Trust in healthcare and wearable use among U.S. adults: A cross-sectional analysis of the 2022 health information national trends survey

Abstract

Background

Wearable devices are increasingly used to monitor and track health, yet the factors shaping adoption remain incompletely understood. Amid longstanding declines in public trust in the U.S. healthcare system, this study examines how trust in healthcare relates to wearable device use.

Methods

Data from the 2022 Health Information National Trends Survey (HINTS 6) were analyzed in a sample of 6,118 respondents with complete data on trust, wearable use, and key sociodemographic and psychosocial characteristics. Structural equation modeling (SEM) evaluated relationships among trust, wearable use, and four latent constructs: technological aptitude, health literacy, social media use, and perceived competence.

Results

Among respondents, 84.8% reported moderate to high trust in the U.S. healthcare system and 33.2% reported using a wearable device in the prior year. Technological aptitude was the strongest correlate of wearable use (β = .88, p < .001), while perceived competence (β = −.68, p < .001) and social media use (β = −.46, p < .001) were inversely associated. Trust was positively associated with technological aptitude (β = .29, p = .005) and health literacy (β = .36, p < .001), and negatively associated with social media use (β = −.19, p < .001). Trust and wearable use demonstrated a negative association (β = −.14, p = .015).

Conclusions

Digital readiness and lower healthcare trust together shape wearable adoption, with implications for how clinicians might leverage self-tracking behaviors to rebuild patient–provider relationships.

Introduction

Wearable devices have become an increasingly prominent part of the digital health landscape. Their rapid growth reflects not only expansion in the consumer technology market, but also growing interest in their ability to capture health-related information in real-world settings, including behavioral measures such as physical activity and sleep. Their ubiquity and ease of use today has allowed individuals to gain greater oversight over their personal health and set goals for their well-being. ¹ Their wireless connectivity also allows for the widespread dissemination of collected data, including with social networks and healthcare providers. ² As a result, wearables are increasingly relevant not only as consumer devices, but also as tools that generate patient-produced health data that may support prevention, self-management, and more informed clinical decision-making. In this context, wearable-generated data may help facilitate more longitudinal insight into health outside traditional clinical encounters and support more informed communication between patients and clinicians. ³

The potential value of wearable technologies in healthcare depends not only on their technical capabilities, but also on whether individuals are willing to use them and share the information they produce. Health data derived from wearables may be perceived as personal and sensitive, and willingness to share such data is shaped by several considerations, including limited understanding of how data are collected or used, privacy and confidentiality concerns, and uncertainty about whether these data are meaningful or useful for clinical care. ⁴ Other factors, including demographic characteristics, socioeconomic background, and health status, may also influence how individuals view wearable use and data sharing.^5,6 Together, these considerations suggest that adoption of wearable technologies is not solely a matter of device availability or functionality, but is shaped by a broader socio-technical context that influence engagement.

Trust in wearable devices is not limited to the technology itself. Rather, it is likely multidimensional and may reflect confidence in the accuracy and usefulness of the device, the privacy and security of the systems through which data are transmitted, and the healthcare institutions or organizations that may receive, interpret, or act on those data. Prior work suggests that trust in technology is shaped by factors such as whether users can relate a new tool to prior technological experiences and whether they perceive its design and functionality positively, both of which influence emotional responses to adoption. ⁷ In the case of wearables, however, these technological considerations do not operate in isolation. Decisions about wearable use and data sharing therefore occur within a broader institutional context shaped by public trust in the healthcare system. That context is especially relevant given longstanding declines in confidence in medicine. Surveys dating back to the 1960s have shown a steady decline in the public’s trust in medicine over time; by 2021, barely four out of ten Americans said that they felt confident in the leaders of the medical system. ⁸ Persistent inequalities in access and quality of care, increasing costs coupled with growing financial motivations in healthcare, and a decrease in meaningful communications between providers and patients have all been identified as contributors to this decline. ⁹ More recently, the COVID-19 pandemic placed multiple stresses on the healthcare system and lead to a further deterioration in trust that has yet to recover.^8–10

