Factors associated with changes in the objectively measured physical activity among Japanese adults: A longitudinal and dynamic panel data analysis.
Daiki WatanabeHaruka MurakamiYuko GandoRyoko KawakamiKumpei TanisawaHarumi OhnoKana KonishiAzusa SasakiAkie MorishitaNobuyuki MiyatakeMotohiko MiyachiPublished in: PloS one (2023)
Factors associated with dynamic changes in the objectively measured physical activity have not been well understood. We aimed to 1) evaluate the longitudinal change in the physical activity trajectory according to sex which is associated with age and to 2) determine the factors associated with the dynamic change in physical activity-related variables across a wide age range among Japanese adults. This longitudinal prospective study included 689 Japanese adults (3914 measurements) aged 26-85 years, whose physical activity data in at least two surveys were available. Physical activity-related variables, such as intensity (inactive, light [LPA; 1.5 to 2.9 metabolic equivalents (METs)], moderate-to-vigorous [MVPA; ≥3.0 METs]), total energy expenditure (TEE), physical activity level (PAL), and step count, were evaluated using a validated triaxial accelerometer. Statistical analysis involved the latent growth curve models and random-effect panel data multivariate regression analysis. During a mean follow-up period of 6.8 years, physical activity was assessed an average of 5.1 times in men and 5.9 times in women. The profiles for the inactive time, LPA (only men), MVPA, step count, PAL, and TEE showed clear curvature, indicating an accelerated rate of change around the age of 70. In contrast, other variables exhibited minimal or no curvature over the age span. The MVPA trajectory was positively associated with alcohol consumption, hand grips, leg power, and trunk flexibility and negatively associated with age, local area, body mass index (BMI), comorbidity score, and heart rate over time. Our results indicated that the physical activity trajectory revealed clear curvature, accelerated rate of change around the age of 70, and determined physical health and fitness and BMI as dynamic factors associated with physical activity changes. These findings may be useful to help support populations to achieve and maintain the recommended level of physical activity.
Keyphrases
- physical activity
- body mass index
- data analysis
- heart rate
- healthcare
- sleep quality
- alcohol consumption
- public health
- blood pressure
- machine learning
- type diabetes
- computed tomography
- weight gain
- magnetic resonance
- high intensity
- skeletal muscle
- climate change
- pregnant women
- peripheral blood
- adipose tissue
- body composition
- neural network
- human health