Social Factors, Wearable Activity Tracker Use Frequency, and Physical Activity Patterns Among U.S. Older Adults: Findings from a National Cross-Sectional Survey

Social Factors, Wearable Activity Tracker Use Frequency, and Physical Activity Patterns Among U.S. Older Adults: Findings from a National Cross-Sectional Survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Social Factors, Wearable Activity Tracker Use Frequency, and Physical Activity Patterns Among U.S. Older Adults: Findings from a National Cross-Sectional Survey Mengchi Li, Chakra Budhathoki, Hae-Ra Han, Sarah L. Szanton, Jennifer A. Schrack, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8217228/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2026 Read the published version in BMC Geriatrics → Version 1 posted 13 You are reading this latest preprint version Abstract Background Few research have investigated wearable activity tracker (WAT) use frequency and physical activity (PA) patterns. The present study aims to describe PA patterns (moderate-vigorous physical activity (MVPA) and sedentary behavior) and to examine the association of social factors and WAT use frequency with PA patterns among U.S. older adults. Methods We used cross-sectional data from 3,370 older adults from the Health Information National Trends Survey. Linear regression models and multinomial logistic regression models were used to identify associations among social factors, WAT use, and PA patterns. Baron and Kenney’s approach was used to examine the mediation effect of wearable activity tracker use on socioeconomic status and activity. Results Participants were 53.9% female and aged 73.9 years (SD = 7.1) on average. Over half of the participants reported less than 150 minutes of weekly MVPA time (63.7%) and over 6 hours of daily sedentary time (60.1%). Older adults who were female and aged 75 and older reported significantly less weekly MVPA time compared to male and 65-to-74 years old. Older adults with high annual household income, compared with those with low income reported longer MVPA time. Frequent use of WATs was significantly associated with longer weekly MVPA time. WAT use mediated the associations between income and weekly MVPA time. Conclusion Social disparities existed in PA patterns, but WAT use was found to partially mediate the associations between income and MVPA time. There is still an urgent need to promote PA patterns especially in socially and economically disadvantaged older adults. Social Factor Wearable Activity Tracker Older Adult Physical Activity Social Determinants of Health Figures Figure 1 Introduction Physical activity (PA) is defined as any bodily movement requiring energy expenditure (World Health Organization., 2010), and sedentary behavior is characterized by low-energy activities such as sitting (Leitzmann et al., 2023 ). To achieve better health outcomes, older adults should engage in moderate-to-vigorous-intensity physical activity (MVPA) for at least 150 minutes per week and reduce sedentary behavior (CDC, 2024a ; World Health Organization., 2010). Although there is a lack of a standard cut-off point for sedentary behavior, 6 hours or more of sitting time has been considered as high sedentary behavior (Heron et al., 2019 ). Unfortunately, the prevalence of inactivity significantly increases with age: an estimation of 26.9% of adults aged 65–74 years and 35.3% of adults aged 75 years and above were inactive (Watson et al., 2016 ). Physical inactivity is strongly linked to mortality and morbidity such as diabetes, cardiovascular disease, cancer, obesity, mental illness, and dementia, threatening the health of the aging population (Booth et al., 2017 ). Physical inactivity in the U.S. population results in approximately $ 117 billion in annual health care costs and about 10 percent of premature mortality (“CDC,” 2024b). There is an urgent need to improve PA among older adults to promote health, prevent chronic illnesses, and reduce health care costs in this population. Studies show that older adults’ PA patterns may be influenced by multiple social factors. For example, older adults who are female, older, or have less education have been found to have lower PA levels on average(Lim & Taylor, 2005 ). Evidence on social factors associated with sedentary behavior in older adults have been mixed. Although some studies found that those who are males, older, with lower education (less than college) are more likely to engage in sedentary behavior in adults and older adults (Da Silva et al., 2020 ; Patterson et al., 2018 ), other evidence suggested no associations with sex, age, education, income and sedentary behavior in older adults (Heseltine et al., 2015 ). Access to innovative technologies such as wearable activity trackers (WATs)—consumer devices that provide feedback to the wearer such as fitness trackers, activity-tracking smartwatches, and pedometers (Tedesco et al., 2017 )—has emerged as an additional determinant of PA among older adults. Although previous evidence suggested that most U.S. older adults have never used a WAT (Xie et al., 2020 ), it has been reported that older adults show general interest and acceptance of using a WAT device to monitor their PA levels (Kononova et al., 2019 ; Li, McPhillips, et al., 2024 ). WATs have demonstrated promising potential for PA enhancement in both observational and experimental studies with the aging population (Zhang et al., 2020 , 2022 ). Given the significant health and economic burden, it is important to confirm risk factors for physical inactivity and sedentary behavior among older adults. To the authors’ best knowledge, no study has investigated social factors associated with older adults’ activity class, simultaneously considering both MVPA and sedentary time. Additionally, limited research has focused on older adults’ WAT use frequency and whether different WAT use frequencies are associated with PA patterns (Chandrasekaran et al., 2020 ). WATs, which often require financial investment and access to compatible technology, may be less accessible to older adults with lower socioeconomic status (SES) (Tedesco et al., 2017 ). Based on the current literature, WATs could potentially mediate the associations between SES and PA patterns in older adults. Understanding the interplay between social factors, WAT use, and PA patterns can inform targeted interventions to reduce health disparities and improve health outcomes among older adults. The purpose of this research study is to understand the associations among social factors, WAT use, and PA patterns among U.S. older adults. The conceptual framework for the current study has been developed based on available evidence and adapted from the Fundamental Cause Theory (Link & Phelan, 1995). The Fundamental Cause Theory posits that SES can influence an individuals’ access to certain resources that may impact their health outcomes. In this study, WAT use was considered as