DEVELOPMENT OF A MOBILE TOOL FOR DUAL-TASK GAIT ASSESSMENT

JENNA BLUMENTHAL, MASc Candidate MARK CHIGNELL, PhD TAMMY SIEMINOWSKI, MD, MEng, CCFP

UNIVERSITY OF TORONTO / MECHANICAL & INDUSTRIAL ENGINEERING

RATIONALE

• Changes in gait have been shown to precede cognitive impairment, and is one of the most consistent predictors of falls1

• Stride time variability has been shown to be low among cognitively healthy older adults, and high among individuals with Alzheimer's disease and MCI2

• Gait assessment while performing a secondary task (“dual-tasking”) has been widely used to assess interaction between cognition, gait and the risk of falling3

• Dual-tasking also a better representation of real-life demands4

RATIONALE

• The smartphone as a self-contained system for gait analysis5

KEY CAPABILITIES: 1. Inertial measurement unit 2. Processing core 3. Touchscreen

IMPROVES: - Cost - Portability - Customizability - Patient tolerance - Deployment scalability

RESEARCH GOALS

1. Develop a simple tool to enable collection,

aggregation and visualization of gait variability data through a co-design process with clinicians

1. Examine the feasibility of integrating a gamified dual-

task assessment 1. Explore the factors that influence rehabilitation

therapist’s willingness to use mobile/wearable technology

Prototype development

1 Gait Assessment

- Foot contact moments determined from the Anterior-Posterior accelerations of the torso6

Fig 1: Gait Cycle: Kordari, K et al. 2013. Method For Step Detection And Gait Direction Estimation, U.S. Patent 20130311134.)

Fig. 2: Signal processing (fast Fourier transform & 4th order low-pass Butterworth filter at 60 Hz)

Prototype development

2 UI/UX Design

Fig. 3: Wireframes to working prototype

Prototype development

3 Dual-Task using Go/No-Go7

“Score” attentional trade-off

Simple to develop

Validated EF assessment

- Traditional assessment used to measure inhibition ability

- Participant required to respond to a certain stimuli, but not to others

- Inhibition ability shown to be sensitive to age and cognitive status

Prototype development

3 Dual-Task using Go/No-Go

GO NO-GO

80% 20%

Fig. 4: Probability of stimulus presentation8

MEASUREMENT VALIDATION

MEASUREMENT VALIDATION

Mean Stride Time (ms)

n = 12

Vico

n

Smartphone

r = 0.89 p < 0.001

Smartphone

Stride Time Variability (%)

Vico

n

r = 0.79 p < 0.01

User Considerations Prototype development

IF WE BUILD IT, WILL THEY COME?

User Considerations

1 Literature review, expert interviews and in-situ observation

2 Questionnaire development

Fig. 1: Construction of a summated rating scale10

Pilot test & Content Validation

Construct definition based on Technology Acceptance Model9

Internal consistency analysis

α

Design of scale

User Considerations

3 Pilot assessment (n = 13, Bridgepoint Health)

- Both males & females

- Wide age range (25-54) and experience level (< 5 – 20+ years)

- No previous experience using MWT in practice

User Considerations

3 Pilot assessment (n = 13, Bridgepoint Health)

- Both males & females

- Wide age range (25-54) and experience level (< 5 – 20+ years)

- No previous experience using MWT in practice

Initial findings 4

Concerns with…

- Privacy - Interoperability with current EMR - More documentation (?!) - Distraction from patient interaction

Interest expressed in…

- Testing emerging MWTs - Undergoing training - Providing time teaching patient to use MWT - Referring to data during planning & evaluation

KEY LEARNINGS

- Opinion of physical and occupational therapists largely omitted from the research in acceptance and uptake in mobile/wearable health

- Barrier to entry may be lower than in other clinical areas - There is opportunity in this field for testing new ideas and mobile

healthcare implementation strategies - Users apprehensive about holding patient data on personal devices

NEXT STEPS

- Use of this app in a hospital rehab setting - Further technical validation - Assessment of secondary task - In-situ usability testing

- Broader send-out of survey

- Diverse geography, clinical setting, etc… - Develop recommendations of implementation strategies, etc.

REFERENCES

1. Amboni M, Barone P, Hausdorff JM. Cognitive contributions to gait and falls: Evidence and implications. Mov Disord. 2013;28(11):1520–1533. doi:10.1002/mds.25674.

2. Muir SW, Speechley M, Wells J, Borrie M, Gopaul K, Montero-Odasso M. Gait assessment in mild cognitive impairment and Alzheimer’s disease: The effect of dual-task challenges across the cognitive spectrum. Gait Posture. 2012;35(1):96–100. doi:10.1016/j.gaitpost.2011.08.014.

3. Al-Yahya E, Dawes H, Smith L, Dennis A, Howells K, Cockburn J. Cognitive motor interference while walking: A systematic review and meta-analysis. Neurosci Biobehav Rev. 2011;35(3):715–728. doi:10.1016/j.neubiorev.2010.08.008.

4. Montero-Odasso M, Muir SW, Speechley M. Dual-task complexity affects gait in people with mild cognitive impairment: the interplay between gait variability, dual tasking, and risk of falls. Arch Phys Med Rehabil. 2012;93(2):293–9. doi:10.1016/j.apmr.2011.08.026.

5. Ellis RJ, Ng YS, Zhu S, et al. A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease. 2015. doi:10.1371/journal.pone.0141694.

6. IJmker T, Lamoth CJC. Gait and cognition: The relationship between gait stability and variability with executive function in persons with and without dementia. Gait Posture. 2012;35(1):126–130. doi:10.1016/j.gaitpost.2011.08.022.

7. De Sanctis P, Butler JS, Malcolm BR, Foxe JJ. Recalibration of inhibitory control systems during walking-related dual-task interference: A Mobile Brain-Body Imaging (MOBI) Study. Neuroimage. 2014. doi:10.1016/j.neuroimage.2014.03.016.

8. Redick TS, Calvo A, Gay CE, Engle RW. Working memory capacity and go/no-go task performance: selective effects of updating, maintenance, and inhibition. J Exp Psychol Learn Mem Cogn. 2011;37(2):308–324. doi:10.1037/a0022216.

9. Davis FD. Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989;13(3):319–340. doi:10.2307/249008.

10. Paul E. Spector. Summated Rating Scale Construction: An Introduction. Sage Publications; 1992. Available at: https://home.ubalt.edu/tmitch/645/articles/Summated Rating Scales.pdf. Accessed April 7, 2016.

CREDITS / Q&A