Abstract
Objective:
This study aimed to investigate the association between wearable device use and physical activity, as well as to assess the mediating role of wearable device use in the relationship between social disparities and achievement of recommended level of physical activity.
Methods:
Data from the 2022 Health Information National Trends Survey (HINTS 6) (n = 4922) were analyzed. Weighted prevalence estimates were presented, and adjusted odds ratios from multivariable logistic regression of wearable device use and meeting recommended levels of physical activity were reported. Causal mediation analysis (CMA) was conducted to examine the mediation effect of sociodemographic factors on physical activity via wearable devices.
Results:
Among participants, 34.9 % used wearable devices, and 37.1 % achieved the recommended level of 150 min of weekly moderate-intensity physical activity. Wearable device users were 1.84 times more likely to achieve recommended physical activity goals than non-users. Significant mediation effects of wearable device use were found, mediating the association between female sex and higher education attainment with physical activity. Furthermore, among wearable users, Asians, and individuals with a body mass index ≥ 30 kg/m2 were less likely to meet recommended levels of physical activity.
Conclusions:
Wearable device use exhibit sociodemographic patterns, influencing physical activity and potentially exacerbating health disparities. Promoting equitable usage of wearables and enhancing skills in utilizing wearable devices can significantly enhance physical activity levels, playing a pivotal role in reducing health disparities and fostering overall well-being.
Keywords: Physical activity, Wearable device, Health disparities, Digital divide, Survey
Lay summary
This study examines the impact of wearable devices, such as fitness trackers, on physical activity levels and their role in addressing disparities in exercise across different social groups. Using data from a national survey of 4922 participants, we found that only 37.1 % met the recommended weekly physical activity levels. However, wearable device users were nearly twice as likely to achieve these goals compared to nonusers. Wearable devices mediated the relationship between being female or having higher education and meeting physical activity recommendations. Notably, among wearable device users, Asians and individuals with a higher body mass index were less likely to meet activity recommendations. These findings suggest that expanding access to wearable devices and enhancing digital literacy may help increase physical activity and reduce health disparities, ultimately improving overall wellbeing.
Introduction
The benefits of physical activity in health and well-being are extensively documented (1–3). Regular physical activity lowers the risk of chronic diseases such as type 2 diabetes, cancer, and cardiovascular disease (4,5), and also contributes to mental health and overall quality of life (6). The lack of physical activity stands as the fourth leading risk factor for global mortality, accounting for 6 % of deaths worldwide (7). Both the World Health Organization and the 2018 Physical Activity Guidelines for Americans recommend at least 150 min of moderate physical activity per week, 75 to 150 min per week of vigorous-intensity physical activity, or a combination of both for adults (8,9). However, approximately 25 % of adults globally and 76 % of U.S. adults fail to meet these physical activity guidelines (10,11). Promoting physical activity is, therefore, a key priority for achieving global public health goals (9).
With the rise of digital health technologies, wearable devices have emerged as widely used tools for supporting individuals in managing their health (12). Wearable devices are defined as technologies worn on or near the body that continuously monitor an individual’s activities and physiological parameters without disrupting daily life (13). They play a significant role in tracking physical activity, heart rate, energy expenditure, and other health-related metrics (14). Empirical studies using national datasets indicate that individuals who use wearable devices are more likely to engage in at least 150 min of physical activity per week compared to non-users (15,16). Systematic reviews and meta-analyses further confirm that wearable devices are effective in increasing physical activity across different age groups and populations, both clinical and non-clinical settings (17–20). The effectiveness of these devices stems from their ability to provide self-monitoring, real-time feedback, and goal-setting features, which are essential for behavior change (21). The potential mechanism through which these devices influence behavior includes increasing awareness of physical activity levels, reinforcing self-regulation, and enhancing motivation through immediate feedback and progress tracking. (22).
The adoption of wearable devices has grown significantly in recent years, driven by advancements in digital health and consumer demand. In 2020, approximately 396 million wearable units were shipped worldwide, with projections increasing to 631.7 million units by 2024 (23). However, only 30 % of U.S. adults report using wearable health devices, highlighting significant sociodemographic disparities in adoption (24). Research suggests that women, non-Hispanic White individuals, those with higher education, and those with higher household incomes are more likely to use wearable devices (24,25). These disparities raise concerns about whether the growing adoption of digital health technologies may reinforce or exacerbate existing inequalities in physical activity levels.
The phenomenon under discussion is commonly referred as the ‘digital divide,’ a term that traditionally signifies the inequal access to emerging information technologies (IT) (26). Ragnedda’s framework (2020) delineates three levels of the digital divide (27). First, the primary focus are issues related to access to information and digital technology, known as the ‘access gap’ (28). Subsequent levels address variations in skills and motivation to employ digital resources, identified as the second level, the ‘use gap.’ Then, the first two levels of digital inequalities collectively contribute to the emergence of the third level gap, the ‘utility gap.’ The third gap encompasses the real-world outcomes and consequences, leading to the reproduction of inequality (26, 29). While extensive research has examined the first two levels of the digital divide, fewer studies have explored the third level, “the utility gap”, particularly in the context of wearable device use and physical activity engagement. Understanding which populations benefit most from wearable devices is crucial for identifying disparities in health technology utilization and developing strategies to ensure equitable health outcomes.