an important technological resource that can influence health outcomes such as PA patterns. We hypothesized that social factors, including sex, age, income, and education are associated with WAT use (Li, Huang, et al., 2024 ) and WAT use is associated with PA (Tang et al., 2020). In addition, social factors are also associated with older adults’ PA (Gidlow et al., 2006 ). Aim 1: Examine social factors (e.g., sex, age, race and ethnicity, education, income) associated with older adults’ PA patterns (MVPA time- minutes per week, sedentary time -hours per day, and activity class). Aim 2: Examine the association between WAT use frequency and older adults’ physical activity. Hypothesis : Older adults who frequently used WATs have higher MVPA time and lower sedentary time. Aim 3: Examine whether frequent use of WATs mediates the associations between socioeconomic status (income and education) and older adults’ PA patterns. Hypothesis : WAT use positively mediates the association between socioeconomic status (income and education) and older adults’ PA: Higher income and education are associated with higher odds of WAT use frequency, which is further associated with longer MVPA time and shorter sedentary time. Methods The HINTS Study Design and Recruitment This was a cross-sectional secondary data analysis using the Health Information National Trends Survey (HINTS) dataset with data collected from January to April 2019 and February to June 2020. Launched by the National Institutes of Health (National Cancer Institute) in 2003, HINTS regularly collects data about the American public’s knowledge of, attitudes toward, and use of cancer-related and other health-related information. To recruit participants, HINTS sent postal mail to random samples of non-vacant U.S. residential addresses for both the 2019 and 2020 cohorts. More details about study methods of the HINTS study are available through HINTS briefs and reports (Westat, 2019 ; Westat, 2020 ). The present study included 3,370 participants aged 65 and above from both cycles 3 and 4. All data collected in HINTS are self-reported on paper and sent back by mail. Measures Social Factors included sex, age, race/ethnicity, annual household income, and education. Participants’ sex included “Male” and “Female.” Based on previous literature and data distribution, the age variable was dichotomized into 65–74 years and 75 years and above (Lee, Oh, Park, Choi, & Wee, 2018). Annual household income was categorized into (1) low income (less than $ 35,000), (2) intermediate income ( $ 35,000- $ 75,000), and (3) high income ( $ 75,000 or more) (Xie, Jo, & Hong, 2020 ). Education was categorized into (1) high school or less, (2) some college, and (3) college degree or higher. Race and ethnicity were categorized into three categories: (1) White, (2) Black, and (3) Hispanic, non-Hispanic Asians, and others. WAT Use The participants’ WAT use and the frequency of use were assessed with two items: (1) “In the last 12 months, have you used a Wearable Activity Tracker to monitor or track your health or activity? For example, a Fitbit, Apple Watch, or Garmin Vivofit.”; and (2) “In the past month, how often did you use a wearable device to track your health?” For those who answered “Yes” in the previous question, options included “Every day,” “Almost every day,” “1–2 times per week,” “Less than once per week,” or “I did not use a wearable device in the past month.” Based on these questions, we developed WAT Use into a categorial variable: (1) Frequent WAT use: those who reported using a WAT “Every day” or “Almost every day” in the past month; (2) Infrequent use: those who reported using a WAT “1–2 times per week,” “Less than once per week,” or “I did not use a wearable device in the past month (but used one in the past year)”; and (3) No use: respondents who did not use a WAT in the past month and those who did not use a WAT over the past 12 months. Physical Activity Patterns (PA Patterns) While MVPA and sedentary behavior represent distinct constructs, they are not mutually exclusive: one can reach guideline recommended activity levels and have a sedentary lifestyle at the same time (Thivel et al., 2018 ). For the purpose of this analysis, PA pattern was created as a 4-category variable considering both MVPA and sedentary time. First, Weekly MVPA time was assessed using two items: (1) “In a typical week, how many days do you do any physical activity or exercise of at least moderate intensity?”; and (2) “On the days that you do any physical activity or exercise of at least moderate intensity, how long do you typically do these activities?” Choices for the first item included: “None”, “One day per week”, “Two days per week” … “Seven days per week”. As for the second item, The respondents were asked to answer the number of minutes per day. Based on these 2 items, the HINTS dataset provided a variable named “Minutes per week of at least moderate-intensity exercise.” Consistent with previous literature (Xie, Jo, and Hong 2020 ), this study also used this variable (Minutes per week of at least moderate-intensity exercise) as a measure of PA. Second, daily sedentary time was assessed using one item: “During the past 7 days, how much time did you spend sitting on a typical day at home or at work?” The respondents were asked to answer with the number of hours per day. Based on current PA guidelines and literature, a weekly MVPA time under 150 minutes and a daily sedentary time more than 6 hours were used as cutoffs for low MVPA time and high sedentary time (DHS, 2018). Four physical activity classes were then created: (1) high MVPA low sedentary, (2) high MVPA high sedentary, (3) low MVPA low sedentary, and (4) low MVPA high sedentary groups. Covariates : Body Mass Index (BMI) was calculated using the respondents’ self-reported heights and weights and was dichotomized into (1) non-obesity: under 30, and (2) obesity: 30 and above. Smoking status was a variable derived from two questions on past smoking experience and current smoking frequency and was categorized into current, former, and never smoker. The marital status question was included in the questionnaire and dichotomized into two categories: (1) married or living with a partner, and (2) divorced, widowed, separated, or never married. Comorbidity was measured by the sum of reported medical conditions reported by respondents and categorized into: (1) one or no comorbidity and (2) multiple comorbidities. Mental health( depression and anxiety) was assessed by the 4-item Patient Health Questionnaire-4 (PHQ-4) is an ultra-brief self-report validated questionnaire that consists of a 2-item depression subscale (PHQ-2) and a 2-item anxiety subscale (GAD-2)(Löwe et al., 2009 ). In each subscale, scores range from 0 to 6, with a score of 3 or greater indicating positive for depression or anxiety. Statistical Analysis: Following the recommended methods, we first merged data from HINTS 5 cycle 