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With an increase in aging population and chronic medical conditions in the United States, the role of informal caregivers has become paramount as they engage in the care of their loved ones. Mounting evidence suggests that such responsibilities place substantial burden on informal caregivers and can negatively impact their health. New wearable health and activity trackers (wearables) are increasingly being used to facilitate and monitor healthy behaviors and to improve health outcomes. Although prior studies have examined the efficacy of wearables in improving health and well-being in the general population, little is known about their benefits among informal caregivers.
This study aimed to examine the association between use of wearables and levels of physical activity (PA) among informal caregivers in the United States.
We used data from the National Cancer Institute’s Health Information National Trends Survey 5 (cycle 3, 2019 and cycle 4, 2020) for a nationally representative sample of 1273 community-dwelling informal caregivers—aged ≥18 years, 60% (757/1273) female, 75.7% (990/1273) had some college or more in education, and 67.3% (885/1273) had ≥1 chronic medical condition—in the United States. Using jackknife replicate weights, a multivariable logistic regression was fit to assess an independent association between the use of wearables and a binary outcome: meeting or not meeting the current World Health Organization’s recommendation of PA for adults (≥150 minutes of at least moderate-intensity PA per week).
More than one-third (466/1273, 37.8%) of the informal caregivers met the recommendations for adult PA. However, those who reported using wearables (390/1273, 31.7%) had slightly higher odds of meeting PA recommendations (adjusted odds ratios 1.1, 95% CI 1.04-1.77;
The results demonstrated a positive association between the use of wearables and levels of PA among informal caregivers in the United States. Therefore, efforts to incorporate wearable technology into the development of health-promoting programs or interventions for informal caregivers could potentially improve their health and well-being. However, any such effort should address the disparities in access to innovative digital technologies, including wearables, to promote health equity. Future longitudinal studies are required to further support the current findings of this study.
With a rapidly aging global population, the number of people living with chronic medical conditions (CCs) is increasing. The role of caregivers for this population, including informal caregivers, has become critical as they get involved in the delivery of care and provision of support to patients inside or outside formal health care settings [
Physical activity (PA) is one of the essential components of maintaining good health [
Information and communication technologies—including smartphones and electronic health and activity trackers (henceforth, wearables)—are increasingly being used as tools to facilitate the delivery of care and help improve health outcomes among patients and caregivers. The number of wearable users, as well as willingness to wear these technologies, has substantially grown in recent years both in the United States and worldwide. As of 2022, there are approximately 67 million adult wearable users in the United States, a figure that has increased by almost 42 million users since 2014 [
Despite numerous studies focused on developing and evaluating wearable devices designed to improve care delivery and health outcomes in the general population [
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 [
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) [
We followed the constructs of the Social-Ecological Model [
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
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 [
The analytical sample of 1273 caregivers represented a national-level estimate of approximately 73.1 million informal caregivers in the United States.
Among those meeting the recommendations of engaging in ≥150 minutes of at least moderate-intensity PA per week, approximately 43.1% (201/466) reported using wearables during the past 12 months (
From our multivariable logistic regression model, informal caregivers who reported wearable use during the past 12 months had higher odds (adjusted OR 1.1, 95% CI 1.04-1.77;
Informal caregiver characteristics in the United States (HINTSa 5—cycles 3, 2019 and 4, 2020; N=1273).