While systematic reviews and meta-analyses have demonstrated the effectiveness of wearable devices in increasing physical activity across clinical and non-clinical populations (17–20), a gap remains in population-level analyses exploring their role in physical activity among nationally representative, socioeconomically diverse groups. Given the sociodemographic differences in wearable device use, it is important to investigate whether wearable device use mediates the relationship between social disparities and physical activity levels. Individuals from socially advantaged groups (e.g., higher income, higher education) are more likely to own and use wearable devices, potentially leading to greater engagement in physical activity. If wearable device use plays a mediating role, this would suggest that differences in digital health technology adoption contribute to existing disparities in physical activity levels. Additionally, even among wearable device users, there may still be variation in who benefits the most from these technologies, the utility gap. Therefore, it is critical to examine whether disparities persist even within the subgroup of wearable device users.
To address these gaps, this study aims to: (1) investigate the association between wearable device use and achievement of the recommended level of physical activity, (2) examine the mediating role of wearable device use in the relationship between sociodemographic factors and physical activity achievement, and (3) explore sociodemographic patterns in achieving the recommended physical activity levels among wearable device users. While previous research, including systematic reviews and randomized controlled trials (RCTs), has established the effectiveness of wearable interventions, there remains a need for population-level analyses that examine how wearable device adoption interacts with sociodemographic factors in real-world settings. By leveraging a nationally representative dataset, this study provides insights into disparities in digital health engagement and explores potential pathways through which wearable devices may influence physical activity across diverse groups. These findings contribute to a broader understanding of how digital health technologies shape physical activity behaviors and inform strategies for promoting more equitable access to and utilization of wearable health technologies.
Methods
Study population, design, and settings
This study is a secondary analysis of nationally representative data from the Health Information National Trends Survey (HINTS), a periodic survey conducted by the National Cancer Institute (NCI) to assess how the U.S. population accesses and uses health-related information. The survey collects data on health behaviors, perceptions, and knowledge through a complex stratified, random sampling procedure, selecting participants from households across the U.S. listed in a nationwide address database (30–32). HINTS is not conducted annually but is administered periodically in separate cycles. For this study, we utilized survey data from HINTS 6, which was collected from March 7, 2022, to November 8, 2022. Data for HINTS 6 were primarily collected via mailed self-administered questionnaires sent to selected households. The publicly available HINTS dataset was accessed and retrieved from the NCI website (https://hints.cancer.gov/). The HINTS protocol was reviewed by the Westat Institutional Review Board and was exempted from review and informed consent by the U.S. National Institutes of Health Office of Human Subjects Research Protections (30).
Measures
Outcome variable
The primary outcome is the attainment of a weekly minimum of 150 min of moderate-intensity physical activity, based on self-reported data. Physical activity was assessed using the following survey question: “In a typical week, how many days do you do moderate- or vigorous-intensity physical activities for at least 10 min at a time?” Participants who reported engaging in moderate-to-vigorous physical activity (MVPA) were also asked to specify the duration of these activities on the days they were performed. The HINTS dataset provided the derived variable ‘minutes per week of at least moderate-intensity exercise,’ which combines both frequency and duration responses. The outcome was dichotomized to indicate whether a participant met the 2018 Physical Activity Guidelines for Americans, which recommend at least 150 min of moderate-intensity physical activity per week (8,10).
Mediator
The mediating role of wearable device use was examined in this study. Assessment of wearable device utilization was based on the following 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.” Participants responded yes or no, with yes indicating wearable device use.
Independent variables
Participants’ self-reported sociodemographic- and health-related factors were used in the analysis. These characteristics included age, sex, race, ethnicity, educational attainment, household income, marital status, health insurance, and areas of residence. Health-related factors included body mass index (BMI), smoking status, and underlying medical conditions. For underlying medical conditions, the study participants were asked to check whether a doctor or other health professional had ever told them that they had any of the following medical conditions: diabetes, high blood pressure, heart condition, lung disease, depression, and cancer.
Statistical analysis
Participants with missing values for the outcome or mediator variables were excluded to ensure the integrity and completeness of the analysis while maintaining a sample relevant to our research objectives, resulting in a final analytic sample of 4922 participants. Full-sample weights were computed to explicitly account for nonresponse and noncoverage of populations and to enhance the accuracy of the estimates and permit generalizability of the results to the national population, using jackknife methods (30). Differences in participant characteristics between users and non-users of wearable devices were compared using χ2 statistics.
Multivariable logistic regression models were employed to investigate factors associated with two key variables: (1) the use of wearable devices and (2) attainment of a weekly minimum of 150 min of moderate-intensity physical activity. These models considered participants’ sociodemographic and health-related characteristics. To examine the mediation effect of sociodemographic factors on physical activity via wearable devices, causal mediation analysis (CMA), a method to dissect total effect of a treatment into direct and indirect effect, was conducted (33). We selected independent variables for the causal mediation analysis based on their significant association with the mediator (wearable device use) in the initial analysis. We calculated both the direct and indirect effects of predictors of physical activity (i.e., sex, age, educational attainment, household income, diabetes, and smoking status) on physical activity using the ‘mediation’ package in R. All potential confounders and associations between participant characteristics and physical activity were carefully controlled for. Average causal mediation effect (ACME), average direct effect (ADE), and total effect are reported.
