%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e59878
%T Validation of Ecological Momentary Assessment With Reference to Accelerometer Data: Repeated-Measures Panel Study With Multilevel Modeling
%A Noh,Jung Min
%A Im,SongHyun
%A Park,JooYong
%A Kim,Jae Myung
%A Lee,Miyoung
%A Choi,Ji-Yeob
%+ Department of Biomedical Sciences, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 02 740 8922, jiyeob.choi@gmail.com
%K telemedicine
%K wearable electronic devices
%K physical activity
%K mobile phone
%K wearables
%K smartphones
%K ecological momentary assessment
%K EMA
%K global physical activity questionnaire
%K GPAQ
%K Bouchard’s physical activity
%K multilevel modeling
%K females
%K women
%K males
%K men
%K sensors
%K evaluation
%K comparative
%K South Korea
%D 2025
%7 1.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: There is growing interest in the real-time assessment of physical activity (PA) 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 PA to address these limitations. Objective: This study aimed to evaluate the usability of EMA by comparing its measurement outcomes with 2 self-report assessments of PA: Global Physical Activity Questionnaire (GPAQ) and a modified version of Bouchard Physical Activity Record (BAR). Methods: A total of 235 participants (137 female, 98 male, and 94 repeated) participated in one or more 7-day studies. Waist-worn sensors provided by ActiGraph captured accelerometer data while participants completed 3 self-report measures of PA. The multilevel modeling method was used with EMA, GPAQ, and BAR as separate measures, with 6 subdomains of physiological activity (overall PA, overall excluding occupational, transport, exercise, occupational, and sedentary) to model accelerometer data. In addition, EMA and GPAQ were further compared with 6 domains of PA from the BAR as outcome measures. Results: Among the 3 self-reporting instruments, EMA and BAR exhibited better overall performance in modeling the accelerometer data compared to GPAQ (eg EMA daily: β=.387, P<.001; BAR daily: β=.394, P<.001; GPAQ: β=.281, P<.001, based on repeated-only participants with step counts from accelerometer as dependent variables). Conclusions: Multilevel modeling on 3 self-report assessments of PA indicates that smartphone-delivered EMA is a valid and efficient method for assessing PA. 
%M 40168069
%R 10.2196/59878
%U https://www.jmir.org/2025/1/e59878
%U https://doi.org/10.2196/59878
%U http://www.ncbi.nlm.nih.gov/pubmed/40168069