop-down menu in two states: sensor disconnected (left) and connected (right). The user’s current breath rate is displayed on the user’s system tray. ............................. 83!
Figure 6.2: ‘Today’s Highs and Lows’ shows three desktop screenshots from each category with relevant metadata (breath rate, time of day). Here, the user’s breath rate was highest when working in email and on a presentation. It was lowest when viewing their calendar and viewing a document. .................................................................... 85!
Figure 6.3: The user’s Breath.fm profile for an imaginary user, ‘KKP’. The top shows their overall data including name, last activity update, total calm points accumulated, mean baseline, and mean BPM. The area below shows their activity stream with event notifications updated in real-time: positive and negative reinforcement messages and milestone images. ........................................................ 86!
Figure 6.4: Calm point milestones. The desktop of a user who achieved the 80-point milestone. The inspiring images are always randomized as an attempt to create anticipation for the different milestones. ..... 87!
Figure 6.5: The prototype system showing two types of notifications in the lower-right corner of the screen: (a) Left, positive feedback gives the user a congratulatory message and a duration for which they were breathing relatively calmly. (b) Right, a cautionary message tells users how long they have been breathing relatively fast. ........... 89!
Figure 6.6: When other users have recently logged in and had data sent to the web repository, the drop-down menu also doubles as the location of the buddy list. Usernames, current point values, and last recorded breath rates are displayed. ............................................ 92!
Figure 7.1: The BreathTray shows 4 components: calm points, breath rate, percent relative to resting rate, and whether they are above (red) or below (blue) their resting breath rate. ......................................... 100!
Figure 7.2: The ‘Serial Sevens’ task adapted to a web-based interface. A starting number was shown (top) and numbers disappeared when participants typed and pressed Enter (bottom). ............................... 102!
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Figure 7.3: The math problem in the ‘Problem-Solving with Audio Distractors’ task. Users were to choose the correct expression using the radio buttons and press the “answer” button. ................. 103!
Figure 7.4: The study setup with the USB-connected wearable respiration sensor (left), computer, headphones, and external mouse. The web browser was maximized to fill the screen for all conditions. ........... 104!
Figure 7.5: Mean breath rates in each task, split by condition and also aggregated across all condition. The difference between BreathTray and No BreathTray is significantly different in the Serial Sevens task. ............................................................................ 107!
Figure 8.1: The initial prototype. Dashboard (left) shows real-time feedback and Settings (right) allows the user to make changes to their breath rate baseline and to manually send data back to the research team via email. ................................................................... 121!
Figure 8.2: Push notifications on an iOS5-based mobile phone based on initial prototype design. Each notification has a type (positive or negative/cautionary) and duration that the system detected the user was in that state. For example, the user had been breathing above their resting rate for 15.2min at left, at or below their resting rate for 12.7min on right. ..................................................... 122!
Figure 8.3: The second iteration of the Breathwear client interface, which includes a recent activity indicator (left, top, in green) and additional configuration options in the Settings screen (not shown). The center image shows when the user is hovering around their baseline (hence the yellow) and the sensor is connected (hence the ‘Time Connected’ indicator). During a high breath rate state, the band is red (not shown). The baseline here is set to 15bpm (the default). ................................................................ 124!
Figure 8.4: Distribution of breath rates of each participant. X-axis is “Breath rate in Breaths per Min”. Y-axis is ‘Frequency in Seconds’. Clockwise from top-left, users 1, 2, 4, 5, and 3. .............. 128!
Figure 8.5: Frequency and type of push notifications received by participant. ‘Calm’ and ‘Zen’ are two types of positive notifications. This graph shows that there was no discernable trend around type or frequency of push notifications but that one can characterize an individual’s respiratory patterns to some degree using this visualization. ......................................................... 130!
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Figure 8.6: Calm points per hour granted to each study participant. This graph shows how User #2, who had a great deal of meditation experience, was rewarded a great deal more calm points per hour than the other participants. .............................................................. 131!
Figure 8.7: A line graph produced by one participant with data she labeled herself, “meditating”, “surfing the web”, and “reading”. ................. 132!
Figure 8.8: A line graph showing the relationship between a user’s breath rate punctuated by the different types of push notifications (green=calm, blue=zen, red=stress). The line indicating the user’s breath rate is by default gray and then colored according to the duration of the state detected by the subsequent push notification.134!
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CHAPTER 1 INTRODUCTION
People across a range of ages and occupations engaged in computer-based work
or study frequently suffer from task interruptions (Iqbal & Horvitz, 2007), difficulty
sustaining attention (Holt & Andrews, 1989), and chronic stress (Mark, Gudith, Klocke,
2008). This can seem like merely an inconvenience to productivity but consider that the
impact of high stress reactivity extends to decreasing cognitive performance (Luine,
Villages, Martinex, McEwen, 1994; McEwen & Sapolsky, 1995), behavioral problems in
children (Obradovi!, Boyce, 2012), and ultimately damage to the brain (Sapolsky, 1996).
The problem of susceptibility to chronic stress can be approached as a problem of
inadequate self-regulation rather than only exasperating environmental stressors. As such,
this dissertation examines how technology can augment one’s self-regulation processes
rather than analyzing exactly how different environmental stressors occur and could be
mitigated. Specifically, the research agenda is to augment respiratory self-regulation, a
common technique used to help one learn to self-modulate one’s own
psychophysiological state. The goal of this agenda is to usher in new tools, technique,
and design examples for amplifying, augmenting, and ultimately strengthening one’s
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innate self-regulatory system. Our approach leverages the recent emergence and unique
affordances of wearable sensors and personal mobile devices that make continuous
monitoring and feedback possible. Thus, the research here represents the first known
study of digital augmentation of continuous respiratory behavior.
To date, technology-mediated respiration influence has required the user to stop
and disengage from their current task and focus full attention on modifying their
respiratory state. This is often done during clinical sessions separate from the work
context creating the stress. However, humans can “regulate their respiration rates in a
relatively short time period” (Ley, 1999), making breath regulation a viable treatment for
sporadic and subtle stressors such as those that may be encountered in everyday settings
such as multi-tasking on a laptop computer, transitioning between tasks, and face-to-face
meetings. Indeed, continuous influence of respiratory patterns represents a fundamentally
different approach than existing modal solutions, which are subject to inconveniences and
compliance issues. Building off of research that studies how tools augment our
intellectual capacities (Pea, 1985; Pea, Gomez, 1992; Pea, 2004), this approach relies on
an understanding of psychophysiology, wearable sensors, interaction design, and
persuasion (Fogg, 2002; Cialdini, 2008).
1.1 THE PROBLEM
Human psychophysiological self-regulation is a crucial component to modern life
given the numerous demands on our attention and arousal. Such consistent arousal causes
mild but chronic stress (McEwen & Sapolsky, 1995), which can lead to poor academic
performance (Cassady & Johnson, 2001), burnout (Etzion, 1984), and even loss of brain
plasticity (McEwen & Gianaros, 2010). While tools grow to extend and amplify our
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cognitive and communicative abilities (Pea, 1985), less research supports our innate self-
regulative abilities, particularly in the domain of stress. Learning to self-regulate in the
face of stressors is an invaluable tool that is only rarely given due focus in traditional
education systems (Pope, 2001; Obradovic, Boyce, 2012).
1.2 PROPOSED SOLUTION
Many non-technological techniques are now common to help people monitor and
adjust to a flood of demands (e.g., conscious breathing, yoga, nature retreats). Many of
these incorporate respiration monitoring and modification as a means of modulating
arousal and reducing tension. They often hinge upon one’s ability to disengage from their
task, monitor their own respiration, and adjust it to a subjectively appropriate pattern.
Both (1) monitoring and (2) pacing one’s respiration are viable places where technology
can augment innate ability. Crucially, both monitoring and pacing can in principle be
done in parallel to existing information work (something we will investigate in Chapter
6), reducing problems of compliance and disruption that emerge when requiring one to
stop a task. This dissertation examines interactive techniques to influence respiratory
self-regulation during existing tasks. Our focus begins with influence breathing during
stationary work at a laptop computer and is then extended to mobile settings.
1.2.1 THESIS STATEMENT
Technological tools can be an effective means of influencing respiratory change and self-regulation during, and without negatively impacting, cognitive work.
1.3 RESEARCH CHALLENGES
There are several research challenges involved in demonstrating the above claim.
First, how can one’s respiration be influenced during meaningful computer work without
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significantly distracting the user given normal patterns of use (i.e., without requiring the
use of headphones or distracting nearby workers), while ensuring the user knows the end
points of both inhalation and exhalation?
Second, given the real-time nature of physiological behavior, what might be the
role of social feedback on motivating peripheral respiratory change during computer
work?
Third, what is the design space of user interaction techniques to encourage the
development of one’s own self-regulation system as opposed to system-generated explicit
respiration-pacing (which, ostensibly, off-loads from the user to the system both
respiration monitoring and pacing)?
Fourth, are such feedback-based attempts to augment one’s self-regulation more
effective than simply being motivated to breathe calmly? Moreover, does the available
feedback take attention away from the user’s task to such a degree that cognitive
performance is compromised?
Fifth, how can such techniques at motivating change be expanded to apply across
contexts in a mobile setting?
These challenges can be expressed in the form of the following research
questions:
• Q1: Is it feasible to augment respiratory patterns of information workers as they
are engaged in meaningful information work?
• Q2: How does synchronous social feedback compare with asynchronous feedback
in peripheral paced respiration?
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• Q3: How can sociotechnical systems be designed so as to motivate respiratory
change without prompting it explicitly?
• Q4: How does peripheral feedback influence respiration and does that feedback
negatively influence cognitive performance?
• Q5: How must these techniques be adapted to be effective in a mobile setting?
1.4 SUMMARY OF FINDINGS
Table 1.1 presents an overview of the studies in this dissertation in order to guide
the reader’s understanding and prepare them for more in-depth consideration ahead.
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Chapter Study Name Contribution(s)
2 Literature review
Identification and motivation of respiration rate as an effective data source for monitoring and feedback to regulate user state.
3 Breathbelt Design and validation of a non-invasive, ambulatory, non-clinical respiration sensor to detect breath rate continuously.
4 Peripheral paced respiration (PPR)
(1) Desktop user interface design technique for visually pacing the user’s respiration in parallel to their existing tasks. (2) Showing that PPR reduces breath rate without subjectively disrupting users.
5 Breathcast (1) Application of synchronous feedback to physiological behavior (i.e., respiration). (2) Method of using social feedback atop the PPR bar magnifies the effect of the bar more when the feedback is thought to be synchronous as compared to asynchronous.
6 Breathaware (1) Identification of 10 design principles for systems that aim to influence respiratory behavior. (2) Design of several desktop techniques for influencing breath rate without using explicit pacing.
7 BreathTray (1) Desktop users self-determined when to use peripheral feedback to significantly regulate breath rate. (2) Peripheral real-time feedback changes breathing without compromising cognitive task performance. (3) The two findings above only apply during single tasks; users were unable to use the feedback while multi-tasking.
8 Breathwear (1) Iterative design of continuous monitoring and influence in a mobile context. (2) Analysis of results from a longitudinal study showing how the system describes respiration patterns over time and adapts notifications to individual behavior.
Table 1.1: An overview of the studies and contributions in this dissertation.
The studies in this dissertation rest upon a thorough review of the literature of the
psychophysiology of stress and calm (Chapter 2). It is in this review the reader is
introduced to both the illustrative and actionable properties of human respiration.
Scholars in medicine frequently have used, and continue to use, respiration to understand
the state of the body and mind as well as to influence it. Technology researchers and
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designers have since used these properties to develop interventions that train or modify
one’s respiration to affect their emotional state, cognitive state, and general health.
The literature review goes forth to describe how technological interventions to
influence the breath have taken primarily a training approach, requiring users to
disengage from their existing tasks and focus their attention on the important task of
retraining their respiration patterns. This small requirement is large enough to keep
compliance down and continues to make breath modification uncommon outside
contemplative practices. As such, the first intervention study is one of influencing
respiration patterns in parallel to existing tasks at the computer during information work
(Chapter 3; Q1), requiring us to build and evaluate a non-invasive and wearable
respiration sensor (0).
Once assured it was possible to influence breath regulation as users engage in
information tasks, we turn our attention to the problem of motivating engagement with
such a system. For this we turn to social psychology, which has found that synchronous
physical behavior increases connectedness between parties. We applied this notion to a
physiological behavior – respiration – to see if synchronous feedback motivates self-
regulation more effectively than asynchronous behavior (Chapter 4; Q2).
Maintaining our focus on influencing respiration during information work, we
turn our attention to designing methods of influencing respiration without explicit pacing.
This requires considerable attention and is, like all interventions, subject to the novelty
effect. We designed and implemented a system that utilizes motivational cues, real-time
feedback, and operant conditioning to influence respiration in a desktop operating system
environment (Chapter 5; Q3).
8
Inserting physiological feedback directly into the operating system could have
adverse effects on productivity and performance, which is in opposition to our goal of
augmenting respiration while complementing existing tasks. Hence, we evaluated the
effect of peripheral, real-time feedback and motivational cues on cognitive performance
in a controlled task. The interesting results of the study shed light on when and how to
incorporate feedback and how it affects task performance for different types of tasks
(Chapter 6; Q4).
We took insights from designing for the desktop and applied them to a mobile
device to realize the goal of continuous respiration augmentation (Chapter 7; Q5). This
transition presents a new set of challenges and exciting opportunities for truly distributing
one’s self-regulatory processes into technological tools. Finally, Chapter 9 concludes the
dissertation with study limitations, directions for future work, and a discussion of
implications for the field.
9
CHAPTER 2 RELATED WORK
This chapter discusses the theories and experimental findings of research
conducted to better understand how stressful and calm states relate to human performance
and how interactive technologies can influence self-regulation based on breath
modification. The three sections of this chapter cover (2.1) the stress response and self-
regulation, (2.2) breath modification for self-regulation, and (2.3) technology-mediated
breath modification. A summary (2.4) concludes the chapter.
2.1 STRESS AND SELF-REGULATION
Stress is a causal or correlational factor to the six leading causes of death: heart
disease, cancer, lung ailments, accidents, cirrhosis of the liver, and suicide (APA, 2011).
Chronic stress negatively impacts one’s health, physiology, and ability to learn and
perform (Lupien et al., 2009; Sapolsky, 1996; Selye, 1998/1936; Sherwood, 2006).
First applied to humans from the world of structural engineering in 1936
(reproduced, Selye, 1998), the word ‘stress’ refers to distress, which differs from
eustress, ‘positive’ stress, or general stimulation (Selye, 1975). For years, Selye tried to
10
identify the specific environmental conditions (physical stressors such as lack of food and
extreme ambient temperatures) that gave rise to a specific physiological ‘stress response’
(e.g., enlargement of the adrenal glands, gastric ulceration, etc.). Ultimately, he decided
the determinants of stress were non-specific – because he could not identify the physical
determinants.
Psychologist John Mason (1968) later studied the psychological determinants that
could produce the stress response measured in hormone levels. He put participants in
different situations (e.g., parachuting, air-traffic control) and created a table summarizing
the difference in hormone levels before and after these situations. He found three
determinants characterize stressful stimuli: interpreted as unfamiliar, unpredictable, and a
lack of control. Later, Dickerson & Kenney (2002) identified a fourth determinant using
meta-analysis: feeling a social evaluative threat.
A stressor is a source causing stress (Selye, 1975), either subjective or
environmental. Stress “is a highly individual experience that does not depend on a
particular event such as time pressure, but rather, it depends on specific psychological
determinants that trigger a stress response” (Lupien et al., 2007). As such, stressors can
be absolute (objective and universal to all humans – e.g., physical danger) or implied
(subjective to the person in question – e.g., giving a presentation) (Lazarus, 1966).
When a situation is interpreted as stressful, a series of reactions begins in the
hypothalamus, causing the stress hormones glucocorticoids (cortisol in humans) and
catecholamines to be secreted by the kidneys. This gives rise to the so-called ‘fight-or-
flight’ response of the sympathetic nervous system characterized by increase in heart rate
and blood pressure. In effect, these hormones instruct the body to “increase the
11
availability of energy substrates in different parts of the body, and allow for optimal
adaptations to changing demands of the environment” (Lupien et al., 2007; p. 211).
While this is a natural means of responding to environmental demands, prolonged
secretion of these hormones is damaging to the body and brain including impairing tissue
repair, suppressing immune function (Lupien et al., 2007) and, ultimately, atrophy of the
hypothalamus (Sapolsky, 1996). The stress response is natural and has evolved to protect
the body. It is prolonged or repeated triggering or the inability to return to a neutral state
that finally causes damage as the body is preoccupied with preparing to react rather than
healing itself (see Figure 2.1).
The stress response is counteracted by the relaxation response, a coordinated
physiological response characterized by decreased arousal, diminished heart rate,
respiratory rate, and blood pressure, in association with a state of “well-being” (Benson,
1975). This response triggers parasympathetic activity while reducing sympathetic
activity (see Figure 2.1). The relaxation response helps mitigate the negative effects of
stress on the body and mind in an effort to return the body to homeostasis (Lang, 2000;
Syrjala et al., 1992; Vasterling, 1993).
Porges’ polyvagal theory (2001) provides a rich explanation of stress reactivity
than one based solely on cardiovascular responses. It states that the human stress
response system has evolved such that more primitive responses (e.g., feigning death,
rage) occur when more evolved ones (e.g., social communication, self-soothing) have
failed. This theory explains behavioral, not only physiological, responses to stress. It aims
to describe the structure and function of the vagus nerve, which influences the heart and
other organs during parasympathetic activation.
12
Figure 2.1: An abbreviated diagram of the complementary functions of the sympathetic and parasympathetic components of the nervous system
(PrecisionNutrition.com, 2012). The stress response is characterized by sympathetic activation and the relaxation response by parasympathetic.
2.1.1 EFFECTS OF STRESS ON LEARNING, PERFORMANCE, AND BEHAVIOR
Even mild chronic stress has deleterious effects on an individual’s capacity to
learn and be productive. While common interpretations of stress are often associated with
significant negative life events, so-called ‘burnout’ (in both school and workplace) results
from “psychological strain that is especially connected with chronic day-to-day stresses
and is marked by a state of physical, emotional, and mental exhaustion” (Etzion, 1984, p.
616). As such, even mild stressors have accumulated effects if an individual does not
13
effectively adapt to them. It is clear how this occurs so frequently given that any “mental
activity appears to invariably be associated with some degree of sympathetic activation,
whose degree depends on the amount of stress involved in performing the mental task”
(Bernardi,Porta, Spicuzza, Sleight, 2005).
In a review, Marin et al (2011) illustrates the link between worker burnout and
cognitive dysfunction. These effects interact with psychological wellbeing when “the
strain of chronic workplace distress overwhelms the psychological capacities of the
individual and puts them at greater risk of psychiatric manifestations such as burnout and
depression” (Marin et al., 2011, p. 3). Such burnout has been linked to decreases in
cognitive performance and the inability to sustain attention (van der Linden et al., 2005).
In a review article, McEwen and Sapolsky (1995) describe how stress hormones
impair cognitive function and result in a loss of neurons in the hippocampus in two time
scales: short-term and long-term. Newcomer et al (1999) simulated realistic elevated
stress levels synthetically and found decreases in verbal declarative memory. Luine et al
(1994) similarly induced chronic stress and found it impaired spatial memory but that the
effects were reversible.
Allostatic load (McEwen, 1999) is “the physiological consequence of chronic
exposure to fluctuating or heightened neural or neuroendocrine response that results from
repeated or chronic stress” (Taylor, 2006) (see Figure 2.2 for a visual explanation). It
can accrue as a result of four processes: 1) frequent activation of the stress response, 2)
failure to habituate to repeated stressors of the same kind, 3) failure to shut off the stress
response appropriately, and 4) inadequate reaction to the stress response.
14
Figure 2.2:A diagram showing McEwen’s theory of allostatic load as emerging from interactions between environmental stressors, individual differences, behavioral responses, and physiological responses (McEwen, Gianaros, 2011).
The deleterious effect of stress on memory has been differentiated from the
effects of emotionally arousing stimuli alone (Lupien et al., 2007). Stress, in effect,
‘distracts’ the brain from focusing on the intended material and impacting retention. This
is clearly a high-priority problem given the sociocultural dynamics of a typical classroom
or workplace.
The Nobel Prize in Medicine for 2009 was awarded to Elizabeth Blackburn for
the discovery of how chromosome-protecting telomeres are damaged by chronic stress
(Epel et al, 2004). Members of the same research team found that three months of
intensive meditation were able to reduce the damage and essentially reduce cell aging
(Epel et al., 2009).
Academic test anxiety is another active area of research at the intersection of
stress and cognitive performance. Cassady and Johnson (2002) found increased
performance on the SAT correlated with lower anxiety on a psychometric instrument
meant to assess test anxiety.
15
Stress and individual reactivity to stress has been identified as a key factor
affecting school readiness and socioemotional behavior (Obradovic et al., 2010). In
children, there are two sociobehavioral patterns of physiological (autonomic) reactivity to
stress: “under-arousal during the resting state or in response to challenging stimuli tends
to be related to externalizing symptoms, while overarousal is associated with
internalizing symptoms” (Obradovic & Boyce, 2009; p. 301). Even low self-esteem, a
predictor of stress reactivity (Pruessner et al., 2004), has been linked to stress due to
associations between chronic stress and reduced hippocampal volume (Pruessner et al.,
2005).
The effects of chronic stress on mental health are apparent and subject to ongoing
study. Prolonged stress is a major risk factor for depression (Mazure & Maciejewski,
2003) and exposure to traumatic stress can cause post-traumatic stress disorder (PTSD)
(Southwick et al., 2005). These are largely subjective or implied, internal stressors
perhaps long after the actual environmental stressor occurred. If stress does not cause
mental health disorders, it likely exacerbates it – e.g., with schizophrenia (Dohrenwend et
al., 2005) and bipolar disorder (Hammen & Gitlin, 1997).
2.1.2 SELF-REGULATION: THERAPEUTIC AND DEVELOPMENTAL
A review by leading neuroendocrinologists describes the effects of stress at each
stage of life, from prenatal to old age (Lupien et al., 2009). Writing about a study on rats,
however, the authors note “it is interesting to note that in contrast to the effects of chronic
or severe stress on the brain and behaviour earlier in life, which are long-lasting, effects
of adulthood stress — even chronic stress — are reversed after a few weeks of non-
stress” (p. 438). Thus, while study of the negative impact of stress continues to be
16
important, some recent research programs have focused on approaches for recovery and
self-regulation against stress and their efficiency and effectiveness. This work suggests
the importance of increasing, or supporting one’s internal process of increasing, states of
“non-stress,” especially in young people, as studies show chronic stress in youth likely
becomes a learned pattern of response and causes permanent damage.
Porges, one of the leading figures in the study of self-regulatory (autoregulatory)
processes, notes “self-regulation is a difficult process to operationalize” (Porges et al.,
1994). It means different things in different contexts – but these contexts impact one
another and cannot be treated completely independently. The body and mind are inter-
connected as are affective and cognitive processes. Porges relates self-regulation to “self-
soothing” (p. 182). The physiological body, too, can be seen as self-regulating or to self-
soothe itself. Self-regulation in this context refers to the adaptive process by which one’s
body and mind respond to environmental stimuli to maintain homeostasis and deliver the
organism back to a neutral (non-stress) state. Though often subconscious and tacit, self-
regulation is also a learned behavior, being impacted by our choices, sociocultural norms,
and environmental influences.
