For this study, we used data from the Health Information National Trends Survey (HINTS), a nationally representative, cross-sectional, probability-based survey conducted by the United States National Cancer Institute every few years since 2003 [33]. The HINTS, focusing on civilian noninstitutionalized adults aged ≥18 years, compiles information on access to, use of, and needs for health-related information, perceptions, knowledge, and behaviors. However, this survey is not a cancer-specific survey per se [34,35]. More recently, HINTS began collecting information on wearables use as well. We specifically used data from HINTS 5 cycles 3 (2019) and 4 (2020) because these 2 recent cycles of data contain information on wearables use. All 4 cycles of HINTS 5 involved self-administered mailed questionnaires [36] except for cycle 3, which involved a multimode survey that, in addition to the mail-in surveys, incorporated 2 experimental conditions of a web pilot. All mail-in surveys in cycle 3 and all groups in the multimode survey in cycle 4 received a US $2 prepaid monetary incentive to encourage participation; they received an additional US $10 Amazon e-gift card for participating in the second web pilot experimental (Web Bonus) survey in cycle 4.

HINTS 5 cycle 3 data collection began in January 2019 and concluded in April 2019 with an overall response rate of 30.3%. HINTS 5 cycle 4 was fielded between February 2020 and June 2020 with a response rate of 36.7%. The combined HINTS 5 cycles 3 and 4 resulted in an initial unweighted sample of 9303 adults aged ≥18 years. However, our analytical sample included 1273 self-identified informal caregivers. The informal caregiver status was assigned based on participant response to the following two survey questions: “Are you currently caring for or making health care decisions for someone with a medical, behavioral, disability, or other condition?” and “Do you provide any of this care professionally as part of a job (for example, as a nurse or professional home health aide)?”

The primary outcome of interest in this study was a binary measure indicating whether the informal caregivers were meeting the current WHO recommendations of moderate PA for adults (ie, ≥150 minutes of at least moderate-intensity PA per week) [15]. The measure was derived from a composite of combined participant responses to 2 questions in the HINTS survey. The participants were asked, “In a typical week, how many days do you do any PA or exercise of at least moderate intensity?” They were then asked, “On the days that you do any PA or exercise of at least moderate intensity, how long do you typically do these activities?” On the basis of the participant’ responses, the number of days per week of at least moderate-intensity PA was multiplied by the number of reported minutes per day of PA to compute weekly minutes of at least moderate-intensity PA for each respondent. We classified survey respondents into those with ≥150 minutes of at least moderate-intensity PA per week and those with <150 minutes of at least moderate-intensity PA per week, indicating meeting versus not meeting the current WHO recommendations of moderate PA for adults, respectively. The main independent variable was a binary measure assessing whether the informal caregiver has used wearables during the past 12 months (yes vs no). This indicator was derived from the participant’s response to the survey question, “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.”

We followed the constructs of the Social-Ecological Model [37] and Social Cognitive Theory [38] to select study covariates. Drawing from these theoretical frameworks, several sociodemographic characteristics related to informal caregivers were included as control measures in our analyses. Respondents’ ages were categorized into “18 to 34,” “35 to 49,” “50 to 64,” and “≥65” years. Sex was a binary variable, “male versus female.” Each respondent’s race and ethnicity was categorized into “non-Hispanic White,” “non-Hispanic Black,” “Hispanic,” and “non-Hispanic Asian and others.” Marital status was represented through a nominal variable, “married or living as married,” “divorced, widowed, or separated,” and “single or never married.” Informal caregivers’ education was categorized as “less than high school,” “high school graduate,” “some college,” and “college graduate or more.” Other incorporated socioeconomic status characteristics included annual household income, which was categorized as “<US $20,000,” “US $20,000 to US $35,000,” “US $35,001 to US $50,000,” “US $50,001 to US $75,000,” and “>US $75,000.” Other variables included whether the respondents had a smartphone, “yes versus no”; metropolitan statistical area (MSA) residency, “MSA versus non-MSA”; and having a regular provider, “yes versus no.” The incorporated measures of health and health behaviors included number of reported CCs, which was categorized as “none,” “1,” and “≥2.” The number of CCs was constructed based on a history of diagnosed medical conditions including diabetes or high blood sugar; high blood pressure or hypertension; heart conditions such as myocardial infarction, angina, or congestive heart failure; chronic lung disease, asthma, emphysema, or chronic bronchitis; depression or anxiety disorder; and cancer. Smoking status was categorized as “current,” “former,” and “never”; each respondent’s BMI (calculated as weight in kilograms divided by the square of height in meters [kg per m²]) was categorized into “underweight or normal (BMI ≤24.9 kg/m²),” “overweight (BMI=25-29.9 kg/m²),” and “obese (BMI ≥30kg/m²).” We also incorporated 2 measures of caregiver self-efficacy: self-rated general health status—which was categorized as “excellent or very good,” “good,” and “fair or poor”—and caregiver’s self-reported confidence in taking care of own health—categorized as “completely confident,” “very confident,” and “somewhat, a little, or not confident at all.”