Although prior research has examined privacy concerns, technology adoption, and willingness to share health information, less is known about how trust in the healthcare system relates to wearable use and wearable-based health tracking behaviors at the population level. The aim of this study was to examine the association between trust in the healthcare system and wearable device use for health monitoring in a nationally representative sample of U.S. adults, and to identify the sociodemographic and psychosocial factors that help explain this relationship. We hypothesized that trust in the healthcare system would be associated with wearable use, although the direction of this relationship was uncertain given competing mechanisms of institutional reliance and self-directed health monitoring. We further hypothesized that digital literacy-related factors, including health literacy and technology aptitude, would be the strongest positive predictors of both trust and wearable use, whereas social media use and demographic data such as minority status would be associated with lower levels of trust and reduced wearable adoption. Characterizing the relationship between trust and wearable device use may help identify which groups are less likely to engage with wearable-enabled health monitoring. It may also inform strategies, such as efforts to strengthen healthcare trust and support patients’ digital and health literacy, aimed at incorporating wearable-generated data more effectively into routine care.

Methods

Data from the 2022 Health Information National Trends Survey (HINTS 6) of the National Cancer Institute were analyzed. HINTS is a nationally representative survey designed to assess the American public’s access to and use of health information, including cancer-related information, among civilian, noninstitutionalized adults in the United States. Data collection for HINTS 6 was conducted between March 7 and November 8, 2022. The survey used a stratified sampling design that included urban and rural populations, and one adult was selected from each sampled household to complete a self-administered questionnaire in either web-based or paper format. A total of 6,252 completed questionnaires were obtained. HINTS items are developed through NCI’s established instrument-development and validation procedures, including cognitive testing to assess comprehension, clarity, and response processes before survey fielding. The present analysis used these standardized public-use HINTS measures as provided in the survey documentation. Additional details regarding survey design, administration, response rates, and weighting procedures are available in the HINTS 6 Instrument Development and Methodology Reports.The English version of the survey instrument has been provided in^11,12 Supplementary material.

For inclusion in the present analysis, respondents were required to report use of a wearable device for health or activity tracking by answering “yes” to the item: “In the past 12 months, have you used an electronic wearable device to monitor or track your health or activity? For example, a Fitbit, Apple Watch, or Garmin Vivofit.” Respondents were also required to have complete data for the outcome variables used in the SEM, including trust in the healthcare system and the observed indicators used to construct the latent wearable device use variable: wearable device use, frequency of wearable device use, willingness to share wearable device data with friends or family, and willingness to share wearable device data with a healthcare provider. Respondents with missing, invalid, skipped, or not ascertained responses for these variables, as defined by the HINTS 6 Methodology Report, were excluded from the sample. ¹¹ A total of 134 respondents were excluded based on these criteria, resulting in a final analytic sample of 6,118 respondents.

Variables and measures

Wearable device use was assessed using a binary item asking whether respondents had used an electronic wearable device to monitor or track their health or activity in the past 12 months (yes/no). Frequency of wearable use was also examined using a categorical measure ranging from daily use to less than weekly use.

Trust in the healthcare system was measured using a single-item indicator assessing respondents’ level of trust in healthcare organizations (“not at all” to “a lot”).

Sociodemographic variables included age, income, education, marital status, and race, categorized according to standard HINTS groupings. These variables were included in the model to account for structural and access-related factors that may influence both trust in healthcare and engagement with wearable technologies.

Several latent constructs were specified for inclusion in the structural equation model. These constructs captured key domains hypothesized to shape both trust in the healthcare system and engagement with wearable technologies, including digital literacy, prior technology use, and health self-efficacy. Each latent variable was derived from multiple HINTS items and standardized prior to analysis. Items were re-coded as needed so that higher values reflected greater levels of the underlying construct.