Self-regulative ability is particularly important in modern society and non-warring
nations because “absolute stressors [such as physical threats to our safety] are rare”
(Lupien et al., 2007). It is implied, self-created stressors that we must respond to most
consistently. These include giving a presentation, facing stereotype threat (Steele, 1995)
(the concern or anxiety of conforming to a negative stereotype about one of one’s social
groups), taking an exam, approaching authority figures, and doing homework. Because
stressors are so often implied, states of non-stress cannot be created solely by controlling
17
the environment. Our psychophysiological state emerges from an interaction between
environment and individual’s self-regulative ability. As a result, human societies have
developed multiple approaches to developing this ability, leading to psychological
(reducing cognitive or emotional stress and anxiety; confidence and relatedness),
physiological (developing calm habits in heart, muscle, and respiration), and behavioral
(calming rituals and practices) .
It is important to distinguish between therapeutic and developmental techniques
for influencing self-regulation. Therapeutic techniques are meant to induce calm states
but not necessarily to train the nervous system for future adaptation to stress (i.e., calm
states, not traits). Developmental techniques are more training-based in this regard, as
they train the body and mind to identify stressors and adaptively self-regulate.
Pranayama (literally ‘extension of the breath’) and qigong (literally ‘skills gained
through working with breathing’) are probably the oldest and most mature forms of
developing one’s self-regulative ability (Iyengar, 1985). Westerners often conflate these
psychophysiological practices with spiritual development but the two are independent.
Both approaches include mechanistic and prescriptive descriptions of techniques to
evaluate and develop one’s self-regulative ability. While focusing on the breath, both
systems describe the mechanistic interactions of the breath with the rest of the
cardiovascular system and patterns of the mind including thoughts, emotions, and focus.
The appearance of a related science of self-regulation in the West was autogenic
training (Luthe & Schultz, 1932) in Germany. This is a still-popular set of relaxation
practices with guided imagery that aims to influence and train the autonomic nervous
system. This was complemented in America by the therapeutic technique of progressive
18
muscle relaxation (Jacobson, 1938). Adoption or invention of other therapeutic
techniques has since blossomed in the West, delivered via haptic (e.g., touch, caress),
environmental (e.g., eco-therapy), auditory (e.g., soothing tones, mantras), and olfactory
(e.g., lavender) modalities.
Physiological approaches to developing self-regulative ability are complemented
by contemplative practices such as mindfulness (Kabat-Zinn et al., 1992; Langer, 1990)
and transcendental meditation (Russel, 2002). As of 2007, complementary and alternative
medicines (including contemplative practices) were in use by over 35% of the US adult
population (Barnes, 2002). Recent randomized controlled trials have also lent insight into
the efficacy of mindfulness training in children (reviewed by Obradovic, Portilla, and
Boyce, in press). These practices are used both therapeutically and developmentally
through the conscious intention of entering calm states despite subtle anxieties and
implied stressors. Contrary to popular misconceptions, contemplative practices are meant
to produce an aroused and focused state (Cahn & Polich, 2006), rather than the inert,
relaxed, and detached state often associated with meditation.
In the learning sciences, ‘self-regulated learning’ is used to describe a conscious,
metacognitive process by which learners can monitor their own learning, motivation, and
behavior (Corno & Mandinach, 1983; Corno & Rohrkemper, 1985). Three components of
self-regulated learning seem paramount (Pintrich and De Groot, 1990): metacognitive
strategies for planning, monitoring, and modifying one’s cognition (e.g., Brown,
Bransford, Campione, & Ferrara, 1983; Corno, 1986; Zimmerman & Pons, 1986, 1988),
self-regulating effort to overcome distraction on academic tasks (Corno & Rohrkemper,
1985), and cognitive strategies (e.g., rehearsal and elaboration) to learn, remember, and
19
understand the material (Corno & Mandinach, 1983). A review by Boekaerts (1997)
discusses how self-regulated learning can be domain-specific, where learners can apply it
in one subject but not in another. Stress has been shown to reduce plasticity of the limbic
system and one’s ability to learn (Sapolsky, 2003). Providing a neurological explanation
for how stress could inhibit metacognitive self-regulation.
Developmentally, behavioral self-control in children grows over time. “Brain
regions essential to self-control are immature at birth and develop slowly throughout
childhood” (Tarullo, Orbradovic, Gunnar, 2009). In children, developmental techniques
include training executive functions (EFs) that regulate behavior, attention, and emotion
(reviewed by Obradovic, Portilla, and Boyce, in press) – skills that are taken to be
precursors to academic and social achievement. Techniques with children have included
meditation, heart-rate variability feedback, behavioral interventions, and attention
training.
2.2 AUGMENTING SELF-REGULATION WITH BREATH MODIFICATION
The link between psychological state and respiration is non-intuitive but has been
well-studied for thousands of years. As B.K.S. Iyengar (1985) translates from Patanjali’s
Yoga Sutras (500-200 BC):
"Pranayama is the regulation of the incoming and outgoing flow of breath with retention. Pranayama has three movements; prolonged and fine inhalation, exhalation and retention; all regulated with precision according to duration and place. The fourth type of pranayama transcends the external and internal pranayamas, and appears effortless and non-deliberate. Pranayama enables the mind to become fit for concentration, and removes the veil covering the light of knowledge and heralds the dawn of wisdom."
20
More recently, in the West, Charles Darwin noted “the breathing to be hurried” in
men in stressful situations (Darwin, 1872). Sigmund Freud (1962) also recognized that
anxious patients often experienced difficulty breathing. This section motivates the use of
breath awareness and regulation as an effective technique for not only assessing one’s
state but influencing it. Table 2.1, at the end of this section, provides a useful summary
of the key concepts along with lay descriptions to ensure the reader can follow the myriad
concepts easily.
It is common knowledge that one’s heart rate increases in stressful conditions.
Less common is the understanding that heart rate variability (HRV) occurs continuously
and illustrates the body’s ability to adapt to even subtle demands of body and mind. An
example is the simple act of inhaling and exhaling: the heart rate increases during each
inhalation and decreases with each exhalation (Sherwood, 2006).
The degree of change in heart rate during respiration, or respiratory sinus
arrhythmia (RSA), is commonly used as a robust measure of parasympathetic nervous
system (PNS) activation. Higher RSA amplitudes (i.e., the difference in heart rate
between inhalation and exhalation) are associated with healthier individuals and longer
life expectancy. RSA can be averaged over time but changes can also be measured in
response to temporary demands. In general, high RSA is commonly used to indicate
autonomic adaptability to both positive and negative demands (Obradovic & Boyce,
2009).
RSA and other psychophysiological indicators such as skin conductance level
(SCL) and salivary cortisol responses do not by themselves alone explain one’s inability
to self-regulate. For example, environmental or familial influences play a role in dictating
21
a child’s adaptability and symptoms independent of individual stress reactivity
(Obradovic, Bush, Stamperdahl, Adler, Boyce, 2010). However, these
psychophysiological indicators can complement or provide cues for self-regulation
development. Respiration, in particular, is well-suited because it is relatively easy to both
consciously observe and modulate (unlike the others). It is the focus of this review.
The influence of stressors on RSA cannot be studied without accounting for
changes in respiration that result from engaging in those activities. Bernardi et al (2000)
found that “simple mental and verbal activities markedly affect HRV through changes in
respiratory frequency” (Bernardi et al., 2000; p. 1462). Simply put: engaging in a task
puts a certain amount of demand on the body, which is reflected by changes in the
interconnected cardiovascular system (including respiration).
Stress causes mild or acute hyperventilation (over-breathing) as dictated by the
stressors’ intensity or duration and the person’s innate or learned stress-reactivity (Suess
et al., 1980; Boyce et al., 1995; Van Diest et al., 2001). Dr. Herbert Fensterheim, clinical
professor of psychology in psychiatry at Cornell University Medical College in New
York City, notes that “depending on the person, any emotional stimulation can set off
over-breathing” (Flippin, 1992, p. 24).
The link between respiratory pattern and stress is often, but not always (Fried,
1990), studied in individuals with panic disorder (Wilhelm, Gevirtz, Roth, 2001; Meuret,
Wilhelm, Roth, 2001; Roth, 2005; Conrad et al., 2007), ostensibly because the
association is plainly evident, effects are more easily measured, and motivation is great
because it is a formalized chronic condition. However, chronic and acute stress are
experienced not only by those with a clinical condition but by almost anybody.
22
Therefore, we draw upon studies done with those panic disorder and find there are
similarities with theories posed by yogic and qigong scientists. There are two competing
theories (‘hyperventilation’ theory and ‘suffocation false alarm’ theory) to explain how
stress and respiration rate are related, summarized by Roth (1999):
The hyperventilation theory of panic postulates that falls in arterial blood CO2 precede and cause attacks, at least many at the beginning of the illness and when other variables such as the buffering effects of blood electrolytes are taken into account. Klein’s suffocation false alarm theory postulates that panic is provoked by sudden feelings of dyspnea triggered aberrantly in the same brain circuits that monitor blood CO2 levels. This theory sees hyperventilation as a compensatory reaction to this alarm rather than a cause of panic. Klein contrasts panic anxiety to ‘‘anticipatory anxiety’’, which is linked to external stimuli through Pavlovian conditioning and which is characterized more by cardiovascular than respiratory symptoms.
It has also been shown that those suffering from panic disorder “do not show a
unique tendency toward hyperventilation, but rather that their hyperventilatory symptoms
and perhaps intermittent over-breathing episodes are a function of the high levels of
anxiety they experience” (Holt, 1989). It follows that subjective stress or self-regulative
inability is the issue at hand rather than limiting the issue only to people labeled as having
panic disorder per se. People who are not classified as having ‘panic disorder’ have also
been repeatedly shown to react with faster breathing when exposed to a stressful situation
(Suess, 1980; Ley, 1999; Conrad et al., 2007; Roth, 2005).
In healthy individuals in non-stressful environments, one’s autonomic nervous
system determines the exact breathing pattern necessary to maintain homeostasis in the
body. As environmental and mental demands are made, this homeostasis is disturbed and
breathing patterns, being “exquisitely sensitive to [external or internal] stress” (Ley,
23
1999), adjust accordingly. Indeed, “distinct effects of stress are apparent even on a small
time scale (seconds-minutes)” (Bernardi, Porta, Spicuzza, Sleight, 2005). A number of
studies have demonstrated the relationship that affective state has with respiratory pattern
(Porges, 1994; van Diest et al., 2001). Just as the stressed mind is under conscious control
to change focus from stress-inducing thoughts, so is respiration under conscious control
to change patterns to calming ones. This is the bi-directional, causal relationship between
respiratory patterns and affective state.
In summary, the stress response prepares the body for quick action – including
shallow and fast respiration resulting from heightened heart rate – but prolonged
activation has significant costs on the brain and body. Respiratory pattern is an indicator
of one’s psychophysiological state that can be assessed without sophisticated feedback
and is one that can be modulated quickly and easily by practically anybody.
Though the link between stress and respiration is physiologically evident, it is
surprisingly not a common focus of research in psychophysiology. As Grossman (2007)
notes, “the significance of respiratory influences upon cardiovascular functioning has
been much neglected in the psychophysiological literature” while more attention is paid
to HRV (Mulder, 1992; Brown et al., 1993; Mulder, de Waad, Brookhuis, 2005). Perhaps
this is because respiration is under conscious control, making it more difficult to control
for in laboratory studies. Another reason could be because respiration is seen as
‘superficial’ compared to heart rate and rhythm. Yet another reason could be because
HRV and RSA are relatively easy to measure compared to subtle respiration
characteristics such as tidal volume, the amount of air breathed in a single inhalation or
exhalation, which are more difficult to measure unobtrusively.
24
Beyond the relatively little research that exists studying how stress and respiration
are linked, we observe even less scholarly work on how this bi-directional relationship
can be exploited to reduce stress-reactivity. In summary, for researchers interested in
augmenting (not only measuring) self-regulation, respiration is the central process to
study because it reflects mental demands, can be consciously regulated, and is relatively
easy to measure.
2.2.1 REGULATING RESPIRATION TO MITIGATE THE STRESS RESPONSE
Voluntary breath regulation is a common, empirically validated technique for
reducing stress and anxiety (Clark & Hirschman, 1990; McCaul, Solomon, Holmes,
1979; Sisto et al., 1995; Ley, 1999), relieving symptoms of asthma (Cooper & Oborne,
2003), reducing blood pressure (Grossman et al., 2001; Schein et al., 2001), and focusing
the mind (Ley, 1999). Changes in breath may result from frustration, pain, stress, test
anxiety (Ley & Yelich, 1998), and post-traumatic stress disorder (PTSD) (Zucker et al.,
2009). Schliefer and Ley (1994) investigated the effect of computer data entry compared
to baseline relaxed periods. They found that breath rate increased 26% during data entry
and that subjects exhibited decreased heart rate variability (HRV) and increased self-
ratings of tension. The differences in breath rate between resting and data entry were
comparable to those between rest and feeling threats of electric shock.
Humans can “regulate their respiration rates in a relatively short time period”
(Ley, 1999), making breath regulation a viable treatment for sporadic and subtle stressors
such as those that may be encountered in everyday settings such as information multi-
tasking. However, developing adequate continuous awareness and motivation to monitor
25
one’s breathing behavior is problematic and requires considerable attention and is often
conflated with spiritual pursuits, making it a suitable candidate for technology mediation.
Breath rate (breaths per min, or bpm) is calculated by the number of times the
chest rises (inhalations) in a minute. Breath rate, like heart rate, changes frequently even
in a resting state as it is affected by arousal, talking, posture, personal health, and other
factors (Ley, 1999). There is no standard resting human breath rate; studies have found
mean rates to range between 12-20bpm for Western adults (Ley, 1999; Sherwood, 2006).
There are different perspectives on what an optimal target breath rate should be. Song
and Lehrer found that an optimal range of 4-6bpm (i.e., very slow), usually in a state of
restful alertness, is correlated with the greatest HRV but that HRV generally increases as
breath rate decreases (Song, Lehrer, 2003). Stark et al. showed that a target rate too
different from one’s resting breath rate requires greater attention and effort to change it
(Stark, Schniele, Walter, Vaitl, 2000).
Referring to the hyperventilation theory, lowering the breath rate can bring carbon
dioxide back to normal levels, allaying neurological alarms (Ley, 1999). A 1970 study of
101 acute psychiatric patients in a hospital found that clinical improvement was
significantly associated with a mean decrease of resting breath rate of 3.4bpm (Skarbek,
1970). However, recent research has shown that if one breathes too slowly, carbon
dioxide levels may decrease so much that feelings of pressure or stress result (Roth,
2005). As a result, interventions must focus not on encouraging users to breathe at
arbitrarily low rates but at their own resting rate or perhaps at a rate in between.
Monitoring the breath, without instructed regulation, is itself an effective means of
regulating the breath. Conrad et al found that in a clinical study, simply “paying attention
26
to breathing significantly reduced respiratory rate and decreased tidal volume instability
compared to the other instructions” (Conrad et al., 2007).
The vast majority of studies we inspected were based on short breathing
instructions where the effects were measured over controlled durations in laboratory
environments. Even in these short durations of breath regulation, parasympathetic
activation is triggered. However, “this effect could be enhanced if respiration could be
maintained at a regular (rather than episodic) slower rate, and (by consequence) increased
tidal volume” (Bernardi et al., 2005). Training or triggering users to regulate their
respiration in light of even mild stress crosses both behavioral and physiological
responses in McEwen and Gianaros’s conceptual diagram of emergent allostatic load in
Figure 2.2. It is a conscious behavior but within the physiological system only, not
requiring tangible action. This means, we hypothesize, it can be done in parallel to
existing tasks and life events.
Meuret, Wilhelm, and Roth (2001) had participants with anxiety disorder use a
handheld sensor that senses the volume of the breath 5 times over a four-week period
(administered by a psychologist). The ultimate goal in their bio-feedback-augmented
respiration influence was slow, shallow, and regular diaphragmatic breathing. They
showed patients had significantly fewer panic symptoms and reduced physiological
indicators for stress and depression solely by inducing regularity and reduced rate of
respiration. Further, they showed that the effects were retained after the study. The team
speculated that the “biofeedback breathing training probably makes patients feel more in
control of their bodily reactions and makes them react less fearfully to them” (p. 600). It
was tidal volume, rather than rate alone, that the team used to explain the effect. This
27
further supports the claim that changes in rate must be complemented by changes in tidal
volume. A key limitation of the study was that the sensor was not ambulatory and could
not sense the patient continuously. The same team later identified a training method that
involves 4 characteristics of the breath: increased ventilation (respiratory rate x tidal
volume), breath-to-breath regularity in rate and depth, rate alone, and chest breathing
(Meuret, Wilhelm, Roth, 2004). It was feedback of breath rate and tidal volume alone that
helped participants “facilitate voluntary control of respiration and reduce symptoms” of
anxiety.
Though it may seem to follow from these studies that optimal respiration is that
which is constantly slow and deep, this is not true. Arousal, broadly speaking, is a natural
part of the experience of life and is crucial to physical and cognitive performance. The
aim is not to avoid all fluctuations in respiratory state but to support the ability to
effectively and efficiently recognize one’s sustained and unnecessary arousal and return
to a neutral state. This is self-regulation in its essence: the self-awareness to monitor
one’s state and the capability to influence it.
Part of the value of this dissertation is that it draws upon multiple fields:
psychophysiology, biofeedback, interaction design, electrical engineering, and behavior
change. Due to this diversity, Table 2.1 illustrates in a plain manner the key concepts
and mechanisms motivating breath regulation as a means of regulating
psychophysiological state.
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# Concept Description
1 Psychophysiology Study of the link between psychological processes such as fear and physiological response such as dilated pupils. The link also exists in the opposite direction, between physiological processes such as increased breath rate and psychological processes such as anxiety.
2 Sympathetic response
The part of the human nervous system that prepares the body for action. Dilates pupils, contracts muscles, etc.
3 Parasympathetic response
The part of the human nervous system that initiates healing processes.
4 Allostatic load When the sympathetic nervous system is over-activated, the presence of stress hormones in the body ‘builds up’.
5 Self-regulation As it is used here: the innate, learned, and controlled process of maintaining balance between the sympathetic and parasympathetic systems.
6 Stress-reactivity One’s innate, learned, and controlled reactivity to environmental or imagined stressors.
7 Heart rate (HR) When the sympathetic system is activated, heart rate increases to pump blood more quickly to muscles so they can take the person more quickly away from the threat.
8 Heart rate variability (HRV)
The heart rate is actually constantly fluctuating. The amount of fluctuation is an indicator of how responsive the body is to its demands. High HRV is an indicator of general health.
9 Respiratory Sinus Arrhythmia (RSA)
The difference in heart rate between respiration inhalation and exhalation. The larger the amplitude, the more healthy the individual.
10 Respiratory pattern
Scientists have found a link between the way one breaths and their cognitive and emotional state. Breathing pattern includes rate, regularity, volume of air, etc.
11 Respiratory regulation
Consciously regulating one’s respiration pattern is a tangible means of influencing one’s mental state & reducing anxiety.
12 Respiration rate (RR)
Also called ‘breath rate’. High breath rate is indicative of anxiety and sympathetic activation. Low breath rate is correlated with a calm state of mind and high HRV.
13 Tidal volume The amount of air inhaled or exhaled. When reducing breath rate you want to increase tidal volume (slow, deep breathing).
14 Chest or diaphragmatic breathing
Another characteristic of the breathing pattern is the location: is the breathing ‘shallow’, where only the top of the lungs moves in and out? Is it from the diaphragm or belly? Or is it full-chest breathing?
15 Breath awareness The notion of simply devoting some amount of attention to the state of the breath, without consciously influencing it. This has been shown to effectively regulate and calm the breath.
Table 2.1: Lay descriptions of the key concepts motivating breath regulation as a means of regulating psychophysiological state. Not meant to be comprehensive.
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2.3 TECHNOLOGY-MEDIATED RESPIRATORY SELF-REGULATION
Based on the review of the psychophysiological literature above, we identified
respiration rate as an appropriate candidate for modulating the stress response because
respiration:
- …is a direct mechanism for modulating the autonomic nervous system (as
opposed to heart rate, which we can control only indirectly).
- …is relatively simple to interpret (i.e., fast, slow, or average).
- …can be sensed relatively easily (e.g., using a thoracic strain gauge) in an
ambulatory manner.
- …can be regulated while one is engaged in other tasks.
- …does not require high precision sensor accuracy for reasonably useful
information. More granular information can be more useful but rate alone can
develop a sense of self-awareness and, as long as users increase tidal volume
accordingly, is a robust method of mitigating stress.
We do this with the caveat that rate is only one of four possible respiration characteristics
that could be used in aiding self-regulation. Tidal volume is relevant to both theories of
the relationship between stress and respiration.
Many people are motivated to, encouraged to, and even clinically prescribed to
practice breath regulation during their daily lives but they need support to not only assess
their state and guide breath regulation but to remember to do it at the most relevant time
(Meuret, Wilhelm, Roth, 2001; Wilhelm, Roth, Sackner, 2003). This section explores
technological methods of providing that support. The question of how best to motivate
users to do this is outside the scope of this dissertation.
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The modern laboratory study of using technology to shape or develop one’s self-
regulative ability started, arguably, in the early 60’s by the research Armenian-Canadian
scientist John Basmajian published in Science (1963) and by the work of Neal Miller
(1972). Research in this vein has been active since it was popularized at Harvard
(Shapiro, Tursky, Schwartz, 1970). It waned but, with the advent of ubiquitous
technologies and the demands of information work, has grown in recent years.
Traditional biofeedback, which attempts to train patients to change their state on
command, is an example of a developmental technique to increase calm because it aims
to develop one’s own self-regulatory processes.
Clinical biofeedback practitioners have long encouraged patients to “use
breathing to focus attention, reduce arousal during the day, and inhibit the somatic
responses induced by stressful stimuli and pain” (Peper, 2003). “Appropriate
modification of the respiratory pattern can in fact induce changes that appear to have
useful clinical applications in different diseases” (Bernardi, Porta, Spicuzza, Sleight,
2005). Practitioners use a number of methods to influence breathing patterns, falling
generally into two categories: (1) self-assessment or (2) guiding breath regulation.
The first category offers users insight into their state for self-assessment. The
iPhone-based MyBreath (2011) infers respiratory pattern from the microphone’s input as
the user breathes into their headset. Azumio’s Stress Check (2011) uses the phone’s
camera as a pulse oximeter to detect heart rate through their finger and give the user
feedback about their state. The LifeShirt offers researchers a glimpse into the
psychophysiological state of the user as well (Wilhelm, Roth, Sackner, 2003). Similar
wearable research systems offer similar feedback to users about their state when they
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synchronize the data with their computers and reflect about the patterns (Fletcher et al.,
2010).
Techniques and tools to guide breath regulation include visual animation (Sisto et
al., 1995), modulating ambient lighting in a room between binary states to guide
inhalation and exhalation (Lehrer, Vaschillo, Vaschillo, 2000; Stark, Schnienle, Walter,
Vatil, 2000), or a virtual metronome (Roth, 2005). Resperate (Gavish, 2010) is a
Walkman-like device that uses auditory tones to guide relaxed breathing to reduce blood
pressure. StressEraser (2005) and emWave (McCraty et al., 1999) measure and guide
users towards larger RSA amplitudes – the latter in coherence with other physiological
rhythms. The iPhone-based Pranayama (2011) uses pie charts and human figures with
animated chests to guide the practice of known stress-reduction techniques using paced
respiration.
