Spann LAK17 Self-Regulation

EXAMINING SELF-REGULATION USING WEARABLE TECHNOLOGY

Catherine Spann, Ph.D.James Schaeffer, M.S.George Siemens, Ph.D.

Learning Analytics & Knowledge Conference Vancouver, CA | March 2017

SELF-REGULATION

Broad term that refers to the full range of ways in which human beings adjust their behavior

Foundation for learning and achievement

PSYCHOBIOLOGICAL MODEL OF SELF-REGULATION

Blair, C., & Raver, C. C. (2015). School Readiness and Self-Regulation: A Developmental Psychobiological Approach. Annual Review of Psychology, 66(1), 711–731. https://doi.org/10.1146/annurev-psych-010814-015221


Neuropsychological Assessment

COMMON NEUROPSYCHOLOGICAL MEASUREMENT: DIMENSIONAL CHANGE CARD SORT TASK

• Rules• Match by color or shape• Switch between rules

• Components of self-regulation tested• Working memory• Inhibitory control• Cognitive flexibility

• Performance greatly depends on prefrontal cortex


Psychophysiology, Affective Computing

COMMON PSYCHOPHYSIOLOGICAL MEASUREMENTS:HEART RATE AND HEART RATE VARIABILITY

Brain and body intimately connected

HR is different form HRV Sympathetic and parasympathetic nervous systems

Vagus nerve is the single most important nerve in the body (Tracey, 2002)

Central Autonomic Network (CAN) of the brain inhibits autonomic arousal via the efferent vagus nerve and regulates the rhythm of the heart

Evidence that HRV at rest predicts self-regulatory abilities Yet to be tested during a task

Tracey, K. J. (2002). The inflammatory reflex. Nature, 420(6917), 853–859.Thayer, J. F., Yamamoto, S. S., & Brosschot, J. F. (2010). The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors. International Journal of Cardiology, 141(2), 122–131. https://doi.org/10.1016/j.ijcard.2009.09.543

HeartRateDataExamplesHigher HF variability(0.15-0.4Hz)

LowerHFvariability(0.15-0.4Hz)FastFourierTransform

HigherHFvariability(yellow)

LowerHFvariability(yellow)

Participant

46Participant

51

CurrentStudy:Self-Regulation attheMuseum• Participants

• Museumvisitors8 yrs.andolder

• Measures• ExecutiveFunctioning• DimensionalChangeCardSorttask

• Self-reportQuestionnaires• Self-regulationquestionnaire• Self-AssessmentManikinformoodandarousal

• Physiologicaldata(viaE4wristband)• HR,HRV(Photoplethysmography (PPG))• Skinconductance (EDA)• Accelerometer Empatica E4











SAMPLE CHARACTERISTICSN = 228 43.4% Males

Mean age = 25.47, SD = 16.35 Minimum 8 Maximum 69

Ethnicity 20.5% Hispanic or Latino

Race 58.2% White 13.2% Black 13.2% Other 11.8% Asian 3.6% More than One Race

BASELINE HR AND HRV MEASURES

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Heart Rate

Baseline

During Task

Significant increase in HR from baseline to during task, t(174) = -7.10, p < .001

3

4

5

6

7

8

9

Heart Rate Variability

No significant change in HRV from baseline to task, t(174) = 1.20, p = .23

HR and Age HRV and Age

CORRELATIONS

Heart Rate Heart Rate Variability Self-Reported Arousal

--

Heart Rate Variability -.55** --

Self-Reported Arousal .17* -.08 --

Executive Functioning -.05 .17* .18*

Note: Controlling for AgeN = 176** p < .001* p < .05

Curvilinear relationship

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90

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150

Very low (-2 SD)

Low(-1 SD)

Average High(+1 SD)

Very high(+ 2 SD)

Executive Functioning

Self-Reported Arousal

MULTIPLE REGRESSION ANALYSESOUTCOME: EXECUTIVE FUNCTIONING

b SE t p Effect Size (%)

Age 5.78 3.29 1.76 .081 1.61%

Heart Rate 0.10 0.15 0.66 .507 0.18%

Self-ReportedArousal

-0.91 0.34 -2.69 .008 3.76%

Heart RateVariability

5.89 1.93 3.05 .003 4.84%

HRV X Age -6.23 2.46 -2.53 .012 3.33%

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Very Low HRV (-2 SD) Low HRV (-1 SD) Mean HRV (6.56) High HRV (+1 SD) Very High HRV (+2 SD)

Executive Functioning

Heart Rate Variability

Age 8 (p = .001)

Age 11 (p = .001)

Age 15 (p = .001)

Age 21 (p = .004)

Age 28 (p = .039)

Age 39 (p = .354)

SUMMARY

-First study to examine HRV during executive functioning task

-HR and HRV are differentHRV predicts Executive Functioning, HR does not

-HRV is a stronger predictor of performance than self-reported arousal

IMPLICATIONSHRV is an indicator of self-regulation A multi-method approach to self-regulation Consider costs and ease of measurement Complements, but doesn’t replace existing methods

Wearable tech can offer a non-invasive, passive measurement

Age is important factor in psychophysiological variables

Adjust schedules depending on self-regulatory pattern

Self-regulation implicated in almost every aspect of life, not just learning

Increase HRV, Increase Self-Regulation

Fort Worth Museum of Science and History

Debbie Cockerham

Families who participated

LINK Research Lab

LAK Participants

Data & Analytics

Spann LAK17 Self-Regulation