We also conducted subgroup analyses among those who used wearable devices (n = 1718) to examine factors associated with physical activity, using a separate logistic regression analysis. All statistical tests were conducted with a two-sided approach, and the level of statistical significance was set at α = 0.05. Statistical analyses were performed using R, version 4.3.3 (R Group for Statistical Computing), and reported results include odds ratios (ORs), 95 % confidence intervals (CIs), and P values.
Results
Study population
Among the respondents, 1718 individuals (weighted 37.8 %) reported using wearable devices for monitoring their health and physical activity in the past 12 months. This represents a weighted estimate of 71.5 million (95 % CI, 67.1–75.1 million) U.S. adults, accounting for approximately 29 % (95 % CI, 27 %–30 %) of the entire U.S. adult population (Table 1).
Table 1.
Characteristics of participants by wearable device use (N = 4922).
| Overall (n = 4922) | Non-user (n = 3204) | User (n = 1718) | P | ||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Sex | n | % | n | % | n | % | |
| Male | 1985 | 49.23 | 1372 | 66.1 | 613 | 33.90 | 0.001 |
| Female | 2937 | 50.77 | 1832 | 58.4 | 1105 | 41.60 | |
| Age group (years) | Mean, M 48.2 years (Standard Deviation, SD 0.36) | ||||||
| 18–34 | 773 | 25.77 | 391 | 55.2 | 382 | 44.80 | <0.001 |
| 35–49 | 1043 | 26.58 | 534 | 51.1 | 509 | 48.90 | |
| 50–64 | 1438 | 27.88 | 971 | 67.5 | 467 | 32.50 | |
| 65–74 | 1067 | 12.31 | 814 | 76.5 | 253 | 23.50 | |
| 75+ | 601 | 7.45 | 494 | 82.6 | 107 | 17.40 | |
| Race/Ethnicity | – | ||||||
| Non-Hispanic (NH) White | 2822 | 61.67 | 1824 | 61.9 | 998 | 38.10 | 0.060 |
| NH Black or African American | 265 | 11.32 | 152 | 51.1 | 113 | 48.90 | |
| Hispanic | 787 | 16.92 | 548 | 70.3 | 239 | 29.70 | |
| NH Asian | 883 | 5.47 | 567 | 61.5 | 316 | 38.50 | |
| NH Other | 165 | 4.62 | 113 | 61.6 | 52 | 38.40 | |
| Residence | – | ||||||
| Metropolitan | 4337 | 88.25 | 2796 | 61.6 | 1541 | 38.40 | 0.364 |
| Micropolitan | 395 | 7.80 | 285 | 67.3 | 110 | 32.70 | |
| Small town | 190 | 3.95 | 123 | 65.6 | 67 | 34.40 | |
| Marital status | – | ||||||
| Married or living as married | 2608 | 56.55 | 1538 | 57.7 | 1070 | 42.30 | 0.001 |
| Divorced, widowed, or separated | 1344 | 11.67 | 1025 | 74.9 | 319 | 25.10 | |
| Single | 970 | 31.78 | 641 | 65.6 | 329 | 34.40 | |
| Education level | – | ||||||
| Less than high school | 275 | 5.73 | 236 | 82.3 | 39 | 17.70 | <0.001 |
| High school graduate | 828 | 20.63 | 650 | 73.2 | 178 | 26.80 | |
| Some college | 1405 | 39.28 | 960 | 64.4 | 445 | 35.60 | |
| College degree or higher | 2414 | 34.36 | 1358 | 49.7 | 1056 | 50.30 | |
| Income level | – | ||||||
| Less than $20,000 | 765 | 13.05 | 649 | 83.5 | 116 | 16.50 | <0.001 |
| $20,000 to <$35,000 | 635 | 10.72 | 491 | 77.0 | 144 | 23.00 | |
| $35,000 to <$50,000 | 640 | 11.55 | 452 | 70.2 | 188 | 29.80 | |
| $50,000 to <$75,000 | 848 | 17.89 | 552 | 61.7 | 296 | 38.30 | |
| $75,000 or more | 2034 | 46.79 | 1060 | 51.1 | 974 | 48.90 | |
| Health insurance | – | ||||||
| Insured | 4526 | 89.48 | 2930 | 62.2 | 1596 | 37.80 | 0.964 |
| Uninsured | 396 | 10.52 | 274 | 62.0 | 122 | 38.00 | |
| BMI (kg/m2) | – | ||||||
| Under 30 | 3142 | 64.95 | 2040 | 61.0 | 1102 | 39.00 | 0.138 |
| 30 and over | 1780 | 35.05 | 1164 | 64.3 | 616 | 35.70 | |
| Smoking status | – | ||||||
| Never | 3159 | 65.75 | 1943 | 59.5 | 1216 | 40.50 | <0.001 |
| Current | 529 | 11.70 | 430 | 77.5 | 99 | 22.50 | |
| Former | 1234 | 22.55 | 831 | 62.2 | 403 | 37.80 | |
| Medical condition | – | ||||||
| Diabetes (No=0) | 3904 | 83.47 | 2440 | 59.3 | 1464 | 40.70 | <0.001 |
| Diabetes (Yes=1) | 1018 | 764 | 76.8 | 254 | 23.20 | ||
| High Blood Pressure (No=0) | 2757 | 64.13 | 1652 | 58.5 | 1105 | 41.50 | <0.001 |
| High Blood Pressure (Yes=1) | 2165 | 1552 | 68.9 | 613 | 31.10 | ||