We first calculated the unweighted frequencies and weighted proportions for the entire sample of informal caregivers and then by subgroups based on PA levels. Wald chi-square was used to test for equal proportions in 2-way analyses. Univariate and multivariable logistic regressions were fit to assess the association between the use of wearables and the binary PA outcome. The fully adjusted model incorporated the primary independent variable and the entire pool of selected covariates. Multicollinearities were checked, and the significance of interaction terms was assessed by the likelihood ratio test. Assessing for multicollinearities was performed by first exploring the correlation matrix and then the variance inflation factor and tolerance. There were no threats of multicollinearity between the model variables. The final generated outputs included odds ratios (ORs), their 95% CIs, and associated P values. In the above analytical steps, final person weights and jackknife replicate weights from the HINTS data set were used to estimate national-level values and more accurate SEs of estimates. The significance threshold was set at P<.05. All analyses were performed using the SAS statistical software (version SAS 9.4; SAS Institute Inc).

This study involved analyses of secondary data from the HINTS 5 data set, which is primarily deidentified and publicly available. The institutional review board of Westat, the organization that administers the survey, and the institutional review board of the National Cancer Institute Office of Human Subjects Research both granted exempted status for the use and analysis of HINTS data. Additional details about the HINTS survey design, methodology, and access to public data can be found on the survey website [39].

Using a nationally representative sample of 1273 informal caregivers in the United States (73.1 million at the national level), we examined whether informal caregivers who use wearables met the current WHO recommendations of ≥150 minutes of at least moderate-intensity PA for adults. Study findings revealed that informal caregivers who reported using wearables during the past 12 months had higher odds of meeting PA recommendations. Informal caregivers are pillars of current health care systems, as they provide essential care and emotional support to their loved ones. However, they often experience significant burden associated with their caregiving roles, which can negatively impact their health and well-being. Those who care for older patients with CCs (ie, cancer, hypertension, etc), dementia or Alzheimer disease are even more likely to report poorer physical and mental health as well as social and financial challenges attributed to the caregiving burden, including limited time and resources.

Mounting evidence exists about the use of information technologies and their health-related benefits among various groups of populations, including informal caregivers [30-32]. For instance, Matthews et al [32] assessed how family caregivers deal with challenging aspects of dementia to inform formal interventions designed to strengthen their caregiving knowledge and skills. The findings indicated that family caregivers of people with dementia could use a novel, wearable camera system to collect evidence of dementia-related behaviors and interactions that may affect the health and safety of the caregiving dyad. Furthermore, Egan et al [30] co-designed and assessed a mobile app named CareFit to educate and support caregivers to perform regular PA at home during and after COVID-19 restrictions by integrating a transtheoretical model of behavior change based on the United Kingdom’s guidelines for PA. They found that integrating PA into the CareFit app with functions such as a weekly planner and educational material for users is feasible. In addition, an observational study by Martinato et al [31] assessed Vivoactive HR (Garmin) smartwatch as a wearable device in quantifying PA in a sample of 49 older adults and found the potential of the wearable device to enhance PA among care recipients by capturing even the low levels of PA. Furthermore, Jaschinski and Allouch [40] found that most informal caregivers had a positive perception about using ambient assisted living and appreciated the help of ambient assisted living technologies in preventing accidents and alerting them immediately in case of an emergency [40].

There is a greater potential for wearable devices to improve health and health care. Wearable technology can serve as a safe and cost-effective intervention to promote health and healthy behaviors such as PA [28]. Incorporating health-related data obtained from wearables into the electronic health record systems could further assist health care professionals in monitoring individual health status and providing relevant care and support efficiently [25,28,41]. Notably, by connecting or linking to mobile apps, wearables can better facilitate motivating and managing individual health and health care [42]. Furthermore, prior systematic reviews and meta-analysis in this domain have provided evidence regarding the positive influence of using wearable technology in increasing PA among various subgroups of population [43-45]. The use of wearable devices as a health-promoting intervention is associated with increased PA and steps per day and a significant increase in moderate to vigorous minutes per week of PA among patients with cardiometabolic diseases [44]. Other studies indicate that using consumer-based wearable activity trackers as an intervention modality significantly increased daily step count and energy expenditure among wearable users compared with those who do not use these devices among a wide range of healthy populations and populations with CC [43].