Characteristics | Sample, frequency (weighted %)b | Minutes per week of at least moderate-intensity exercise, frequency (weighted %)b | |||
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≥150 minutes (n=466) | <150 minutes (n=764) |
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≥150 minutes | 466 (37.8) | —c | — | — |
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<150 minutes | 764 (62.2) | — | — | — |
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Yes | 390 (31.7) | 201 (42.1) | 179 (26.4) | .03 |
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No | 873 (68.3) | 262 (57.9) | 581 (73.6) | — |
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18-34 | 103 (11.7) | 35 (9.9) | 67 (13.1) | .62 |
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35-49 | 318 (32) | 136 (35.7) | 180 (30.5) | — |
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50-64 | 457 (39.6) | 166 (39.1) | 284 (40.7) | — |
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≥65 | 356 (16.7) | 117 (15.3) | 218 (15.7) | — |
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Male | 426 (40) | 184 (45.3) | 232 (37.4) | .06 |
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Female | 757 (60) | 252 (54.7) | 488 (62.6) | — |
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Hispanic | 188 (15.3) | 63 (15.8) | 119 (14.7) | — |
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Non-Hispanic Asian and others | 104 (12) | 38 (14.0) | 66 (11.1) | — |
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Non-Hispanic Black | 150 (9.1) | 54 (9.3) | 93 (9.1) | — |
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Non-Hispanic White | 706 (63.6) | 278 (60.9) | 410 (65.1) | .86 |
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Married or living as married | 815 (70) | 315 (74.8) | 484 (67.5) | .13 |
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Divorced, widowed, or separated | 268 (12.4) | 85 (10.2) | 172 (13.3) | — |
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Single or never married | 149 (17.6) | 55 (15) | 91 (19.2) | — |
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Less than high school | 66 (5.2) | 21 (5.4) | 40 (4.5) | .002 |
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High school graduate | 182 (19.1) | 45 (10.5) | 128 (23.7) | — |
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Some college | 372 (43.8) | 138 (45.4) | 226 (43.2) | — |
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College graduate or more | 618 (31.9) | 243 (38.7) | 357 (28.6) | — |
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<20,000 | 184 (16.3) | 47 (8.3) | 124 (19.6) | .004 |
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20,000-35,000 | 121 (10.9) | 37 (9.2) | 81 (11.8) | — |
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35,001-50,000 | 138 (11.2) | 51 (9.6) | 84 (12.3) | — |
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50,001-75,000 | 210 (17.1) | 69 (17.6) | 137 (16.9) | — |
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>75,000 | 491 (44.5) | 224 (55.3) | 262 (39.4) | — |
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Yes | 1058 (89.7) | 411 (92.2) | 624 (88.7) | .17 |
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No | 188 (10.3) | 45 (7.8) | 128 (11.3) | — |
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MSA | 1144 (88.1) | 424 (89.6) | 682 (86.9) | .46 |
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Non-MSA | 129 (11.9) | 42 (10.4) | 82 (13.1) | — |
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Yes | 935 (72) | 345 (71.7) | 570 (73.0) | .77 |
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No | 323 (28) | 116 (28.3) | 190 (27) | — |
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None | 384 (32.7) | 173 (37.6) | 200 (30.2) | .09 |
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1 | 393 (31.6) | 146 (32.4) | 235 (31) | — |
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≥2 | 492 (35.7) | 147 (30) | 328 (38.8) | — |
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Current | 138 (13.2) | 54 (14.4) | 82 (12.3) | .04 |
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Former | 323 (25.1) | 99 (19) | 214 (28.1) | — |
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Never | 790 (61.7) | 310 (66.6) | 461 (59.6) | — |
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Underweight or normal (≤24.9) | 379 (31.6) | 176 (37.3) | 187 (27.5) | .02 |
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Overweight (25-29.9) | 385 (30.4) | 155 (31.7) | 222 (29.5) | — |
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Obese (≥30) | 477 (38) | 127 (31) | 339 (43) | — |
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Excellent or very good | 578 (44.2) | 278 (56.6) | 283 (37) | <.001 |
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Good | 478 (38.6) | 145 (33.5) | 320 (41.7) | — |
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Fair or poor | 207 (17.2) | 41 (9.9) | 156 (21.3) | — |
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Completely confident | 304 (23.6) | 153 (31.2) | 138 (19) | <.001 |
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Very confident | 600 (44.3) | 223 (46.7) | 362 (42.9) | — |
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Somewhat, a little, or not confident at all | 367 (32.1) | 90 (22.1) | 264 (38.1) | — |
aHINTS: Health Information National Trends Survey.
bFrequencies represent sample frequencies; proportions are population-level estimates that were generated by adjusting for complex survey features of the HINTS data (N=73.1 million).
cNot available.
dSuch as Fitbit, AppleWatch, or Garmin Vivofit.
eSuch as iPhone, Android, Blackberry, or Windows phone.
fMSA: metropolitan statistical area.
Logistic regressions modeling the association between use of electronic activity trackers (wearables) and meeting recommendations of physical activity (≥150 minutes per week of at least moderate-intensity exercise) among informal caregivers.