3 and cycle 4, then accounted for the complex survey design using survey weights and the “delete one jackknife replication method” that deletes one sampling unit at a time from the full sample to create a set of 50 replicate weights(Westat, 2019 ; Westat, 2020 ). The reported percentages are weighted and the sample sizes are unweighted. Analyses were conducted using Stata (version 17.0; StataCorp). We first used descriptive analyses including frequency and percentages for categorical variables and means and standard deviations for continuous variables to summarize sample characteristics. We also used Pearson’s Chi-square to compare sample characteristics by PA patterns (weekly MVPA time, daily sedentary time, and activity class). We then conducted multivariate linear regression analyses to identify social factors associated with older adults’ PA patterns. A test with a p-value of less than 0.05 was considered statistically significant. Multinomial logistic regression models were built to examine social factors associated with the PA classes, with the low MVPA high sedentary group being the reference group. WAT use frequency associated with weekly MVPA time and daily sedentary time were examined using simple linear regression models adjusting for covariates. WAT use frequency associated with activity classes was assessed with a multinomial logistic regression model adjusting for covariates. Finally, mediation analyses were performed to illustrate the association of SES (income and education) with physical activity levels (weekly MVPA minutes and daily sedentary hours) mediated by the use of WATs (Fig. 1 ). Baron and Kenny’s approach to test mediation (MEDSEM procedure in Stata version 17) was used to estimate the total effects, indirect effects (IE), and direct effects (DE) of SES on physical activity levels. Two models were estimated: a multivariate logistic regression model for WATs use (mediator) conditional on social factors (exposure), and all study confounders and a multivariate linear regression model for physical activity levels (outcome) conditional on social factors. The DE represented the effect of social factors on physical activity levels that were independent of WAT use. An IE represented the proportion of social factors that could be explained by its association with WATs use. Sobel Test was used to test the significance of IE. To quantify the magnitude of mediation, the study estimated the proportion of the association mediated by the use of WATs (IE/[DE + IE]). Human Ethics and Consent to Participate The HINTS 5 general population survey received expedited approval from the Westat Institutional Review Board (IRB) on March 28, 2016 (Project #6048.14). Furthermore, the NIH Office of Human Subjects Research issued a“Not Human Subjects Research”determination from the NIH Office on April 25, 2016 (Exempt #13204). Therefore, informed consent was not required for the primary data collection process. Ethical Considerations The analysis utilizing HINTS 5 data was classified as non-human subjects research by the Johns Hopkins University School of Medicine IRB. As the study involved secondary analysis of existing, de-identified, and publicly available datasets, it was exempt from further ethical review and approval. Clinical trial number Not applicable. Results Sample characteristics by MVPA time, sedentary time, and activity classes. Sample characteristics were described in total and by MVPA time, sedentary time, and activity classes (Table 1 ). The current study included a total of 3370 participants, with 53.9% female, average age 73.9 years (SD = 7.1), 21.9% had a college degree or higher, and most were Non-Hispanic White (78.0%). Over half of the participants exercised less than 150 minutes per week (63.7%) and sat for more than 6 hours per day (60.1%). The percentage of participants classified as the “Low MVPA and High Sedentary” class was 32.1%. Table 1 Weighted Sociodemographic and Clinical Characteristics by PA Patterns Total Weekly MVPA Time Daily Sedentary Time Activity Classes N = 3370 < 150min/wk ≥ 150 min/wk p-value < 6 hrs/day ≥ 6 hrs/day p-value Low MVPA High Sedentary Low MVPA Low Sedentary High MVPA High Sedentary High MVPA Low Sedentary p-value n = 2,142 (63.7%) n = 1,228 (36.3%) n = 1,330 (39.9%) n = 2040 (60.1%) n = 1,086 (32.1%) n = 1056 (31.6%) n = 476 (13.9%) n = 752 (22.4%) Sex Male 1,374 (46.1%) 807 (58.8%) 567(41.2%) < 0.001 515 (38.9%) 859 (61.1%) 0.341 411 (31.0%) 396 (27.7%) 229 (15.9%) 338 (25.4%) 0.003 Female 1,675 (53.9%) 1,142 (67.9%) 533 (32.1%) 683 (41.5%) 992 (58.5%) 569 (32.4%) 573 (35.5%) 194 (11.9%) 339 (20.2%) Age (years) 73.9 +- 1.7 75&above 1,330 (41.6%) 912 (68.5%) 418 (31.5%) 0.003 491 (37.4%) 839 (62.6%) 0.116 473 (34.0%) 439 (34.5%) 180 (13.9%) 238 (17.6%) 0.003 65–74 2,040 (58.4%) 1,230 (60.4%) 810 (39.7%) 839 (41.7%) 1,201 (58.3%) 613 (30.8%) 617 (29.5%) 296 (13.9%) 514 (25.8%) Education High school degree and lower 1,024 (38.7%) 735 (70.1%) 289 (29.9%) < 0.001 414 (40.0%) 610 (60.0%) 0.626 368 (35.8%) 367 (34.2%) 123 (12.3%) 166 (17.7%) < 0.001 Some college 1,036 (39.4%) 682 (62.9%) 354 (37.1%) 387 (39.0%) 649 (61.0%) 361 (32.4%) 321 (30.5%) 130 (13.6%) 224 (23.5%) College degree or higher 1,237 (21.9%) 683 (54.3%) 554(45.7%) 503 (42.1%) 734 (57.9%) 332 (24.7%) 351 (29.6%) 204 (15.8%) 350 (29.9%) Race and Ethnicity Non-Hispanic White 2,044 (78.0%) 1,280 (63.0%) 764 (37.0%) 0.135 760 (38.8%) 1,284(61.2%) 0.005 672 (33.4%) 608 (29.9%) 285 (13.2%) 479 (23.8%) < 0.001 Non-Hispanic Black 358 (9.2%) 255 (72.5%) 103 (27.5%) 149 (39.6%) 209 (60.4%) 121 (32.9%) 134 (39.7%) 43 (11.1%) 60 (16.3%) Hispanics, Asians, and others 486 (12.8%) 307 (64.4%) 179 (35.6%) 236 (50.8%) 250 (49.2%) 119 (20.6%) 188 (43.9%) 63 (13.9%) 116 (24.7%) Annual Household Income Less than 35k 1,150 (36.5%) 831(71.8%) 319 (28.2%) < 0.001 411 (33.7%) 739 (66.3%) 0.002 448 (38.1%) 383 (33.7%) 145 (14.2%) 174 (14.0%) < 0.001 35k to less than 75k 964 (36.5%) 601 (63.4%) 363 (36.6%) 389 (41.4%) 575 (58.7%) 283 (30.7%) 318 (32.6%) 132 (11.8%) 231 (24.9%) 75K or more 765 (27.0%) 396 (51.7%) 366 (48.3%) 319 (46.1%) 443 (53.9%) 184 (22.7%) 212 (29.0%) 130 (16.2%) 236 (32.1%) Smoking Status Current Smoker 285 ( 8.6%) 203 (72.4%) 82 (27.6%) 0.031 110 (38.4%) 175 (61.6%) 0.012 101 (35.4%) 102 (37.0%) 34 (11.3%) 48 (16.3%) 0.030 Former Smoker 1,189 (37.2%) 775 (65.6%) 414 (34.4%) 420 (35.9%) 769 (64.1%) 430 (36.8%) 345 (28.8%) 148 (12.4%) 266 (22.0%) Never Smoker 1,835 (54.2%) 1,133 (61.6%) 702 (38.4%) 792 (43.7%) 1043 (56.3%) 535 (28.5%) 598 (33.1%) 267 (14.3%) 435 (24.2%) Body Mass Index (BMI) BMI under 30 2,345 (70.8%) 1,397 (60.3%) 948 (39.7%) < 0.001 989 (43.4%) 1,356 (56.6%) < 0.001 679 (28.9%) 718 (31.5%) 339 (14.3%) 609 (25.4%) < 0.001 BMI 30 or higher 1,025 (29.2%) 745 (72.0%) 280 (28.0%) 341 (31.5%) 684 (68.5%) 407 (40.1%) 338 (31.9%) 137 (12.9%) 143 (15.1%) Marital Status Married or living with a partner 1,559 (58.3%) 916 (60.9%) 