Nonetheless, there is the issue of the digital divide related to these innovative technologies, which could worsen the already existing disparities [2,46]. The digital divide pertains to differences between have’s and have-not’s with information and communication technologies [47]. The gap in access to digital technologies, including wearable devices, can widen the already present health and health care disparities [2]. Evidence shows that the younger, more educated, wealthier, and tech-savvy adults are more likely to use wearables compared with older adults, those with lower education and income, and non–tech-savvy individuals [21,28,48]. Farivar et al [49] explored the extent to which factors were associated with the adoption of wearable devices among older adults by conducting a mixed methods study and found their perceived complexity in using wearable devices as a barrier to adoption. In addition, it was found that cognitive age itself does not considerably affect wearable device use intention, which is moderated by subjective well-being in older adults, indicating that older adults’ use intention is influenced by their subjective well-being [49].

Our findings show that informal caregivers with higher income, compared with those with lower income (≤US $20,000), were more likely to engage in PA by meeting the recommended guidelines. A meta-analysis conducted by Pinquart and Sörensen [50] examined correlates of informal caregivers’ physical health and found that having higher income was associated with better physical health. They suggested that less access to health care and inadequate health practices may be attributed to the relationship of income with physical health [51]. As one of the socioeconomic indicators, income is considered a correlate of financial resources and tangible well-being that may affect individual’s healthy behaviors [51]. Insufficient tangible resources may influence individual health behaviors from financial limitations that can hinder people from making healthy choices, albeit not all those choices are based on income or money [51]. Nonetheless, evidence also suggests that the relation of socioeconomic components, including income, to individual health behaviors is not clear or straightforward [51]. Moreover, other evidence from the United States indicates that use of wearable devices and other digital technologies is greatly impacted by individuals’ socioeconomic status [21]. Approximately 31% of the residents of the United States with an annual household income of ≥US $75,000 reported wearing a smart watch or fitness tracker on a regular basis, whereas only 12% of those with an annual household income ≤US $30,000 reported using these devices [21]. Similarly, people with higher educational attainment are more likely to adopt these technologies than those with lower levels of education [21].

Interestingly, our findings also indicated that informal caregivers who assessed their health as fair or poor, when compared with those who rated their health as excellent or very good, were less likely to meet PA recommendations. This finding was in line with that of prior studies that reported positive associations of higher self-efficacy and self-rated health with initiation of exercise, higher PA, and better health-promoting behaviors [52-55]. Thus, these psychosocial factors are important predictors of PA among informal caregivers. Specifically, lower levels of perceived health may actually be a potential barrier to PA [54]. It is also possible that this group of informal caregivers may not have had sufficient time to engage in PA owing to their caregiving burden. Indeed, informal caregivers have challenging circumstances or situations such as financial difficulties, time constraints, and other barriers related to their caregiving role [2]. In this respect, innovative technologies, including wearables, could help reduce the burden associated with caregiving by providing beneficial features (eg, motivation, self-tracking, information gathering and exchange, and communication with a provider), which could potentially help improve individual health and well-being [2]. Notably, they could be used by individuals to further engage them in health-related activities, including PA [30], particularly caregivers who are more likely to have poorer physical and mental health and emotional well-being.

There is a greater need for providing support and help to informal caregivers, given that they play an essential role in the health care system. Incorporating elements of innovative information and communication technologies such as wearables could contribute to beneficial health outcomes. Wearables can help individuals be proactive in monitoring, tracking, and managing their health and health care and can help improve their quality of life [56]. Wearables are becoming more affordable and easier to use for monitoring health-related physical activities or conditions, particularly among older people. Wearable devices are beneficial to overcome the diverse needs related to aging among older adults [57]. Essentially, health-promoting strategies, such as providing health education materials or counseling, should be provided using these technologies to promote or enhance health and health care [23,58]. To reduce the burden associated with caregiving, training should be offered to them, especially those who are new to caregiving. For example, caregiver stress and burden could be reduced by providing instructions about how to perform tasks such as offering mobility assistance, advocating for medical treatment, administering medications and injections, and using digital technologies and other electronic tools (eg, personal health record tracking and medication support systems) [59,60].

