| Summary: | Objectives The purpose of this study was to develop a replicable methodology of mobile ecological momentary assessments (EMAs) to capture prospective physical activity (PA) within free-living social and physical contexts by leveraging citizen-owned smartphones running on both Android and iOS systems.Design Data were obtained from the cross-sectional pilots of the SMART Platform, a citizen science and mobile health initiative.Setting The cities of Regina and Saskatoon, Canada.Participants 538 citizen scientists (≥18 years) provided PA data during eight consecutive days using a custom-built smartphone application, and after applying a rigid inclusion criteria, 89 were included in the final analysis.Outcome measures EMAs enabled reporting of light, moderate, and vigorous PA, as well as physical and social contexts of PA. Retrospective PA was reported using International Physical Activity Questionnaire (IPAQ). For both measures, PA intensities were categorised into mean minutes of light and moderate-to-vigorous PA per day. Wilcoxon signed ranks tests and Spearman correlation procedures were conducted to compare PA intensities reported via EMAs and IPAQ.Results Using EMAs, citizen scientists reported 140.91, 87.16 and 70.38 mean min/day of overall, light and moderate-to-vigorous PA, respectively, whereas using IPAQ they reported 194.39, 116.99 and 98.42 mean min/day of overall, light and moderate-to-vigorous PA, respectively. Overall (ρ=0.414, p<0.001), light (ρ=0.261, p=0.012) and moderate-to-vigorous PA (ρ=0.316, p=0.009) were fairly correlated between EMA and IPAQ. In comparison with EMAs, using IPAQ, citizen scientists reported significantly greater overall PA in active transportation (p=0.002) and recreation, sport and leisure-time domains (p=0.003).Conclusions This digital epidemiological and citizen science methodology adapted mobile EMAs to capture not only prospective PA, but also important physical and social contexts within which individuals accumulate PA. Ubiquitous tools can be leveraged via citizen science to capture accurate active living patterns of large populations in free-living conditions through innovative EMAs.
|
|---|