In addition to mobile use cases, desktop computers hold potential use, primarily
because of the long durations that many users spend with them. The publicly available
‘Calm Down’ desktop application (2011) dims the screen to pace respiration in a calming
inhalation/exhalation pattern at predetermined intervals or upon user demand. Another
common technique is “break-reminder” software (Morris et al., 2008). Morris et al.
(2008) acknowledged the desire to maintain productivity while taking physical breaks.
Desktop-based biofeedback games have also been explored to motivate HRV regulation
and neurophysiological coherence (McCraty et al., 1999).
Pacing stimulus design can lead to ambiguity about how long each inhalation and
exhalation will be. Methods with binary states (e.g., when will the light turn off?) or
intensity (e.g., what is maximal brightness?) do not clearly indicate the end point of the
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current inhalation or exhalation. These problems are not frequently acknowledged in the
literature to our knowledge, assumedly because the physiological implications are of
primary interest.
A noted trend in the literature is the practice of using multiple physiological
sensors to gain a comprehensive picture of the user’s state. If the user is shown multiple
feedback channels, perhaps they could more adeptly self-regulate their state. This
increases cognitive load so an important question becomes, ‘what physiological data is
best to feedback to users to most effectively help them self-regulate?’ A study by Reyes
del Paso, Godoy, and Vila (1992) comparing the effects of RSA amplitude, RSA
amplitude plus respiration, respiration biofeedback alone, and respiration instructions
showed that it was the respiration conditions that produced the most efficient changes in
RSA amplitude (as opposed to directly showing the user their RSA amplitude). Even the
condition that used RSA amplitude and respiration biofeedback resulted in slower
changes because of the extraneous cognitive load required for users to “search for [RSA]
control strategies” (p. 272). Further, participants reported they even though there was no
explicit instructions to do so, they used respiration to modulate RSA amplitude, a clear
indicator that RSA amplitude feedback is an indirect feedback element that requires
conscious attention, rendering it less useful for user feedback. The study showed
conclusively that “the parasympathetic cardiac outflow seems to be controlled easily in a
voluntary way in normal subjects by means of simple strategies for changing the
respiratory pattern toward slower respiratory rates and greater respiratory amplitudes,
with or without the help of biofeedback” (p. 273).
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HCI research has also explored human breath as an input modality. Marshall et al.
(2011) reviewed relevant literature on using breath for two purposes: (1) an assistive
device to aid otherwise challenged users and (2) as expressive input into systems
attempting to infer affective state (e.g., games (Nacke et al., 2011)). Neither category
attempts explicitly to produce desired respiratory patterns in the user. Affective
computing (Picard, 2003), a related class of computing systems, attempts to infer, detect,
or influence affective state (rather than psychophysiological state). Some of these are
more recently being applied to health and self-awareness, e.g., the digital mirror (Poh,
McDuff, Picard, 2010). What differentiates our work is (a) the continuous nature of the
monitoring and feedback and (b) a focus on respiration and not affective state or valence.
Respiration rate is arguably the ideal data source for user feedback. Existing
systems suffer from requiring high amounts of self-discipline to use because they are
essentially means of explicitly training the user. We have not seen in the published
literature methods of continuously influencing breathing patterns to augment self-
regulation in a continuous manner.
2.3.1 SOCIAL INFLUENCE ON PHYSIOLOGICAL BEHAVIOR
Social awareness is “an understanding of the activities of others, which provides a
context for your own activity” (Dourish & Belotti, 1992). It can emerge via social activity
indicators (Ackerman & Starr, 1995) without requiring direct social interaction.
Peripheral displays have been used to make such indicators perceivable in a glance. For
example, EventManager (McCarthy & Anagnost, 2000) uses a peripheral display to
notify users of location and person-specific events that are relevant to collaborative
organizational work. Sideshow (Cadiz, Venolia, Janke, Gupta, 2001) displays personal
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information management data in an always-visible peripheral desktop display, allowing
users to drill-down to details at any time.
In addition to studies of improved productivity, recent HCI research focuses on
using social systems to motivate behavioral change. For example, Maitland and Chalmers
(2011) conducted a study of how peer influence can be designed into systems promoting
weight management. They review systems (e.g., ActiveShare (Fialho et al., 2009))
employing socially motivating mechanisms such as setting commitments, sharing data
with others, and normative influences (Cialdini, 2008).
Though we draw on social systems for behavior change, our primary focus is on
mitigating stressors. In this vein, researchers have studied affective social games in which
users “out-calm” one another as measured by skin conductance and/or heart rate. Bersak
et al.’s (2001) early example found two primary problems with such systems: (1) players
were frustrated that they could not control these physiological measures directly and (2)
falling behind in competitive games can add to the user’s frustration.
In social psychology, synchrony can foster cooperation, rapport, in-group identity,
and altruism by strengthening social attachment among group members (Valdesolo et al.,
2010). Valdesolo et al. (2010) note that “[s]ynchronous others are not only perceived to
be more similar to oneself but also evoke more compassion and altruistic behavior than
asynchronous others experiencing the same plight.” Synchronous physical behavior is
rarely employed in virtual tools, assumedly because individual use is the norm. The
“parallel games” of Mueller et al. (2010) showed how shared physical experience bonds
and motivates distributed participants by connecting users as they jog or run.
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We have not seen in the literature exploration into using the psychology of
synchronous physical behavior applied to cardiovascular or physiological behavior such
as breathing in order to intentionally influence respiration patterns. The literature points
to three reasons that synchrony may be useful for CSCW designers: synchrony may be
used to (1) address the free-rider problem by increasing empathy and relatedness
(Wiltermuth & Heath, 2009), (2) motivate and facilitate learning via imitation, and (3)
build connectedness.
2.4 SUMMARY AND IMPLICATIONS FOR CURRENT RESEARCH
The literature demonstrates a significant research opportunity to augment
continuous respiratory self-regulation with continuous respiration rate monitoring,
feedback, and incentive systems. Augmenting self-regulation, rather than motivating
users to do therapeutic exercises, represents a significant departure from prior approaches
and leads to interesting research questions that are of interest to researchers in HCI. The
creation of such novel tools could complement intensive training, regular practice, or
stand alone as a means of integrating calming influences into our technological
ecosystem.
In the present dissertation, our focus is solely on supporting and incentivizing
users to breathe at their personal resting rate in parallel to existing work tasks while at the
computer and beyond. We will investigate design techniques and evaluate their efficacy
while also investigating their effect on cognitive performance. It is our intention that
research in this domain can inform a class of future ‘calming technologies’ to improve
individual and collective health and productivity.
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37
DESIGN AND VALIDATION OF A NON-CLINICAL AMBULATORY RESPIRATION SENSOR
This chapter provides a description of the sensors used in subsequent chapters.
There are many methods of sensing respiratory rate; Al-Khalidi et al., (2011) provide a
review of nine methods used in clinical research. Because the goal of this dissertation is
to support research on non-clinical interaction design and behavior modification, we do
not require clinical-grade accuracy, which allows one to assess precise tidal volume
among other characteristics. Rather, we are interested in sensing of respiration rate in a
continuous manner. As such, a high degree of wearability is desired. The data must also
be sent in real-time to a computing device.
Existing commercial sensors did not support our requirements. As a result, we set
out to design and build a sensor that fit our needs. We identified two dimensions,
accuracy and wearability, that describe a continuum of respiration sensors for the purpose
of continuous feedback of respiration rate. We then inspected the various methods of
sensing respiration and looked to see which would be amenable to our needs. A list of
known methods is shown in Table 0.1.
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Method Description Acoustic modeling Uses a microphone near throat/nose. Thermistor Airflow measurement by measuring temperature using a
thermistor near the mouth (e.g., by putting sensor on a pair of glasses and requiring user to breathe only using nose).
Accelerometer Detection of movement of the chest or air passages in the throat using a tri-axial accelerometer.
Transcutaneous CO2 monitoring
Measuring CO2 diffusion using electrodes on skin.
Pulse oximetry Blood-oxygen saturation measurement inferred from changed in heartrate using pulse oximeter (on earlobe or finger), high-accuracy heart rate monitors, or plethysmogram.
Doppler Radar-based measurement on chest expansion at a distance. Optical Sensing of chest movement of stationary subjects using infrared
light projection or static cameras. Thermal Sensing of semi-stationary temperature changes in around the
mouth and nose at a distance. Strain gauge A stretch sensor measures force that the chest emits when
expanding upon inhalation. Elastomeric plethysmography
Detects changes in chest and/or abdomen expansion.
Respiratory inductance plethysmography
Uses thin bands of sinusoid coils woven to measure .changes in the magnetic field generated when they are stretched. One band on the abdomen, one on the chest. Maximum number of uses is 40-60 before they wear out.
Impedance plethysmography
Two electrodes are put on the skin and measures changes in current between them because of respiration expanding the chest.
Table 0.1: Known methods of sensing respiration.
A thoracic strain gauge to detect expansion and contraction of the chest and/or
abdomen (elastomeric plethysmography) was deemed the best choice given our needs:
wearability, non-invasiveness, and capability of sensing respiration rate accurately
enough for practical use. It is important to note that because our needs are not purely on
accurate description of tidal volume, we were able to focus on the most wearable means
of deriving respiration rate. Measuring changes in thoracic circumference is a robust and
straightforward measure of respiration for healthy individuals (i.e., those without apnea
or obstructed airways) as long as they are not engaged in high-movement activities such
as running (Al-Khalidi et al., 2011). In clinical research, Napal, Biegeleisen, and Ning
39
(2002) were able to use a thoracic strain gauge to detect sleep apnea and parametric
modeling, illustrating its accuracy.
2.4.1 SENSOR DESIGN
This section describes the basic design of the ‘breathbelt’ sensor. There are three
primary components: the belt, the sensor, and the communication mechanism. The strain
gauge sensor is a thin, 2-inch long cylindrical conductive rubber-like material that
increases resistance linearly as it is stretched. It is very sensitive to even subtle
movements – necessary for respiration.
The initial belt design (see Figure 0.1) consists of a single strap with an
adjustable buckle. Such a sensor could be built into clothing or undergarments and does
not require direct contact with the skin. Contrary to the name, the sensor is worn at an
arbitrary location on the abdomen, not around the waist (where belts are normally worn).
40
Figure 0.1: The initial breathbelt sensor. (Top) The adjustable sensor band with the Arduino Uno board. (Bottom) Close-up of the stretch sensor held in
place by two clips.
Our initial sensor was connected to an Arduino Uno, which in turn is connected to
a computer using a standard wired USB connection. The sampling frequency is 20Hz
(50ms) plus a small OS-level variable error from reading serial data (~2ms), making it
near-real-time.
The communication mechanism varies from wired to wireless methods. Initial
version used USB and grew to wireless (XBee and Bluetooth 4.0). Power consumption
did become an issue we had to solve when conducting continuous sensing.
41
The sensor delivers a continuous stream of integers representing the amount of
stretch in the sensor, with some noise. This raw signal, filtered signal, and detected peaks
(i.e., inhalation endpoints) are shown over a period of 30 seconds in Figure 0.2. Due to
the presence of high-frequency noise, we smoothed the signal using a Hanning window.
The window width is selected based on a reasonable maximum breath rate of 40bpm. The
peaks are detected by comparing the value of each data point in the filtered signal to data
points taken immediately before and after that point. The breath rate is then calculated
based on the number of peaks in a 30 second interval proceeding that time.
Figure 0.2: The respiration sensor’s raw signal (red) is filtered (blue) and then peaks (black) are detected using well-studied signal processing techniques. The Y-axis refers to raw sensor values (not normalized).
2.4.2 INDUSTRIAL DESIGN EVOLUTION
This section describes the evolution of the breathbelt’s industrial design from user
feedback, communication needs, and other necessities.
2.4.2.1 Iteration 1: Wired USB
The initial version of the strain gauge sensor used a rather bulky Arduino Uno
glued to the belt with a wired connection direct to the computer via USB (Figure 3.1).
The protective elastic material atop the stretch sensor was meant to protect the sensor
from breaking from too much tension, which happens occasionally.
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2.4.2.2 Iteration 2: Wireless USB
The second version of the sensor used an XBee wireless communication device
housed on a Lilypad Arduino (see Figure 0.3) to allow for more free range of motion
while the user is at the computer. The XBee communicates with an XBee receiver
plugged into the USB port of a laptop computer.
Figure 0.3: Wireless USB version of the original breathbelt, using paired XBee wireless communication widgets and a Lilypad Arduino (the circular PCB).
The black plastic case on the top-most image holds 2 AAA batteries.
2.4.2.3 Iteration 3: Wireless Bluetooth
The third version of the sensor attempted to collect and transmit respiration data
more or less continuously (to a mobile device), rather than only when the user was close
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to a laptop computer. This was a significant difference with previous versions of the
breathbelt. This resulted in two design goals:
1. A less intrusive belt that could be worn for multiple days on end.
2. Continuous data transmission.
To address the first design goal, bulk on the belt was minimized and we ensured
the battery did not require frequent change. The idea was that that sensor would be worn
under one’s primary shirt but over another shirt (i.e., not directly on the skin). Wires and
other components would be kept to a minimum so as to reduce strange bumps on the shirt
or the possibility of catching clothes on the breathbelt. Another innovation here was to
componentize the breathbelt so that the strain gauges, which have been known to break
during harsh use, can be replaced easily. To this end the existing sensors were modified
to be replaceable using snap-on/off buttons (see Figure 0.4).
Figure 0.4: The strain gauge (black) has two hooks (top), which was modified to include buttons to snap in and out of the sensor (bottom). This was necessary
because the strain gauge occasionally breaks and must be replaced.
To address the second design goal, the belt transmits data directly to a mobile
phone. To address power issues, this sensor uses the Bluetooth 4.0 Low Power protocol,
which consumes significantly less power than its predecessor, ideal for sensors. The
iPhone 4S is the first commercial mobile phone to support Bluetooth 4.0. A Texas
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Instruments (TI) development kit board with on-board Bluetooth 4.0 chip was used to
make prototyping possible. The board uses a battery and is optimized for low power
consumption. This prototype also had two red interaction buttons on the transceiver itself,
allowing for on-belt feedback (see Figure 0.5).
Figure 0.5: The most recent breathbelt design includes 3 components: (a) adjustable band, (b) strain gauge, and (c) microprocessor Bluetooth 4
transceiver, allowing it to communicate continuously with a mobile phone.
2.5 SENSOR VALIDATION
The aforementioned thoracic strain gauge sensor was compared to a non-clinical,
commercial respiratory sensor manufactured by PASCO (see Figure 0.6) and used in
college and high school science experiments around the world. The PASCO sensor works
by having the participant wear an 8-inch-wide belt around their abdomen which is then
pumped full of air. Changes in thoracic circumference are then reflected in changes in air
pressure in the belt. A gas pressure sensor senses these changes and sends them to a
custom amplifier that is then connected via USB to the computer. Proprietary software
allows us to record and export data from the gas pressure sensor to compare it to changes
in values from our breathbelt sensor.
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Figure 0.6: The PASCO respiration sensor used to validate our strain gauge sensor. It uses a gas pressure sensor to measure how air pressure in the belt
changes as the wearer breathes.
A researcher in our team wore both the PASCO device and the USB-wired
version of our thoracic strain gauge simultaneously for a period of 20 minutes. Both
devices were worn around the abdomen, the strain gauge immediately above (and not
touching) the PASCO device. The researcher then worked on a variety of unrelated tasks
on the computer, sometimes consciously changing his breath rate and sometimes not.
We were interested in the similarity between the frequency (not amplitude) of the
two signals. An excerpt of the two raw signals side-by-side is shown in Figure 0.7. We
attribute the small differences between the sensors to OS-level timing interruptions of
other processes which can delay writing to the log file in our sensor. For example, at 235
seconds in Figure 3.5 one can see the breathbelt (in red) skips a cycle.
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Figure 0.7: X-axis is Time in seconds and the Y-axis is normalized sensor values. A 5-minute excerpt of our thoracic strain gauge breathbelt (red)
compared with the PASCO sensor (blue) shows their similarity, with some noise. Each peak is the apex of an inhalation.
We ran a smoothing algorithm on both signals to identify the breath rates
averaged over a 120sec. The width of the Hanning window used to smooth both signals
was 4 seconds. The value was selected after trial and error to most optimally delineate
peak detection. We normalized both signals and plotted them on the same graph, as
shown in Figure 0.8 over the full 20-minute period.
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Figure 0.8: X-axis is Time in seconds. A comparison between the sensor gauge-based breathbelt and the commercial PASCO sensor that uses highly sensitive air
pressure fluctuations to measure breath rate.
First investigating the signals visually, we see there is a high amount of variance
in the signals as the user worked across different tasks including having a conversation.
The signals are very similar, sometimes diverging but returning to a similar breath rate
and neither signal is notably above or below the other consistently.
The mean difference of -0.028bpm was found between the two sensors. I.e., the
breath rate calculated by the strain gauge was, on average, 0.028bpm higher than that
calculated using the PASCO sensor. The cross-correlation, a measure of correspondence
between two time-series signals, between the two signals was 0.985, making the sensor
more or less identical to the commercial PASCO sensor.
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2.6 CONCLUSION
This chapter motivated, described, and validated the design of an ambulatory
respiration sensor optimized for wearability, accuracy, and continuous data transmission.
Four iterations of the breathbelt’s industrial design were described along with a
comparison with a non-clinical commercial sensor, showing the accuracy between the
two sensors is comparable. Different iterations of the breathbelt are used in subsequent
studies presented in this dissertation as the studies progressed and wearability became a
greater emphasis.
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CHAPTER 3 PERIPHERAL PACED RESPIRATION: INFLUENCING RESPIRATORY PATTERNS DURING INFORMATION WORK
This chapter explores methods for mitigating stress by motivating calming
physiological habits during computer work, with the advantages that such systems (1)
need not interrupt users’ work, (2) provide continuous, rather than sporadic, self-
regulation goals, and (3) complement relaxation therapies to work across computer tasks.
I investigate methods of integrating respiration-pacing techniques into the desktop
computing environment to enable peripheral paced respiration (PPR). Such a system
would allow users to engage in other tasks while regulating the breath, increasing the
accessibility and frequency of breath regulation. To that end, this chapter makes two
contributions. The first is a peripheral visual feedback technique to influence respiration
of a desktop user. The second is the results of a study evaluating the effects of PPR
compared to a control condition lacking visual feedback.
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3.1 A PERIPHERAL PACED RESPIRATION INTERFACE
The prevailing interaction paradigm for pacing respiration is modal: interfaces
require the focused attention of the user. This is due to the common assumption that to
create a calm state, an interface must require the user to stop their current activity, which,
it is assumed, contains stressors. Given the demonstrated ease with which users can
entrain their breath to visual stimuli, this chapter proposes using short but frequent pacing
sessions that are peripheral, allowing the user to engage in other tasks while concurrently
pacing their respiration. Peripheral pacing requires continuous monitoring and a medium
for intermittent biofeedback. To our knowledge, the work here is the first instance of
peripheral respiration pacing being integrated directly into the desktop.
Our design goal was to evaluate the feasibility of peripherally regulating the
respiration of a desktop computer user. Such a system requires two functions: (1) sensing
user respiration and (2) feedback to pace the respiration across computing tasks.
3.1.1 WIZARD-OF-OZ PROTOTYPE
The research team first created a Wizard-of-Oz prototyping tool to assess
qualitative reactions to different forms of feedback. The researcher would sit behind the
user as they worked at their desk where a researcher would manually observe the user’s
breathe rate due to chest rises. They would approximate the rate and input it using a slider
onto a web form (see Figure 3.1). The pacing prototype installed on the user’s computer
read this data from the webpage and updated its current user breath rate accordingly.
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Figure 3.1: The user interface of the web-based Wizard-of-Oz interface that study administrators would use to control the visualization of the desktop-based feedback of a user in real-time. The panel included elements that were
not yet in use (e.g. breath regularity).
An important early consideration was whether feedback should be integrated into
a specific application (e.g., programming code editor, web browser, or productivity
software) or system wide. Because information work involves multiple applications, the
team opted for the latter.
The team used visual pacing because information workers often listen to music
and work in office areas where others are working. The first prototype used a pulsing
circle atop other windows in the top-right screen corner as pacing stimulus.
Using this prototyping method with several users in our lab, two primary issues
were found. First, though seemingly useful, real-time feedback regarding one's breath
rate is highly distracting, as users frequently check the accuracy of the detected rate.
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Second, the rate of the pulsing circle was not noticeable when users were deeply engaged
in their work, even when the pulse rate was exaggerated.
3.1.2 USER INTERFACE
The final design does not require any mouse- or keyboard-based interaction and
does not require researcher interaction. Our team implemented three pacing techniques
and a calibration mode. ‘Screen Dim Feedback’ sets the pacing stimuli to dim the entire
screen from near-black to maximum brightness at the target rate. ‘Menu Dim Feedback’
does the same but only to the Mac OS menu bar. ‘Bounce Feedback’ uses an animated
horizontal bar to pace respiration (see below). The calibration mode option toggles the
display of a gray bar whose y-position is controlled in near real-time by the resistance of
the stretch sensor. In calibration mode, the user can determine if the gray bar is indeed
moving up and down as they breathe or if the band requires tightening or repositioning.
Figure 3.2 shows how the study administrator selects the current pacing mode.
Figure 3.2: The user interface of the client was used by the study administrator to select the current peripheral pacing method.
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3.1.3 PACING RESPIRATION PERIPHERALLY
Our team implemented and tested a pulsing light technique, two dimming
techniques (screen and menu bar) and one object animation technique. We chose the
animation technique because it is recommended to biofeedback practitioners (Lehrer,
Vaschillo, Vaschillo, 2000) and performed best in our early tests: users could identify
clear end points to inhalation and exhalation in their periphery.
The object animation technique (Figure 3.3) works by moving a screen-wide,
semi-translucent grey bar up and down across the screen, representing inhalation (up) and
exhalation (down). The ratio of up to down is 1:1 but could be adjusted or user-
configurable in future versions. Slow-in and slow-out animation (Lasseter, 1987) is used
to provide smooth movements and aid tracking. The bar moves across the lower third of
the screen to reduce both distraction and distance travelled (using the full screen height
would require a fast and distracting animation).
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Figure 3.3: The peripheral paced respiration feedback used an animated, semi-translucent grey bar stretching across the screen. Vertical arrows on the left
indicate the full range of motion.
Each user’s target breath rate is set relative to an individual resting baseline,
rather than a universal target that might require too much effort (Stark, Schnienle, Walter,
Vatil, 2000). The target rate is set to 20% below their baseline to exaggerate the slow
rhythm of resting breath. This particular value was determined through internal testing to
be slow enough to not be distracting but not so slow so as to make it unrealistic for users
to do while engaged in another task.
The software continuously samples sensor data and determines when to display
visual feedback. If the user’s current breath rate is 20% above their resting rate, pacing is
triggered. This value was chosen through iteration with our research team members so as
to reduce frequent triggering as the user’s breath rate fluctuates frequently. The software
will also automatically trigger pacing at least once every six minutes to evaluate its
influence on breath rate. Similar to prior studies, a 2-minute duration was used (Bloch,
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Lemeignan, Aguilear, 1991; Sisto et al., 1995). A study was conducted to evaluate the
efficacy and feasibility of using a PPR system to pace users’ respiration to a resting rate
while authentically engaged in information work.