| Heart Condition (No=0) | 4468 | 87.95 | 2872 | 61.5 | 1596 | 38.50 | 0.003 |
| Heart Condition (Yes=1) | 454 | 332 | 72.5 | 122 | 27.50 | ||
| Lung Disease (No=0) | 4246 | 71.14 | 2742 | 61.9 | 1504 | 38.10 | 0.481 |
| Lung Disease (Yes=1) | 676 | 462 | 64.4 | 214 | 35.60 | ||
| Depression (No=0) | 3561 | 90.45 | 2347 | 62.9 | 1214 | 37.10 | 0.318 |
| Depression (Yes=1) | 1361 | 857 | 60.3 | 504 | 39.70 | ||
| Cancer (No=0) | 4204 | 2691 | 61.8 | 1513 | 38.20 | 0.279 | |
| Cancer (Yes=1) | 716 | 511 | 65.6 | 205 | 34.40 | ||
| Physical activity (PA) | M 184.34 min (SD 10.15) | ||||||
| No=0 | 3096 | 61.22 | 2155 | 68.4 | 941 | 31.60 | <0.001 |
| Yes=1 | 1826 | 38.78 | 1049 | 52.4 | 777 | 47.60 | |
Factors related to the use of wearable devices
Table 2 and Fig. 1 present the results of multivariable logistic regression models examining the factors associated with wearable device utilization. Females compared to males (OR, 1.59 [95 % CI, 1.25–2.01]; P < 0.001), the youngest group (18 to 34 years) compared to those aged 50–64 years (OR, 0.47 [95 % CI, 0.34–0.64]; P < 0.001) were more likely to use wearable devices. High education levels, specifically a college degree or higher (OR, 2.44 [95 % CI, 1.13–5.24]; P = 0.03), and an annual household income of $75,000 or more (OR, 3.53 [95 % CI, 2.04–6.12]; P < 0.001) were associated with an increased likelihood of using wearable devices. In terms of health-related factors, current smokers were less likely to use wearable devices (OR, 0.57 [95 % CI, 0.37–0.89]; P = 0.02), as were respondents with diabetes (OR, 0.67 [95 % CI, 0.49–0.90]; P = 0.01).
Table 2.
Multivariable logistic regression of factors associated with wearable device use (N = 4922).
| Odds Ratio | 95 %C.I. | P-value | ||
|---|---|---|---|---|
|
| ||||
| Sex | ||||
| Male | Ref. | |||
| Female | 1.59 | 1.25 | 2.01 | < 0.001 |
| Age group (years) | ||||
| 18–34 | Ref. | |||
| 35–49 | 1.02 | 0.72 | 1.44 | 0.91 |
| 50–64 | 0.47 | 0.34 | 0.64 | < 0.001 |
| 65–74 | 0.30 | 0.22 | 0.42 | < 0.001 |
| 75+ | 0.19 | 0.12 | 0.31 | < 0.001 |
| Race/Ethnicity | ||||
| Non-Hispanic (NH) White | Ref. | |||
| NH Black or African American | 0.86 | 0.59 | 1.24 | 0.40 |
| Hispanic | 1.12 | 0.79 | 1.58 | 0.52 |
| NH Asian | 1.23 | 0.69 | 2.20 | 0.47 |
| NH Other | 0.90 | 0.46 | 1.74 | 0.74 |
| Residence | ||||
| Metropolitan | Ref. | |||
| Micropolitan | 1.02 | 0.63 | 1.66 | 0.92 |
| Small town | 0.98 | 0.59 | 1.63 | 0.95 |
| Marital status | ||||
| Married or living as married | Ref. | |||
| Divorced, widowed, or separated | 0.80 | 0.55 | 1.17 | 0.25 |
| Single | 0.80 | 0.48 | 1.33 | 0.39 |
| Education level | ||||
| Less than high school | Ref. | |||
| High school graduate | 1.34 | 0.61 | 2.95 | 0.45 |
| Some college | 1.78 | 0.89 | 3.56 | 0.10 |
| College degree or higher | 2.44 | 1.13 | 5.24 | 0.03 |
| Income level | ||||
| Less than $20,000 | Ref. | |||
| $20,000 to <$35,000 | 1.51 | 0.88 | 2.58 | 0.13 |
| $35,000 to <$50,000 | 1.98 | 1.16 | 3.38 | 0.02 |
| $50,000 to <$75,000 | 2.85 | 1.63 | 5.00 | < 0.001 |
| $75,000 or more | 3.53 | 2.04 | 6.12 | < 0.001 |
| Health insurance | ||||
| Insured | Ref. | |||
| Uninsured | 1.07 | 0.70 | 1.64 | 0.74 |
| BMI (kg/m2)_ | ||||
| Under 30 | Ref. | |||
| 30 and over | 1.05 | 0.83 | 1.34 | 0.67 |
| Smoking status | ||||
| Never | Ref. | |||
| Current | 0.57 | 0.37 | 0.89 | 0.02 |
| Former | 1.17 | 0.89 | 1.52 | 0.24 |
| Medical condition | ||||
| Diabetes (No=0) | Ref. | |||
| Diabetes (Yes=1) | 0.67 | 0.49 | 0.90 | 0.01 |
| High Blood Pressure (No=0) | Ref. | |||
| High Blood Pressure (Yes=1) | 1.08 | 0.83 | 1.41 | 0.53 |
| Heart Condition (No=0) | Ref. | |||
| Heart Condition (Yes=1) | 1.30 | 0.89 | 1.91 | 0.16 |
| Lung Disease (No=0) | Ref. | |||
| Lung Disease (Yes=1) | 1.05 | 0.72 | 1.54 | 0.79 |
| Depression (No=0) | Ref. | |||
| Depression (Yes=1) | 1.12 | 0.83 | 1.50 | 0.44 |
| Cancer (No=0) | Ref. | |||
| Cancer (Yes=1) | 1.25 | 0.87 | 1.78 | 0.21 |
Fig. 1.
