Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Apr 24, 2024
Open Peer Review Period: Apr 30, 2024 - Jun 25, 2024
Date Accepted: Jan 22, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Comparative Evaluation of Ecological Momentary Assessment, Global Physical Activity Questionnaire, and Bouchard’s Physical Activity Record for Measuring Physical Activity: A Multilevel Modeling Approach
ABSTRACT
Background:
There is growing interest in the real-time assessment of physical activity and physiological variables. Acceleration, particularly those collected through wearable sensors, has been increasingly adopted as an objective measure of physical activity. However, sensor-based measures often pose challenges for large-scale studies due to their associated costs, inability to capture contextual information, and restricted user populations. Smartphone-delivered Ecological Momentary Assessment (EMA) offers an unobtrusive and undemanding means to measure physical activity to address these limitations.
Objective:
To evaluate the usability of EMA by comparing its measurement outcomes with two self-report assessments of physical activity: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard’s Physical Activity Record (BAR).
Methods:
235 participants (137 females, 98 males, 94 repeated) participated in one or more 7-day study. Waist-worn sensors provided by Actigraph™ captured accelerometer data while participants completed three self-report measures of physical activity. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with eight sub-domains of physiological activity (overall physical activity; overall excluding occupational; move; moderate and vigorous exercise; moderate and vigorous occupational; sedentary) to model accelerometer data.
Results:
Among the three measurement outcomes, EMA (β = .185, p = .005) and BAR (β = .270, p < .001) exhibited higher overall performance over GPAQ (β = .140, p = .019). EMA also showed a more balanced performance, compared to other measurement tools, in modeling various physical activity domains, including occupational, leisure, and sedentary behaviour.
Conclusions:
Multilevel modeling on three self-report assessments of physical activity indicates that smartphone-delivered EMA is a valid and efficient method for assessing physical activity.telemedicine; smartphone; wearable electronic devices; physical activity
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