Given that most caregivers have a smartphone, they can download and use mobile health apps for health-related purposes. For example, downloading a pedometer app could help enhance their PA. Moreover, facilitating the use of wearables and making them less noticeable and user-friendly, while providing appropriate operating instructions, could help reduce anxiety about technology use [61,62]. Today, there are different types of products (smartwatches, fitness trackers, smart clothes, implantable gadgets, head-mounted displays, etc) from various manufacturers (Xiaomi, Huawei, Polar, Samsung, Apple, Garmin Ltd, Withings, and Fitbit just to name a few) that are available in the market; the variety of options can be daunting for informal caregivers when making decisions about which wearable device to adopt and use [62,63].

Although this study provides novel insights that have implications for health care policy and practice, it has several limitations. First, owing to the cross-sectional nature of the HINTS survey, we were unable to infer causality in the reported associations. Second, despite capturing the relevant variables based on the conceptual and theoretical frameworks, it is possible that there could be a more appropriate conceptual model. However, given that the use and implementation of wearable devices are still developing and in their infancy, unified and better-fitting theories and frameworks related to this field are still being developed [64]. Third, provided that the data used for this study were based on a national survey including the self-reported information, there could be recall bias if the respondents did not provide correct information, and there is a possibility that other types of bias may be introduced, including social desirability bias. Fourth, despite ongoing efforts to improve the HINTS survey response rates [65], these rates have remained low, and there is potential for selection bias; thus, population representativeness and generalizability of the findings could be questionable. Fifth, although we adjusted for several related factors based on the theoretical frameworks and literature review, there may be other factors that were not included in our model. For example, we could not adjust for many contextual factors or attributes possibly associated with the use of wearables or PA. Sixth, no thorough assessment of muscle-strengthening activities was provided in the database, which could have supplemented our evaluation of PA. Finally, other critical information about consistency of wearables use, device features, and quality of engagement of those who used the device during the past 12 months were also not collected in this survey. Thus, we were constrained in our analyses by the information provided in the HINTS data set. All these limitations present an opportunity for our team and other researchers to expand the study in the future. Potentially more prospective studies can be designed to further support the findings of this study. In addition, investigating these associations among groups of caregivers who specifically care for patients with CCs (eg, diabetes, hypertension, cancer, and dementia or Alzheimer disease) will be of particular interest in the caregiving field.

In this study, we used a nationally representative sample of 1273 self-identified informal caregivers in the United States to assess the associations between use of wearables and the status of meeting the current WHO recommendations of moderate-intensity PA for adults (ie, ≥150 minutes of at least moderate-intensity PA per week). We found that informal caregivers who reported wearable use during the past 12 months were modestly more likely to engage in ≥150 minutes of at least moderate-intensity PA per week compared with caregivers who did not use wearables. The results demonstrated the potentials of wearables as a means of increasing PA among informal caregivers, thus their role in health promotion and improving quality of life among this important segment of the population.

Long-term adoption could potentially be critical for the delivery of the benefits promised by wearable technology, and yet, this particular area of research requires further scrutiny [64]. There are several acceptance- and abandonment-related issues that substantially influence long-term wearable use. Some of these factors include device appearance, display and interaction, wearability, perceived usefulness or risks, and other technical issues such as data measurement and presentation [29,64]. Other factors that influence wearable use include development of more personalized devices for various groups of people who have different needs and preferences. Usually, designing of an all-purpose wearable is unreasonable and less impactful and is less likely to have sustained use or benefits over time [29].

There are many other issues related to wearable development and use that need to be properly addressed. A few of these issues include data security and protection, consumer privacy concerns, device accuracy, discoverability risks, ethical issues related to the tracking features of wearables, and the fact that many wearables are not regulated by the United States Food and Drug Administration [22,23,25,29,66]. Furthermore, other issues related to wearable technology adoption such as lack of awareness regarding the health and well-being benefits of wearable device use and their implications for physical and mental health, specifically among caregiving and older adults, need to be particularly addressed [23]. To overcome these challenges and concerns, wearable technology developers, researchers, interventionists, health care providers, and policy makers should work in synergy to design and develop personalized, effective, and validated wearables with an optimum impact on health behavior change and health promotion for the caregiver population [25]. The role of digital divide in the adoption and effectiveness of wearables should be emphasized and addressed [64]. As most health technology adoptions are lagging among those in low-income and low-education subgroups, the design and development of wearables should overcome the initial and long-term barriers of adoption among this disadvantaged segment of the population.