WEARABLE RESEARCH
TECHNOLOGY
TRACKING TOOLS FOR ALL OCCASIONS?
FABIOLA GATTRINGER, MANUELA SCHMID,
BARBARA STIGLBAUER & BERNAD BATINIC
AOM.JKU.AT
WEARABLE RESEARCH TECHNOLOGY
PROJECT GOAL
 multimethod approach
 consumer-oriented
 portable accessories
subjective
self-report
 least intrusive
 common
physiological
data
behavioral
data
 Literature review
 Cross-sectional study
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ALL THAT DATA…
physical data
• steps, heart rate, miles, sleep,…
diet
• calories consumed, portions, carbs, fat, protein,…
psychologocial states and traits
• mood, happiness, emotion, anxiety,...
mental and cognitive states and traits
• IQ, reaction, alertness, memory,…
environmental variables
• location, weather, noise,…
situational variables
• time of day, day of week, context,…
social variables
• influence, trust, current status in social network,…
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PROS & CONS
+
-
• continous
• big amounts of data
• non-reactive, objective
• reactivity concerns
• 1 for n
• data security concerns
• use of additional apps
• device/software updates
• easy to use
• accuracy, validity
• cost-effective
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VALUABLE ADDTION FOR
PSYCHOLOGICAL RESEARCH
 accounts for multi-faceted phenoma
 counteract common-method bias
subjective
self-report
 counteract self-report bias
 evaluate validity of findings
physiological
data
behavioral
data
 diagnostic
 intervention
 measurment
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EXPLORATIVE STUDY
n = 98
59% ♀, age ø = 32 years (SD = 11)
?
what‘s been tracked and with what device/software
?
what could they imagine to track in the future
?
obstacles for tracking
 66% regularly engage in self-tracking, only about half of them use
self-tracking devices (e.g. FitBit, Runtastic)
 Self-trackers work longer hours than non-trackers, (t(94)=1.7, p=.092)
and they are signficantly older than non-trackers (t(93)=2.8, p=.006)
 Engaging in self-tracking behavior was directly related to selfconcept clarity (B=0.34, SE=0.16, β=.21, p=.039, ) and indirectly
related to self-esteem (B=0.22, SE=0.10, 95%CI [0.04; 0.46])
 Trackers reported higher goal-achievment than non-trackers
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EXPLORATIVE STUDY
 55% of non-trackers do not track because it‘s time consuming
46% reported not having thought about it, yet
21% have data security issues
0% reported financial obstacles
 82% of non-trackers can imagine starting to track themselves




Movement (e.g. steps)
Diet (e.g. food log)
Weight
Sleep
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ON-GOING & UP-COMING
PROJECT
Wearable Insights: The Potential of Tracking Technology to
Enrich Psychological Research
 What is the quality of wearable technologies for measuring
psychological constructs?
 What is the benefit of combining reactive questionnaire data with
non-reactive data obtained through wearable technologies in applied
(psychological) research settings?
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SOURCES
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and opportunites. Sensors, 14, 9153–9209.
Cadmus-Bertram, L.A., Marcus, B.H., Patterson, R.E., Parker, B.A., & Morey, B.L. (2015). Randomized trial of a Fitbit-based physical activity
intervention for women. Am J Prev Med, 49, 414–418.
eMarketer (October 28, 2015). Wearable usage will grow by nearly 60% this year. Retrieved from: http://www.emarketer.com/Article/WearableUsage-Will-Grow-by-Nearly-60-This-Year/1013159
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers.
International Journal of Behavioral Nutrition and Physical Activity, 12(1). https://doi.org/10.1186/s12966-015-0314-1
Kelly, P., Thomas, E., Doherty, A., Harms, T., Burke, Ó., Gershuny, J., & Foster, C. (2015). Developing a method to test the validity of 24 hour time
use diaries using wearable cameras: a feasibility pilot. PloS One, 10(12), e0142198.
Miller, G. (2012). The Smartphone Psychology Manifesto. Perspectives on Psychological Science, 7(3), 221–237.
https://doi.org/10.1177/1745691612441215
MMA Mobile Marketing Association. (2015). Mobile communications report 2015. Vienna: MindTake Research. Retrieved from:
http://www.mmaaustria.at/download-infos
Morris, M. E., & Aguilera, A. (2012). Mobile, social, and wearable computing and the evolution of psychological practice. Professional Psychology:
Research and Practice, 43(6), 622–626. https://doi.org/10.1037/a0029041
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. J
NeuroEng Rehabil, 9, 21.
Piwek, L., Ellis, D.A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLOS Med, 13, e1001953.
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literature and recommended remedies. J Appl Psychol, 88, 897–903.
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inquiry (pp. 548–560). Oxford: University Press.
Schmid Mast, M., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology: Automated interpersonal
behavior assessment. Curr Dir Psychol Sci, 16, 154–160.
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https://doi.org/10.1089/big.2012.0002
Wrzus, C., & Mehl, M. R. (2015). Lab and/or Field? Measuring Personality Processes and Their Social Consequences: Lab and/or field? European
Journal of Personality, 29(2), 250–271. https://doi.org/10.1002/per.1986
pictures and graphics by freepic.com & pixabay.com
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WEARABLE RESEARCH
TECHNOLOGY
TRACKING TOOLS FOR ALL OCCASIONS?
FABIOLA GATTRINGER, MANUELA SCHMID,
BARBARA STIGLBAUER & BERNAD BATINIC
AOM.JKU.AT
[email protected]