One-Year Trajectory of Step Counts and Weight Loss in Adults With Overweight/Obesity: Retrospective Cohort Study.

BACKGROUND

Being overweight and obese are major health concerns worldwide, contributing to lifestyle-related diseases such as hypertension, dyslipidemia, type 2 diabetes, and cardiovascular disease. Increasing physical activity is an effective strategy for weight management. However, earlier step count studies have remained limited to small populations, short-term measurements of 1-2 weeks, and mainly cross-sectional comparisons of average step counts. The effects of long-term step count changes on weight loss remain unclear.

OBJECTIVE

This study was conducted to assess the effects of long-term patterns of step counts on weight loss using data from the "Asmile" mobile health app in Japan. We hypothesized that participants with continuously increasing step counts over time would have a higher likelihood of significant weight reduction than participants who show steady or fluctuating patterns, even if their average step counts were similar.

METHODS

We analyzed data of 2778 Asmile users aged 40-74 years with BMI ≥25 kg/m² who underwent a specific health checkup during fiscal years 2019-2023 and who had valid step count records for 10-14 months. Step count trajectories, reflecting long-term trends in physical activity, were classified using a latent class mixed model into four patterns: UP (increasing), FLAT (steady), DOWN (decreasing), and UP/DOWN (increasing then decreasing). Logistic regression was applied to estimate odds ratios for achieving ≥3% weight loss, with step trajectory as the explanatory variable and weight loss as the outcome.

RESULTS

Among participants, 1601 (57.6%) were men and 1177 (42.4%) were women, with respective mean ages of 65.8 (SD 7.9) and 64 (SD 8.2) years. Step count trajectories were distributed as 28.5% UP, 36.2% FLAT, 20.1% DOWN, and 15.2% UP/DOWN. Compared with the FLAT group, participants in the UP group had a significantly higher likelihood of achieving ≥3% weight loss (adjusted odds ratio 2.45, 95% CI 1.78-3.38).

CONCLUSIONS

Long-term tracking of step counts using the Asmile app revealed distinct activity patterns. Continuous increases in step counts were associated with the greatest likelihood of weight loss, emphasizing the importance of sustained physical activity. These findings support the use of long-term step monitoring to guide interventions for obesity and lifestyle-related disease prevention.