3.2 STUDY
A study was designed to determine if PPR influences user respiration across
computing tasks in a naturalistic manner. Participants were recruited who had existing
work to do (e.g. research, programming, writing). Thirteen university students (9 male, 4
female, mean age=25.5) were recruited from computer science and related disciplines to
participate. They were told they could conduct their existing work during the experiment.
Participants were desired to be genuinely engaged and to work naturally (i.e., switch
windows and tasks as they normally do). Participants were not compensated. According
to a post-study questionnaire, no participants had existing respiratory conditions.
The study design was a counterbalanced, within-subjects experiment in which
participants were exposed to two conditions: (1) no feedback and (2) PPR feedback. As a
control, participants wore the respiration sensor in both conditions.
3.2.1 PROCEDURE
Participants first wore the sensor band and the administrator tightened it. They
were told that the sensor measures their respiration. A short calibration period ensured the
band was positioned accurately. Participants sat in a chair in front of their laptops,
working alone and not speaking. Their posture was not controlled, again to ensure a
naturalistic testing environment. Hence, they were allowed to lean backwards and
forwards in the chair, which can have an effect on respiration rate (Sisto et al., 1995).
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Participants first completed a consent form. As in prior studies (Ley, Yelich,
1998), participants were then asked to close their eyes for 3-minutes and relax.
Unbeknownst to participants, baseline data was collected during this relaxation period.
Three participants did not take part in the relaxation period; their baseline was recorded
as they completed a pre-survey.
After recording the baseline, the experiment began with the participant being told
they could start working on their own tasks. They were also told that when the gray bar
appeared, it represents their target breath rate.
To guarantee that pacing would occur at least three times during the PPR
condition (at least once every six minutes), the duration of each condition was 20
minutes. When activated, PPR occurred for 2 minutes.
3.3 RESULTS
Consistent with prior studies, the mean breath rate across conditions was
16.67bpm (SD=4.28) and the breath rate of the relaxation period baseline was 9.33bpm
(SD=5.31). See Figure 3.4 for a visual illustration of the results.
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Figure 3.4: (Top) Mean breath rate for the No Feedback and PPR conditions with standard error bars. (Bottom) Mean breath rate during the PPR condition when
PPR was on and off.
Figure 3.4 (top) shows the mean for each condition with 95% confidence
intervals. Paired t-tests were used to compare the means of each condition. A significant
difference was found between no-feedback (M=17.58, SD=4.18) and PPR (M=15.7,
SD=4.49) conditions; t(12)= 3.83, p<0.005. The mean difference between conditions was
1.8bpm.
Figure 4 (bottom) shows the mean breath rate during the PPR condition when
PPR was active or inactive. A paired t-test was conducted to compare the means between
when PPR was activated (M=14.96, SD=4.44) and when it was not (M=17.09, SD=5.25);
t(12)=3.5647, p<0.005. The mean difference was 2.13bpm. The mean proportion of time
that the feedback was activated was 0.60 (SD=0.14).
A paired t-test was used to compare the breath rate in the PPR condition when
feedback was unavailable (M=17.09, SD=5.25) and the breath rate during the no-
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feedback condition (M=17.58, SD=4.18); t(12)=0.989, p>0.05. When feedback was not
present, breath rates returned to their working rates.
To illustrate how the breath rate is impacted by PPR, Figure 3.5 depicts the
breath rate of one participant in each of the conditions. The no-feedback condition (top)
shows a relatively consistent, high breath rate. While PPR was active (bottom) the breath
rate decreased.
Figure 3.5: Breath rate for one participant in both no feedback (top) and PPR
(bottom) conditions. Bold (orange) areas indicate where PPR occurred
Using Likert scales from 1 to 5 where higher scores represent greater levels of
‘annoyingness’, participants rated PPR 2.0 (SD=0.87). Concerning how much it
adversely affected productivity, PPR was rated 2.2 (SD=0.6). Lastly, participants gave a
score of 3.7 (SD=1.0) as to how likely they would be to use the software all day long
while working, were the sensor non-invasive.
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3.4 DISCUSSION
Our hypothesis that PPR influences user respiration while they are engaged in
naturalistic tasks is supported; the peripheral feedback reduced breath rate significantly.
The 1.9bpm difference is almost identical to the 2bpm interval changes shown by Song
and Lehrer (2003) to correspond with significantly higher HRV amplitude.
Breath rates were observed to return to working levels between pacing instances.
Hence, there was no evidence of persistent rate change. This is a viable area for future
study; cues, social feedback, and game mechanics, among other methods, could help
motivate users to maintain low respiratory rates as they work.
Based on self-report data, PPR feedback was not too distracting and participants
expressed motivation to use PPR for sustained periods in the future. The results motivate
longitudinal research that attempts to motivate, trigger, or incentivize users to pace their
respiration even when pacing is not active. This allows for long-term respiration pacing
that could complement existing methods of respiratory habit-change. The pacing
algorithm could factor in the physiological and work history of the user. Such ‘context-
sensitive biofeedback’ could be used to influence the physiological factors underlying
cognition, and affect.
Quantitative measures of the level of distraction caused by PPR (such as working
while pacing) were not collected in this study. Further, the PPR method used goes beyond
breath rate and implicitly proposes time and duration for inhalation and exhalation, which
is beyond our goals and may have required greater effort than is necessary. This presents
an opportunity for future work to identify the optimal balance between pacing efficacy
and distraction.
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The PPR method is but one possibility for visual PPR techniques. Similar
research could be done with auditory techniques that require headphones (or not), haptic
techniques in the mouse, chair, keyboard, or other peripheral, or other visual techniques.
The design space of integrated biofeedback and PPR is large and this chapter outlined the
most important design parameters. It is crucial to note that the goal is not feedback per se,
but rather feedback that is appropriately used to help users change their
psychophysiological state without requiring them to mentally search for control strategies
which would be distracting. That distraction would reduce compliance, being self-
defeating. The goal of systems that augment self-regulation is different from the goal of
enabling self-reflection. In the former, the purpose is to augment state change in real-
time. In the latter, the purpose is more intellectual, to give users feedback and data to
reflect on their state to motivate future state change.
As a tertiary contribution, this study is the first known that quantifies the effect of
naturalistic information work on respiration rate. In our case, resting breath rate was
almost half the working rate. This result highlights the issue of mild but chronic stress
that occurs during information work, and again recommends longitudinal studies.
3.5 CONCLUSION
This chapter presented the design of a peripheral paced respiration technique and
evaluated its efficacy in a naturalistic task environment. It was found that peripheral
pacing significantly reduces breath rate, but these changes are not sustained for the
duration of the tasks. The results recommend further research on using motivational cues
to amplify or sustain the effects. Further, the results motivate more research on
incorporating biofeedback directly into the desktop operating system to complement long
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durations of information work in an attempt to reduce stress and increase productivity,
general health and wellness.
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CHAPTER 4 BREATHCAST: A STUDY OF SOCIAL INFLUENCE ON BREATH MODIFICATION
The peripheral paced respiration (PPR) technique shown in the previous chapter
overlays a semi-transparent bar across the user’s screen, floating above other windows,
and animates it up and down across the lower third of the screen at a rate contingent upon
the target breath rate and rhythm. The user’s resting breath rate is used as their target rate.
The bar’s visibility is triggered by the user’s current breath rate (i.e., cycles of breathing
per minute). Our aim was to extend this method with social feedback; our study examines
the effect that synchronicity of feedback has on breath rate.
There are many possible methods of adding social activity indicators to PPR. The
Breathcast system is grounded in two design goals:
1. There is a shared goal for Breathcast users to breathe calmly while working, and
2. The more one user breathes calmly, the more they influence others to stay calm
(and vice versa).
In the Breathcast version of the PPR bar, participants are told these principles outright but
in future work these will be communicated via the interface. Avatar icons of other
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Breathcast users are displayed atop the PPR bar (see Figure 4.1). The selection of which
icons to display is dictated by the breath performance of those users: the more one
breathes at their resting rate, the more their picture appears on the bars of others. ‘Breath
performance’, here, is defined as the proportion of time one is breathing at one’s
individual resting rate in a recent time range.
Figure 4.1: Breathcast works by intermittently animating a semi-transparent bar across the bottom third of the user’s screen. The inset shows how profile icons
of other Breathcast users are discreetly displayed on the bar. The vertical arrow on the lower right illustrates the range of bar movement. In asynchronous
mode, the bar is blue to aid differentiation.
Rather than offering social presence indicators to simply enhance behavioral
awareness of others, the system attempts to motivate desired breathing patterns by
making the visibility of those indicators contingent upon the performance of the social
agents in question. This introduces a subtle but potentially effective motivational cue that
one’s behavior is being measured and will receive due positive recognition.
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Breathcast supports two types of temporality: (1) asynchronous, where users’ past
performance dictates display on your bar and your performance dictates how often your
profile is displayed on future users’ screens, and (2) synchronous, where bi-directional
influence occurs with other concurrent Breathcast users. A third, “coupled” form of
synchrony, in which the actual inhalation and exhalation intervals are conveyed, was not
included because users found such pacing distracting in early tests.
In sum, users are only aware that others are intending to maintain their own
respective resting breath rate. The social feedback received from other users concerns
how well they are able to do this (their “breath performance”), in order to motivate the
current user.
4.1 STUDY
A controlled, within-subjects laboratory study was designed to compare the
effects of synchronous or asynchronous peer influence on PPR efficacy. The study design
is comparable to that shown in the previous chapter.
4.1.1 PARTICIPANTS
Thirteen university students (8 male, 5 female, mean age 26.9) with no respiratory
conditions or prior PPR experience participated, receiving a $5 gift card at the end of the
study. They were recruited by being told they could participate in a study while doing
their own computer work. To sense respiration, participants were fitted with the wired
USB thoracic strain gauge sensor used in prior studies.
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4.1.2 PROCEDURE
Participants were asked not to leave their seat for the duration of the study. They
first spent five minutes completing a consent form, adjusting and calibrating the sensor,
and establishing a resting breath rate by closing their eyes for three minutes (as in prior
studies (Ley, 1999; Moraveji et al., 2011). Each user was asked for a profile picture and
username that would represent them on the PPR bars of other participants.
Participants were then told they could start working and asked not to play video
games but to do their other work as usual. The tasks were not controlled in order to
ensure naturalistic work. They first worked for 10 minutes without any PPR feedback in
order to establish a working baseline rate. The subsequent two 20-minute conditions were
counter-balanced and participants were told that when a bar appeared, it would “guide
them back to their resting breath rate.” In actuality, the rate of the bar was slower than
their (often quite fast) resting rate so as to not distract. They were told that the shared
goal was for all students on campus to breathe calmly. In the asynchronous condition,
participants were told that the visible icons were from prior participants, and that their
own performance would dictate their appearance on the bars of future participants. In the
synchronous condition, they were told that icons correspond to other real-time
participants in the study.
At the outset of the synchronous condition, an administrator spoke on their phone,
pretending to coordinate the start of the “real-time” feature. Participants were told that
three other students were doing the study at the same time and therefore the start time
was important.
To control for differentiated feedback, the social data was identically simulated
across participants in each session. The icons of the simulated social agents were all
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“unknown similar peers” represented using icons of random Twitter users across genders.
Different bar colors (green and blue) were used to differentiate the conditions but
otherwise feedback was identical. It was hypothesized that synchronous feedback would
motivate users to be more vigilant with their respiratory regulation than with
asynchronous.
The bar was activated when the user’s breath rate was significantly higher than
their resting breath rate, at least once and at most three times every four minutes so as to
avoid distraction. When activated, PPR lasted a random amount of time between 1-2
minutes. This is because the goal was to make the study feel ‘real’ and that it was
automatically detecting the appropriate amount of time to pace the user’s respiration
(over a minimum of 1 minute).
4.2 RESULTS AND DISCUSSION
The mean breath rates for each participant in each condition are shown in Figure
4.2, with a dotplot illustrating the distribution in each condition in Figure 4.3. Both
figures omit a single outlier to enhance readability.
An omnibus repeated measures ANOVA test found mean breath rates differed
significantly across conditions, F(3)=2.88, p<0.05. Planned paired t-tests were then used
to compare the mean breath rates of the different conditions. Replicating prior work
(Moraveji et al., 2011), the mean resting baseline breath rate (M=15.75, SD=4.5) was
lower than that of the working baseline (M=20.17, SD=2.48), t(12)=-5.135, p<0.01.
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Figure 4.2: Mean breath rates for participants in each condition.
Mean breath rate during the asynchronous (M=17.78, SD=4.16) condition was
lower than during the working baseline (M=20.17, SD=2.48), t(12)=3.09, p<0.01. The
same was true for synchronous (M=16.91, SD=4.43) feedback, t(12)= 3.868, p<0.01.
More surprising was that both synchronous (t(12)=-0.0873, p=0.3991) and asynchronous
(t(12)=-1.668, p=0.1212) conditions did not differ significantly from the resting baseline.
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Figure 4.3: Mean breath rate for each condition. BL=baseline, WBL=working baseline, A=asynchronous, S=synchronous.
The average breath rate in the synchronous feedback condition was 0.87bpm
lower than the asynchronous condition, supporting our primary hypothesis. However, this
small difference is only weakly significant, t(12)=2.156, p=0.052. There are two
hypotheses that might explain the observed difference: a magnify effect, in which breath
rate changes were more pronounced for comparable durations, and a persist effect, which
supposes users maintained a lower breath rate over a longer duration.
Visually inspecting each participant’s data, there is no noticeable trend. The
difference in breath rates when the PPR bar was shown and hidden in each condition was
also inspected. In the asynchronous condition, when PPR guidance was activated it
produced a mean difference of 0.74bpm while in the synchronous condition it was
1.49bpm. This is evidence of a potential magnify effect, albeit small. Figure 4.4 below
shows a time series of a single representative user that illustrates this dynamic.
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Figure 4.4: Breath rate of a user with a resting rate of 19.5bpm. PPR occurrences are orange. The working baseline condition (top) saw the breath
rate climb upwards. With synchronous feedback (middle), it decreases noticeably during PPR. Asynchronous feedback (bottom) saw rates drop little and continue
to climb overall.
Survey results show that PPR was occasionally distracting (Likert scale of 6,
M=3.3, SD=1.1) and, when asked who they would expect to use the system with,
participants claimed it would be known friends or perhaps clinical caregivers, yoga
instructors, meditators, or other authoritative people.
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This study represents the first known instance of social feedback being used to
influence psychophysiological behavior. Clearly, this study has only demonstrated the
feasibility of utilizing social feedback for influencing breathing behavior – it has not duly
investigated all the ways in which social feedback could be used and for different
purposes. Other social influences may also be explored including indicators of another
user’s breathing patterns over time. Another dimension could be to manipulate the
reputation of the social agent in order to magnify the effect more (e.g., if one’s doctor,
yoga instructor, or favorite celebrity was the social agent in question).
4.3 CONCLUSION
This chapter offered two primary contributions. The first is the interaction design
method for socially motivating respiratory regulation in parallel to information work. The
second is an evaluation of temporality in the system, showing that expectations of
synchronous feedback magnified the effect of social feedback. The implications for
researchers are that continuous physiological behaviors such as respiration can be
socially influenced and that synchronous behavioral feedback from unknown peers, even
at a distance, can enhance the effect. These results recommend further investigation into
design techniques to motivate consistent respiratory change without pacing.
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CHAPTER 5 BREATHAWARE: CONTINUOUS INFLUENCE OF SELF-REGULATION DURING INFORMATION WORK
The previous two chapters examined how visual stimuli could prompt users to
pace their respiration rate during information work. However, the system relied on
repeated intermittent visual cues to remind the user to bring their breath rate down. The
drawbacks in that model are (1) that the system is not adaptive to task valence, (2) the
user is not relying upon their innate self-regulation, and (3) the user only has negative
motivation (in the form of keeping the PPR bar hidden) to maintain low breathing rates.
This chapter investigates different methods of augmenting self-regulation without
explicitly pacing respiration. It contains three contributions. The first is the set of 10
design principles, motivated by the literature on stress and stressors, which describe
necessary attributes of a system that effectively augments a user’s respiratory self-
regulatory processes. The second is the design, implementation, and exploratory
evaluation of a prototype system that realizes these design principles. The third
contribution is the outline of a research agenda and accompanying research questions for
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further study of interactive systems that influence and augment our fundamental innate
self-regulatory processes.
5.1 DESIGN PRINCIPLES
Based on a review of the literature, related tools, and our own experience
designing interfaces and getting user feedback from previous chapters, a set of 10
generalizable design principles (DPs) is presented, each responsive to a challenge of
influencing a user’s psychophysiological state using interactive technology. We refer to
these systems as autonomic interaction systems (AISs) and, when used in these
principles, generally refers specifically to interaction with respiration. The challenge is
shown in italics and the rationale follows. These principles are not meant to educate or
motivate the user to engage in breath awareness or regulation. They assume the user is
motivated to a significant degree and that the use of the technology is only meant to
complement their own self-regulatory processes.
A caveat about these principles is that they are principles, not empirically
evaluated design heuristics. That is, they are drawn from research on self-regulation,
breath regulation for panic disorder, the yogic and qigong practices, human-computer
interaction, and contemplative science. As such, they contain a certain degree of
speculation and, though we attempted to make them as objective as possible, may contain
bias towards the intention to influence users in the direction of calm states or neutral
arousal. A summary of all 10 principles is listed in Table 5.1 below.
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# Design Principle Challenge
1 Accommodate different levels of attention
One’s breath is ‘always-on’ but shouldn’t be all-consuming.
2 Sustain motivation Because we are always breathing, regular attention and motivation to regulate it can be easily dismissed as unimportant.
3 Demonstrate desired patterns of breathing
Few people have a good sense of their desirable breathing pattern at work and at rest.
4 Personalize the feedback Desirable breathing patterns vary widely between users.
5 Reinforce the relationship between the breath and the body
The relationship between breathing patterns and the rest of the body (such as muscles) is not often apparent
6 Avoid exasperating stress with negative feedback
Negative feedback can cause feelings of inadequacy, competition, or resentment.
7 Develop awareness at different timescales
Breath awareness is different at different timescales.
8 Encourage internal self-assessments
Technology could create dependency, hindering true self-regulation
9 Consider secondary components of respiration
Sighing, wheezing, and related events are important contributors to improved self-awareness.
10 Protect the privacy of the breath
One’s breathing patterns are intimate and private and should be treated as such.
Table 5.1: 10 design principles for interactive systems aiming to influence respiratory self-regulation.
5.1.1 ACCOMMODATE DIFFERENT LEVELS OF ATTENTION
One’s breath is ‘always-on’ but shouldn’t be all-consuming. The goal of
respiratory self-regulation is not to have users constantly watching their breath and ignore
their primary tasks. Instead, systems should support the user in returning from a
heightened state of arousal in a reasonable time frame or to refrain from repeated,
frequent sympathetic activation (Obradovi!, Boyce, in press). This means that users may
not pay direct attention to their breath for spans of time where they are focused on their
task yet in a calm manner. As such, AISs must support various levels of attention:
sometimes full attention, sometimes partially aware and looking for feedback in intervals,
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and other times attempting to maintain a certain state while focused on another task (e.g.,
writing an essay, creating a spreadsheet, or manipulating an image).
‘Level of attention’ encompasses granularity of data; precise values may not be
necessary to communicate their importance to users. Gross, relative values may have the
highest impact and require less cognitive load to process. Providing relative values may
require the system have some sort of baseline, whose definition should be communicated
to user (e.g., compared to their individual resting rate, yesterday’s rate, similar peers,
prior best, and so on). Level indicators can also be relative, and descriptions of the levels
should make this apparent. Social comparison is an oft-used method of influence but, in
the context of self-regulation, should be used in a manner that supports self-regulation
and understanding of one’s own state and behavior. It should not compromise one’s
privacy (see 5.1.10).
This principle could be evaluated by identifying what kinds of information users
are able to glean from what kinds of feedback. Using different feedback conditions, an
evaluator could interview participants to ascertain the accuracy of that user’s self-
awareness as augmented by the feedback. Motivation and task would need to be
controlled in such a study.
5.1.2 SUSTAIN MOTIVATION
Because we are always breathing, regular attention and motivation to regulate it
can be easily dismissed as unimportant. It is easy to ignore or not notice the effects and
implications of changing one’s breathing patterns or awareness of breath. Unlike
exercise, a user may not notice outwardly visible effects that others can comment on (or
that a mirror can make salient). Instead, maintaining a calm state or disposition is
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primarily an internally visible change, the effects of which may include greater subjective
self-awareness, less agitation, less cognitive rumination, greater positive affect, and
greater awareness of issues that were invisible before such as minute physical
discomforts. Sustaining one’s motivation to develop and practice breath awareness and
self-regulation can be done in myriad ways and, in this domain in particular, users have
very different levels of motivation. Some users with consistently high levels of stress may
consider that the norm and not even realize the benefits of calm states. They may equate
stress with productivity and calm with sleep, a non-productive state that they address
nightly. In truth, many people dedicate their lives to practicing breath awareness,
teaching others about the breath, and using the breath for personal growth. AISs may
assume high levels of motivation or attempt to inform users about the various reasons to
develop their self-regulatory ability. AISs may appeal to the user’s desire for greater
health, productivity, more positive affect, reduced negative affect, and so on. Such
systems must also take care to not overwhelm users with information and triggers to pay
attention to their breathing; it requires a delicate balance of bringing attention back to a
user’s existing motivation to enhance their own self-regulation.
5.1.3 DEMONSTRATE DESIRED PATTERNS OF BREATH
Few people have a good sense of their desirable breathing pattern while at work
compared to at rest. Most individuals in modern society either do not have a good sense
of what a calm breathing pattern is or how to maintain it in parallel with their
participation in other tasks. Many have never taken part in breathing exercises or
dedicated time to consciously experiencing different breathing patterns. It is important
that any system be able to guide users to experience calm patterns, such as is often done
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in current commercial solutions reviewed in section 2.3. Even users who have
experienced calm breathing patterns may not know how to incorporate them into a
workday. Due to the nature of psychophysiological state itself influencing attention and
psychology, it is difficult to maintain cognitive awareness of a reference point because
cognition itself is affected by aberrant breathing patterns. One’s respiratory state is not
normally quantified in the same way that heart rate is, making respiratory state less
tangible.
Further, the breath has multiple characteristics including depth and regularity,
further complicating the process of remembering one’s neutral patterns of calm breathing.
Simply measuring one’s resting rate is an imprecise practice because it does not define
explicitly what ‘rest’ means. ‘Rest’ normally refers to physical rest but breathing patterns
are influenced by both physical and mental activity. For example, watching a video, even
a calm one, influences the state of the mind, which influences the state of the breath.
Even when closing one’s eyes to meditate, one finds that their breathing patterns and
heart rate rhythms fluctuate consistently with their emotional state.
5.1.4 PERSONALIZE FEEDBACK
Desirable breathing patterns vary widely between individuals. Resting breathing
patterns vary significantly between individuals (Ley, 1999; van Diest et al., 2001) and
depending on their current state of mind (Porges, Doussard-Roosevelt, Malit, 1994;
Biotin, Brigade, Witnesses, 1994), physiological health (Grossman, 1983), and physical
state (e.g., posture). Interactive systems will need to take these differences into account,
rather than guiding users towards a non-existent ‘universally optimal’ breathing pattern.