Technological aptitude included measures of confidence in accessing online health information, use of health-related mobile applications, and engagement with patient portals. This construct was included as a core indicator of digital literacy, which has been shown to influence both trust in health information systems and adoption of digital health tools.^13,14 Social media use captured both frequency of exposure to health-related content and use of such information in health decision-making. This construct was included to represent engagement with informal digital health information environments, where users may encounter health information through peer networks, user-generated content, public health messaging, and noninstitutional sources. ¹⁵ Social media use was modeled separately from technological aptitude because it reflects exposure to and reliance on informal online health information rather than confidence or skill in using formal digital health tools, such as patient portals, mobile health applications, or online health information systems. This distinction was important given the study’s focus on pathways linking digital information environments, healthcare trust, and wearable device use.

Perceived competence was included as a psychosocial construct reflecting respondents’ confidence in their ability to manage health-related tasks and decisions. Conceptually, this construct aligns with self-efficacy, a central component of health behavior theories including the Health Belief Model, in which an individual’s perceived ability to take action may influence engagement with health-related behaviors.^16,17 In the context of wearable device use, higher perceived competence may shape whether individuals feel capable of monitoring, interpreting, and acting on personal health information. Perceived competence was therefore modeled as distinct from health literacy, which reflects the ability to obtain, understand, and use health information, and from technological aptitude, which reflects comfort or skill with digital tools.^18,19

A higher-order latent construct representing wearable engagement was specified to capture device use, frequency of use, and willingness to share wearable-generated data with healthcare providers and others.

Data analysis

Structural equation modeling (SEM) was conducted using AMOS version 29.0, a graphical software package from IBM SPSS Statistics specifically designed for path analysis and latent variable modeling. ²⁰ Model parameters were estimated using maximum likelihood estimation. Prior to analysis, data were screened for missing or invalid values using the HINTS 6 codebook definitions. Prior to analysis, data were screened for missing or invalid values using the HINTS 6 codebook definitions. HINTS 6 uses negative-value codes to distinguish valid skips, inappropriate or nonconforming responses, multiple responses, items not seen because of web breakoff, and missing or not ascertained responses. These values were treated as missing where applicable. Missing data were handled using full information maximum likelihood (FIML), which estimates model parameters using all available observed information rather than excluding cases with incomplete responses. ²¹

The hypothesized model included both measurement and structural components. Measurement paths linked latent constructs to their observed indicators, and structural paths were specified to test the relationships between exogenous constructs and the endogenous construct representing wearable device use. Covariances among exogenous variables were freely estimated. The final model included 45 variables, including 22 observed variables and 23 latent variables, with 117 free parameters and 158 degrees of freedom.

Correlations among exogenous variables were examined before model estimation to assess potential multicollinearity. Although several constructs were moderately correlated, such as technological aptitude and health literacy (r = .68), no correlations exceeded commonly cited thresholds for concern in SEM. ²² The hypothesized SEM is shown in Figure 1.

Figure 1.

SEM Mapping including demographic and latent construct variables, individual associations with trust in the healthcare system and wearable device use, and the overall association between trust and wearable device use.

Results

Sample characteristics

Among 6,252 total respondents, 6,250 completed the survey item regarding wearable device use to monitor their health in the past 12 months. Of these, 2,074 (33.2%) reported using a wearable device during this time frame. The sample was predominantly under 50 years of age (50.3%), had annual incomes above $50,000 (65.2%), and reported at least some college education (74.7%). Most respondents identified as non-Hispanic White (64.0%). Among marital status categories, married individuals represented the largest group (48.9%). Additional sample characteristics are shown in Tables 1 and 2.

Table 1.

Population estimates for sociodemographic characteristics of wearable users.

