JMIR Publications

ABSTRACT

Background:

Overweight and obesity has become a medical and social problem in economically developed and developing countries, including Japan. Overweight and obesity are associated with lifestyle-related diseases such as hypertension, dyslipidemia, type 2 diabetes, and arteriosclerotic cardiovascular disease. Increasing physical activity is an extremely effective method to promote weight loss as a common way to de-obesify and thereby improve lifestyle-related diseases. Earlier studies of step counts and weight were aimed at small populations and short-term step counts, which were recorded for 1–2 weeks while volunteers wore pedometers. Nevertheless, the relation between long-term and longitudinal step count changes and weight loss remains unclear.

Objective:

For this study, we analyzed effects of long-term steps on weight loss by accessing the database of "Asmile": a mobile health application provided by Osaka prefecture.

Methods:

We targeted Asmile users in their 40s to 70s who had undergone a Specific Health Checkup during fiscal years 2019–2023. Of these, we selected 2,778 users with a BMI of 25 kg/m2 or higher and continuous step count data of 10–14 months. Then, we conducted a cluster analysis of trajectories of a year's accumulated data of steps using a Latent Class Mixed Model (LCMM). Finally, odds ratios of step trajectories for weight loss were calculated using logistic regression with step trajectories as explanatory variables and more than 3% weight loss or not as the response variable.

Results:

Of the 2,778 members, 1,601 (57.6%) were men and 1,177 (42.4%) were women, with respective mean ages of 65.8±7.9 and 64.0±8.2. Step counts of around one year were classified into the four latent classes of UP, FLAT, DOWN, and UP/DOWN, respectively representing increasing, steady, decreasing, and increasing/decreasing trajectories by LCMM. Logistic regression shows UP as having an adjusted odds ratio of 2.45 (95% confidence interval: 1.78–3.38), compared to FLAT.

Conclusions:

Asmile provided a reliable dataset that enabled tracking of users’ long-term step counts. We provided an effective analytical method to classify different tendencies of users' step counts, which can be explained easily as physical activities. The results revealed that different clusters of step count changes can differently affect weight loss.