Characteristics | Crude ORa (95% CI) | Adjusted OR (95% CI) | |||
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Yes | 1.9 (1.12-2.26)c | 1.1 (1.04-1.77)c | ||
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No | Reference | Reference | ||
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18-34 | Reference | Reference | ||
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35-49 | 1.55 (0.77-3.13) | 1.06 (0.46-2.45) | ||
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50-64 | 1.27 (0.67-2.44) | 0.79 (0.38-1.64) | ||
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≥65 | 1.3 (0.64-2.64) | 0.98 (0.38-2.51) | ||
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Male | Reference | Reference | ||
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Female | 0.72 (0.51-1.02) | 0.72 (0.43-1.22) | ||
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Hispanic | 1.15 (0.67-1.96) | 1.5 (0.79-2.85) | ||
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Non-Hispanic Asian and others | 1.35 (0.61-2.99) | 1.39 (0.49-3.99) | ||
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Non-Hispanic Black | 1.09 (0.61-1.94) | 1.85 (0.77-4.43) | ||
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Non-Hispanic White | Reference | Reference | ||
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Married or living as married | 1.42 (0.82-2.48) | 1.08 (0.53-2.21) | ||
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Divorced, widowed, or separated | 0.98 (0.52-1.88) | 1.19 (0.45-3.13) | ||
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Single or never married | Reference | Reference | ||
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Less than high school | Reference | Reference | ||
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High school graduate | 0.36 (0.14-0.94)c | 0.32 (0.1-1.26) | ||
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Some college | 0.86 (0.33-2.25) | 0.55 (0.15-1.99) | ||
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College graduate or more | 1.11 (0.46-2.69) | 0.57 (0.15-2.13) | ||
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<20,000 | Reference | Reference | ||
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20,000-35,000 | 1.86 (0.95-3.67) | 2.67 (1.01-7.08)c | ||
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35,001-50,000 | 1.86 (0.94-3.65) | 2.91 (1.17-7.24)c | ||
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50,001-75,000 | 2.46 (1.21-4.99)c | 3.72 (1.28-10.81)c | ||
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>75,000 | 3.32 (1.85-5.95)d | 3.8 (1.51-9.6)e | ||
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Yes | 1.52 (0.81-2.85) | 0.82 (0.31-2.13) | ||
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No | Reference | Reference | ||
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MSA | Reference | Reference | ||
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Non-MSA | 0.77 (0.36-1.64) | 1.3 (0.54-3.16) | ||
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Yes | 0.94 (0.6-1.46) | 1.13 (0.61-2.08) | ||
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No | Reference | Reference | ||
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None | Reference | Reference | ||
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1 | 0.84 (0.53-1.32) | 1.08 (0.61-1.92) | ||
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≥2 | 0.62 (0.41-0.95)c | 1.19 (0.66-2.15) | ||
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Current | Reference | Reference | ||
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Former | 0.58 (0.28-1.2) | 0.47 (0.19-1.13) | ||
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Never | 0.96 (0.49-1.88) | 0.73 (0.31-1.68) | ||
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Underweight or normal (≤24.9) | Reference | Reference | ||
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Overweight (25-29.9) | 0.79 (0.49-1.29) | 0.90 (0.47-1.69) | ||
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Obese (≥30) | 0.53 (0.33-0.85)e | 0.62 (0.31-1.23) | ||
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Excellent or very good | Reference | Reference | ||
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Good | 0.53 (0.38-0.73)d | 0.66 (0.38-1.13) | ||
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Fair or poor | 0.30 (0.16-0.57)d | 0.39 (0.16-0.94)c | ||
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|||||
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Completely confident | Reference | Reference | ||
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Very confident | 0.66 (0.43-1.02) | 0.81 (0.45-1.47) | ||
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Somewhat, a little, or not confident at all | 0.35 (0.22-0.56)d | 0.74 (0.37-1.46) |
aOR: odds ratio.
bSuch as Fitbit, AppleWatch, or Garmin Vivofit.
c
d
e
fSuch as iPhone, Android, Blackberry, or Windows phone.
gMSA: metropolitan statistical area.
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 [
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 [
Nonetheless, there is the issue of the digital divide related to these innovative technologies, which could worsen the already existing disparities [
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 [
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 [
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 [
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 [
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 [
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 [
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 [
chronic medical condition
Health Information National Trends Survey
metropolitan statistical area
odds ratio
physical activity
World Health Organization
The authors would like to thank Shiva S Malepu and Atiya Khan for their assistance in manuscript preparation. This project was supported by the University of Southern Mississippi Research (Start-up) Fund.
The data analyzed for this study were obtained from the National Cancer Institute’s Health Information National Trends Survey; it is publicly available for access and download at https://hints.cancer.gov/.
AM and HK conceptualized and developed the initial draft of the manuscript. SK and PD critically reviewed and revised the manuscript and made appropriate edits or changes.
None declared.