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BackgroundThe use of Web-based physical activity systems has been proposed as an easy method for collecting physical activity data. We have developed a system that has exhibited high accuracy as assessed by the doubly labeled water method.ObjectiveThe purpose of this study was to collect behavioral data from a large population using our Web-based physical activity record system and assess the physical activity of the population based on these data. In this paper, we address the difference in physical activity for each urban scale.MethodsIn total, 2046 participants (aged 30-59 years; 1105 men and 941 women) participated in the study. They were asked to complete data entry before bedtime using their personal computer on 1 weekday and 1 weekend day. Their residential information was categorized as urban, urban-rural, or rural. Participant responses expressed the intensity of each activity at 15-minute increments and were recorded on a Web server. Residential areas were compared and multiple regression analysis was performed.ResultsMost participants had a metabolic equivalent (MET) ranging from 1.4 to 1.8, and the mean MET was 1.60 (SD 0.28). The median value of moderate-to-vigorous physical activity (MVPA, ≥3 MET) was 7.92 MET-hours/day. A 1-way ANCOVA showed that total physical activity differed depending on the type of residential area (F2,2027=5.19, P=.006). The urban areas (n=950) had the lowest MET-hours/day (mean 37.8, SD, 6.0), followed by urban-rural areas (n=432; mean 38.6, SD 6.5; P=.04), and rural areas (n=664; mean 38.8, SD 7.4; P=.002). Two-way ANCOVA showed a significant interaction between sex and area of residence on the urban scale (F2,2036=4.53, P=.01). Men in urban areas had the lowest MET-hours/day (MVPA, ≥3 MET) at mean 7.9 (SD 8.7); men in rural areas had a MET-hours/day (MVPA, ≥3 MET) of mean 10.8 (SD 12.1, P=.002). No significant difference was noted in women among the 3 residential areas. Multiple regression analysis showed that physical activity consisting of standing while working was the highest contributor to MVPA, regardless of sex.ConclusionsWe were able to compile a detailed comparison of physical activity because our Web-based physical activity record system allowed for the simultaneous evaluation of physical activity from 2046 Japanese people. We found that rural residents had greater total physical activity than urban residents and that working and transportation behaviors differed depending on region type. Multiple regression analysis showed that the behaviors affected MVPA. People are less physically active while working, and sports and active transportation might be effective ways of increasing physical activity levels.