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Wearable Technologies and Health Behaviors: New Data and New Methods to Understand Population Health

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  • Benjamin Handel
  • Jonathan Kolstad

Abstract

We study a randomized control trial in a large employer population of access to "wearable" technologies and the associated planning and monitoring tools on improved health behaviors (sleep and exercise). Both ITT and IV estimates based on actual plan enrollment for the treatment group suggest statistically significant but economically small changes in behavior after three months. We then implement machine learning-based models to assess treatment effect heterogeneity. We find little evidence for heterogeneous treatment effects base on observables. We also present detailed data on sleep patterns underscoring the value of this new data source to researchers.

Suggested Citation

  • Benjamin Handel & Jonathan Kolstad, 2017. "Wearable Technologies and Health Behaviors: New Data and New Methods to Understand Population Health," American Economic Review, American Economic Association, vol. 107(5), pages 481-485, May.
    • Handle: RePEc:aea:aecrev:v:107:y:2017:i:5:p:481-85
    Note: DOI: 10.1257/aer.p20171085
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    Citations

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    Cited by:

    1. Muhammad Zia Hydari & Idris Adjerid & Aaron D. Striegel, 2023. "Health Wearables, Gamification, and Healthful Activity," Management Science, INFORMS, vol. 69(7), pages 3920-3938, July.
    2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    3. Giuntella, Osea & Hyde, Kelly & Saccardo, Silvia & Sadoff, Sally, 2020. "Lifestyle and Mental Health Disruptions during COVID-19," IZA Discussion Papers 13569, Institute of Labor Economics (IZA).
    4. Burgess, Simon & Metcalfe, Robert & Sadoff, Sally, 2021. "Understanding the response to financial and non-financial incentives in education: Field experimental evidence using high-stakes assessments," Economics of Education Review, Elsevier, vol. 85(C).
    5. Iizuka, Toshiaki & Nishiyama, Katsuhiko & Chen, Brian & Eggleston, Karen, 2021. "False alarm? Estimating the marginal value of health signals," Journal of Public Economics, Elsevier, vol. 195(C).
    6. Lunyu Xie & Tianhua Zou & Joshua Linn & Haosheng Yan, 2024. "Can Building Subway Systems Improve Air Quality? New Evidence from Multiple Cities and Machine Learning," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(4), pages 1009-1044, April.
    7. Carrera, Mariana & Royer, Heather & Stehr, Mark & Sydnor, Justin & Taubinsky, Dmitry, 2018. "The limits of simple implementation intentions: Evidence from a field experiment on making plans to exercise," Journal of Health Economics, Elsevier, vol. 62(C), pages 95-104.
    8. Miller, Steve, 2020. "Causal forest estimation of heterogeneous and time-varying environmental policy effects," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    9. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
    10. Joan Costa-Font, 2022. "Incentivizing sleep?," IZA World of Labor, Institute of Labor Economics (IZA), pages 502-502, November.
    11. Idris Adjerid & George Loewenstein & Rachael Purta & Aaron Striegel, 2022. "Gain-Loss Incentives and Physical Activity: The Role of Choice and Wearable Health Tools," Management Science, INFORMS, vol. 68(4), pages 2642-2667, April.

    More about this item

    JEL classification:

    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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