This study aggregated data from 3 consecutive years (2020-2022) of the Rock Health Digital Health Consumer Adoption Survey [8]. The total sample consisted of 23,974 US-based respondents (7,980 in 2020, 7,980 in 2021, and 8,014 in 2022). Survey respondents were not excluded from participating in the study in subsequent years and repeat respondents comprised 3.3% (n=784) of the total cohort. The survey was administered by Toluna, a survey management organization. Toluna used its existing panel, initially recruited via online advertising, to identify participants. Eligible participants were 18 years or older, and the sample was Census-matched each year according to age, race/ethnicity, geographic region, gender, and annual household income.

In accordance with the Common Rule (Federal Policy for the Protection of Human Subjects, 82 Federal Regulation 7259, January 19, 2017 [9]), this study was exempt from regulations for research with human subjects as the data were deidentified.

Surveys were digitally administered through Toluna’s platform, and respondents used their personal desktop, laptop, smartphone, or tablet to complete the survey in English. The survey encompassed 4 main domains: (1) sociodemographic factors, (2) health status, (3) adoption of digital health tools (eg, telemedicine, wearables), and (4) attitudes toward and perceptions of digital health technology. For this study, we focused on questions about wearable technology adoption.

The primary outcome was wearable ownership as measured by the survey question, “Do you own a wearable device or smartwatch (note: this excludes smartphones) that helps you track your health? This could include: number of steps/exercise, sleep, heart rate, or blood pressure.” Covariates included sociodemographic characteristics (eg, age, gender, race/ethnicity, income level, and educational level) and self-reported health status. The secondary analysis was exploratory, delving into the cohort of wearable owners to investigate the source of their devices and their reasons for using wearables. Respondents were asked: “Do or did you use your wearable device or smartwatch for any of the purposes listed below? Select all that apply.”

We performed a descriptive analysis of the pooled sample and examined associations with wearable ownership. The χ² test was used to assess variation across covariates and multivariate logistic regression was used to identify factors associated with wearable ownership. Additional analyses explored how respondents acquired their wearables and what they used the devices for. Analyses were conducted in Stata v.16.

Despite steadily increasing wearable ownership from 2020 to 2022 among US consumers, sociodemographic disparities persist related to age, income, education, and residence. Ownership skews toward younger, more affluent, and highly educated respondents living in urban areas. This is consistent with prepandemic findings [2,10], suggesting continuity in the digital divide [11]. This unequal access impacts the potential benefits of wearables on health promotion and health outcomes [12-15].

Surprisingly, non-Hispanic Black and Hispanic/Latine respondents have slightly higher ownership odds than non-Hispanic White respondents. If corroborated by future research indicating active usage among these communities, this finding suggests that wearables can be harnessed to monitor chronic conditions prevalent in these communities, such as hypertension and diabetes [16,17]. The inclination of wearable ownership among these groups presents an opportunity to inform the design and development of new wearable technologies [18].

Reliance on direct-to-consumer channels (eg, direct purchase or gifts) for acquiring wearables underscores the discretionary nature of wearable purchase and signals a possible relationship between health consciousness and wearable ownership [19]. The self-motivated nature of acquisition points to the importance of consumer preferences in uptake and indicates a potential barrier to access among those with limited discretionary means. This signals a need to improve equitable access through alternative distribution pathways.

As wearables are considered for clinical care [20] and public health surveillance applications [21-23] such as communicable respiratory (eg, COVID-19) and infectious (eg, dengue) diseases, it will be critical to continue to track ownership patterns and address biases in the wearable user population related to age, income, education, and area of residence [24,25]. Thus, effective wearable-based public health surveillance efforts must actively mitigate such biases within the current wearable-owning population and increase wearable uptake among subgroups reporting lower rates of ownership. In the near term, acknowledging and adjusting for biases is essential. This requires recognizing barriers that lead to uneven ownership. Over the longer term, there is an opportunity to address barriers to wearable ownership, which can allow the user cohort to gradually better represent the general population.

Study limitations include sampling bias, as the survey undersamples those without digital devices (eg, smartphones, tablets, or computers), regular internet connectivity, and non-English speakers. While the study used a pooled sample across 3 years, a small proportion of respondents were surveyed in multiple waves, which may affect comprehensiveness. Further, the study focuses on ownership, not wearable adoption, impacting findings related to active usage patterns of wearables. Additionally, respondents who indicated ownership may or may not own more than one device, which could impact the responses to subsequent questions.

This study provides an updated benchmark on wearable device ownership among US consumers, highlighting sociodemographic disparities in ownership across age, income, education, and residence. While some traditionally disadvantaged groups demonstrate higher ownership rates, other findings reflect persisting disparities, painting a mixed picture of progress toward a more equitable wearable distribution. This underscores the need for further research, particularly into higher ownership rates among minority groups. Future research may also explore reasons for nonownership among wearable nonowners, such as lack of interest, privacy concerns, cost, or other barriers. Understanding these differences is critical for designing inclusive wearable products and business models, and for ensuring the appropriate use of wearable data in health care and public health research by accounting for biases in sample representation.