Characteristics	Users, n (%)
Age (years)
18-29	257 (12.49%)
30-39	419 (20.36%)
40-49	359 (17.44%)
50-59	382 (18.56%)
60+	627 (30.47%)
Annual Household Income (USD $)
<$19,999	139 (6.75%)
$20,000-$49,999	363 (17.64%)
$50,000-$99,999	595 (28.91%)
>$100,000	747 (36.3%)
Education
12 years (high school)	207 (10.06%)
Some college	394 (19.14%)
College graduate	637 (30.95%)
Postgraduate	507 (24.64%)
Marital Status
Married	1007 (48.93%)
Living as married	166 (8.07%)
Divorced	224 (10.88%)
Widowed	122 (5.93%)
Separated	31 (1.51%)
Single never married	360 (17.49%)
Race
White, non-Hispanic	1317 (63.99%)
Black, non-Hispanic	295 (14.33%)
Other race	236 (11.47%)

Table 2.

Standardized direct effects predicting trust in the healthcare system and wearable device use.

Outcome	Predictor	Standardized beta	p-value
Trust in the healthcare system	Health literacy	0.36	< .001
Trust in the healthcare system	Technological aptitude	0.29	0.005
Trust in the healthcare system	Older age	0.21	< .001
Trust in the healthcare system	Married	0.08	< .001
Trust in the healthcare system	Minority status	0.06	< .001
Trust in the healthcare system	Social media use	-0.19	< .001
Trust in the healthcare system	Education	-0.09	< .001
Trust in the healthcare system	Income	-0.06	0.013
Wearable device use	Technological aptitude	0.88	< .001
Wearable device use	Perceived competence	-0.68	< .001
Wearable device use	Social media use	-0.46	< .001
Wearable device use	Trust in the healthcare system	-0.14	0.015

Note. This table summarizes standardized direct effects from the structural equation model. Only direct paths shown in the final model are reported.

Model fit

Model fit was evaluated using multiple standard fit indices. The chi-square test was significant, χ²(158) = 1649.81, p < .001, although this statistic is known to be sensitive to large sample sizes. ²³ Therefore, interpretation focused primarily on approximate fit indices. The RMSEA was 0.039 (90% CI, 0.037 to 0.041; PCLOSE = 1.000), consistent with close fit, and the CFI was 0.914, indicating acceptable fit. Overall, the model demonstrated good fit.

Structural model results

Technological aptitude emerged as the strongest positive correlate of wearable device use (β = .88, p < .001), indicating that individuals with greater confidence and experience using digital health technologies were substantially more likely to engage with wearable devices. In contrast, perceived personal competence (β = –.68, p < .001) and social media use (β = –.46, p < .001) were significant negative correlates. No sociodemographic variables were significantly associated with wearable device use.

The latent construct of wearable device use was significantly reflected by device ownership (β = .72, p < .001), frequency of use (β = .44, p < .001), and willingness to share data with family (β = .45, p < .001) and healthcare providers (β = .33, p < .001).

Trust in the healthcare system was positively associated with health literacy (β = .36, p < .001) and technological aptitude (β = .29, p = .005), and negatively associated with social media use (β = –.19, p < .001).

Among sociodemographic variables, age (β = .21, p < .001), minority status (β = .06, p < .001), and marital status (β = .08, p < .001) were positively associated with trust, whereas education (β = –.09, p < .001) and income (β = –.06, p = .013) were negatively associated.

Measurement model

All standardized factor loadings were statistically significant (p < .001) and ranged from moderate to strong. No standardized residuals exceeded ±2.5, indicating acceptable local fit. ²⁴

Discussion

This aim of this study was to examine how trust in the healthcare system relates to wearable device use and identify sociodemographic and psychosocial correlates of both constructs in a nationally representative sample. Overall, trust and wearable use appeared to be embedded within a broader socio-technical context shaped by age, socioeconomic position, health literacy, technological aptitude, and patterns of social media use.