Forest plot of odds ratios for wearable device use (N = 4922).
Factors related to achievement of 150-minute weekly moderate-intensity physical activity
Table 3 and Fig. 2 present the outcomes of logistic regression models examining the factors related to the achievement of a weekly 150-minute moderate-intensity physical activity. Among sociodemographic factors, females (OR 0.71, CI [0.55 – 0.90], P = 0.01) and the age group of over 75 years (OR, 0.63 [95 % CI, 0.42–0.95]; P = 0.03) were less likely to achieve the recommended or higher levels of physical activity. Race and ethnicity were associated with physical activity, with Black (OR, 0.65 [95 % CI, 0.46–0.92]; P = 0.02) and Asian (OR, 0.52 [95 % CI, 0.32–0.84]; P = 0.01) participants being less likely to achieve the recommended level of physical activity compared to non-Hispanic white. The ‘some college’ group (OR, 2.00 [95 % CI, 1.10–3.64]; P = 0.03) and the ‘college degree or higher’ group (OR, 2.04 [95 % CI, 1.14–3.68]; P = 0.02) were significantly associated with achieving the recommended level of physical activity.
Table 3.
Multivariable logistic regression of factors associated with meeting the 150 min weekly physical activity guideline (N = 4922).
| OddsRatio | 95 %C.I. | P-value | ||
|---|---|---|---|---|
|
| ||||
| Sex | ||||
| Male | Ref. | |||
| Female | 0.71 | 0.55 | 0.90 | 0.01 |
| Age group (years) | ||||
| 18–34 | Ref. | |||
| 35–49 | 0.89 | 0.67 | 1.18 | 0.38 |
| 50–64 | 1.08 | 0.77 | 1.52 | 0.63 |
| 65–74 | 1.44 | 1.04 | 1.99 | 0.03 |
| 75+ | 0.63 | 0.42 | 0.95 | 0.03 |
| Race/Ethnicity | ||||
| Non-Hispanic (NH) White | Ref. | |||
| NH Black or African American | 0.65 | 0.46 | 0.92 | 0.02 |
| Hispanic | 0.72 | 0.51 | 1.02 | 0.06 |
| NH Asian | 0.52 | 0.32 | 0.84 | 0.01 |
| NH Other | 0.83 | 0.46 | 1.49 | 0.52 |
| Residence | ||||
| Metropolitan | Ref. | |||
| Micropolitan | 0.69 | 0.45 | 1.06 | 0.08 |
| Small town | 0.91 | 0.53 | 1.54 | 0.70 |
| Marital status | ||||
| Married or living as married | Ref. | |||
| Divorced, widowed, or separated | 1.36 | 1.00 | 1.85 | 0.05 |
| Single | 1.31 | 0.94 | 1.84 | 0.11 |
| Education level | ||||
| Less than high school | Ref. | |||
| High school graduate | 1.55 | 0.92 | 2.62 | 0.10 |
| Some college | 2.00 | 1.10 | 3.64 | 0.03 |
| College degree or higher | 2.04 | 1.14 | 3.68 | 0.02 |
| Income level | ||||
| Less than $20,000 | Ref. | |||
| $20,000 to <$35,000 | 0.72 | 0.44 | 1.18 | 0.18 |
| $35,000 to <$50,000 | 1.08 | 0.62 | 1.88 | 0.79 |
| $50,000 to <$75,000 | 1.05 | 0.59 | 1.86 | 0.87 |
| $75,000 or more | 1.10 | 0.72 | 1.69 | 0.63 |
| Health insurance | ||||
| Insured | Ref. | |||
| Uninsured | 0.88 | 0.51 | 1.52 | 0.62 |
| BMI (kg/m2) | ||||
| Under 30 | Ref. | |||
| 30 and over | 0.54 | 0.42 | 0.69 | < 0.001 |
| Smoking status | ||||
| Never | Ref. | |||
| Current | 0.80 | 0.51 | 1.25 | 0.31 |
| Former | 1.02 | 0.75 | 1.37 | 0.91 |
| Medical condition | ||||
| Diabetes (No=0) | Ref. | |||
| Diabetes (Yes=1) | 0.72 | 0.53 | 0.98 | 0.04 |
| High Blood Pressure (No=0) | Ref. | |||
| High Blood Pressure (Yes=1) | 0.71 | 0.56 | 0.88 | < 0.001 |
| Heart Condition (No=0) | Ref. | |||
| Heart Condition (Yes=1) | 0.97 | 0.70 | 1.34 | 0.86 |
| Lung Disease (No=0) | Ref. | |||
| Lung Disease (Yes=1) | 0.77 | 0.56 | 1.08 | 0.12 |
| Depression (No=0) | Ref. | |||
| Depression (Yes=1) | 0.77 | 0.60 | 0.99 | 0.04 |
| Cancer (No=0) | Ref. | |||
| Cancer (Yes=1) | 0.77 | 0.55 | 1.09 | 0.13 |
| Use of Wearable Devices | ||||
| Non-user (No=0) | Ref. | |||
| User (Yes=1) | 1.84 | 1.45 | 2.33 | < 0.001 |
Fig. 2.
