Association Between Wearable Device Adoption and Health-Related Lifestyle Behaviors: Retrospective Cohort Study

Clinical Snapshot

Key Findings

• P Value: Not explicitly reported but implied significant given confidence intervals

• Effect Size: Total activity RR 1.24 (95% CI 1.08-1.35), Physical activity RR 1.36 (95% CI 1.12-1.64), Cultural activity RR 1.78 (95% CI 1.31-2.42)

• Primary Outcome: Health-related lifestyle activity counts across physical, social, and cultural domains

• Nnt Or Sensitivity: 24% relative increase in total health-related activities associated with wearable device adoption

• Confidence Interval: All significant associations had confidence intervals excluding 1.0

Clinical Application

Wearable devices widely available and increasingly affordable, though effectiveness may depend on sustained engagement Relevant to Australian digital health initiatives and preventive care strategies, though cultural differences in technology adoption and health behaviors may limit direct applicability Adults considering wearable devices for health behavior modification, particularly those interested in physical activity and cultural engagement

Abstract

BACKGROUND: Wearable devices are increasingly adopted for personal health monitoring, but evidence on their long-term associations with health-related lifestyle behaviors in real-world population settings remains limited. OBJECTIVE: This study examined the longitudinal association between wearable device adoption and engagement in health-related lifestyle behaviors using a nationally representative panel dataset from South Korea. METHODS: We analyzed data from the 2016 and 2022 waves of the Korea Media Panel survey. Health-related lifestyle behaviors in the physical, social, and cultural domains were operationalized as estimated annual activity counts based on self-reported frequency measures. We used a difference-in-differences framework with generalized estimating equations to compare changes in these behaviors between new wearable adopters and nonadopters adjusting for demographic and socioeconomic characteristics. Relative changes were estimated using Poisson models with a log link, and subgroup analyses were conducted to explore variation across sociodemographic groups. As a sensitivity analysis, inverse probability of treatment weighting was additionally applied to assess the robustness of the findings to observed baseline imbalance. RESULTS: Wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time. In the difference-in-differences model, adopters showed greater relative increases in total activity (rate ratio [RR] 1.24, 95% CI 1.08-1.35), physical activity (RR 1.36, 95% CI 1.12-1.64), and cultural activity (RR 1.78, 95% CI 1.31-2.42) than nonadopters. Subgroup analyses showed limited evidence of consistent heterogeneity and should be interpreted cautiously. Sensitivity analyses using inverse probability of treatment weighting showed overall patterns broadly similar to those of the primary analyses. CONCLUSIONS: In this nationally representative panel study, wearable device adoption was associated with greater increases in total, physical, and cultural health-related lifestyle activities over time, whereas no clear association was observed for social activity. These findings should be interpreted as associative rather than causal given the observational design and the inability to directly assess parallel trends.

References

1. 1.Choi, E., Choi, S., & Jang, S. Y. (2026). Association between wearable device adoption and health-related lifestyle behaviors: Retrospective cohort study. Journal of Medical Internet Research. https://doi.org/10.2196/88276