Further, AISs should aim to personalize the strictness with which they trigger changes in
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behavior. That is, users have very different desires for stringency based on their personal
goals, environmental contexts, and the ebb and flow of work intensity. Feedback while at
home may be very different than feedback at work. Likewise, feedback in the early part
of the workday may be different from the latter.
5.1.5 REINFORCE THE RELATIONSHIP BETWEEN BREATH AND BODY
The relationship between breathing patterns, the body, and the mind is not often
apparent. Users have very different motivations for wanting to develop self-regulation.
However, any AIS should ideally develop the user’s innate self-regulatory ability to
avoid over-dependence and to develop that user’s experiential understanding of the
nature of the mind-body relationship. Developing a user’s understanding and experience
of this relationship is among the most profound intentions that a tool can have for a
human being. An ideal AIS system would reinforce the relationship between breathing
patterns and affect, ability to focus, and other aspects of the human experience (Biotin,
Brigade, Witnesses, 1994; van Diest et al., 2001). For example, systems could uncover
when stressful events lead to halted or hyperactive breathing. In the other direction, a
system could show how a change in breathing patterns brought the user from fragmented
to focused attention. Awareness of any component of this interconnected system could
strengthen self-regulation and self-awareness.
5.1.6 AVOID EXASPERATING STRESS WITH NEGATIVE FEEDBACK
Negative feedback can exasperate the problem. Environmental stressors (i.e., real
physical threats to one’s safety) account for only a small proportion of stress typical
experienced by a modern human being in the West. However, many individuals see
external stimuli as stress-inducing because it triggers their own anxieties or needs to
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comply with external pressures. As a result, interactive technologies are often seen as
stress-inducing.
Feelings of social evaluative threat from a social agent, even a piece of
technology, can do the same. Any interactive system that gives feedback on performance
has the potential of delivering what can be construed as negative reinforcement. That is,
the feedback could make explicit bad breathing performance or, worse, falsely diagnose
what was in reality a calming breathing pattern (false negatives). In the domain in
question, this is particularly dangerous because negative feedback could cause additional
stress, exasperating rather than mitigating the problem. Even accurate positive feedback
(e.g., “Great job, you just had 23 minutes of calm.”) can interrupt a calm state by
triggering feelings of social evaluation and so must be designed in light of this.
5.1.7 DEVELOP AWARENESS AT DIFFERENT TIMESCALES
Breath awareness is different at different timescales. Breathing patterns can be
observed at different levels of analysis: e.g., second-to-second, recent past, portions of
the day, day, few days, week, month, segment of year, year, life stage, and lifetime. There
are different reasons to focus on different levels of analysis at different times: they
influence motivation differently, reinforce the relationship between mind and body
differently, and help diagnose overarching patterns differently. Because changes in the
breathing patterns are constantly occurring, it may be possible to become ‘over-vigilant’
and hyper-focused on moment-to-moment changes. Testing the effects of different levels
of analysis is non-trivial because users must be wearing the sensor for such durations of
time in order to make the data useful and ensure they take the feedback seriously. For this
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purpose, simulated data can be used to evaluate impact on users if it is explained that they
should suspend disbelief.
5.1.8 ENCOURAGE INTERNAL SELF-ASSESSMENTS
Technology could create dependency, hindering true self-regulation. Any AIS
system faces the risk of creating an unhealthy dependency between the user and the
system. This is a well-known problem in “scaffolding” human performances in education
(Pea, 2004). Though humans can be conceptualized as co-evolving with technology, as
described by Douglas Engelbart (Bardini, 2000), self-regulation is ultimately an
introspective process independent of external tools (which serve only to augment and
strengthen one’s innate self-regulatory process). It is therefore important that the system
take measures to encourage an introspective or reflective internal self-assessment.
From relevant literature on stress-reduction techniques and programs, we
identified a practice of an internal awareness ‘check-in’ where one assesses their own
state to determine their own heart rate, breath rate, cognitive state, affective state, muscle
tension, or other indicators of sympathetic or parasympathetic activity. Different
techniques or exercises use different phrases to describe this process: centering, stress
check, cool-down, spot-check, etc. The general goal is to assess one’s state on a regular
but non-disruptive basis; the simple process of assessment is usually enough to influence
one’s state. This practice should complement tool-based feedback, which brings the
user’s attention out from their bodies and onto the external feedback display. One
difficulty in designing for internal self-assessment lies in evaluation because, by
definition, they often do not involve system interaction.
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5.1.9 CONSIDER SECONDARY COMPONENTS OF RESPIRATION
Sighing, wheezing, and related events are important contributors to improved
self-awareness. A number of relevant phenomena or events influence one’s breathing
patterns such as breath-holding during work tasks (Stone, 2008), sighing (found to
increase during high cognitive load tasks (Vlemincx et al., 2011)), sleep apnea during
sleep, and speaking. These events often go unnoticed and their impact on overall
breathing patterns unacknowledged. Note these are not studied in this dissertation though
they are measurable and potentially significant for learning purposes. They can act as
punctuated events to give practical texture to more persistent feedback such as breathing
rate and depth. They can even act as primary feedback mechanisms, making their
detection easy to subjectively validate.
5.1.10 PROTECT THE PRIVACY OF BREATH
One’s breathing patterns are intimate and private and should be treated as such.
Like many other aspects of life, if privacy is broached in what was once a private,
intimate aspect of one’s life, problems can arise. Breathing patterns of others can be
interpreted in undesirable ways, requiring a means of protecting one’s privacy. The
patterns could be used by insurance companies, employers, interviewers, doctors,
therapists, romantic partners, and other social agents as a reflection of the user’s mind,
health, emotional patterns, and cognitive load. As in other aspects of data privacy, users
may be willing to relinquish data privacy to garner well-documented benefits, but such
trade-offs require study outside the scope of this dissertation.
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5.2 PROTOTYPE DESIGN
The prototype described here instantiates the aforementioned design principles
using an iterative, user-centered design research process. The prototype, Breathaware, is
meant to help users develop awareness of their breathing patterns while doing work on a
laptop or desktop. This differs significantly from PPR, whose aim was to visually pace
users to a specific breath rate. An assumption of the present prototype is that the user is
already aware of the benefits of maintaining a calm state. The prototype system has three
components the user interacts with: sensor, client, and social network.
To date, methods of influencing physiological change relied on traditional modal
biofeedback, requiring the user’s full attention. A stationary, as opposed to mobile,
context was chosen for multiple reasons. First, it is a more controlled environment.
Second, it was expected that the technology would be somewhat easier to evaluate. Third,
it allows experimentation with both interruptive and peripheral means of influence.
Fourth, sensor readings of breath rate during user verbalizations are notoriously difficult,
so a context was chosen where users do not regularly speak.
5.2.1 CLIENT
The software client, written in Objective-C for OSX, is a system tray application.
It reads data from a USB receiver and creates a log of all respiration sensor readings.
Because users are expected to go out of range of the computer (e.g. to attend meetings
and meals), the client will automatically detect signal loss, pause, then restart sensor
recordings. The user interface and features of the client are described in the next section.
All log data is stored both on the local drive (retaining the user’s respiratory
history while the sensor is on) and is uploaded to a centralized web repository, associated
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with a unique identifier for each user (email address). A separate process is launched
every 3 minutes to upload both the physiological data and client events via HTTP POST.
The client also stores an adjustable baseline breath rate to ensure that it is tailored
not only to one’s resting rate, but also to their different states and goals (addressing DP4).
The default baseline is 15bpm but it can be set at any time by toggling the record button
on and then off.
5.2.2 SOCIAL NETWORK
The web repository (Figure 5.3), Breathcast, is accessed via a client menu item or
by web browser. An online profile stores their log data and those of social connections. In
future versions, the user will be able to control their privacy settings at a more granular
level.
5.3 INTERACTION DESIGN
This section describes the strategies used to meet the design principles identified
earlier. Because the research here is focused on stationary users at work, it is crucial that
such strategies are usable in a longitudinal setting in parallel to existing work, unlike
dedicated tools or exercises used in isolation. The goal of the prototype was to help users
understand their breathing patterns and support them to breathe in their desired manner.
This section outlines the features of Breathaware and the design principle(s) they address.
Principle 9 is not addressed, as algorithms for secondary characteristics were not included
in the prototype.
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Figure 5.1: The BreathTray and its drop-down menu in two states: sensor disconnected (left) and connected (right). The user’s current breath rate is
displayed on the user’s system tray.
5.3.1 BREATH RATE (IMMEDIATE) – DP3, DP7
A core feature is displaying the user’s current breath rate in near real-time, available
at a glance. This is done by using the drop-down menu itself as the feedback panel
(Figure 5.1). The update interval can be adjusted manually but is set to every second.
Further study is required to ascertain an optimal interval update rate to reduce distraction
while maintaining desired impact on breath rate.
As the active application changes on OSX, system tray icons can be ‘pushed off’ and
made invisible. We wanted to reduce this occurrence as much as possible so that users are
able to see their current status while in any application. The system tray displays three
fields at all times:
• Breath rate: The user’s current breath rate, in units of bpm (breaths per minute).
• Difference from user’s baseline breath rate: The user’s current breath rate in
relation to the current baseline (breath rate while at rest). Here, “131.2%” means
the user is breathing 31.2% faster than their personal resting rate (recorded earlier
or set manually).
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• Calm points: an indicator of past performance (described below)
After several iterations, these three fields above were considered most interesting to
display and display technique was the smallest way to display them without taking too
much room on the system tray. This display is what we call the BreathTray. When the
sensor is disconnected or is not in use, the BreathTray reverts to “<Breathe>” to indicate
to the user that their breath rate is not currently being detected.
5.3.2 BREATH RATE (DAILY) – DP5, DP7
Another user need identified is to be aware of trends that impact one’s daily life
(e.g., stressful situations or activities). This can help users develop an awareness of
particularly stressful or calm moments in their day to develop their self-awareness for the
future. To this end, the user can select “Today’s Highs and Lows” from the drop-down
menu (shown in Figure 5.2) to trigger the display of a window that displays the three
highest and lowest breath rates (consistent within 1bpm for at least 1min). The client goes
a step further to associate a context of work (represented by a screenshot of the entire
screen) and the time of day for each high and low. This strategy aims to strengthen the
mental association between one’s respiratory state and work activities. This can lead to
insights such as “I find that I begin to stress out when I check email too often – or vice
versa”.
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Figure 5.2: ‘Today’s Highs and Lows’ shows three desktop screenshots from each category with relevant metadata (breath rate, time of day). Here, the user’s breath rate was highest when working in email and on a presentation. It was
lowest when viewing their calendar and viewing a document.
5.3.3 BREATH RATE (IMMEDIATE BUT RELATIVE) – DP3, DP7
Rather than displaying to the user a decimal value, another means is using color to
communicate a general relative breath rate frequency. This enables the user to have a
peripheral view of his or her breath rate without looking at precise percentage values.
When this function is toggled on, the entire menu text is displayed in the color according
to the proportion of the baseline (blue: at or below baseline, red: above baseline). Other
levels (e.g., particularly calm or stressed) could easily be added by using other colors or
different intensities of the chosen colors.
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5.3.4 BREATH RATE (LONGITUDINAL) – DP7
The last way to inspect one’s breathing patterns by breath rate is to use the
website to inspect one’s profile. This allows one to see a cumulative average (Figure
5.3, top), a day-by-day breakdown in list form, and a visualization of one’s history data
in a line graph. This also helps realize DP7.
Figure 5.3: The user’s Breath.fm profile for an imaginary user, ‘KKP’. The top shows their overall data including name, last activity update, total calm
points accumulated, mean baseline, and mean BPM. The area below shows their activity stream with event notifications updated in real-time: positive and
negative reinforcement messages and milestone images.
5.3.5 CALM POINTS – DP2
Displayed continuously in the system tray alongside the user’s current breath rate
is the current number of calm points they have accumulated (see Figure 5.1). It is a daily
running total, resetting each morning to zero. A user gets points by breathing calmly; the
calmer they breathe relative to their baseline, the bigger the increment. Points are never
taken away, they are always positive. One’s point total at any time is, therefore, a
function of how long they’ve been at the computer, wearing the sensor, and breathing
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calmly. Breathing at one’s resting rate for 1min adds 2 points. The increment increases
linearly as the user’s breath rate decreases. Because points are accrued while doing any
activity at the computer, they can be accrued while working, watching videos, chatting
with friends, reading, or any computer activity.
5.3.6 DAILY MILESTONES – DP2
Based on the number of calm points achieved, a window is displayed atop all
other open windows at the 40, 80, 120, 160, 200, 240, 300, 400, 600, 800, and 1000-point
milestone markers. At each milestone, one of 50 different nature-inspired, calming
images is selected at random and displayed in a large window, above all other windows
(Figure 5.4). The user must close the window manually to hide the image. The sequence
of images is never repeated.
Figure 5.4: Calm point milestones. The desktop of a user who achieved the 80-point milestone. The inspiring images are always randomized as an attempt to
create anticipation for the different milestones.
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5.3.7 ENCOURAGING MESSAGES – DP2
Giving the user positive feedback when they have been breathing calmly is useful
because (a) the user may not realize it, (b) if they do realize it, they may not know for
how long, and (c) the knowledge that durations (not only instances) of calm breathing are
acknowledged can be motivating. Figure X depicts one of the two types of positive
notification windows included in this prototype. The user can manually dismiss the
notifications as well.
Modeless ‘toast’ notification windows (see Figure 5.5) were implemented that
fade away after five seconds (the default duration in Growl, a popular notification
management system). The notifications appear in the bottom-right of the screen. Future
versions would use standard notification practices, customizable by the user.
• Notable duration: “Very cool – 7:16 of calm.” This contains two elements: a
congratulatory message and the duration that they user stayed below their
baseline. An interesting motivational mechanism included is that the user sees this
positively reinforcement message at an opportune moment: when they are
breathing above their baseline.
• Exceptional duration: “Moment of Zen. 12:37 of calm breathing.” When the user
breathes calmly for at least 10min, a different category of positive reinforcement
is denoted which contains a different congratulatory message to motivate users to
reach higher (and breathe calmer).
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Figure 5.5: The prototype system showing two types of notifications in the lower-right corner of the screen: (a) Left, positive feedback gives the user a congratulatory message and a duration for which they were breathing relatively calmly. (b) Right, a cautionary message tells users how long they have been
breathing relatively fast.
5.3.8 CHECK-IN – DP8
A “Check-in” button on the system tray dropdown (see Figure 5.1) that simply
reports back the user’s breath rate averaged over a 30-second window. This allows the
user to communicate their intention to conduct an internal self-assessment, even while
they continue working. Our original design had the breath rate over the 30-seconds
becoming the user’s new baseline, but this proved too onerous in internal testing. In sum,
pressing the “Check-in” button could connote one of several different intentions such as:
• “I am coming back from a stressful state.”
• “I have been feeling calm recently.”
• “I have been feeling stressed recently but would like to be calm.”
There are no extrinsic motivators for pressing the check-in button. It is meant to draw
only on a user’s latent intention to maintain a calm state as they work.
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5.3.9 CAUTIONARY FEEDBACK – DP6
The notion of giving users negative reinforcement around stress can seem
counterproductive. However, reminders about sustained stress can be motivating for
some users if they are not penalizing. As such, two types of notifications exist in the
prototype:
• Notable duration (Figure X): “Caution – watch your breath. Your breath rate is
17.2 (115%)!” A cautionary message is displayed alongside the duration for
which the user has been breathing over their baseline for at least 3min. A soothing
audible chime is played when the window appears.
• Exceptional duration: “Moment of Stress: 7:18 of especially rapid breathing
detected.” The user’s long duration of fast breathing is called a ‘Moment of
Stress’.
5.3.10 ACTIVITY STREAM – DP2, DP10
The events that one experiences with the client are all stored on their profile and,
at present, are public to other users of the (private and closed) system on the social
network (see Figure 5.3). Cautionary and encouraging feedback, and milestones
achieved, are all listed with their corresponding icon and background color to aid
delineation. The knowledge that one’s activity stream is part of one’s profile could
motivate users to remain engaged in the system. Future iterations would support
manipulation of privacy settings for different types of events. Screenshots of the user’s
desktop are not stored in their web profile.
One intention of the website is to enable people to develop reputations for being
‘always green’ and possibly establish a following by other users, as in Twitter or other
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social media. In this way, one’s state of mind and body can act as a physiological status
indicator alongside traditional online status indicators (i.e., available, busy, away).
Clearly, there is a need for fine-grained data privacy controls at this level as well (DP10).
5.3.11 BUDDY LIST – DP10
As multiple users could be using the system simultaneously, one research goal is
to experiment with methods of motivating calm breathing socially. The first strategy is to
show other users who are using the system and their respective breath rates precisely and
relative to their baseline (if they opted in to revealing that data to you – DP10). Figure
5.6 shows the drop-down menu with other users online. Their username (email address)
is displayed, alongside the number of points they currently have and their most recently
reported breath rate.
In its current incarnation, all users of the system are in a fully connected graph.
This creates a scenario that actually violates DP10 because users have no privacy over
their respiration data (and this information is told to them upon using the prototype). This
is only a temporary solution as we experiment with the impact of social influence and
before privacy settings and the ability to construct one’s own social graph or inherit it
from other social networks are implemented.
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Figure 5.6: When other users have recently logged in and had data sent to the web repository, the drop-down menu also doubles as the location of the buddy list. Usernames, current point values, and last recorded breath rates are
displayed.
5.3.12 RE-RECORD AND MODIFY BASELINE – DP3, DP4
At any time, the user can re-record (by toggling the ‘Record Baseline’ button) or
manually modify the current baseline (by pressing ‘Set New Baseline’) that points and
notifications are in terms of. This allows users to adapt the system to their desired
feedback level or arousal level. This is one way to support goal-setting in that a user can
decide to record a low baseline if they want to ensure they stay especially calm while
working. This could happen in response to a stressful episode, headache, pain, or training
session.
5.4 TEST DEPLOYMENT
A small longitudinal test deployment of our prototype system was implemented to
get feedback that would guide further design iteration and to shed light on how relevant
the design principles are. Two male users self-identified as ‘early adopters’, aged 26 and
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28 (User A and B, respectively), used the system for a total of 34 hours over 3 and 4.5
workdays, respectively. Each worked primarily at their laptop computer during the day.
At the culmination of the deployment, participants were individually interviewed. The
buddy list feature was deactivated for the purposes of the deployment and users were not
allowed to view the data of other users so the website could be used only to view one’s
own past data (DP10).
5.4.1 RESULTS AND FEEDBACK
It quickly became apparent that the two users represented two very different
personas. What was most interesting about this deployment was the way in which these
different users interacted with the system over time. User A had an average breath rate of
18.3 (SD=4.91) while User B’s was 24.0 (SD=5.1). The users gained a very different
number of calm points: 402 for User A and 41 for User B. User B received 74 cautionary
messages while User A received 0. User A received 22 notifications with positive
reinforcement while user B received 5. User B hit 2 milestones while User A reached 14.
The baseline was adjusted/ recorded four times by User B (ranging from 18.78 to 28.75)
and only once by User A (Baseline=18.81). The longest non-‘Zen’ duration of calm
breathing sensed was 8.8 (by User A) and there was 1 ‘Moment of Zen’ total (User A).
In some ways, the users acted similarly. They checked in very little (4 times total)
and visited the website only once each. Each user also used the check-in feature 3 times.
User A viewed their daily highs and lows a total of 5 times while User B viewed it twice.
After the users returned their sensors, both users reported a sense of breath
awareness that they did not have previously. User A: “I didn't think about it [my breath]
much before. The only time I used to think about it was when I'd get nervous before a
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presentation or something like that. Now I use it to keep stress down so it doesn’t build
up.” This is an indicator of initial success of the prototype’s primary intent. Both
participants also noted that their awareness would be stronger and last longer if they used
the prototype for a longer duration.
From user interviews it was discovered that User A was already motivated to keep
their breath rate low throughout the day while User B was more interested in the
relationship between breath rate and task. This difference speaks to the fact that the
application does not clearly impose a preferred model of use (for better or for worse).
User A developed a goal to maintain a breath rate at or below their baseline. He
reported that when he received a notification informing him of a notable duration of
calm, it motivated him to maintain that low breath rate. This positive reinforcement had
the intended effect given that it appears when the breath rate climbs above the baseline
and motivated the user to bring (and keep) it down.
User B found their resting breath rate to be consistently lower than the breath rate
they felt comfortable working at. As such, he accrued very few points each day until he
“cheated” by raising his baseline manually. This user felt he had a significantly different
resting and working rate and this was not a problem to him.
User A found the milestone intervals to be “unpredictable” but “it didn’t bother
[him]”. The unpredictability speaks to the general feeling among the users that the
algorithm for increasing calm points was not clear. This was by design: we wanted to
avoid attempts at gaming the system and distractions from the user’s task. Both users
voiced appreciation for the inspirational imagery shown at milestones (one even took
several screenshots of those images as an expression of pride).
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Both users noted the system was not disruptive despite the regular desktop
notifications. Regarding negative feedback, both users were not discouraged by it (even
User B). Rather, they felt it was “more of a reminder”. We also noted the suggestion to
see one’s breath rate at different times of day and in different contexts (e.g., morning,
Fridays, at home). This deeper level of analytics could come by opening up a user’s data
to third party components via an API, being sure to heed privacy control settings.
5.5 DISCUSSION AND IMPLICATIONS FOR RESEARCH
Breathaware is the first system we are aware of whose goal is to develop
respiratory self-awareness in parallel to other tasks. The test deployment confirmed it met
its goals and motivates further inquiry into this area of research.
We can use insights from this deployment to iterate further. First, the system’s
simple threshold values and lack of adaptation to user performance means users who do
not perform in an expected fashion may very well ignore feedback over time. Attempts at
motivating users who are not breathing at their resting rate cannot rely on cautionary
messages alone. Perhaps the baseline must be automatically adjusted and respiratory
consistency encouraged. To this end, future improvements can draw upon prior research
in adaptive feedback and pedagogical agents that adapt to user performance and adjust
difficulty accordingly.
Second, setting breathing goals is currently not straightforward and it may be best
to add a goal-setting feature.
Third, the user cannot be expected to remember to ‘check-in’; perhaps introducing
a random notification (if the user’s breath rate has high variance) would be a welcome
feature.
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Fourth, the system provided a greater variety of feedback to the user who was
more consistently calm. This positive bias was an artifact of our desire to avoid
extraneous negative reinforcement. However, it is clear the cautionary messages did not
have the intended effect and perhaps violated DP6, causing the user to ignore them.
Finally, the system should not assume that users always want to keep their breath
rate low; identifying patterns and relationships between breath rate and activity may be
more interesting. This would strengthen the user’s own self-regulative awareness, rather
than only enforcing a particular type of breathing.
The primary contribution of this study is the design of a system that augments
respiratory self-regulation by developing breath-awareness, based on design principles
inferred from a review of relevant literature and tools. Further iteration and evaluation of
individual components of the system will shed further light on those design principles
while motivating further research into systems that augment human self-regulation.
These results triggered a number of research questions listed below to motivate
other researchers and designers to answer pressing questions into techniques for most
effectively augmenting the human self-regulatory system.
• How (if at all) should adaptive feedback be integrated?
• What is the role of goal-setting in such systems without distracting users from
their task?
• What is the role of negative feedback –in both rare and regular cases?
• How can the system help users make educated choices about what baseline value
to choose?
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• What privacy settings are optimal for protecting user privacy while still
promoting social motivation?
• How should the system adapt to user context and user preference? E.g., should
systems differentiate between different work activities?
