Examining Self-Regulation Using Wearable Technology

1. 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

2. SELF-REGULATION
Broad term that refers to the full
range of ways in which human
beings adjust their behavior
Foundation for learning and
achievement

3. 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

4. PSYCHOBIOLOGICAL MODEL
OF SELF-REGULATION
Neuropsychological
Assessment

5. 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

6. PSYCHOBIOLOGICAL MODEL
OF SELF-REGULATION
Psychophysiology,
Affective Computing

7. 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

8. Heart Rate Data Examples
Higher HF variability (0.15-0.4 Hz)
Lower HF variability (0.15-0.4 Hz)
Fast Fourier
Transform
Higher HF variability (yellow)
Lower HF variability (yellow)
Participant
46
Participant
51

9. Current Study:
Self-Regulation at the Museum
• Participants
• Museum visitors 8 yrs. and older
• Measures
• Executive Functioning
• Dimensional Change Card Sort task
• Self-report Questionnaires
• Self-regulation questionnaire
• Self-Assessment Manikin for mood and arousal
• Physiological data (via E4 wristband)
• HR, HRV (Photoplethysmography (PPG))
• Skin conductance (EDA)
• Accelerometer Empatica E4

10. Current Study:
Self-Regulation at the Museum
• Participants
• Museum visitors 8 yrs. and older
• Measures
• Executive Functioning
• Dimensional Change Card Sort task
• Self-report Questionnaires
• Self-regulation questionnaire
• Self-Assessment Manikin for mood and arousal
• Physiological data (via E4 wristband)
• HR, HRV (Photoplethysmography (PPG))
• Skin conductance (EDA)
• Accelerometer Empatica E4

12. Current Study:
Self-Regulation at the Museum
• Participants
• Museum visitors 8 yrs. and older
• Measures
• Executive Functioning
• Dimensional Change Card Sort task
• Self-report Questionnaires
• Self-regulation questionnaire
• Self-Assessment Manikin for mood and arousal
• Physiological data (via E4 wristband)
• HR, HRV (Photoplethysmography (PPG))
• Skin conductance (EDA)
• Accelerometer Empatica E4

13. SAMPLE CHARACTERISTICS
N = 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

14. BASELINE HR AND HRV MEASURES
60
65
70
75
80
85
90
95
100
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

15. HR and Age HRV and Age

16. 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 Age
N = 176
** p < .001
* p < .05
Curvilinear relationship

17. 80
90
100
110
120
130
140
150
Very low
(-2 SD)
Low
(-1 SD)
Average High
(+1 SD)
Very high
(+ 2 SD)
Executive
Functioning
Self-Reported Arousal

18. MULTIPLE REGRESSION ANALYSES
OUTCOME: 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-Reported
Arousal
-0.91 0.34 -2.69 .008 3.76%
Heart Rate
Variability
5.89 1.93 3.05 .003 4.84%
HRV X Age -6.23 2.46 -2.53 .012 3.33%

19. 60
70
80
90
100
110
120
130
140
150
160
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)

20. SUMMARY
-First study to examine HRV during executive functioning task
-HR and HRV are different
HRV predicts Executive Functioning, HR does not
-HRV is a stronger predictor of performance than self-reported
arousal

21. IMPLICATIONS
HRV 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

22. Fort Worth Museum of Science and History
Debbie Cockerham
Families who participated
LINK Research Lab
LAK Participants