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The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey

Author

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  • Shiyuan Yu

    (School of Medicine and Health Management, Huazhong University of Science & Technology, 13 Hangkong Road, Wuhan 430030, China
    These authors have contributed equally to this work and share first authorship.)

  • Zhifeng Chen

    (School of Medicine and Health Management, Huazhong University of Science & Technology, 13 Hangkong Road, Wuhan 430030, China
    These authors have contributed equally to this work and share first authorship.)

  • Xiang Wu

    (School of Medicine and Health Management, Huazhong University of Science & Technology, 13 Hangkong Road, Wuhan 430030, China)

Abstract

Background: Wearable devices are shown to be an advanced tool for chronic disease management, but their impacts on physical activity remain uninvestigated. This study aims to examine the effect of wearable devices on physical activity in general people and chronic patients. Methods: Our sample was from the third cycle of the fifth iteration of the Health Information National Trends Survey (HINTS), which includes a total of 5438 residents. Genetic matching was used to evaluate the effect of wearable devices on physical activity in different populations. Results: (1) Both using wearable devices and using them with high frequency will improve physical activity for the whole population. (2) Wearable devices may have greater positive effects on physical activity for chronic patients. (3) Especially in patients with hypertension, high-frequency use of wearable devices can significantly improve the duration and frequency of physical activity. Conclusions: Wearable devices lead to more physical activity, and the benefit is more noticeable for chronic patients, particularly those with hypertension.

Suggested Citation

  • Shiyuan Yu & Zhifeng Chen & Xiang Wu, 2023. "The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey," IJERPH, MDPI, vol. 20(1), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:1:p:887-:d:1024171
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    References listed on IDEAS

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    1. Marta Gorina & Joaquín T. Limonero & María Álvarez, 2019. "Educational diagnosis of self‐management behaviours in patients with diabetes mellitus, hypertension and hypercholesterolaemia based on the PRECEDE model: Qualitative study," Journal of Clinical Nursing, John Wiley & Sons, vol. 28(9-10), pages 1745-1759, May.
    2. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    3. Xiaowen Li & Tong Li & Jianying Chen & Yuanling Xie & Xia An & Yunhong Lv & Aihua Lin, 2019. "A WeChat-Based Self-Management Intervention for Community Middle-Aged and Elderly Adults with Hypertension in Guangzhou, China: A Cluster-Randomized Controlled Trial," IJERPH, MDPI, vol. 16(21), pages 1-12, October.
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