643 (39.1%) 0.003 655 (41.9%) 904 (58.1%) 0.081 425 (29.4%) 491 (31.5%) 229 (13.7%) 414 (25.4%) 0.003 Divorced, Widowed, Separated, or never married 1,744 (41.7%) 1,191 (68.4%) 553 (31.6%) 649 (37.3%) 1095 (62.7%) 641 (36.0%) 550 (32.3%) 228 (13.1%) 325 (18.6%) Comorbidity One or No Comorbidity 1,777 (56.1%) 1,044 (57.8%) 733 (42.2%) < 0.001 781 (45.0%) 996 (55.0%) < 0.001 482 (26.7%) 562 (31.1%) 243 (13.5%) 490 (28.8%) < 0.001 Multiple Comorbidity 1,423 (43.9%) 988 (71.4%) 435 (28.6%) 480 (33.0%) 943 (67.0%) 550 (40.0%) 438 (31.5%) 206 (13.8%) 229 (14.8%) Mental health Depression & Anxiety No or Mild 3008 1876 (62.3%) 1132 (37.7%) 0.002 1224 (41.2%) 1784 (58.9%) 0.010 925 (31.2%) 951 (31.1%) 427 (14.0%) 705 (23.6%) 0.019 Moderate or Severe 362 266 (75.2%) 96 (24.8%) 106 (29.8%) 256 (70.2%) 161 (39.8%) 105 (35.5%) 49 (12.6%) 47 (12.2%) Weekly MVPA minutes mean 152.3; sd: 269.0. Daily sedentary hours mean:6.5; sd: 3.6. Row percentages were shown in this table. Social factors associated with MVPA time, sedentary time, and activity classes (Table 2 ) Table 2 Associations between Social Factors and Physical Activity Patterns Social Factors Weekly MVPA Minutes n = 2430 Daily Sedentary Hours n = 2218 High MVPA time Low sedentary time High MVPA time High sedentary time Low MVPA time Low sedentary time Coef 95% CI Coef 95% CI AOR 95% CI AOR 95% CI AOR 95% CI Sex Male (ref) Female -55.79* -101.05, -10.52 -0.43 -0.90, 0.04 0.85 0.57, 1.26 0.78 0.48, 1.28 1.22 0.86, 1.73 Age Group 65–74 (ref) 75 and older -51.47* -98.90, -4.04 0.12 -0.35, 0.59 0.69 0.46, 1.02 0.81 0.51, 1.30 1.15 0.82, 1.61 Race/Ethnicity Non-Hispanic White (ref) Non-Hispanic Black -31.20 -6.81, 68.47 -0.63* -1.18, -0.08 0.89 0.44, 1.80 0.96 0.44, 2.11 1.18 0.72, 1.93 Hispanic, Asian, and Others 23.23 3.98, 117.82 -0.72* -1.31, -0.13 1.27 0.79, 2.03 1.35 0.72, 2.56 2.06**8 1.35, 3.13 Annual Household Income (US Dollars) Low: 75,000 60.90* 3.98, 117.81 -0.42 -1.13, 0.28 2.16** 1.26, 3.68 1.68 0.85, 3.31 1.26 0.78, 2.01 Education High school or less (ref) Some college 21.60 -19.52, 62.72 0.33 -0.19, 0.86 1.23 0.76, 1.97 1.19 0.71, 1.97 1.03 0.76, 1.40 College or higher 14.37 -38.87, 67.60 0.14 -0.37, 0.64 1.65* 1.08, 2.53 1.71* 1.05, 2.78 1.29 0.92, 1.81 AOR: Adjusted Odds Ratio. Reference group: Low MVPA time & High sedentary time. * 0.01 < p < 0.05; ** 0.001 < p < 0.01; *** p < = 0.001 This table presents weighted associations between Social Factors and Physical Activity (MVPA time, Sedentary time, and Activity classes) All results were adjusted for sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, bmi, comorbidity, and mental health condition. Social factors associated with MVPA time. Table 2 shows factors associated with MVPA time and sedentary time using multivariate linear regression. Older adults who were female (b=-55.79, 95% Confidence Interval [CI]: -101.5, -10.52), aged 75 years and above (b=-51.47, 95% CI:-98.90, -4.04) had significantly less weekly MVPA time compared to their counterparts. Older adults who had an annual household income of over 75000 US Dollars (b = 60.90, 95% CI: 3.98, 117.81) had significantly more weekly MVPA time compared to those with lower annual household income. Social factors associated with sedentary time. Non-Hispanic Black older adults had significantly fewer daily sedentary time (hours) than Non-Hispanic Whites (Coefficient: -0.63, 95% CI: -1.18, -0.08, p = 0.026). Similarly, Hispanic, Asian, and other ethnic groups exhibited significantly lower sedentary hours (Coefficient: -0.72, 95% CI: -1.31, -0.13, p = 0.017). Social factors associated with activity class. Older adults with higher annual household income was significantly associated with increased odds of falling into the“High MVPA time & Low sedentary time”activity class, with middle-income (AOR: 1.77, 95% CI: 1.17, 2.66, p = 0.007) and high-income groups (AOR: 2.16, 95% CI: 1.26, 3.68, p = 0.005) showing higher odds of falling in this class compared with the reference class (“Low MVPA time & High sedentary time”). Individuals with college or higher education had higher odds of falling into the “High MVPA time & Low sedentary time” (AOR: 1.65, 95% CI: 1.08, 2.53, p = 0.022) and “High MVPA time & High sedentary time” (AOR: 1.71, 95% CI: 1.05, 2.78, p = 0.033) classes. Compared with the reference class, older adults who were Hispanic, Asian, and other ethnic groups had significantly higher odds of falling into the class “Low MVPA time & Low sedentary time” compared to Non-Hispanic Whites (AOR: 2.06, 95% CI: 1.35, 3.13, p = 0.001). Frequent WAT Use was associated with PA Patterns Table 3 shows the associations between WAT use frequency and older adults’ physical activity adjusting for covariates (sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, BMI, comorbidity, and mental health condition). We found that older adults who frequently used WATs (i.e., using a WAT “Every day” or “Almost every day” in the past month) had significantly longer weekly MVPA times (b = 58.54, 95%CI: 4.73, 112.34), and had higher odds of falling in the activity class of “High MVPA time & Low sedentary time” (AOR = 1.92, 95%CI: 1.10, 3.34) compared to the reference group. Table 3 Weighted Adjusted Associations between WAT Use Pattern and Physical Activity Patterns WAT Use Weekly MVPA Time (Minutes) Daily Sedentary Time (Hours) Activity Classes High MVPA time Low sedentary time High MVPA time High sedentary time Low MVPA time Low sedentary time Coefficient (95% CI) p-value Coefficient (95% CI) p-value AOR (95% CI) p-value AOR (95% CI) p-value AOR (95% CI) p-value No Use (ref) Infrequent Use -36.52 (-79.17, 6.12) 0.572 0.47 (-0.46, 1.40) 0.314 0.92 (0.40, 2.13) 0.840 1.01 ( 0.31, 3.22) 0.992 0.95 ( 0.43, 2.10) 0.890 Frequent Use 58.54 (4.73, 112.34) 0.033 -0.25 ( -1.02, 0.52) 0.518 1.92 (1.10, 3.34) 0.022 1.63 (0.78, 3.42) 0.195 0.6718 (0.40, 1.14) 0.138 AOR: Adjusted Odds Ratio. Activity Classes reference group: Low MVPA time & High sedentary time. All results were adjusted for sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, bmi, comorbidity, and mental health condition. WAT use significantly mediates the associations between income and MVPA time. Table 4 demonstrates the mediating effects of WAT use in the associations between income, education and older adults’ physical activity patterns. Income, education and weekly MVPA time : WAT use explained 21% of the association (IE: 4.08, 95% CI 0.04, 8.13) between income and weekly MVPA time. No mediation effect of WAT use was detected in the association between education and weekly MVPA time. Income, education and daily sedentary time : No mediation effect of WAT use was detected in the associations between SES and daily sedentary time in the study sample. Table 4 Mediation Analysis: Adjusted Direct and Indirect Association with MVPA and Sedentary Time Weekly MVPA Time Daily Sedentary Time Variables Coefficients 95% CI p Variables Coefficients 95% CI p With