Abstract
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.