• What positive motivation exists beyond the novelty of ‘points’?
• For mobile contexts, how can systems provide feedback without constant
interruption (which may be while driving or handling heavy machinery)?
• What are the optimal sensors to be used for sensing the regulatory processes
(including respiration)?
• What other processes are most interesting and productive to experiment with (e.g.
muscle tension)?
• What are the most important patterns to display to users that would best influence
their self-regulatory practices? E.g., differentiation by time period, application
use, time of week/year?
• How best to determine for any given system user, what is the optimal update rate
of peripheral biofeedback for them such that it provides maximum benefit and
influence while minimizing distraction?
5.6 CONCLUSION
There is no question that society is turning its attention to methods and techniques
for sustainable living in an environmental sense and an individual health and productivity
sense. As a result, interactive tools are emerging that hope to influence user state in such
a way as to produce the psychophysiological states appropriate for productive work. The
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focus of this chapter was to investigate this space by creating and testing a prototype that
aims to reduce stress by increasing the prevalence of calm states through breath
regulation. Also guiding this examination are 10 design principles for designing such
applications, which yielded several research questions to provoke further research. The
number of such research programs will likely increase in the coming years as
corporations and governments are economically incentivized to ensure optimal
psychophysiological states in their members more frequently.
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CHAPTER 6 BREATHTRAY: CONTINUOUS RESPIRATORY FEEDBACK AND ITS EFFECTS ON COGNITIVE PERFORMANCE
The previous chapter outlined the design space and research questions for methods
of designing self-regulatory feedback directly into the desktop. One of the fundamental
questions to this research agenda is the effect that such feedback has on the user’s breath
and their work; this chapter addresses that question.
Methods to augment user self-regulation during information work have so far relied
on notifications and explicit prompting of physiological change. Peripheral paced
respiration intermittently paces respiration according to the user’s individual resting rate.
Earlier, it was shown that this method had users rely on external prompts as reminders
rather than strengthening or amplifying their own internal self-regulatory habits.
This chapter presents an empirical evaluation of an interaction design technique to
motivate and incentivize self-regulation without explicit prompts and pacing. Further, this
study uses controlled tasks that induce cognitive load to evaluate the effect that such
feedback, and respiratory self-regulation itself, has on cognitive tasks.
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Our interest is in augmenting the ability of information workers and students to
self-regulate despite cognitive demands. To that end, the research question asked was,
“Can continuous feedback be used for ongoing self-regulation and, if so, does it come at
the cost of task performance from the additional cognitive load?” This chapter makes two
contributions by (1) studying the extent to which peripheral respiratory feedback and
monitoring impacts respiratory patterns as the user is engaged in other tasks and when
compared to motivation alone and (2) examining the extent to which these changes are
accompanied by impacts on cognitive performance on the user’s tasks.
6.1 BREATHTRAY DESIGN
BreathTray continuously displays respiratory feedback in the system tray,
implemented here on Mac OSX. It contains four feedback elements: (1) one’s breath rate
in real-time updated every second, (2) breath rate displayed as a percentage of their
individual resting breath rate, (3) earned ‘calm points’, an indicator of the number and
duration of previous calm moments (see below), and (4) being below or above one’s
resting breath rate, which colors the text of the entire display blue or red, respectively.
Figure 6.1: The BreathTray shows 4 components: calm points, breath rate, percent relative to resting rate, and whether they are above (red) or below
(blue) their resting breath rate.
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BreathTray can be complemented with notifications (see the ‘Breathaware’ chapter)
and prompts but can stand alone as a peripheral awareness cue of respiratory behavior.
Text color provides peripheral awareness without requiring reading.
6.2 STUDY DESIGN
A counter-balanced, controlled, within-subjects study was administered that first
evaluates the effect of the interface on breath rate (calculated as the number of inhalations
in one minute) versus when the interface is not available, controlling for motivation.
Effect of the additional cognitive load of the interface on two different tasks was also
measured.
6.2.1 PARTICIPANTS
There were 14 participants (7 female, average age 34.2, SD=9.23), each given a
$5 gift card to a local eatery as remuneration. Participants ranged from working
professionals to university students; all had a working understanding of Mac OSX and
had not taken part in studies in this domain.
Two different tasks were used to evaluate whether or not the task type affected
whether or not the feedback helps regulate breathing. Both tasks are designed to
introduce significant cognitive load but in different ways, as described below.
The two tasks used are straightforward; most participants had no clarifying questions
after a practice round (see below). The durations of each task, based on study iterations,
were adjusted to be as long as possible without significantly frustrating participants while
fitting the 30-minute study duration.
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6.2.2 SERIAL SEVENS
The first task was the Serial Sevens test adapted to a web-based environment. The
Serial Sevens test (reviewed by Taylor, 1988) is a well-studied tool to assess cognitive
impairment because it introduces significant cognitive load (Sweller, 1988). Participants
are presented with a number from which they are instructed to serially subtract in sevens.
The participant must retain that last number in working memory, perform a mental
calculation, derive the new number, write that new number down, go to the next problem
and ensure that they hold that resulting number in working memory to start the process
again as quickly and accurately as possible.
Figure 6.2: The ‘Serial Sevens’ task adapted to a web-based interface. A starting number was shown (top) and numbers disappeared when participants typed
and pressed Enter (bottom).
In the present study, the test was 5 minutes. Text instructions were standardized,
asking that the participant aim for speed and accuracy. The user was shown an empty text
box below the (randomized three-digit number between 900 and 1000) ‘starting number’
(see Figure 6.2). They typed the number which was 7 below that starting number and
pressed Enter, upon which both the starting number and the number they typed would
disappear. After inputting that starting number minus 7, no other text was shown and the
user had to remember the previous number in order to subtract 7 from it and type in the
new number.
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The task was scored as total number of correct subtractions. The number of incorrect
responses was also recorded, to ensure that there was no condition-specific ‘speed-
accuracy trade-off’. In the case of an error, subsequent responses were scored as positive
if they were correct in relation to the last number entered.
6.2.3 PROBLEM-SOLVING WITH AUDITORY DISTRACTORS
The second task simulated a form of multi-tasking with high cognitive load:
problem-solving with auditory distractors. For 6 minutes, participants had to choose the
true mathematical expression of two side-by-side expressions (order randomized) (see
Figure 6.3). Each expression had a combination of multiplication and addition in it, so
order of operations had to be adhered to. Concurrently, users listened to pre-recorded
conversations (order of segments randomized). The conversations were between a man
and woman, who are discussing various topics such as where to move. The conversations
varied in affective tone and valence and were recorded specifically to be distracting:
numerous questions are asked between each conversant and distracting comments were
made. Most participants remarked that they were indeed distracted.
Figure 6.3: The math problem in the ‘Problem-Solving with Audio Distractors’ task. Users were to choose the correct expression using the radio buttons and
press the “answer” button.
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This task is fundamentally different from the Serial Sevens task in that it contains
a primary task (a math problem that does not require one to use significant working
memory) and a secondary ‘task’ (hearing an interesting conversation). The mathematical
calculations required were not as intensive as working memory was not used as heavily;
users could rely on reading the numbers visually and nothing needed to be remembered
from one problem to the next.
Figure 6.4: The study setup with the USB-connected wearable respiration sensor (left), computer, headphones, and external mouse. The web browser was maximized
to fill the screen for all conditions.
6.2.4 PROCEDURE
The participant sat down at a desk jkwith a computer in front of them (see Figure
6.4). The study administrator sat in an available chair in the room and read a book while
the participant completed the study. Participants first completed a consent form, and then
filled out a short web-based pre-survey with demographics questions and no information
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about breathing or self-regulation. The web browser was maximized such that only the
browser and the menu bar were visible. The resolution was 1440x900, making the
dimensions of the BreathTray 1.0”x0.16”. Participants then wore a USB-connected
thoracic strain gauge respiration sensor whose function was earlier described (see Figure
6.4). They watched a 2-minute web-embedded video of the study administrator
describing the benefits of respiratory self-regulation during mild and acute stressors. The
video motivates viewers to maintain calm respiration during work, play, and life in
general. During this 2-min video, the BreathTray feedback was hidden but the user’s
resting breath rate was recorded in the background. Participants wore headphones during
the task. The video was not shown at full screen and participant posture was not
controlled throughout the study, though they remained seated.
Each user did a 90-second practice session of each task in the same order: (1)
Serial Sevens and (2) Problem-Solving with Auditory Distractors. There was no
BreathTray feedback during the practice round. Following these practice sessions, the
administrator asked if there were any questions about each task – rarely were there any.
Participants performed each series of tasks twice, once in each condition. The two
conditions were BreathTray and NoBreathTray. The order of the conditions was
randomized, removing any practice or training effects and minimizing novelty effects.
Novelty effects were also minimized by having participants do the repetitive tasks for a
significant period of time (22min total). Before the BreathTray condition, the participants
were asked to read text that explained what the BreathTray was showing. They were
allowed to ask any questions about BreathTray but were never told to breathe a certain
way. After doing both conditions, participants did a short post-survey.
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Our hypothesis was that the feedback would help augment the user’s self-
regulation in multiple ways: (1) the red/blue color in their periphery while they were
focusing on a task, (2) the visual reminder of their own breath rate, (3) the subtle desire to
accrue calm points. However, our study design does not distinguish between which of
these would be the primary cause of any obtained difference in breath rate or cognitive
performance.
6.3 RESULTS
Table 0.1 shows the mean (and SD) breath rates for each of the conditions and
tasks as well as aggregated across both conditions. Figure 6.5 illustrates these results.
Task BreathTray No BreathTray Both
Both 19.7 (1.9) 20.7 (2.2) 19.9 (2.2)
Serial Sevens 19.5 (2.6) 21.3 (2.5) 20.4 (2.6)
Problem-Solving with Audio Distractors
19.4 (1.6) 19.9 (2.3) 19.9 (2.1)
Table 6.1: Mean (and standard deviation) breath rates across both BreathTray and NoBreathTray conditions in each task and across both tasks together.
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Figure 6.5: Mean breath rates in each task, split by condition and also aggregated across all condition. The difference between BreathTray and No
BreathTray is significantly different in the Serial Sevens task.
6.3.1 BREATHTRAY IMPACT ON BREATH REGULATION
The first research question our analysis serves to answer is “Did the BreathTray
feedback influence breath rate significantly more than NoBreathTray condition (i.e.,
motivation alone)?”
There was a condition effect on breath rate. Paired t-tests with Welch corrections
were conducted because the variances between tasks and conditions were not equal.
Breath rates in the BreathTray condition (M=19.7, SD=1.9, Med=19.7, where ‘Med’ is
the mean median breath rate across participants) were significantly less than that of the
NoBreathTray condition (M=20.7, SD=2.2, Med=20.9) when comparing both medians
(t=3.17, df=13, p=0.007) and means (t=2.12, df=13, p=0.028). The stronger result in the
median comparison is especially encouraging given how median somewhat captures the
variance of individual breath rate. The median difference in breath rate in the BreathTray
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condition was 1.2bpm. A difference of 1bpm corresponds with significant differences in
HRV amplitude (Song, Lehrer, 2003).
The number of calm points received by participants in the BreathTray condition
(M=2.7, SD=3.8), a measure of episodic breath regulation, was larger than that of the
NoBreathTray condition (M=1.6, SD=2.9) but the effect was not statistically significant
(t=1.61, df=13, p=0.13). The majority of users received 0 calm points, indicating that,
more than anything else, the current requirement for calm points is currently too
stringent.
There was a condition effect on the breath rate of the Serial Seven task but not
Problem-Solving. There was a significant difference between conditions in breath rate
between the Serial Seven task (t=3.17, df=13, p=0.007) but not in the Problem-Solving
task (t=0.07, df=13, p=0.944). Thus, it was the Serial Sevens task that accounted for the
majority of the effect between conditions. Participants breathed markedly differently
between the BreathTray (M=19.5, SD=2.6, Med=19.7) and NoBreathTray (M=21.3,
SD=2.5, Med=21.2) conditions during Serial Sevens.
6.3.2 MAGNIFY OR PERSIST?
An important question in the study of peripheral paced respiration (Chapter 4)
was whether participant respiration was decreasing overall or only when the PPR bar was
displayed. It was shown that it was the latter. While one cannot ask exactly the same
question here because BreathTray does not explicitly prompt users to change their
breathing pattern, one can ask, “How were respiration patterns influenced when
BreathTray was available?” It is impossible to know exactly when and how participants
utilized the feedback given that it was peripheral. We visually surveyed the individual
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differences in the Serial Sevens task when BreathTray feedback was available or not. One
trend was noted. We quantitatively inspected the standard deviation of the two conditions
of Serial Sevens with the expectation that if the standard deviation of the BreathTray task
was significantly lower, then the feedback caused breath to be lower more consistently.
The two were not significantly different, t=0.059, df=13, p=0.954. This means that there
is no clear sign indicating exactly how the BreathTray feedback influenced breathing: a
persistently lower breath rate or occasional glances up that brought the user’s attention to
their breath to lower for a short period of time.
6.3.3 IMPACT ON COGNITIVE PERFORMANCE
The second research question was “Did having the continuous feedback negatively
impact the performance on the tasks?” This is a non-obvious question because both tasks
required a good deal of cognitive load. Participants frequently remarked about the
difficulty of each of the tasks and that they were unable to spend the desired amount of
time looking at the BreathTray or focusing on their breath. The difference in each task
between conditions was non-significant in number correct, error rate, and, crucially,
response time. There was no condition effect on task performance. I.e., users in the Serial
Seven condition were able to regulate their breath rate without adverse impact on task
performance or response time.
6.3.4 QUALITATIVE FEEDBACK
Post-survey Likert scales of 7 (1=not at all, 7=very much so) revealed that
participants, on average, reported they were not distracted by the feedback (M=2.4,
SD=1.8) and that they felt it influenced their breathing (M=4.1, SD=2.2) but not their
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performance (M=3.3, SD=1.6). They also expressed their likeliness to use BreathTray or
similar feedback on their own computers (M=4.6, SD=1.9).
6.4 DISCUSSION
The results of the study show that technology-augmented self-regulation is
effective for a certain class of cognitively demanding tasks represented by Serial Sevens.
That task contains a single intensive task with no distractors. The cognitive load imposed
by the task is significant: users had to focus and were very silent during the task. It
requires users to store elements in working memory, unlike the second task. Unlike the
multi-tasking task, the user was able to attend to the BreathTray feedback while
regulating their respiration. The results suggest that self-regulatory feedback will be
effective during cognitively intensive tasks such as writing and programming. On the
contrary, the results show that one cannot multi-task and expect to be able to use
peripheral biofeedback simultaneously.
The BreathTray design exemplified an approach to designing feedback to
accommodate both elements of self-regulation: monitoring and influence. The changes in
color augmented the user’s ability to monitor changes in their state. The fact that they
knew it was about their breathing gave them actionable feedback to modify that state,
aiming to reinforce their own self-regulative ability.
Levy et al (2011) showed that mindfulness-based practices reduce stress and
improve memory during multi-tasking but that it does not change performance of those
tasks. The present study motivates study of integrating mindfulness-related information
into multi-tasking environments but that it may be the times in between multi-tasking that
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the feedback can effectively be used by participants. Perhaps, then, this could reduce
multi-tasking behavior.
The quantitative difference in breath rate between the two conditions during the
Serial Sevens task was 1.8bpm (see Table 6.1), greater than results found in previous
research that used intermittent pacing (i.e., peripheral paced respiration in Chapter 4). It
is noteworthy that BreathTray was able to effect breathing comparable with intermittent
pacing. Future systems would rely on both explicit prompting and peripheral feedback to
augment self-regulation in a form of autonomic interaction design. This concept refers to
the notion that our interactive systems interact not only with our conscious minds and
commands but also with our autonomic nervous systems. Specifically, they can not only
detect and react to changes in our psychophysiological state but can be designed to
influence that state as well.
6.4.1 STUDY LIMITATIONS
Some participants mentioned they felt some ‘math anxiety’ initially; it is assumed
this was reduced in all but the most extreme cases during the practice tasks or early on in
the study. Similarly, the exact seating location of the study administrator may have
caused more or less anxiety in different participants in case they felt they were being
spied on. Again, it is proposed that any such effects waned over the course of the practice
sessions.
Participants may have inferred that the study administrator was interested in calmer
breathing during the BreathTray condition and may have been especially motivated by
this, but (a) they were not told of the BreathTray until it was used (and the order of the
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conditions was randomized) and (b) any extraneous motivation was likely reduced due to
the cognitive load of having to do the task itself.
This study does not explicitly identify which of BreathTray’s feedback elements
was responsible for the influence. Our hypothesis is that there is no ‘silver bullet’
feedback that is effective but rather that different people use different influence
mechanisms and that it depends also on task difficulty, eyesight, multi-tasking ability,
and self-regulation performance. Note that specific contributions of different design
elements can be studied in future work.
6.5 CONCLUSION
The BreathTray feedback technique was introduced. A study demonstrated that (1)
peripheral feedback alone can modulate breath rate better than motivation alone without
explicitly prompting or pacing users’ respiration and (2) peripheral physiological
feedback does not draw sufficient attention away from intensive cognitive tasks so as to
negatively impact them. When engaged in a task that simulated multi-tasking, users were
unable to utilize the feedback to regulate their respiration. During an intensive cognitive
task, however, they were. Future operating system enhancements may profitably include
physiological feedback. This study sheds light into how we can expect such feedback to
impact both user physiology and cognitive performance.
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CHAPTER 7 BREATHWEAR: AMBULATORY INFLUENCE ON RESPIRATORY PATTERNS
Breathwear is a system for continuous (1) monitoring and (2) influence of one’s
psychophysiological state as reflected by respiratory pattern. In this chapter, design
challenges and opportunities are presented for sociotechnical systems that influence state
for augmented self-regulation (ASR). The iterative, user-centered design of the system is
then described, followed by the qualitative and quantitative results of a longitudinal
exploratory user study to assess user impact and usage. The results suggest further
inquiry into game mechanics and goal-setting as means of conditioning changes in
respiratory pattern and also indicate the robustness of the non-invasive respiration sensor
described. The chapter concludes with a discussion of the derived insights, which are
useful to researchers and designers of systems meant to induce real-time physiological
change using wearable devices.
There has been a surge in the availability of wearable sensors for both research
and consumer use. There are two primary aims in this trend: (a) consumer desire for self-
knowledge to improve health or performance behavior and (b) researcher desire for
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ecologically valid and longitudinal data that can reveal insights into human health and
performance phenomena.
These two desires overlap when systems emerge that both help researchers collect
useful data and provide feedback or data that is immediately useful to users themselves.
An example of this is wearable sleep monitors such as the Zeo for sleep researchers but
also for people with sleep disorders (which commonly use accelerometer or EEG
sensors).
In addition to diet, fitness, and sleep, psychophysiological state is a domain of
health that is impacted by lifestyle and behavior. It underlies many, if not all, aspects of
physical and mental health and performance yet few technological approaches have been
made to influence one’s state continuously. Such approaches might attempt to reduce
stress, increase calm, and train users to recognize and adapt to changes in state even when
not using a technological device.
Breath regulation currently requires a great deal of practice, training, and a high
level of compliance that stymies adherence. Our long-term goal is to employ wearable
sensors for continuous monitoring and mobile phones for continuous feedback to
augment respiratory self-regulation and, by association, psychophysiological state. This
chapter first explores related literature, and then describes how user feedback led to two
significant design iterations. The user study presented here is the first known exploratory
study of the ambulatory influence of respiratory patterns using interactive technology.
The chapter concludes with an analysis of the results and discussion of the insights.
Clinical research on the link between psychophysiological state and respiratory
patterns has placed an emphasis on accurate monitoring, detection, and description of the
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stress response but not necessarily on influence. For example, Wilhelm et al. (2006) had
users wear electrodes and a computer waist pack (Wilhelm, Roth, Sackner, 2003) and
collected ECG, skin conductance (on fingers), two thoracic strain gauges, finger
temperature gauges, accelerometer, ambient temperature, and barometric pressure in
three situations: quiet sitting, physical exercise, and a short commercial airplane flight.
Their goal was to distinguish between psychophysiological influence on sensors and
physical activity (exercise). They found that, of all the indicators, “certain parameters of
irregularity in breathing were [responsive] to anxiety” and that it is only respiratory
patterns that separate the resting, physical activity, and emotional conditions,
“emphasizing the high informational content of respiratory pattern analysis”.
Pfaltz et al (2009) conducted a robust ambulatory respiration monitoring study –
in this case two 24-hour time periods separated by 1 week, comparing subjects with
‘panic disorder’ (PD) with healthy controls. They replicated effects found in the
laboratory that distinguished changes in respiration during states of anxiety in both
groups. However, the results did not show the two groups were significantly different.
These results can be interpreted to mean that (a) wearable sensors can detect states of
anxiety and that (b) anxiety spans all populations of people and that separating treatment
for only those with PD is not meaningful. The sensor system employed to conduct these
studies is wearable but is not something one can be expected to wear for many
consecutive days.
Our intent, differentiated from related work, is not to improve or validate the
sensing of ambulatory respiratory patterns but to influence it. Clinical-grade accuracy is
not required for our goal, nor are all related respiration parameters. The respiratory
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characteristic with the greatest utility for this purpose (breath rate) was identified,
motivating focus on studying the interaction design of a system for feedback and to
influence that characteristic by a gross amount. The next section outlines design
opportunities and challenges for designing an ambulatory version of such a system.
7.1 DESIGN OPPORTUNITIES AND CHALLENGES
Using a mobile device is a qualitatively different user experience than designing
for a laptop (Pea & Maldonado, 2006). The device sits on your person, feels like an
extension of the body/mind, has a dramatically different screen size, has opportunities for
other forms of physiological sensing, and perhaps most important, is with the user more-
or-less continuously. Mobile phones already extend one’s cognition (Pea, 1985) and are
the logical medium for augmenting self-regulation. Before designing, we identified
potential design opportunities and challenges for augmented self-regulation (ASR) using
wearable sensors and feedback on a personal device (e.g., mobile phone). ASR systems
are a class of autonomic interaction design (AIS) systems that use AIS approaches
explicitly to augment self-regulation.
7.1.1 CONTINUOUS STATE INFLUENCE
Imagine the difference between a pedometer that gives you feedback only at the
end of the day compared to one that tells you how many steps you’ve logged thus far in
the day. Now apply that analogy to psychophysiological state and stress. With desktop-
based influence, demonstrated with peripheral paced respiration (Chapter 3), the user
associates monitoring and influence with a stationary laptop that must be turned on.
Using continuous feedback, the user can then begin to notice differences between
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information tasks as well as difference between physical tasks or activities such as eating,
reading, listening to a lecture, driving, watching a movie, and so on.
7.1.2 CONTEXT-AWARENESS
Using mobile devices, feedback from the system can take into account different
characteristics of the environment or user’s behavior (Fogg, 2002). For example, if they
are walking, behavioral triggers can be delayed. If the user is co-located or even socially
connected with other users, feedback can take this into account. Time of day can
influence cortisol levels and other stress-related indicators in the body (Sherwood, 2006)
and can also be considered.
7.1.3 SLEEP
One’s state has an impact on sleep quality and patterns (Carskadon & Dement,
1981; Shahar, et al., 2001). The body’s physiological indicators also change according to
stage of sleep, including respiration (Shewood, 2006). Though not an ostensible goal of
our work, there is a great deal of research on the role of respiration in sleep disorders.
Through a mobile device, users can monitor and perhaps even influence sleep patterns.