Income via WAT use frequency With Income via WAT use frequency Total Effect 19.27 -4.17, 42.70 0.106 Total Effect -0.39 − .04, .48 0.093 Indirect Effect 4.08 0.04, 8.13 0.048 Indirect Effect -0.01 -0.05, 0.05 0.746 Direct Effect 15.18 0.10, 0.22 0.000 Direct Effect -0.38 − .03, .49 0.082 Mediated Proportion 21% No Mediation With Education via WAT use frequency With Education via WAT use frequency Total Effect 19.48 -1.89, 40.85 0.074 Total Effect -0.19 -0.07, 0.44 0.157 Indirect Effect 1.32 -0.57, 3.22 0.170 Indirect Effect -0.00 -0.02, 0.02 0.743 Direct Effect 18.15 -3.33, 39.64 0.097 Direct Effect 0.19 -0.07, 0.45 0.151 Mediated Proportion No Mediation No Mediation Discussion The present study described U.S. older adults’ PA patterns and illustrated the associations among social factors, WAT use, and PA patterns. We found that over half of study sample reported less weekly MVPA time than the WHO recommendation (at least 150 minutes of moderate-intensity physical activity), and daily sedentary time more than 6 hours. Sex, age, and income were significantly associated with MVPA time, while race and ethnicity were significantly associated with sedentary time. We also learned that frequent use of WATs was associated with more MVPA but not less sedentary behavior. Our finding that sex, age, and income were social factors significantly associated with older adults’ physical MVPA time is consistent with previous literature. Previous research consistently showed gender differences in PA, with males generally engaging more PA than females across different age groups (Azevedo et al., 2007 ; Hamrani et al., 2015 ). The association between younger age and increased PA may be attributed to the natural decline physiological function with age, which leads to reductions in MVPA (McPhee et al., 2016 ). Additionally, higher income has been linked to higher leisure time PA or exercises (Kari et al., 2020 ; Zapata-Lamana et al., 2021 ). In this study, we also examined social factors associated with older adults’ sedentary behavior and found that ethnic minority older adults (Non-Hispanic Black Americans, Hispanic, Asian and other race and ethnicity) were less sedentary than Non-Hispanic White older adults. There are currently mixed findings in the literature regarding race and ethnicity differences in sedentary behavior. For example, one study examining low-income African American and White adults in the Southeastern U.S. found minimal differences in sedentary behavior between the two groups (Cohen et al., 2013 ). Whereas other studies reported a higher prevalence of sedentary behavior among racial/ethnic minority groups compared to Non-Hispanic Whites, particularly in adolescents (Kenney et al., 2014 ) and in women (Seguin et al., 2014 ). Given the limited and inconsistent evidence regarding racial and ethnic differences in sedentary behavior among U.S. older adults, further research is warranted, with a particular emphasis on ethnic minority populations. In addition to investigating social factors associated with MVPA and sedentary behavior, this study created four activity classes to capture older adults’ PA and sedentary time synergistically. Notably, older adults with intermediate or high annual household income were more likely to belong to the “High MVPA & Low Sedentary time” class compared to the reference class (“Low MVPA & High Sedentary time”). Previous studies with similar findings have suggested that higher income affords more resources to engage in physical activity and allows more time to engage in activities that reduce sedentary hours (Alobaid et al., 2023 ; Da Silva et al., 2020 ; Zapata-Lamana et al., 2021 ). We observed a similar pattern among older adults with college degree or higher in the HINTS sample, consistent with literature that identified education as an important predictor of both MVPA and sedentary behavior (Kari et al., 2020 ; Prince et al., 2020 ; Zapata-Lamana et al., 2021 ). These findings may inform the design of future interventions targeting populations at risk for insufficient PA or high sedentary behavior. The present study is among the first to illustrate associations between the frequency of WAT use and older adults’ self-reported PA patterns. While previous literature has largely focused on determinants of long-term use and adherence to WATs among older adults (Hermsen et al., 2017 ; Paolillo et al., 2022 ; Peng et al., 2021 ), limited attention has been given to WAT use patterns or frequency in the general adult population (Brickwood et al., 2020 ) and specifically among aging adults (Li, Huang, et al., 2024 ). Given that only 65.5% of older adult WAT users reported frequent use (daily or almost daily) in 2019 and 2020 (Li, Huang, et al., 2024 ), it was especially important to examine how WAT use frequency may influence PA patterns. The findings of this research highlight the value of not only long-term WAT use but also frequent WAT use in promoting PA. Importantly, frequent WAT use was found to partially mediate the association between income and weekly MVPA time, supporting the study’s hypothesis. This suggests that older adults with higher annual household income may be better positioned to afford and maintain regular WAT use, which in turn might further promote their PA. Interestingly, this mediation effect was not observed in the association between income and sedentary behavior, possibly due to the design of WATs, which tend to emphasize features like steps monitoring and caloric expenditures aimed at increasing MVPA, rather than reducing sedentary behavior. Although some WATs offer sedentary behavior prevention functions, such as reminders to stand up and walk around, these features may not be widely adopted or effective. To the authors’ knowledge, few studies have examined how WAT use interacts with SES to influence PA in older adults. The findings of this study suggest that enhanced access to WATs could be a key mechanism through which SES influences PA in the aging population (Gidlow et al., 2006 ). Several limitations existed in the present study. In the HINTS survey, all items were self-reported by the participants, therefore the data were prone to have recall bias and social-desirability bias. Second, the cross-sectional study design limited the power of the mediation analysis and does not present a causal relationship between the exposure, mediator, and outcome. Additionally, the frequency of WAT use was developed based on two questionnaire items which assess participants' WAT user frequency in the past months while not evaluating prolonged behavior of WAT use. Despite the limitations, the findings of the present study filled in scientific gaps and provided important implications for future research, policy, and clinical practice focused on WATs and physical activity patterns in older adults. First, inequities exist among older adults with different levels of social backgrounds and result in various PA