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Digital Technology and Health: Evaluating the Impact of Mobile Health-Tracking Applications on Patients Well-Being

Author

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  • Keita, Moussa

Abstract

The number of mobile health applications has witnessed a soaring during the recent years. According the IMS Institute for Healthcare Informatics, more than 165,000 digital health applications have been available in the Apple iTunes Store and the Android App Store in 2015. Despite the enthusiasm aroused by such a growth, the main concern is the lack of evidence regarding the safety and the efficacy of these devices in terms of health benefits. This study attempts to bring in new insight on this problematic by trying to identify the causal effect of the use of technology on health status. For this purpose, we focus on the specific case of health-tracking applications which are among the most used health applications. Our analysis is based on 1020 subjects suffering from Diabetes and High Blood Pressure to compare the results of those using health-tracking applications to monitor their health and those who are not using these applications. We have estimated the model by Ordinary Least Squares (OLS) and multinomial logit regressions methods. We have also corrected potential selection bias in technology adoption by using the Heckman approach. In terms of results, our estimations show significant positive association between technology use and reported health status and quality of life. In particular, we have found that patients who use digital health-tracking feel better and report better health status than those who do not use them. For example, we have found that HealthApps users are 38 % more likely to achieve "good" health status and are 27% more likely to achieve "excellent" health status as compared to non-HealthApps users. These results appear robust to various sensitivity and robustness checks. However, although its promising nature, the effect of technology identified in this study should be regarded as short-term effect since the configuration of the data does not allow to capture potential contextual-effect in health status declaration and possible novelty-effect in technology use.

Suggested Citation

  • Keita, Moussa, 2016. "Digital Technology and Health: Evaluating the Impact of Mobile Health-Tracking Applications on Patients Well-Being," MPRA Paper 71453, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:71453
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    References listed on IDEAS

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    1. Jayanta Kumar Bora & Nandita Saikia, 2015. "Gender Differentials in Self-Rated Health and Self-Reported Disability among Adults in India," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-14, November.
    2. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
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    Keywords

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    JEL classification:

    • I1 - Health, Education, and Welfare - - Health
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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