Forest plot of odds ratios for attaining the weekly minimum of 150 min of moderate-intensity physical activity (N = 4922).
In terms of health-related factors, individuals with a BMI 30 kg/m2 and higher (OR, 0.54 [95 % CI, 0.42–0.69]; P < 0.001) and underlying conditions like diabetes (OR, 0.72 [95 % CI, 0.53–0.98]; P = 0.04), high blood pressure (OR, 0.71 [95 % CI, 0.56–0.88]; P < 0.001), and depression (OR, 0.77 [95 % CI, 0.60–0.99]; P = 0.04) were negatively associated with achieving the recommended level of physical activity. Notably, the utilization of wearable devices was positively related to physical activity. Respondents who used wearable devices were 1.84 times more likely to achieve the weekly recommended level of physical activity (OR, 1.84 [95 % CI, 1.45–2.33]; P < 0.001).
The mediation effect of wearable devices on the relationship between sociodemographic factors and physical activity
Among the factors tested, the mediation effect of wearable device use was significant for sex, which demonstrated a positive and significant indirect effect (Est, 0.012 [95 % CI, 0.005 – 0.02]; P = 0.002). Education exhibited a significant positive indirect effect (Est, 0.023 [95 % CI, 0.004 – 0.040]; P = 0.018) on physical activity (Table 4). In addition, having diabetes was related to physical activity indirectly via wearable device use (Est, −0.057 [95 % CI, −0.093 – −0.020]; P = 0.004). These findings indicate that the influence of sex, educational attainment, and diabetes diagnosis on reaching the recommended level of physical activity is mediated by the utilization of wearable devices. However, both direct and indirect effects of age and annual household income were not statistically significant.
Table 4.
Causal mediation analysis assessing direct, indirect, and total effect of independent variables on attainment of a weekly minimum of 150 min of moderate-intensity physical activity via wearable device usage.
| Independent Variable | Effect | Estimate | 95 % CI Lower | 95 % CI Upper | P-value |
|---|---|---|---|---|---|
|
| |||||
| Sex (Female) | ACME | 0.012 | 0.005 | 0.02 | 0.002 |
| ADE | −0.073 | −0.120 | −0.03 | 0.002 | |
| Total Effect | −0.061 | −0.107 | −0.01 | 0.006 | |
| Age | ACME | 0.001 | −0.010 | 0.01 | 0.94 |
| ADE | −0.025 | −0.085 | 0.03 | 0.39 | |
| Total Effect | −0.024 | −0.084 | 0.03 | 0.42 | |
| Education Level | ACME | 0.023 | 0.004 | 0.04 | 0.018 |
| ADE | 0.142 | 0.039 | 0.24 | 0.008 | |
| Total Effect | 0.161 | 0.058 | 0.26 | 0.004 | |
| Income Level | ACME | 0.009 | −0.002 | 0.02 | 0.1 |
| ADE | −0.063 | −0.151 | 0.03 | 0.21 | |
| Total Effect | −0.054 | −0.145 | 0.04 | 0.28 | |
| Diabetes (yes) | ACME | −0.003 | −0.01 | 0.00 | 0.08 |
| ADE | −0.06 | −0.09 | −0.02 | 0.004 | |
| Total Effect | −0.06 | −0.10 | −0.03 | 0.002 | |
| Smoking (Current) | ACME | −0.01 | −0.02 | −0.01 | <0.001 |
| ADE | −0.03 | −0.07 | 0.02 | 0.20 | |
| Total Effect | −0.04 | −0.09 | 0.00 | 0.07 | |
ACME average causal mediation effect, ADE average direct effect, ATE average total effect.
Predictors of physical activity among wearable device users
In a multivariable logistic regression analysis among wearable device users (n = 1718), we examined factors associated with physical activity (Table 5, Fig. 3). The age group of 65 to 74 years (OR, 1.96 [95 % CI, 1.04–3.69]; P = 0.04) was more likely to achieve 150 min of physical activity per week. Asians were negatively associated with physical activity, suggesting Asian wearable users were less likely to achieve physical activity goals compared to other racial and ethnic group wearable users (OR, 0.55 [95 % CI, 0.31–0.96]; P = 0.04). Regarding health-related factors, individuals with a BMI ≥ 30 kg/m2 (OR, 0.54 [95 % CI, 0.36–0.79]; P < 0.001) and those with high blood pressure (OR, 0.62 [95 % CI, 0.41–0.94]; P = 0.03) were negatively and associated with physical activity.
Table 5.
