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Published on: February 3, 2022
Usage Trends and Data Sharing Practices of Healthcare Wearable Devices Among US Adults: Cross-Sectional Study
Ranganathan Chandrasekaran1, Muhammed Sadiq T2, Evangelos Moustakas3
1Department of Information & Decision Sciences / Department of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL, United States.
Background:
Health care wearable devices can transform health care delivery by enabling real-time, continuous monitoring that facilitates early disease detection, personalized treatments, and improved patient engagement. The COVID-19 pandemic has heightened awareness of the importance of health technology, accelerating interest in wearables as tools for monitoring health and managing chronic conditions. As we navigate the postpandemic era, understanding the adoption and data-sharing behaviors associated with wearable devices has become increasingly critical. Despite their potential, challenges and low adoption rates persist, with significant gaps in understanding the impact of sociodemographic factors, health conditions, and digital literacy on the use and data-sharing behaviors of these devices.
Objective:
This study aimed to explore the usage and data-sharing practices (willingness to share wearable data and actual data-sharing behavior) of wearable devices among US adults specifically during the later phases of the COVID-19 pandemic.
Methods:
Using cross-sectional data from the National Cancer Institute's Health Information National Trends Survey 6, conducted from March to November 2022, this study uses responses from 5591 US adults to examine wearable use, willingness to share wearable data with providers, family, and friends, and the wearable data-sharing behavior.
Results:
The results indicate an increase in wearable device adoption to 36.36% (2033/5591) in 2022, up from 28%-30% in 2019. We also find a significant discrepancy between the willingness to share data, with 78.4% (1584/2020) of users open to sharing with health care providers, and the actual sharing behavior, where only 26.5% (535/ 2020) have done so. Higher odds of using wearables were associated with female gender (odds ratio [OR] 1.49, 95% CI 1.17-1.90, P<.01) and higher income levels (OR 2.65, 95% CI 1.42-4.93, P<.01 for incomes between US $50,000 and US $75,000, and OR 3.2, 95% CI 1.71-5.97, P<.01 for incomes above US $75,000). However, the likelihood of usage and data sharing declines significantly with age. Compared with African American respondents, Hispanic respondents were more willing to share wearable data with providers (OR 1.92, 95% CI 1.02-3.62, P<.05), though the odds of their actual sharing of wearable data with providers was relatively less (OR 0.44, 95% CI 0.20-0.97, P<.05). Frequency of provider visits (OR 1.23, 95% CI 1.08-1.39, P<.01), and total medical conditions (OR 1.35, 95% CI 1.05-1.73, P<.01) were significant predictors of data-sharing behavior. The study also identified weight, frequency of provider visits, technological self-efficacy and frequent physical activity as predictors for higher wearable use.
Conclusions:
Insights from this study are crucial for health care providers and policy makers aiming to leverage wearable technology to enhance health outcomes. Addressing the disparities and barriers identified can lead to more effective integration of these technologies in health care systems, thereby maximizing the potential of digital health tools to improve public health outcomes.
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