patterns. Efforts should be focused on eliminating health inequities through future research and policy revision. Second, this research shed light on the potential of using WATs to promote older adults’ physical activity patterns and the importance of emphasizing adherence to both length and frequency of WAT use. Meanwhile, it is a priority to ensure equal access to WATs for older adults with lower social-economic positions to avoid the risk of digital technologies becoming another social determinant of health (Sieck et al., 2021). Future WAT-based interventions should focus on the inclusion of low-SES older adults and tackle health disparities among older Americans. Conclusion This present study found that U.S. older adults reported overall low PA and frequent WAT use was significantly associated with improved PA patterns. Social disparities existed in PA patterns, and WAT use frequency was found to partially mediate the associations between Income and PA patterns. There is still an urgent need to promote PA patterns in U.S. older adults especially in socially and economically disadvantaged older adults. Frequent WATs may improve PA patterns and should be encouraged in older adults. In addition, increasing adoption of WATs among socially and economically disadvantaged older adults may help better promote PA in this population. Declarations Acknowledgment We thank all the participants for their participation. The authors declared that they have no conflicts of interest. Funding Declaration This study was supported by the [blinded for review] University Center for Equity in Aging Pilot Grant, the Shaanxi Provincial Department of Science and Technology -Key R&D Program - Social Development Field (2025SF-YBXM-236), the Shaanxi Post-doctoral Research Fund (2024BSHSDZZ044). Consent for publication No identifying images or other personal or clinical details of participants were presented that compromise anonymity. Therefore, consent for publication is not applicable for this study. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the authors did not used any AI or AI-assisted technology. 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16:35:26","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167096,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8217228/v1/6982dbc075cd84cde5cdbf99.html"},{"id":98248471,"identity":"d767e8f1-c224-4511-8e2d-98fa1df1bef8","added_by":"auto","created_at":"2025-12-15 16:35:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":28374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation of WAT Use Frequency on the association between SES and PA Patterns\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8217228/v1/6bc95a928c1afb7c102527b1.png"},{"id":107927649,"identity":"6f628996-da11-4f11-9c69-1835f6b7bf0a","added_by":"auto","created_at":"2026-04-27 16:00:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":636172,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8217228/v1/f547009b-2d43-4fd1-bf04-cd52c521d619.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Factors, Wearable Activity Tracker Use Frequency, and Physical Activity Patterns Among U.S. Older Adults: Findings from a National Cross-Sectional Survey","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePhysical activity (PA) is defined as any bodily movement requiring energy expenditure (World Health Organization., 2010), and sedentary behavior is characterized by low-energy activities such as sitting (Leitzmann et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To achieve better health outcomes, older adults should engage in moderate-to-vigorous-intensity physical activity (MVPA) for at least 150 minutes per week and reduce sedentary behavior (CDC, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; World Health Organization., 2010). Although there is a lack of a standard cut-off point for sedentary behavior, 6 hours or more of sitting time has been considered as high sedentary behavior (Heron et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unfortunately, the prevalence of inactivity significantly increases with age: an estimation of 26.9% of adults aged 65\u0026ndash;74 years and 35.3% of adults aged 75 years and above were inactive (Watson et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Physical inactivity is strongly linked to mortality and morbidity such as diabetes, cardiovascular disease, cancer, obesity, mental illness, and dementia, threatening the health of the aging population (Booth et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Physical inactivity in the U.S. population results in approximately \u003cspan\u003e$\u003c/span\u003e117\u0026nbsp;billion in annual health care costs and about 10 percent of premature mortality (\u0026ldquo;CDC,\u0026rdquo; 2024b). There is an urgent need to improve PA among older adults to promote health, prevent chronic illnesses, and reduce health care costs in this population.\u003c/p\u003e\u003cp\u003eStudies show that older adults\u0026rsquo; PA patterns may be influenced by multiple social factors. For example, older adults who are female, older, or have less education have been found to have lower PA levels on average(Lim \u0026amp; Taylor, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Evidence on social factors associated with sedentary behavior in older adults have been mixed. Although some studies found that those who are males, older, with lower education (less than college) are more likely to engage in sedentary behavior in adults and older adults (Da Silva et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Patterson et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), other evidence suggested no associations with sex, age, education, income and sedentary behavior in older adults (Heseltine et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Access to innovative technologies such as wearable activity trackers (WATs)\u0026mdash;consumer devices that provide feedback to the wearer such as fitness trackers, activity-tracking smartwatches, and pedometers (Tedesco et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u0026mdash;has emerged as an additional determinant of PA among older adults. Although previous evidence suggested that most U.S. older adults have never used a WAT (Xie et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it has been reported that older adults show general interest and acceptance of using a WAT device to monitor their PA levels (Kononova et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Li, McPhillips, et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). WATs have demonstrated promising potential for PA enhancement in both observational and experimental studies with the aging population (Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the significant health and economic burden, it is important to confirm risk factors for physical inactivity and sedentary behavior among older adults. To the authors\u0026rsquo; best knowledge, no study has investigated social factors associated with older adults\u0026rsquo; activity class, simultaneously considering both MVPA and sedentary time. Additionally, limited research has focused on older adults\u0026rsquo; WAT use frequency and whether different WAT use frequencies are associated with PA patterns (Chandrasekaran et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). WATs, which often require financial investment and access to compatible technology, may be less accessible to older adults with lower socioeconomic status (SES) (Tedesco et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Based on the current literature, WATs could potentially mediate the associations between SES and PA patterns in older adults. Understanding the interplay between social factors, WAT use, and PA patterns can inform targeted interventions to reduce health disparities and improve health outcomes among older adults.