Multivariable logistic regression of factors associated with meeting the 150 min weekly physical activity guideline among wearable device users (N = 1718).
| OddsRatio | 95 %C.I. | P-value | ||
|---|---|---|---|---|
|
| ||||
| Sex | ||||
| Male | Ref. | |||
| Female | 0.71 | 0.49 | 1.03 | 0.07 |
| Age group | ||||
| 18–34 | Ref. | |||
| 35–49 | 0.84 | 0.54 | 1.31 | 0.41 |
| 50–64 | 1.29 | 0.81 | 2.07 | 0.26 |
| 65–74 | 1.96 | 1.04 | 3.69 | 0.04 |
| 75+ | 1.13 | 0.49 | 2.61 | 0.76 |
| Race/Ethnicity | ||||
| Non-Hispanic (NH) White | Ref. | |||
| NH Black or African American | 0.65 | 0.36 | 1.18 | 0.15 |
| Hispanic | 0.64 | 0.34 | 1.23 | 0.17 |
| NH Asian | 0.55 | 0.31 | 0.96 | 0.04 |
| NH Other | 1.60 | 0.56 | 4.59 | 0.37 |
| Residence | ||||
| Metropolitan | Ref. | |||
| Micropolitan | 0.42 | 0.17 | 1.00 | 0.05 |
| Small town | 0.64 | 0.19 | 2.11 | 0.44 |
| Marital status | ||||
| Married or living as married | Ref. | |||
| Divorced, widowed, or separated | 0.92 | 0.55 | 1.55 | 0.75 |
| Single | 1.84 | 0.97 | 3.49 | 0.06 |
| Education level | ||||
| Less than high school | Ref. | |||
| High school graduate | 0.92 | 0.22 | 3.89 | 0.90 |
| Some college | 1.05 | 0.26 | 4.19 | 0.94 |
| College degree or higher | 0.87 | 0.22 | 3.40 | 0.83 |
| Income level | ||||
| <20,000 | Ref. | |||
| 20,000 to <35,000 | 1.25 | 0.42 | 3.70 | 0.67 |
| 35,000 to <50,000 | 1.98 | 0.75 | 5.21 | 0.16 |
| 50,000 to <75,000 | 2.04 | 0.70 | 5.94 | 0.18 |
| 75,000 or more | 1.98 | 0.69 | 5.71 | 0.19 |
| Health insurance | ||||
| Insured | Ref. | |||
| Uninsured | 1.03 | 0.43 | 2.46 | 0.94 |
| BMI | ||||
| Under 30 | Ref. | |||
| 30 and over | 0.54 | 0.36 | 0.79 | < 0.001 |
| Smoking status | ||||
| Never | Ref. | |||
| Current | 1.02 | 0.53 | 1.99 | 0.94 |
| Former | 1.06 | 0.67 | 1.66 | 0.80 |
| Medical condition | ||||
| Diabetes (No=0) | Ref. | |||
| Diabetes (Yes=1) | 0.79 | 0.45 | 1.39 | 0.39 |
| High Blood Pressure (No=0) | Ref. | |||
| High Blood Pressure (Yes=1) | 0.62 | 0.41 | 0.94 | 0.03 |
| Heart Condition (No=0) | Ref. | |||
| Heart Condition (Yes=1) | 1.07 | 0.46 | 2.48 | 0.86 |
| Lung Disease (No=0) | Ref. | |||
| Lung Disease (Yes=1) | 0.74 | 0.47 | 1.17 | 0.18 |
| Depression (No=0) | Ref. | |||
| Depression (Yes=1) | 0.66 | 0.43 | 1.02 | 0.06 |
| Cancer (No=0) | Ref. | |||
| Cancer (Yes=1) | 0.76 | 0.42 | 1.38 | 0.35 |
Fig. 3.
Forest plot of odds ratios for attaining the weekly minimum of 150 min of moderate-intensity physical activity among wearable device users.
Discussion
In this national survey, over one-third of US adults (34.9 %) used wearable devices, while 37.1 % achieved the recommended level of 150 min of weekly moderate-intensity physical activity. Sociodemographic patterns in wearable device usage were observed, with higher usage among females, younger age groups, and individuals with high education and household incomes. Wearable device users were 1.84 times more likely to meet the recommended activity level. A significant indirect effect of sex (female) and high education levels on achieving the recommended level of physical activity mediated by wearable device use was confirmed. Additionally, sociodemographic patterns were observed among wearable device users, with Asians and individuals with a BMI ≥ 30 kg/m2 less likely to achieve the recommended level of physical activity. These findings align with previous research demonstrating sociodemographic disparities in wearable device adoption. Prior studies have consistently shown that higher socioeconomic status, younger age, and female gender are associated with a greater likelihood of using wearable health technology compared to their counterparts (24, 25). Our findings further support this trend, reinforcing concerns that digital health engagement is unevenly distributed across population subgroups.
Findings of this study provide valuable insights into achieving widespread digital health engagement and enhancing physical activity in population health. First, this study’s results indicate the potential of wearable devices to exacerbate health disparities in physical activity. Sociodemographic patterns in wearable device use identify the first level of the digital divide in adopting wearable devices, termed the “access gap.” Interestingly, the sociodemographic pattern of younger age groups and those with high socioeconomic status were more likely to use wearable devices, echoing early studies on the digital divide in the late 1990s that highlighted connections between race, sex, education level, and income in internet accessibility (34). This phenomenon signifies an ongoing gap in digital technology use, evident in both internet usage then and the current utilization of digital health technologies. Given the rapid evolution of new digital technologies (12,35), there is an urgent call for mitigating digital inequalities.