Trust in the healthcare system

The overwhelming majority of survey item respondents had some level of above-average trust in the healthcare system (n=5,187, 84.8%). The SEM analysis (Figure 1) suggests that within the assessed demographics, being older (β = .21, p < .001) was one of the strongest positive correlates of trust, while education (β = –.09, p < .001) and income (β = –.06, p = .013) were inversely associated with trust in this model. Previous studies have shown that there is more trust in the extremes (<30 years and >70 years of age) and less for in-between groups.^25–27 While the pattern observed with education and income may seem counter to the conventional assumption that greater socioeconomic advantage corresponds to greater institutional trust, prior research suggests that these relationships may be more heterogeneous than traditionally assumed. For example, a Danish study reported an inverse U-shaped association between social position (which incorporated income and education metrics) and trust in the healthcare system, with lower trust at both ends of the social spectrum and higher trust among those in the middle. ²⁸ Cross-national work has also found that higher education may be associated with lower trust in healthcare systems while lower income is associated with higher trust, suggesting that trust may depend not only on material advantage but also on expectations, prior experiences, and how individuals engage with healthcare institutions.^29,30

Marital status (β = .08, p < .001) and minority status (β = .06, p < .001) were both slightly associated with increased trust. Marital status has long been associated with better healthcare utilization, encouragement of healthy behaviors, and overall better health outcomes, and this model corroborates these findings.^31,32 Minority status was associated with slightly higher trust in this sample, a finding that differs from much of the existing literature and should therefore be interpreted cautiously. ²⁷ It is possible that this reflects features of the surveyed population rather than a broader population-level pattern.

Trust in the healthcare system is shaped not only by demographics, but also by individuals’ ability to navigate contemporary health and information environments. To analyze this, we constructed four latent variables: technological aptitude, social media use, perceived competence, and health literacy. Technological aptitude (β = .29, p = .005) and health literacy (β = .36, p < .001) emerged as moderate positive correlates with trust, while greater social media use (β = –.19, p < .001) was negatively associated with trust. Together, these findings suggest that trust may be influenced by how effectively individuals can engage with health information and digital systems used in care. As healthcare delivery becomes increasingly mediated by patient portals, telehealth, remote monitoring, and other digital platforms, those with greater digital and health-related capacity may find it easier to navigate the system and may therefore experience it as more accessible and responsive. Indeed, Kruse et al. (2018) showed that a main barrier to adopting telemedicine is computer literacy and access to the correct equipment. ³³ By contrast, lower technological confidence or lower health literacy may contribute to frustration and disengagement, which could undermine trust over time.^13,34 Conversely, inverse association between social media use and trust may also be important in understanding the broader information environment surrounding healthcare. One possible explanation is that greater exposure to fragmented or low-quality health information online may weaken confidence in healthcare institutions. ³⁵ Social media may also reinforce skepticism when users are repeatedly exposed to content that emphasizes controversy, poor experiences, or mistrust in professional expertise. Although this interpretation remains speculative, it is consistent with growing concern that digital information ecosystems shape how individuals evaluate healthcare credibility and authority. ³⁶

Wearable device usage

Wearable use was most strongly associated with technological aptitude (β = .88, p < .001), suggesting that digital readiness remains central to adoption of self-tracking devices. User adoption of a new technology is a complex process that involves the interplay between social, economic, and behavioral factors. In the context of wearables, technological literacy has been shown to be a strong predictor of intention to adopt, alongside consumer confidence and personal innovativeness.^37,38 Wearable adoption may be less a reflection of health needs alone and more a marker of comfort with technology, familiarity with digital interfaces, and willingness to engage in data-driven forms of self-monitoring.