Partners could watch over one another during sleep or parents could care for children by
monitoring their respiration (co-located or remote).
7.1.4 SOCIAL
Opportunities exist in co-located and distributed scenarios for socially influenced
synchronization or influence on motivation (see Chapter 4). Synchronous physical
behavior could be complemented or replaced with synchronized physiological behavior,
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synchronizing states between users. This could be useful for meetings, meditation classes,
yoga courses, or other intimate social gatherings.
7.1.5 ANNOYANCE
A primary challenge of our domain is that it attempts to influence a process that is
continuously occurring. This is dissimilar from exercise, diet, and other related domains
of intervention. There is a high probability that system designs in this area will annoy
users with frequent notifications or making the user feel that a very intimate aspect of
themselves is being monitored.
7.1.6 EVOLVING USER GOALS
Users have different goals and intentions for different times of day, weeks out of
the year, contexts, and tasks (Fogg, 2002). While studying for an exam, the user may
have one goal that the system could support but while taking an exam, they could have a
completely different goal. While meditating, for example, the user may have very
stringent goals for system guidance. However, they may wish monitoring to be
continuous. Either the system must automatically detect these different contexts and
intentions or must be very simple to customize according to not only individual desires
but also contextual desires.
7.1.7 OVER-DEPENDENCE
A potential issue in ASR systems is the level of granularity at which feedback can
be useful. One’s state is constantly being managed by the body and the mind; looking to
an external device for feedback on self-regulation can create an over-dependence on the
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system. Systems could customize the amount and granularity of feedback given or the
situations that should produce feedback.
7.1.8 INACCURACY
Monitoring one’s state is different from monitoring steps. In the latter, if there is
some level of inaccuracy, the user can clearly blame the system. Because breathing
patterns are not as easily discernible as steps taken yet are more visible than indicators
like heart rate or skin conductance, feedback of breathing patterns can be suspect. Worse,
false negatives can create stress when it did not exist or had subsided.
7.2 INTERACTION DESIGN GOALS
This section describes the interactive client component of the Breathwear system
(on the mobile phone). At first glance, respiration feedback alone may seem like the
primary function of the client. However, our goal is to influence the user’s
psychophysiological state in a continuous manner, not only when the user wants to reflect
about their state or remembers to check the interface. Feedback per se is not the goal of
the system. As such, three primary design goals (monitor, influence, and customize) are
presented below. They follow the components of self-regulation identified earlier, namely
the self-awareness to monitor one’s own state and the capability to influence it.
7.2.1 MONITOR
At its most basic level, the system must ensure the user that their state is being
monitored while the user is engaging in other tasks. This must be as invisible as possible
so the user can be fully engaged in their work and life tasks. Reducing sensor bulkiness is
a big part of this but the software design must also ensure the user knows the system is
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‘on’, is recording breath rate to an appropriate level of accuracy, and is storing the data
for later perusal (without compromising the user’s privacy).
7.2.2 INFLUENCE
What differentiates the system from related systems in the literature is that the
system has a goal beyond assessment: it attempts to influence the user’s behavior. One
method of doing this is by simply knowing the system is monitoring one’s behavior.
Another method is with behavioral triggers (Fogg, 2002). To support this, Breathwear
supports ‘push notifications’ on the iPhone user interface. These are both
positive/congratulatory messages and negative/cautionary messages to support operant
conditioning of breathing behavior (Ley, 1994).
7.2.3 CUSTOMIZE
The user should be able to re-record or adjust their baseline easily because people
naturally breathe at different rates. The baseline becomes not only a baseline of one’s
state at rest but also an intention of their desired state. This can change quickly, as was
found in early user evaluations.
7.3 DESIGN ITERATION 1
An initial prototype on an iPhone 4S was created to receive data continuously and
wirelessly from the breathbelt sensor. The initial prototype attempts to address the three
design goals identified above.
The Dashboard (Figure 7.1, left side) shows the user’s real-time breath rate, raw
sensor value (‘Raw Data’), number of calm points (see Chapter 5.3.5), and number of raw
sensor records counted so far. Sensor values are provided for debugging purposes.
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Buttons to connect/disconnect and trigger a buzzer on the belt are also shown. The
progress bar on the bottom is the sensor’s battery level.
Calm points are analogous to steps with a pedometer. They only increase and are
reset to zero at the start of each day. A user gets points by breathing calmly relative to
their own resting rate; the calmer they breathe, the bigger the increment. They are
awarded a point for each 30sec they are at or below their resting rate. Breathing at or
below one’s own resting rate for 30sec adds 1 point. The increment increases linearly as
the user’s breath rate decreases.
Figure 7.1: The initial prototype. Dashboard (left) shows real-time feedback and Settings (right) allows the user to make changes to their breath rate baseline and to manually send data back to the research team via email.
When the user clicks ‘Notifications’ in the bottom, they are shown a simple list of
notifications that have appeared so far. The Settings (Figure 7.1, right) screen allows for
debugging and testing of parameters during this phase of research. The user can change
their breath rate manually and can manually send the data via email to research team
members for analysis.
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To influence user respiration, the system employs real-time behavioral triggers in
the form of push notifications for operant conditioning (learning based on modifying the
environment to reinforce particular behaviors): negative feedback (Figure 7.2, left) and
positive encouragement (Figure 7.2, right). The notifications that appear to the user
mirror those used in Breathaware (see Figure 5.5). These are shown on the phone’s lock
screen as well as during phone use (Figure 7.2, center). The number of notifications that
can be ‘stacked’ on the lock screen is first determined by the system settings
Figure 7.2: Push notifications on an iOS5-based mobile phone based on initial prototype design. Each notification has a type (positive or
negative/cautionary) and duration that the system detected the user was in that state. For example, the user had been breathing above their resting rate for
15.2min at left, at or below their resting rate for 12.7min on right.
7.4 Primary insights (PIs) from participant feedback
The first iteration of the prototype was tested with three friends of the lab wearing
it for several hours a day for 2-5 consecutive days. The following primary insights (PIs)
were found from user feedback and interviews:
1. Users felt a part of them was being monitored that they were not used to: “How
I’m being”.
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2. Users desired feedback about not only the present moment but also their recent
history. 10 minutes seemed to be a suitable amount of time.
3. Users desired a qualitative measure of performance, not only quantitative
indicators.
4. Users wanted to change the baseline according to their context (or that the system
would automatically do it). At times, they wanted support for being very calm but
at other times they wanted fewer notifications.
5. Notifications became ‘stale’ quickly. If they were not seen immediately, they were
not useful later.
6. Battery consumption on the phone was noticeably increased.
7. Users had no precedent for what were ‘low’ and ‘high’ breath rates so the
feedback was difficult to interpret.
8. When notifications ‘piled up’, they felt like spam and were stacked on the phone’s
lock screen.
Design of the client interface was iterated upon to address a significant number of these
PIs. These are presented in the next section.
7.5 DESIGN ITERATION 2
The primary goals of the second design iteration were first to support quick-
glance, qualitative comprehension of one’s state and, second, to support customization
that would improve subsequent studies. Based on initial user testing, the interface was
changed (see Figure 7.3). The changes are summarized in the sub-sections below and
include reference to the primary insights (PIs) from the previous section they are meant to
address. They still aim to address the three design goals described in Chapter 7.2.
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7.5.1 RECENT ACTIVITY INDICATOR
To address PI2, a visual indicator of recent activity and performance was added
based on 10 minutes of prior data. This number was selected from user interviews with
prior users of the system and domain experts. The bar can be three different colors: red
(mean of last 10 minutes is more than 1.5bpm above resting rate), yellow (within a range
of 1.5bpm above or below resting rate), or green (below 1.5bpm of resting rate) (to
address PI3). The user can make a quick glance at the interface to infer how the recent
past has been for them. There were a number of possible designs, including adding a
slider to adjust the time window in real-time (to see, for example, mean performance over
the past hour or day). However, we elected to maintain a low level of complexity given
that the design goal was to create an interface that could be consumed with a glance.
Figure 7.3: The second iteration of the Breathwear client interface, which includes a recent activity indicator (left, top, in green) and additional configuration options in the Settings screen (not shown). The center image
shows when the user is hovering around their baseline (hence the yellow) and the sensor is connected (hence the ‘Time Connected’ indicator). During a high breath rate state, the band is red (not shown). The baseline here is set to
15bpm (the default).
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7.5.2 RELATIVE BREATH RATE
To address PI7, the relative breath rate indicator was changed to show not just the
total percent comparison but to use the words “below” and “above” to indicate exactly
how much above or below the baseline the user is breathing right now (see Figure 7.3,
left and center).
7.5.3 BREATHBELT FEEDBACK
Additional feedback was added to the breathbelt to address user complaints. First,
the belt will beep when one presses one of the two buttons (in order to confirm it has
battery power and is reacting to user input). Second, the belt beeps multiple times when it
loses connection with the phone (either via distance, Bluetooth software problem, or
other). This can help avoid long periods of time without data collection.
7.5.4 ADDITIONAL SETTINGS
Three additional settings were added, not shown in the screenshots above. First,
the option was added to disable push notifications completely (to partially address PI5). It
is unknown if users would want to use the application this way and, if so, how it would
be used. For example, perhaps users would turn off notifications but glance at the
application more often but only during periods in which they desired to be calm (e.g.,
taking a test). Second, the ability to hide breath rate feedback on the dashboard was
added. The reason for this was research: the desire to use Breathwear to measure baseline
breath rates for users as a control and then use the system with feedback (and
notifications) to see if it influenced their breath rate. Third, another parameter added was
the ability to modify the breathbelt sample rate (to address PI6). This was added to
address prior concerns that the application drains the iPhone battery too quickly. The
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team was not certain how frequent one could make the sample rate before the breath rate
accuracy was compromised so multiple levels exist that the user can select from.
The application was instrumented to log anonymous but granular information
including application usage, changing settings, respiration rate, baseline, sensor data, date
and time, notification events, and application usage summaries.
7.6 STUDY
An exploratory in situ, longitudinal user study was conducted with 6 users (2
female, 4 male, median age 31.5) who used Breathwear over the course of a 5-day
workweek. The users varied in profession and background but all were technologically
savvy and were interested in maintaining a calm state. Participants were recruited through
university and common interest email lists. Participants were warned the prototype may
contain bugs and that the goal was to get feedback and improve the design of the sensor
and feedback. One user’s data was not collected because their sensor repeatedly broke.
This was the only user who broke a sensor.
Participants met with the study administrator individually and were given
instructions for how to manually install the application on their phones and were shown
how to wear the adjustable sensor. They were asked to use the system as much as was
comfortable for several days and the study administrator would send them intermittent
SMS reminders and updates over the course of the study. The participants started on
different days and each had the same model of mobile phone (Apple iPhone 4S).
Participants were given a short URL to access system troubleshooting tips and
feedback form (see Appendix D). They were told they should visit this page and add any
comments, feedback, questions, or criticisms at any time. In this way, the research team
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hoped to collect user feedback close to the time it occurred. Each user was occasionally
sent an SMS reminder to disconnect the sensor before going to sleep each night (to
conserve battery). Before doing so, they were also to send the log file to the study
administrator and clear yesterday’s data using the Settings screen (see the right side of
Figure 7.3). They were also asked to remove the sensor during exercise.
7.6.1 RESULTS
Participants used the system for an average of 37.26 hours (median=20.5,
SD=30.2) over a 5-day period for a total of 186.3 hours. Of that possible duration, 140.5
hours (75.4%) of the breath rate data was deemed usable and the remaining was deemed
unusable due to sensor battery problems, software malfunctions, or a mis-worn
breathbelt. This data loss was expected given the exploratory nature of the study and the
fact that participants were wearing the sensor in their daily lives. It can be improved by
improved robustness in belt and sensor design. Users opened the application an average
of 3.0 times per hour (median=2.9, SD=1.7). Generally speaking, users did not manually
adjust their baseline breathing rates, keeping them at the default 15bpm.
The mean breath rate across all participants over the entire duration was 13.64
(median=13.77, SD=1.97). However, individual breath rates were distributed differently
over the course of the study, as shown in Figure 7.4 below. Drawing upon the literature
on stress and the stress response, one can always expect to encounter relatively high
breath rates even over a relatively calm week. These can occur for myriad reasons that, in
Western culture, are mostly subjective or psychological (rather than resulting from a
physical threat to one’s safety).
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Figure 7.4: Distribution of breath rates of each participant. X-axis is “Breath rate in Breaths per Min”. Y-axis is ‘Frequency in Seconds’.
Clockwise from top-left, users 1, 2, 4, 5, and 3.
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The reader will note here that, in all four cases (including the omitted), breath rate
distribution is normally distributed. This is as one would expect, where one’s breath rate
has regularity to it although there are different patterns of breathing discernable. Because
ambulatory respiration sensing is so rare in the literature, figures such as these are rarely
(if ever) found and interesting to analyze. For example, User #3 has almost two ‘styles’
of respiration: high (10-15bpm) and low (4-9bpm) breath rate. This user later was found
to have used the system during meditation practices. Users #1 and #4 have a very normal
distribution of breath rates while User #5 had little slow respiration duration. An
intelligent mobile system could identify such longitudinal patterns and recommend
changes in lifestyle or behavior. User #2, a frequent meditator, has a distribution that
illustrates the amount of breath awareness he maintained, with a spike in frequency
appearing around 5bpm. In post-hoc user interviews, the user meditated on average about
1 hour per day. This consistent practice is clearly evidenced in the graph.
Users received an average of 45.2 (median=41, SD=43.1) push notifications (1.9
per hour). Of those, 32.4 were positive (‘Calm’ and ‘Zen’), 12.8 were negative (‘Stress’)
feedback. ‘Zen’ notifications were given during especially long calm episodes and were
relatively rare and difficult to achieve. Interestingly, one user received almost no
notifications over 50 hours of usage. Figure 7 shows the frequency and types of
notifications received across the five study participants. No clear trend is evident,
speaking to the system adapting to each user’s individual behavior.
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Figure 7.5: Frequency and type of push notifications received by participant. ‘Calm’ and ‘Zen’ are two types of positive notifications. This graph shows that there was no discernable trend around type or frequency of push notifications but that one can characterize an individual’s respiratory patterns to some
degree using this visualization.
User #2, the frequent meditator, is shown to exhibit very little fast breathing
episodes while Users #3 and #4 both experienced more stress states than calm. User #5
received no push notifications during the 18 hours he used the system. This could be
because he hovered exactly at his resting rate for the duration of the study. In post-hoc
interviews, it was discovered he was a computer programmer and spent the vast majority
of his day working in a consistent environment and pace.
Users were awarded an average of 105.9 calm points per hour (median=78.6,
SD=68.3). This number, which seems high, is attributed to the fact that one user received
disproportionately more calm points and to the fact that users could receive greater
numbers of calm points the slower they breathed. User #2 (the frequent meditator) was an
outlier, receiving 226.9 calm points per hour (see Figure 7.6). The median number of
calm points granted hourly, 78.6, is more useful here, and is still higher than expected.
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Given the baseline was usually 15bpm and that the average breath rate across participants
was 13.6bpm, one can see how users were granted calm points so frequently. Calm points
were designed to be relatively easy to get (i.e., as easy as steps are using a pedometer).
The user interface did nothing with the calm points and users were not explicitly told how
many points they received yesterday or how many their peers received. Later, it was
found that participants noticed them and wished to use them more explicitly.
Figure 7.6: Calm points per hour granted to each study participant. This graph shows how User #2, who had a great deal of meditation experience, was rewarded
a great deal more calm points per hour than the other participants.
Individual breath rates varied over the course of the study as illustrated by one
user who took the liberty to send herself a day’s worth of data, label it, and produce a
graph to gain insight into how her breath rate changed across tasks (see Figure 7.7). This
was an exciting and unanticipated instance of a user being personally interested in her
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data. She later described this insight clearly to us in user interview although it had
happened days earlier. It had clearly made an impression on her.
Figure 7.7: A line graph produced by one participant with data she labeled herself, “meditating”, “surfing the web”, and “reading”.
Looking at the user-generated figure, the reader will note three distinct breathing
patterns (although we cannot objectively confirm their validity): meditation, information
work, and reading. The breath rate during meditation hovered around 4bpm while reading
was slightly more erratic however still low at 6bpm. Surfing the web, albeit for only
10min, resulted in an increasingly fast breath rate, replicating findings from our earlier
related studies. It is not clear how fast the breath rate would have been had the user
continued using the computer for an hour.
The experience of seeing a visualization of their own data made the user more
aware of their daily experiences. She later told us “It was surprising to see how my breath
rate would go up and down over the course of an hour. [The system] gave me an
understanding of my breath rate during different activities. So, like, in meditation my
breath rate could get very low versus walking. But there’s even a difference between
reading and watching a show on television. I’m 10-13 when I’m chilling out but 15 when
I’m working on the computer.”
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The occurrence and effect of push notifications is difficult but interesting to
measure. The notification itself is meant to give the user a sense of their recent
performance but also to influence their subsequent respiratory patterns. depicts a
representative session of use (a session being the time the sensor was connected to being
disconnected) with the different types of push notifications represented (User #3).
First, one notes that the placements of the notifications are in logical locations.
For example, when the user’s breath rate is elevated above 15bpm, a stress notification
appears in a group (approximately 250-270min) until the user notices and takes action
(from 280-350min). Though the data is not labeled by activity, the reader will see that the
feedback does seem to maintain a breath rate at or under the baseline (15bpm).
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Figure 7.8: A line graph showing the relationship between a user’s breath rate punctuated by the different types of push notifications (green=calm, blue=zen, red=stress). The line indicating the user’s breath rate is by default gray and then colored according to the duration of the state detected by the subsequent
push notification.
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7.6.2 USER FEEDBACK
Given the relatively small sample size and exploratory nature of this study, user
feedback is highly valued and can lead to important design considerations. The study
participants were enthusiastic about the possibilities of such a system but had a number of
concerns that must be addressed in future iterations.
The notion that the system is monitoring something that was previously
unavailable was not lost to the participants. This speaks to a simple measurement effect
that, in and of itself, is a useful feature of the system. One participant noted that “having
my phone track this data is making me more self-aware about it.”
The ‘baseline’ concept became used more like a ‘target’ because users could
manually change it and would change it arbitrarily in order to instruct the system to give
different types and frequency of feedback.
Breathwear monitors something that is changing continuously: one’s
psychophysiological state. As such, it is something that isn’t only useful upon later
reflection (such as how pedometers and other fitness devices are used) but in real-time.
Users voiced the desire for an even easier and quicker access to their state via the
iPhone’s lock screen (which cannot be modified programmatically without ‘jail-breaking’
the phone). As one user put it, “I got into the habit of looking at the screen and I wish it
were easier to see that.”
Calm points as an experiment were useful to a degree but were clearly not used to
their potential. One user said they “could be interesting but I’m not sure about milestones
or what I’m shooting for. Maybe I can be told the # of points that others reached? It
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didn't give me something to aim for. Like ‘today 56min versus yesterday 32min’. I
wanted something to aim for day to day.”
After one user’s battery died, they mentioned they "miss wearing it and getting
information about breath rate." After using the system for 5 days, that user found there
were “definitely times when I'm walking and will think about my breath, not something
I'm used to doing.”
One concern our team had was whether sensor accuracy would hold up outside
the lab. Indeed, one user asked, “if it could detect if I was moving it could have a separate
moving state.” She found that the sensor readings looked jumpy while she walked.
User #1 took intricate notes about how her behaviors were affecting her breathing
patterns. She then sent SMS messages directly to the study administrator as they
happened. Examples included screenshots of the application screen and text
accompanying it describing that it was taken “Fri night after a dinner and then two
colas”. Later: “Never realized how much caffeine affects me!” and “rate still high two
hours after caffeine. Even run-walking w my dog, Savannah, I only reached yellow--still
a lesser effect than caffeine!” Clearly, one cannot draw a causal connection between
caffeine and breath rate but providing users with the data to self-experiment can be a
robust means of identifying causal relationships for the purpose of changing one’s
behavior.
User #1 also mentioned that the system told her she was in a ‘green’ state when in
fact she was so stressed that she was engaging in breath-holding. This is clearly a new
feature of the system that future research must address.
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7.7 DISCUSSION
This study explored the impact and usage of a system for continuous monitoring and
influence of psychophysiological state based on sensing, feedback, and incentivizing
changes in respiratory patterns. To interpret these results, we refer back to our three
design goals: monitor, influence, and customization.
To support the goal of effective monitoring, the system performed well for a research
prototype. 25% of the data was lost due to disconnections, sensor drops, and other
unexplained incidents. This can clearly be improved. Another place for improvement is
the potential for using additional sensors to detect physical activity such as walking. The
placement of the sensor, over or under one’s shirt, resting on the hips, and the tightness of
the belt itself, seemed to work well. The system was able to differentiate between
different activities and illustrated differences between people who regularly practice
meditation.
To effectively influence the user, the chapter discussed how notifications were used
and iterated towards user interface design techniques that allow quick glances at both
quantitative and qualitative indicators of respiratory performance. Calm points were
indicative of a user’s performance but are not clearly designed as motivators. There is a
great deal of work to be done around arranging goals, milestones, and other methods of
influence.
The different ways in which calm points and push notifications were delivered to the
user shows one way the system tailors feedback to users. We did not see users explore
changing their baseline breath rate. This would be a way of influencing the frequency and
type of notifications (and calm points) but we did not discuss it with users in great depth.
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This presents a large area for future possible research: automatically adjusting the
baseline breath rate so that system responses influence and reward the user appropriately.
Participants were intrinsically interested in their own data, asking to wear the sensor
longer, for visualization of their data, and for more features.
The push notification logic in this prototype was rather simplistic and though only
occasionally useful, one can imagine the potential utility of taking the user’s context into
consideration in the notification logic. Such context-sensitive influence could be
improved over time such that the system does not attempt to influence the user during
exercise, in a meeting, or similar.
This chapter began by describing the intention of some system designers to create
systems that are useful to both researchers and users themselves. The early results of this
system deployment lend insight into the design of sociotechnical systems that help users
in a real-time manner while providing researchers with data previously unavailable
without highly invasive sensors. The team plans to iterate and use this system to amass a
large, labeled dataset of respiration data of users in their real lives. This is of use to the
research community to both educate and influence the public to take steps to regulate
their state to ensure a healthy and a productive population.
Another next step is to explore design methods of enabling users to label their own
data in real-time, as this has proved to deliver insights to users about the effect that their
behaviors are having on their state. The system could detect changes in breath rate and
prompt the user to label their current activity. These results could then be used on graphs
to provide contextual data of their state and perhaps to identify patterns that were buried
before.
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Lastly, the system must differentiate between slow breathing and breath-holding.
This is non-trivial because while User #1 engaged in breath-holding, it could look similar
to being in a meditative state with a slow breath rate. Differentiating between them may
require looking at tidal volume, respiration regularity, and inhalation depth, all things the
research team is now attempting to infer from the sensor data.
7.8 CONCLUSION
Two design iterations of an ambulatory respiration monitoring and influence system
were presented. The results of an exploratory, longitudinal user study showed the system
effectively assessed different individual respiratory patterns and responded to changes in
state. User feedback shed light into how the system could better motivate and make
tangible the process of breath regulation using goals, visualizations of individual episodes
and data, and improved methods of quickly assessing one’s state in the present moment
and recent past.
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CHAPTER 8 CONCLUSIONS AND FUTURE WORK
This dissertation motivated then investigated technology-mediated
psychophysiological self-regulation via stationary and ambulatory respiration monitoring
and influence as a means of augmenting human self-regulation.