\u003c/p\u003e\u003cp\u003eThe purpose of this research study is to understand the associations among social factors, WAT use, and PA patterns among U.S. older adults. The conceptual framework for the current study has been developed based on available evidence and adapted from the Fundamental Cause Theory (Link \u0026amp; Phelan, 1995). The Fundamental Cause Theory posits that SES can influence an individuals\u0026rsquo; access to certain resources that may impact their health outcomes. In this study, WAT use was considered as an important technological resource that can influence health outcomes such as PA patterns. We hypothesized that social factors, including sex, age, income, and education are associated with WAT use (Li, Huang, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and WAT use is associated with PA (Tang et al., 2020). In addition, social factors are also associated with older adults\u0026rsquo; PA (Gidlow et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAim 1: Examine social factors (e.g., sex, age, race and ethnicity, education, income) associated with older adults\u0026rsquo; PA patterns (MVPA time- minutes per week, sedentary time -hours per day, and activity class).\u003c/p\u003e\u003cp\u003eAim 2: Examine the association between WAT use frequency and older adults\u0026rsquo; physical activity. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHypothesis\u003c/span\u003e: Older adults who frequently used WATs have higher MVPA time and lower sedentary time.\u003c/p\u003e\u003cp\u003eAim 3: Examine whether frequent use of WATs mediates the associations between socioeconomic status (income and education) and older adults\u0026rsquo; PA patterns. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eHypothesis\u003c/span\u003e: WAT use positively mediates the association between socioeconomic status (income and education) and older adults\u0026rsquo; PA: Higher income and education are associated with higher odds of WAT use frequency, which is further associated with longer MVPA time and shorter sedentary time.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eThe HINTS Study Design and Recruitment\u003c/h2\u003e\u003cp\u003eThis was a cross-sectional secondary data analysis using the Health Information National Trends Survey (HINTS) dataset with data collected from January to April 2019 and February to June 2020. Launched by the National Institutes of Health (National Cancer Institute) in 2003, HINTS regularly collects data about the American public\u0026rsquo;s knowledge of, attitudes toward, and use of cancer-related and other health-related information. To recruit participants, HINTS sent postal mail to random samples of non-vacant U.S. residential addresses for both the 2019 and 2020 cohorts. More details about study methods of the HINTS study are available through HINTS briefs and reports (Westat, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Westat, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The present study included 3,370 participants aged 65 and above from both cycles 3 and 4. All data collected in HINTS are self-reported on paper and sent back by mail.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eSocial Factors\u003c/b\u003e included sex, age, race/ethnicity, annual household income, and education. Participants\u0026rsquo; sex included \u0026ldquo;Male\u0026rdquo; and \u0026ldquo;Female.\u0026rdquo; Based on previous literature and data distribution, the age variable was dichotomized into 65\u0026ndash;74 years and 75 years and above (Lee, Oh, Park, Choi, \u0026amp; Wee, 2018). Annual household income was categorized into (1) low income (less than \u003cspan\u003e$\u003c/span\u003e35,000), (2) intermediate income (\u003cspan\u003e$\u003c/span\u003e35,000-\u003cspan\u003e$\u003c/span\u003e75,000), and (3) high income (\u003cspan\u003e$\u003c/span\u003e75,000 or more) (Xie, Jo, \u0026amp; Hong, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Education was categorized into (1) high school or less, (2) some college, and (3) college degree or higher. Race and ethnicity were categorized into three categories: (1) White, (2) Black, and (3) Hispanic, non-Hispanic Asians, and others.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWAT Use\u003c/b\u003e The participants\u0026rsquo; WAT use and the frequency of use were assessed with two items: (1) \u0026ldquo;In the last 12 months, have you used a Wearable Activity Tracker to monitor or track your health or activity? For example, a Fitbit, Apple Watch, or Garmin Vivofit.\u0026rdquo;; and (2) \u0026ldquo;In the past month, how often did you use a wearable device to track your health?\u0026rdquo; For those who answered \u0026ldquo;Yes\u0026rdquo; in the previous question, options included \u0026ldquo;Every day,\u0026rdquo; \u0026ldquo;Almost every day,\u0026rdquo; \u0026ldquo;1\u0026ndash;2 times per week,\u0026rdquo; \u0026ldquo;Less than once per week,\u0026rdquo; or \u0026ldquo;I did not use a wearable device in the past month.\u0026rdquo; Based on these questions, we developed \u003cem\u003eWAT Use\u003c/em\u003e into a categorial variable: (1) Frequent WAT use: those who reported using a WAT \u0026ldquo;Every day\u0026rdquo; or \u0026ldquo;Almost every day\u0026rdquo; in the past month; (2) Infrequent use: those who reported using a WAT \u0026ldquo;1\u0026ndash;2 times per week,\u0026rdquo; \u0026ldquo;Less than once per week,\u0026rdquo; or \u0026ldquo;I did not use a wearable device in the past month (but used one in the past year)\u0026rdquo;; and (3) No use: respondents who did not use a WAT in the past month and those who did not use a WAT over the past 12 months.