Two additional themes also emerged in relation to wearable use. First, greater social media use (β = –.46, p < .001) was associated with decreased wearable use. Although digitally engaged individuals might be expected to be more receptive to wearable technologies, this finding suggests that different forms of digital engagement may not operate in the same way. Several explanations may help account for this pattern, although these interpretations remain speculative and cannot be fully distinguished with the cross-sectional nature of the data. Recent studies have shown that roughly a third of U.S. adults receive some amount of their health information from social media sites. ³⁹ Frequent social media use may place individuals in a complex information environment filled with conflicting claims, often leading to greater exposure to health-related misinformation. ³⁵ In this context, greater social media use could possibly contribute to mistrust toward health-related technologies, like wearables. Social media use is also naturally linked with users’ privacy concerns and skepticism towards data collection. National survey data suggest that privacy concerns are common among social media users, including concerns about advertisers and businesses accessing information shared on social media platforms. ⁴⁰ Because wearable devices collect, store, and analyze personal health data, these privacy concerns may plausibly extend to data-tracking health technologies. Cilliers et al. (2020) demonstrated that there was limited user awareness of how wearable-derived data are protected after collection. ⁴¹ The general uncertainty around user data utilization, privacy, and protection may deter some from adopting these tools to track their health. Studies have also demonstrated that increased social media usage results in an increased odds of depression and increased passive media consumption, reflecting patterns of online behavior that do not translate into interest in active health monitoring, though these likely play a lesser role in the overall interaction between social media use and wearables.^42,43 Addressing these issues is crucial to improving wearable uptake amongst a population of increasingly online users.

Perceived competence was inversely associated with wearable device use, suggesting that individuals who report lower confidence in managing their health may view wearables as supportive tools that provide structure, feedback, and a greater sense of control. This finding may indicate that individuals with lower perceived self-management capacity are more likely to rely on wearable devices as external supports for monitoring and managing health-related behaviors. Prior research on wearable activity tracker use has similarly incorporated self-efficacy within health belief-based models of wearable technology engagement. ⁴⁴ Within this framework, wearable devices may help compensate for lower confidence by offering guidance, reinforcement, and real-time feedback. Notably, perceived competence was not associated with trust in the healthcare system, suggesting that confidence in self-management and trust in institutions may represent distinct pathways influencing digital health engagement.

Taken together, these findings suggest that wearable use is shaped by both digital readiness and self-management needs, rather than by a single pathway of technology enthusiasm or institutional engagement. Although this study focused on wearable use, the broader literature suggests that initial adoption and sustained engagement are not equivalent. Some individuals may adopt wearables briefly but discontinue use if the device is experienced as burdensome, inaccurate, emotionally aversive, or lacking clinical relevance. Future work should therefore examine whether trust in healthcare institutions is associated not only with uptake, but also with sustained use, data-sharing behaviors, and long-term integration of wearable information into self-management or clinical care.

Trust in healthcare and use of wearable devices

The inverse relationship between trust and wearable device use via a direct pathway (β = –.14, p < .015) was one of the more notable findings of this study given the expectation that greater healthcare trust would lead to greater engagement with health technologies. While this inverse association between trust and wearable use was statistically significant, its magnitude was modest relative to that of technological aptitude (β = .88), indicating that digital readiness, rather than healthcare trust, was the dominant correlate of adoption. The trust relationship is therefore best interpreted as a meaningful but secondary influence. Several interpretations for this observation are possible. First, while wearables continue to improve each year, yielding powerful capabilities constantly, there is still some concern around their efficacy, safety, and privacy.^34,38 Individuals with greater trust in clinicians and healthcare institutions may feel less need to rely on self-tracking technologies as supplementary sources of health information. Second, individuals who are more drawn to self-monitoring technologies may place relatively greater emphasis on personal control, autonomy, or independent health management, which could coexist with lower institutional trust.^39,40 This may especially be true in those who are predisposed to health anxiety and would want to continuously track their metrics outside of formal directions from their primary care physician. ⁴¹ However, because the data are cross-sectional, these pathways cannot be disentangled and should not be interpreted as causal. It is also worth noting that trust in the healthcare system likely represents only one dimension of the broader trust landscape relevant to wearable adoption. A critical distinction not captured by the present measure is that between trust in traditional medical institutions and trust in the technology companies that design, manufacture, and manage the data infrastructure of wearable devices. These two forms of trust may not align: an individual could have low confidence in the healthcare system broadly while nonetheless trusting a specific consumer technology brand, or vice versa. It is plausible that trust in device manufacturers and technology companies plays a more proximate role in the decision to adopt a wearable than trust in the healthcare system as a whole, given that initial adoption typically occurs outside of any clinical encounter and is driven largely by the consumer technology marketplace. ⁴⁵ However, willingness to share wearable-generated data with clinicians may depend more on institutional healthcare trust than on trust in the technology brand. ⁴ Future longitudinal work is needed to determine whether trust shapes subsequent wearable uptake, whether wearable engagement alters trust over time, or whether both are driven by other factors such as prior healthcare experiences, health anxiety, or privacy concerns.