Second, on a more optimistic note, the study finding suggests that digital health technologies can also have the potential to narrowing health disparities gaps in physical activity. The study results imply the risk of not achieving recommended physical activity is significantly reduced among individuals with a college degree or higher who use wearable devices compared to those who do not use them, in contrast to individuals with the lowest educational level. Moreover, in the absence of wearable device utilization among females, there is a notable escalation in the risk of inadequate physical activity. Therefore, the implementation of public health policies or strategies aimed at ensuring an equitable usage of these technologies could be considered as part of the public health solution to enhance physical activity on a population-wide scale.
Third, vulnerable population of not meeting recommended level of physical activity were identified. While there are well-known benefits physical activity, a significant portion of comprising three-quarters, does not meet this goal. Even the individuals with health conditions who would benefit from regular physical activity, were less likely to attain the recommended level of physical activity. Given the positive effects of physical activity on health, it is crucial to continue providing targeted interventions and programs to engage in more physical activity. These groups could be prioritized for promoting the use of wearable devices to enhance physical activity. Utilizing wearable devices, personalized interventions can be created by setting achievable physical activity goals based on patients’ age, physical abilities, and current level of physical activity (36). Moreover, healthcare provider can use this information to encourage physical activity to their patients (37,38).
Lastly, the study highlights the significance of enhancing the public’s ability to utilize it, with the aim of optimizing wearable devices to contribute meaningfully to real-world outcomes and consequences. This aim goes beyond mere adoption and increased accessibility of health digital technology. Variations in physical activity outcomes among wearable device users become apparent. Extant research on the digital divide consistently underscores the role of social determinants of health in skills related to the use of new digital technologies. Social gradient in digital utilization level lead to network building and strengthening (39) and aspects of daily life (40). To address the gap, insights from studies on the digital divide propose strategies for improving digital literacy, internet skills, and efficacy. The field of public health can glean valuable lessons from these studies to shape its approach. However, further exploration of the digital divide in the health context is not only crucial but also essential.
These findings have important practical implications for public health and technology-driven health interventions. To maximize the public health impact of wearable devices, efforts should focus on expanding accessibility by promoting affordability and equitable distribution to underserved populations. Enhancing digital literacy programs is essential to ensure individuals can effectively use wearable devices and interpret feedback to guide behavior change. Integrating wearable device data into clinical and public health strategies can further support physical activity monitoring in real-world settings. Additionally, developing tailored interventions that address individual barriers to engagement, such as motivation, self-monitoring preferences, and behavioral reinforcement, can improve user adherence and effectiveness. By addressing these research and policy gaps, future studies and public health initiatives can better leverage wearable technology to promote equitable physical activity engagement and reduce health disparities. These insights align with the broader goals of health policy and technology in optimizing digital health interventions for population health.
Limitations and future research directions
Several limitations should be considered when interpreting these findings. First, the cross-sectional nature of HINTS prevents causal inferences regarding the relationships between social determinants of health, wearable device use, and physical activity. Second, the survey item assessing wearable device use did not differentiate between device types, features, or usage patterns, such as duration and frequency of use, which may influence physical activity levels. Third, due to the use of secondary survey dataset, we may have not included factors influencing wearable device adoption, such as digital literacy, attitudes toward technology, or psychological motivators (41). Additionally, self-reported data are subject to recall bias and misclassification in reporting wearable device use and physical activity behaviors. Fourth, while the response rate of 30 % is comparable to similar national surveys, nonresponse bias may still exist despite survey weighting (42). Finally, physical activity measures were based on self-reports, making them prone to social desirability bias. Future studies should incorporate objective measures, such as wearable device data or accelerometers, to address limitation of subjective measures (43).
Future research using longitudinal designs and objective measures of physical activity will enhance understanding of the meditating effects of wearable devices and related pathways (17,18,20,20). Investigating into device-specific features and engagement patterns could provide deeper insights into their role in behavior change mechanism. Also, exploring behavioral and psychological factors, such as digital literacy and motivation, could help clarify disparities in wearable device adoption and effectiveness. Examining targeted interventions to improve access and engagement, particularly among underserved populations, will be essential for promoting equitable digital health utilization.
Conclusion
This study examined physical activity levels and differences in wearable devices use in the US adults, addressing the potential contributing factors to health inequalities that need to be mitigated through the integration of digital health technologies. A thorough investigation has the potential to shed light on the longstanding question of whether digital health technologies, such as wearable devices, can either mitigate or exacerbate health disparities. Implementing public health interventions and developing relevant health policy and strategies that ensure an equitable use of health technologies are critical in enhancing physical activity and improving population health.
Equal distribution of digital health technology is crucial, and interventions should also prioritize enhancing the public’s utilization of digital technology. There is a pressing need to enhance digital literacy and digital technology skills in promoting digital health equity. Marginalized groups in digital technology utilization should be considered as priority of this intervention. In conclusion, future efforts should include approach advancing our understanding of roles of digital technology in health and equity promoting health policies and practice to improve population health and well-being.
Acknowledgements
This research is supported by National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (R01DK132069).
Role of the funder/sponsor
The funding entities had no role in the design and conduct of the study or in the collection, management, analysis, and interpretation of the data.
Footnotes
Declaration of competing interest
There are no conflicts of interest to disclose.
Statements of ethical approval
The HINTS protocol has been reviewed by the Westat Institutional Review Board and was exempted from review and informed consent by the US National Institutes of Health Office of Human Subjects Research Protections.
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