8.1 SUMMARY OF FINDINGS
We refer to our initial research questions to summarize the findings.
• Q1: Is it feasible to augment respiratory patterns of information workers as they
are engaged in meaningful information work?
Chapter 3 demonstrated that visual pacing feedback can be designed to complement, not
interrupt, the user’s task across operating system windows and applications (PPR). The
pacing method reduced mean breath rate by almost 2bpm but failed to develop user
consistency in maintaining that low breath rate. This motivated further inquiry into social
feedback to motivate that consistency.
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• Q2: How does synchronous social feedback compare with asynchronous feedback
in peripheral paced respiration?
Chapter 4 investigated two types of social feedback: asynchronous and synchronous.
While it did not develop the respiratory consistency expected, it did uncover an
interesting effect of synchronous feedback magnifying the effect of the social feedback.
We also replicated findings from the prior study showing that information work in
general results in significantly breathing rates.
• Q3: How can sociotechnical systems be designed so as to motivate respiratory
change without prompting it explicitly?
Chapter 5 identified 10 design principles for designing desktop-based systems that
influence respiratory patterns and habits without requiring user interruption. The systems
utilize motivational cues and are clear in their intent to develop (not replace) the user’s
innate self-regulation skill. The Breathaware system was designed and implemented to
demonstrate the designs.
• Q4: How do peripheral feedback and motivation cues influence respiration and
does that feedback negatively influence cognitive performance?
Chapter 6 also included a controlled evaluation of a primary component the Breathaware
system: the BreathTray. It aided respiratory self-regulation without compromising
cognitive performance in two types of tasks: problem-solving while multi-tasking and a
more intensive single cognitive task. Further, users were able to effectively utilize the
peripheral feedback, which lacked any prompting or pacing during the single task
condition only (i.e., not while multi-tasking).
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• Q5: How must these techniques be adapted to be effective in a mobile setting?
This final study presents the design opportunities and challenges for mobile, continuous
monitoring and ambulatory influence of respiration. Two design iterations, user feedback,
and results from a longitudinal user study were presented showing that the system
adapted feedback according to individual respiration patterns. Recent activity indicators
were used to give users both quantitative and qualitative understanding of their state in
the present moment and the recent past. Users were intrinsically interested in their own
data and the sensing method was found to be relatively robust for daily use.
8.2 LIMITATIONS
This section contains overall limitations of the approach taken here and, as a
result, of conclusions that can be drawn.
8.2.1 BREATH RATE ALONE IS NOT ILLUSTRATIVE OF AUTONOMIC ACTIVITY
We identified breath rate as the optimal characteristic to begin with but some
studies focus also on tidal volume as a key to assessing user state. The reason is to
understand whether users are breathing slower but more shallow, which is associated
with anxiety (Roth, 2005). At present, the breathbelt does not detect tidal volume but we
are working towards that goal. The Breathwear client can also work with other respiration
sensor systems that do detect tidal volume. Our focus has not been on sensor design but
on behavioral influence.
Further, breath rate has been studied less than heart rate and heart rate variability,
which has been shown to be a highly robust and rich measure of arousal and
psychophysiological state. Future studies should collect both measures to ensure the
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findings are validated. Further, a bi-directional link between breath rate and heart rate has
not been firmly established and is likely mediated by other factors in the autonomic
nervous system. In short, replicating the effects with heart rate as an additional measure
will provide needed validation of the study claims.
8.2.2 THE EVALUATOR EFFECT
In the laboratory studies, the presence of the evaluator can influence user
behavior, perhaps motivating them to ‘do what the administrator wants’ so that they get
desired results on their study. We attempted to mitigate these as much as possible. Even
in ambulatory studies, there can exist an evaluator effect that may wane when the study is
over.
Few of the study participants had had experience with biofeedback or
physiological computing in general. As a result, users could have also been subject to
novelty effects that may have stimulated arousal and sympathetic nervous systems,
resulting in even higher breath rates (but perhaps more attention paid to respiration
feedback).
8.2.3 COMPETITION CONFOUND
In controlled studies there is often a competitive undertone given that participants
know that the study will involve other participants as well. In at least one study
(BreathTray), we did ask users to do the best they could – and this may have been
interpreted to mean breath regulation as well. As a result, users could have felt that they
were ‘competing to be calm’, influencing results at least in the initial part of the studies.
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8.2.4 SINGLE DATA SOURCE
I have motivated the use of breath regulation as a means of influencing
psychophysiological self-regulation using prior controlled and ambulatory studies but we
have not validated it ourselves. We run the risk of relying upon prior studies without
replicating those results here. For example, adding multiple sources of physiological
input to our client could discriminate between high arousal/low breath rate and true
resting breath rates.
8.3 SUPPLEMENTARY CONTRIBUTIONS AND IMPLICATIONS
This section offers supplementary contributions encountered while addressing the
research questions above. Following these is a discussion of the implications of these
supplementary contributions for the field and humanity writ large.
8.3.1 AUTONOMIC INTERACTION DESIGN
The studies presented in this dissertation are essentially a means of initiating and
motivating a field of autonomic interaction design (AID) through interactive technology
that aims explicitly to influence psychophysiological state during everyday usage (rather
than limiting such impact to biofeedback ‘training’ sessions and the like). AID is a field
relevant to the study of autonomic interaction systems (AISs) outlined earlier. Each study
attempts to operationalize the notion of augmenting human self-regulation by creating or
furthering real, working systems and evaluating real, observed changes on human
physiology.
A working definition of autonomic interaction design (AID) is designing to
purposefully influence or interact with the human autonomic nervous system. Principles
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of AID are also useful in affective computing (Picard, 2003), which aims to interact and
adapt to human emotions. AID is concerned less with human emotions and more with
autonomic self-regulation itself, which influences and regulates emotions experienced by
the mind. The utility in making this distinction between affective computing and AID is
that the latter can serve to help augment the human ability to experience, amplify, and
regulate emotions. This helps develops agency in how one chooses to experience
emotions and is a key to wellbeing. Augmented self-regulation systems (ASRs) are AISs
that explicitly aim to augment the user’s innate ability to self-regulate (rather than simply
to interact with the autonomic system in general).
The AID approach maintains that technology must be consciously and
compassionately designed to augment our innate self-serving abilities, not only to replace
or manipulate them. When we approach technology design with this perspective,
technology becomes again a meaningful tool to support human beings to live meaningful
lives, rather than creating new technologies for its own sake or to solve inadequacies
found in prior technology.
8.3.2 ASR AND INCENTIVIZING SELF-REGULATION
The irony of the idea of ASR systems, of course, is that users can develop over-
dependence on external tools to improve their own self-regulatory ability (see Section
7.1.7). Though this is a common concern in HCI systems, it is especially interesting in
this case because the systems is aiming to develop the user’s self-regulatory ability,
which itself would monitor and protect one from over-dependence.
What differentiates ASR systems from others is the notion that such systems must
not incentivize their own use but, rather, effective self-regulation. Though this seems a
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logical conclusion, we consider it novel because inherent to so many interactive systems
is to design continued engagement into the system.
The implicitly valued HCI principle that effective tools are ones that are used
frequently, or even well liked by users, is a crucial issue that fails to honor the humanity
of the user. By making themselves indispensable, technology tools can become
‘permanent crutches’ that produce in the user a feeling of need or dependence, similar to
one addicted to a pain-killing substance that was once a tool but has now become the
source of the problem and must itself be escaped. Such technologies unwittingly serve
themselves (or their creators), rather than their users. We do not make the claim that the
studies presented here overcome the over-dependence problem, but the issue is openly
acknowledged and the very notion of ASR motivates further study and evaluation of the
issue.
8.3.3 TECHNIQUES FOR INCENTIVIZING SELF-REGULATION
The innate incentive system of human stress and suffering often motivates one to
learn to effectively self-regulate affective and cognitive state. However, not all see
emotion or cognition as something that can be regulated. Those not well versed or aware
of emotion regulation are often left victimized by the way in which environment
influences psychophysiological state and one’s neural patterns.
In this dissertation, we have taken steps to augment these incentives in digital
form by quantifying psychophysiological state and providing socialized feedback and a
more accessible context to interpret them (i.e., more accessible than the psychology of
cognition or emotion regulation). Many users are more familiar with video games and
‘gamification’ than with the nature of their own minds. We have used technology to
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attempt to make the vague and unfamiliar territory of the human mind more accessible
and familiar through familiar gaming mechanics and socialized feedback. This is not
always ideal, as the designs presented here are laden with value judgments of ‘good’ and
‘poor’ breathing patterns as proxies for self-regulative ability. Future designs must
address this important issue.
8.3.4 ASR, BEING, AND DOING
Since the industrial age, speculators have promised technology would make our
lives easier to the point where we would barely need to ‘work’ at all. It is interesting to
question why this idea, which seems silly now, was taken so seriously then. First, it
assumes a crucial duality between ‘life’ and ‘work’ that is slowly eroding. This is not
necessarily a negative thing as we see many information workers volunteering to work
long hours even when they have enough money to retire. Clearly, people enjoy being
productive. Perhaps we should question what being ‘productive’ means in the
technological age. For many, the ultimate productivity is increasingly not about work but
about progress in life: wellbeing, self-awareness, and self-actualization.
Using technology to access and produce more information can amplify existing
human conditions such as the anxiety, inadequacy, and stress that go along with increased
time pressure, unpredictability, social comparison, and competition. Attempts to reduce
or avoid the use of technology are often impractical or not desirable for many users or
contexts. We ask ourselves, “Why can’t we have the benefits of technology without the
hindrances?” One logical conclusion is to use the very technologies that introduce
stressors to mitigate them or their effects or to strengthen the human ability to deal with
such unprecedented volume. This dissertation lays out research that, more than any
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known previously, grapples with exactly how to operationalize this idea, how feasible it
is, and what factors must be considered and designed for.
With this new view of how technology can influence our lives, we can imagine a
world where designers have specific intentions regarding not only what capabilities a
product offers but how a product affects the user’s state. It may not always be in the
direction of calm – but at least with some awareness of how the product will affect the
user, these ideas can be debated and discussed and different products can be evaluated
not only on their feature list but also on their intention to influence the user’s state.
Without this distinction, users are left wanting ‘more’ or ‘better’ versions of technologies
that currently exist to let them ‘do stuff’, not necessarily ‘transformative’ ones that
concretely and explicitly transform the user’s experience of their own lives.
One of Douglas Engelbart’s seminal contributions is the notion of co-evolution,
where human and tool systems co-evolve with one another towards greater collective
intelligence (Bardini, 2000). The notion usually connotes greater or more effective
human ‘doing’: meaning-making, authoring, identifying and describing, collaborating,
and the like. The research presented in this dissertation focuses more on evolving the
human self-regulatory system, not in attempt to ‘do’ anything in particular but rather to
augment our ability our ability to regulate our very ‘being’. Clearly, the nature of one’s
‘doing’ in life will always be dissected and discussed; but it has become increasingly
clear that one’s ‘being’ (i.e., psychophysiological state) is of fundamental import.
8.3.5 ASR AND THE PURPOSEFUL EVOLUTION OF HUMAN SOCIETY
The state of perpetual productivity often attempted in the ‘pursuit of happiness’
has a fundamental flaw: one is perpetually ‘in pursuit’. Stress and anxiety exasperate this
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problem because they distract the mind from observing this simple fact. That is, if we are
always in pursuit, we always want something ‘there’, never appreciating what is already
here: the perfection of the present moment and state of affairs; the perfection of the
nature of the universe and nature. If a tool or technology can help one develop this
understanding or help reduce the stress that distracts one from it, the tool would be of a
value that is markedly different than one which improves the user’s ‘productivity’ or
ability to ‘do’ more. Extending one’s ability to be productive is qualitatively different
from transforming one’s ability to experience life.
We must not fool ourselves into thinking that ‘stress’, as it is colloquially defined,
is only a problem of the modern age. The effects of stress have been described (albeit
differently) since ancient times (Patanjali 500-200 BC; Iyengar, 1985) and this cannot be
ignored. We can look upon the current age of personalized technologies and objective
feedback as one where the possibility of grappling with, and effectively addressing, stress
is finally possible in a mechanistic way. Though ultimately, interactive technologies are
only tools, they clearly have become intricately woven into our cognition. If extending
cognition were the ultimate goal of interactive technology (Pea, Gomez, 1985; Bardini,
2000), this would be fine. However, dealing with stress and understanding its roots is not
a question of cognition but of self-awareness and self-regulation (i.e., one cannot think
their way out of stress, they must develop awareness of their own mind to become
unleashed from it). As a result, in the same way that society has built tool after tool to
extend the capacities of our minds, we must extend the capacities of our self-awareness
and self-regulation – i.e., machines must help us be.
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8.4 NEAR-TERM FUTURE WORK
The primary next step of this research is to investigate the different design
techniques for ambulatory influence of respiration in mobile settings, when it is
ineffective or opportune, and what types of feedback or motivational cues are most
effective. There is a great deal of work in identifying and differentiating patterns and
techniques for labeling them either automatically or manually by the user. Automatically
adjusting respiration baselines (or targets) according to user performance is another
logical next step, as is the utilization of other respiration parameters such as depth and
regularity in an effort to better describe the user’s respiration patterns and influence them
with highly specific, targeted behavioral goals.
The efficacy of game mechanics motivating respiratory change must be evaluated,
as must the utility of different visualizations of user data at different timescales and
overlaid with different data (e.g., time, geography, social). We are interested in
investigating so-called ‘stress maps’ that help users uncover patterns. Further work is also
needed on the sensor to improve wearablity further, rendering it very lightweight and
easy to wear over days, weeks, even months or years.
8.5 CONCLUDING REMARKS
This dissertation lays a foundation for continued work in the continuous
augmentation of psychophysiological self-regulation. It demonstrated the efficacy of
peripheral techniques for cueing and motivating respiratory change. The work, ideas, and
studies found here have implications beyond physiology and into the learning sciences,
work productivity, physical health, mental health, cognitive performance, pre-natal
health, and interaction design. It investigated techniques, effects, and motivation behind
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how physiological data and feedback will play a role in our moment-to-moment lives for
the purpose of strengthening our internal connection with our own bodies and minds.
Whereas biofeedback has traditionally been training sessions to identify linkages between
the conscious mind and internal organs, this research focused on augmenting self-
regulatory processes in daily life. It is controversial to imagine that personal technologies
can help us connect with our inner selves. Once beyond that paradox, we find there is
ample room for study towards improving the human experience of life.
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Appendix A PPR study post-survey
Age: Gender: Prior to this experiment, how aware or how much attention did you pay to your breathing habits?
• Very Aware • Somewhat Aware • Unaware/Never considered
How would you rate your expertise with breathing techniques such as those learned in meditation or yoga?
• Expert - • Intermediate - • Novice - • No experience at all with such breathing techniques.
How often do you practice the breathing techniques you learned in the above?
• Very Frequently • Frequently • Occasionally • Rarely • Very Rarely • Never
Do you have a respiratory condition such as asthma or emphysema that interferes with your ability to breathe? Yes No What was the highest level of discomfort you felt at any time during the experiment?
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1 2 3 4 5 Why? How often did software distract you from your work?
• Very Frequently • Frequently • Occasionally • Rarely • Very Rarely • Never
How annoying was the software?
• Extremely Annoying • Very Annoying • Somewhat Annoying • A Little Annoying • Not at all annoying
If the sensor were invisible, would you use this software all day?
• Definitely • Very Probably • Probably • Possibly • Probably Not • Very Probably Not
If the sensor were invisible, would you recommend other people use this?
• Definitely • Very Probably • Probably • Possibly • Probably Not • Very Probably Not
To what extent did the software affect your productivity? 1 2 3 4 5
• A Great Deal • Much • Somewhat • Little • Not at all
If you answered anything other than not at all, was it a positive or negative effect?
• Positive • Negative
How many years of programming experience do you have? ________
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Briefly describe the task you were working on. Include the programming language, name of the class the assignment was for if applicable. What do you think we were trying to measure? Copy the screen video off their computer on a USB stick Please un-install the software off their computer.
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Appendix B Breathcast study post-survey
Prior to this experiment, how aware or how much attention did you pay to your breathing habits? (1-7)
How often do you practice breathing techniques such as those learned in meditation or yoga? (1-7)
Do you have a respiratory condition such as asthma or emphysema that interferes with your ability to breath? Yes/No
To what extent did the software distract you from your work? (1-7)
If the breath sensor is not required, how likely are you to use this software in the future? (1-7)
How much do you agree or disagree with the following statement: “The software influenced my breathing.” (1-7)
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Of the two types of bars, seeing which type of bar influenced your performance more? (real-time, not real-time)
How much do you agree or disagree with the following statement: “Seeing the other people influenced my breathing.” (1-7)
If you were using the software as part of your daily life, who would you want to use it with? _____________
Age: _________ Gender: M/F
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Appendix C BreathTray study materials
C.1 Pre-survey
Welcome
Instruction
Hi there!
This study takes 30min. Please silence your cell phone.
Select your age:
Select your gender:
Enter your major/field:
We're going to ask you to do a few different tasks a few times.
Ready to move on?
C.2 Video motivating breath awareness and regulation
This video was embedded into a webpage that the user viewed. It was shown at normal
resolution (i.e., not full-screen). The user wore headphones to listen. The heading of the
webpage was “Please wear the headphone and breath sensor. The breath sensor tells the
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computer how you are breathing.”. The video is available at
http://www.youtube.com/watch?feature=player_embedded&v=2_K4lqdMR6M and the
transcript is as follows:
“Most people don’t realize this but each of us usually has a significant amount fo
stress and anxiety each day. I’m not talking about the kind of the stress that causes you to
have a meltdown or burnout – those can be infrequent and rare. I’m actually talking about
chronic, consistent, but mild stress. Let’s call it a mini-stress.
“The problem with mini-stress is that it’s often invisible but it can still have a bad
effect on our brain, our bodies, and our hearts. So all this mini-stress, well it piles up.
And honestly it can be even worse than a meltdown. And what’s interesting is that stress
and emotions actually cause changes in the way that we breathe. That’s right, it’s pretty
incredible. And what’s useful about that is that cause effortless breathing has been shown
by a number of studies to have a calming effect and reduce stress and anxiety. Bonus: it
also helps you focus and be productive.
“So the best way to do this is through effortless diaphragmatic breathing that makes
your entire chest and abdomen move subtlety together in an effortless manner – it’s
breathing in a natural way. You’re not under any pressure, and that’s really how you want
to be breathing. So you don’t have to do yoga to relieve stress. You can just breathe
calmly while you work, while you walk, while you play. So don’t forget, effortless
diaphragmatic breathing is the key. Good luck.”
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C.3 Textual Explanation of BreathTray
This text was shown to the user on a web page before they began work on the two tasks
in the BreathTray condition:
“During these next tasks, you can see your current breath rate and calm points in the top-
right of the screen.
The first number refers to how many 'calm points' you have received. You receive points
when you breathe at or below your personal resting breath rate.
You will get more points the more calmly you breathe.
The second number refers to current breath rate.
For example: '21.8bpm 143%' means you are currently breathing at 21.8bpm, 43% above
your resting rate.
The text is RED when you are breathing above your resting rate and BLUE when you are
at or below.”
C.4 Text explanation of Serial Sevens task
Counting Backwards : Practice
We will ask you to count backwards by 7's. Just type the number and press [Enter].
The number which you input will clear, and enter the next number.
For example:
428 (we give you)
421 (you write - correct)
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414 (you write - correct)
408 (you wrote - incorrect!)
401 (you write - correct)
394 (you write - correct)
Go for speed and accuracy.
Do not press Back, Reload, Forward, or any other keys.
Ready for a 90 seconds practice round?
Please press start button when you are ready.
C.5 Text explanation of Problem-Solving with Audio Distractors task
Problem-solving while hearing sounds : Practice
You'll do your best to solve simple math problems.
You need headphones because tones will play while you work. Please wear the
headphones now.
Continue working to solve the math problems. Go for speed and accuracy.
Some of the problems are difficult, don't worry and just try your best. :-)
Do not press Back, Reload, Forward, or any other keys.
Ready for a 90 seconds practice round?
Please press start button when you are ready.
C.6 Post-survey
This survey was completed on a webpage.
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How distracting was the breath feedback? (1=not at all, 7=very much so) How much did you think breathing differently helped your performance? (1=not at all, 7=dramatic difference) How much did you think the breath feedback influenced your breathing? (1=not at all, 7=dramatic difference) How much did you think the breath feedback influenced your performance? (1=not at all, 7=dramatic difference) How likely would you be to use such computer-based feedback while you work on a normal day? (1=no chance, 7=definitely would use) How much experience do you have with conscious breathing and/or meditation? (1=none, 7=daily practice)
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Appendix D Breathwear Instructions and Feedback Form
The URL for this form was given to participants to send feedback and
troubleshoot any issues that would arise when using the system. The live form is
available at http://bit.ly/bwstudy.
Breathwear FAQ & feedback
INSTALL APP
1. give us your iphone's udid by installing http://itunes.apple.com/us/app/udid-
sender/id306603975?mt=8 and emailing it to us
2. we will email you with some file attachments
3. save the .ipa & .mobileprovision files to your desktop
4. drag .mobileprovision file to your computer's itunes library: http://bit.ly/wEMbRf
5. drag the .ipa file to your computer's itunes library's apps folder (same way)
6. sync your iphone to itunes
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UPDATE APP (when we send it to you)
1. save the .ipa file to your desktop
2. drag it to the “apps” section of your computer's itunes library: http://bit.ly/wEMbRf
3. drag it from your itunes library to your iphone's app library.
WEAR THE SENSOR
1. adjust the size first, make it very loose
2. put it on, then tighten the belt
3. it should not be tight like a belt - it should be loose
4. if any connections come off you can slide them back on
CONNECT SENSOR TO IPHONE
1. make sure app is not running (i.e., kill the process).
2. start the breathwear “bc” app.
3. press the belt's left red button. you should hear a beep. if it's a long beep you toggled it
off.
4. on the app, press "connect to sensor".
5. belt should do 3 short beeps when it is connects. app takes 5 sec to load.
6. "connect" button switches to "disconnect". data shows after 30sec. (7).
troubleshoot: in iphone's settings go to “general” then “bluetooth”. toggle bluetooth
off/on.
FIRST 24 HOURS
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1. in the settings, set “show notifications” and “show data on home screen” to OFF.
2. after 24 hours, set them to ON.
EACH NIGHT: SEND DATA, CLEAR, DISCONNECT
1. press the SEND DATA button and send the email to [email protected].
2. press CLEAR DATABASE
3. press “disconnect” in the app. no need to press any sensor button.
ANY TIME
Note: change your baseline to your desired breath rate any time you want.
Having a sensor connection issue? See directions above.
• Not sure if my belt is on.
• My belt was working but it stopped.
• I can't get my belt working at all.
• Other:
Having trouble installing the app? See directions above.
• I can't seem to do the first install.
• I can't seem to update the app.
• Other:
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Having trouble with the iPhone app? See directions above.
• Want to turn off notifications.
• Uses too much battery.
• My breath rate is not correctly detected.
• My baseline needs to be changed.
• Other:
I have a question, comment, idea, reflection, critique, or feedback not addressed
above.
Breathwear helps me increase my self-awareness.
1 2 3 4 5 6 7 8 9 10
Not at all Dramatically
Email address *
Mobile phone number *
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