Limitations

Several considerations should be kept in mind when interpreting these findings. The use of self-reported, cross-sectional data limits causal inference and does not allow determination of the temporal direction of the observed associations. In addition, the measure of trust utilized in this study may not fully capture the multidimensional nature of trust relevant to digital health, including trust in clinicians, devices, data privacy, and technology companies. Residual confounding remains possible because factors such as prior healthcare experiences, privacy concerns, physician recommendation, chronic disease burden, and health anxiety were not fully accounted for. Several latent constructs were also derived from available survey items and may not fully represent the broader concepts they were intended to measure, while broad demographic categories may obscure important within-group heterogeneity. Trust in the healthcare system was also assessed using a single self-reported item, which cannot capture the multidimensional nature of trust relevant to digital health, nor distinguish between trust in healthcare institutions and trust in the technology companies that manufacture and manage wearable devices; future studies should incorporate multidimensional trust measures to address this limitation.

Conclusion

This study examined the relationship between trust in the healthcare system and wearable device use for health tracking in a nationally representative sample. Trust was associated with several demographic factors, while technological aptitude, social media use, and health literacy were significant correlates of trust. Wearable use, by contrast, was most strongly associated with technological aptitude, while a greater sense of perceived competence and increased social media use were inversely associated with adoption. The inverse association between trust in the healthcare system and wearable use suggests that, in settings of lower healthcare system trust, individuals may view wearable technology less as an extension of institutional care and more as an alternative pathway for tracking their health. At the same time, trust in the healthcare system likely represents only one dimension of the broader trust landscape relevant to wearable adoption. Decisions to use wearables may also depend on trust in the device itself, the platform or company handling the data, the adequacy of data privacy and security protections, and the extent to which clinicians are perceived as willing and able to interpret wearable-generated information appropriately.

These findings underscore the importance of considering trust, digital readiness, and health-related capabilities in efforts to integrate wearables into healthcare. They suggest that wearable adoption cannot be assumed to follow automatically from increased access to these devices, and that patients’ willingness to use wearables and share the data they generate is linked to their broader confidence in the healthcare system as well as trust in their own digital and health literacy. Many users, especially those who might lack faith in traditional healthcare, may see wearables as a way to maintain ownership over their own health. This points to a potentially underrecognized opportunity: patients who are skeptical of or disengaged from the healthcare system may already be self-monitoring with wearables, and clinicians who proactively and non-judgmentally engage with them may find this a meaningful entry point for rebuilding the patient–provider relationship. We recommend that future efforts to expand wearable use in healthcare should focus not only on access, but also on digital readiness, health literacy, and the design of care models that make wearable data clinically meaningful, trustworthy, and usable for patients and providers alike.

ORCID iDs

Shashank Madhu https://orcid.org/0000-0001-9070-0892

Justice Centrello https://orcid.org/0009-0002-3092-4717

Cameron Riopelle https://orcid.org/0000-0002-0668-0516

Consent for publication

The Health Information National Trends Survey data set is a public data set and hence does not require consent for publication.

Data Availability Statement

All data and materials are available upon request.*