The purpose of this cross-sectional study is to investigate the difference in physical activity among elderly living in different areas in Japan (“Bunkyo Ward in Tokyo” (Bunkyo) and “Fuchu City in Tokyo” (Fuchu) as urban areas, and “Oyama Town in Shizuoka” (Oyama) as a non-urban area). Participants were 1859 community-dwelling residents aged 65-74 years, randomly selected from the residential registry (response rate: 68.9%). A mail survey using self-administered questionnaires was conducted. Multivariate logistic regression analyses were used to calculate the adjusted odds ratios (ORs) and 95% confidence intervals (95% CI) of various types of physical activity (eg, walking (Walking), going out (Going-out), bicycling (Bicycling), exercise habits (Exercise)), according to residential areas (reference category: Fuchu), stratified by gender, adjusting for socio-demographic variables. There was a significant difference in Going-out (ORs = 0.61 (95% CI: 0.44-0.86) for men, 0.48 (0.33-0.69) for women)), and Bicycling (0.04 (0.03-0.07) for men, 0.04 (0.02-0.07) for women) in Oyama compared to Fuchu. Furthermore, for women, there was a significant difference in Walking (0.56 (0.38-0.81)) and Exercise (0.59 (0.41-0.85)) in Oyama compared to Fuchu. There was a significant difference in Bicycling and Going-out for men in Bunkyo compared to Fuchu, but there was not a significant difference in other items. Low physical activity levels were observed in the elderly in the non-urban area compared to urban areas. The association was stronger in women. Regional difference might need to be taken into account for an effective physical activity intervention.

Evidence is growing on adverse health outcomes associated with less physical activity (PA) and more sedentary behavior (SB). As most workers generally spend approximately one-third of the day at work, measuring the amount of occupational PA/SB time would contribute to understanding the health risks of physical inactivity and SB among workers. In this review article, we describe the amount of time spent in different intensities of activity (moderate-to-vigorous PA [MVPA]; light PA [LPA]; and SB), on work and non-work days, while at work and outside working hours among workers. The review used objective measurements made by an accelerometer and showed the proportion of MVPA that office workers engaged in on work and non-work days accounted for 3-5% and 2-4% of accelerometer wear time, respectively. Moreover, office workers spent more time in SB and less time in LPA on work days than non-work days (SB, 66-76% vs. 60-69%; LPA, 20-28% vs. 23-36%). Especially on work days, office workers spent the greatest proportion of the day in SB during working hours (71-82%), compared with that outside working hours (63-67%). The proportion of MVPA accounted for 2-5% during working hours and 3-7% outside working hours. In addition, there were considerable differences in the proportions of LPA and SB between white-collar and blue-collar workers during working hours (SB, 73 vs. 55%; LPA; 22 vs. 40%), whereas the MVPA time was similar (5 vs. 5%). Some occupational exposure to unfavorable PA/SB at work might be unavoidable; however, inadequate PA/SB during working hours should be corrected.

Approximately 40%–65% of new fitness club (FC) members cancel their membership within 6 months. To prevent such cancellations, it is essential to identify members at high risk of doing so. This study developed a model to predict the probability of discontinuing FC membership among new members. We conducted a cohort study and enrolled participants from 17 FCs in Japan. We asked 5,421 individuals who became members from March 29, 2015 to April 5, 2016 to participate in the study; 2,934 completed the baseline survey, which was conducted when the participants became FC members. We followed up the participants until September 30, 2016. We excluded 883 participants with missing values and 69 participants under aged 18 years; thus, our analysis covered 1,982 individuals. We conducted the random survival forest to develop the prediction model. The mean follow-up period was 296.3 (standard deviation, 127.3) days; 488 participants (24.6%) cancelled their membership during the follow-up. The prediction model comprised 8 predictors: age; month of joining FC; years of education; being under medical follow-up; reasons for joining FC (health improvement, relaxation); and perceived benefits from exercise (maintaining good body weight, recognition of one’s ability by other). The discrimination and calibration were acceptable (C statistic: 0.692, continuous ranked probability score: 0.134). Our findings suggest that the prediction model could assess the valid probability for early FC cancellation among new members; however, a validation study will be needed.