\u003c/p\u003e\n\u003ch3\u003ePhysical Activity Patterns (PA Patterns)\u003c/h3\u003e\n\u003cp\u003eWhile MVPA and sedentary behavior represent distinct constructs, they are not mutually exclusive: one can reach guideline recommended activity levels and have a sedentary lifestyle at the same time (Thivel et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For the purpose of this analysis, PA pattern was created as a 4-category variable considering both MVPA and sedentary time. First, \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eWeekly MVPA time\u003c/span\u003e was assessed using two items: (1) \u0026ldquo;In a typical week, how many days do you do any physical activity or exercise of at least moderate intensity?\u0026rdquo;; and (2) \u0026ldquo;On the days that you do any physical activity or exercise of at least moderate intensity, how long do you typically do these activities?\u0026rdquo; Choices for the first item included: \u0026ldquo;None\u0026rdquo;, \u0026ldquo;One day per week\u0026rdquo;, \u0026ldquo;Two days per week\u0026rdquo; \u0026hellip; \u0026ldquo;Seven days per week\u0026rdquo;. As for the second item, The respondents were asked to answer the number of minutes per day. Based on these 2 items, the HINTS dataset provided a variable named \u0026ldquo;Minutes per week of at least moderate-intensity exercise.\u0026rdquo; Consistent with previous literature (Xie, Jo, and Hong \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), this study also used this variable (Minutes per week of at least moderate-intensity exercise) as a measure of PA. Second, \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003edaily sedentary time\u003c/span\u003e was assessed using one item: \u0026ldquo;During the past 7 days, how much time did you spend sitting on a typical day at home or at work?\u0026rdquo; The respondents were asked to answer with the number of hours per day.\u003c/p\u003e\u003cp\u003e Based on current PA guidelines and literature, a weekly MVPA time under 150 minutes and a daily sedentary time more than 6 hours were used as cutoffs for low MVPA time and high sedentary time (DHS, 2018). Four physical activity classes were then created: (1) high MVPA low sedentary, (2) high MVPA high sedentary, (3) low MVPA low sedentary, and (4) low MVPA high sedentary groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e: \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eBody Mass Index (BMI)\u003c/span\u003e was calculated using the respondents\u0026rsquo; self-reported heights and weights and was dichotomized into (1) non-obesity: under 30, and (2) obesity: 30 and above. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSmoking status\u003c/span\u003e was a variable derived from two questions on past smoking experience and current smoking frequency and was categorized into current, former, and never smoker. The \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003emarital status\u003c/span\u003e question was included in the questionnaire and dichotomized into two categories: (1) married or living with a partner, and (2) divorced, widowed, separated, or never married. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eComorbidity\u003c/span\u003e was measured by the sum of reported medical conditions reported by respondents and categorized into: (1) one or no comorbidity and (2) multiple comorbidities. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eMental health(\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003edepression and anxiety)\u003c/span\u003ewas assessed by the 4-item Patient Health Questionnaire-4 (PHQ-4) is an ultra-brief self-report validated questionnaire that consists of a 2-item depression subscale (PHQ-2) and a 2-item anxiety subscale (GAD-2)(L\u0026ouml;we et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In each subscale, scores range from 0 to 6, with a score of 3 or greater indicating positive for depression or anxiety.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eFollowing the recommended methods, we first merged data from HINTS 5 cycle 3 and cycle 4, then accounted for the complex survey design using survey weights and the \u0026ldquo;delete one jackknife replication method\u0026rdquo; that deletes one sampling unit at a time from the full sample to create a set of 50 replicate weights(Westat, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Westat, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The reported percentages are weighted and the sample sizes are unweighted. Analyses were conducted using Stata (version 17.0; StataCorp).\u003c/p\u003e\u003cp\u003eWe first used descriptive analyses including frequency and percentages for categorical variables and means and standard deviations for continuous variables to summarize sample characteristics. We also used Pearson\u0026rsquo;s Chi-square to compare sample characteristics by PA patterns (weekly MVPA time, daily sedentary time, and activity class).\u003c/p\u003e\u003cp\u003eWe then conducted multivariate linear regression analyses to identify social factors associated with older adults\u0026rsquo; PA patterns. A test with a p-value of less than 0.05 was considered statistically significant. Multinomial logistic regression models were built to examine social factors associated with the PA classes, with the low MVPA high sedentary group being the reference group.\u003c/p\u003e\u003cp\u003eWAT use frequency associated with weekly MVPA time and daily sedentary time were examined using simple linear regression models adjusting for covariates. WAT use frequency associated with activity classes was assessed with a multinomial logistic regression model adjusting for covariates.\u003c/p\u003e\u003cp\u003eFinally, mediation analyses were performed to illustrate the association of SES (income and education) with physical activity levels (weekly MVPA minutes and daily sedentary hours) mediated by the use of WATs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Baron and Kenny\u0026rsquo;s approach to test mediation (MEDSEM procedure in Stata version 17) was used to estimate the total effects, indirect effects (IE), and direct effects (DE) of SES on physical activity levels. Two models were estimated: a multivariate logistic regression model for WATs use (mediator) conditional on social factors (exposure), and all study confounders and a multivariate linear regression model for physical activity levels (outcome) conditional on social factors. The DE represented the effect of social factors on physical activity levels that were independent of WAT use. An IE represented the proportion of social factors that could be explained by its association with WATs use. Sobel Test was used to test the significance of IE. To quantify the magnitude of mediation, the study estimated the proportion of the association mediated by the use of WATs (IE/[DE\u0026thinsp;+\u0026thinsp;IE]).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate\u003c/strong\u003e\u003cp\u003eThe HINTS 5 general population survey received expedited approval from the Westat Institutional Review Board (IRB) on March 28, 2016 (Project #6048.14). Furthermore, the NIH Office of Human Subjects Research issued a\u0026ldquo;Not Human Subjects Research\u0026rdquo;determination from the NIH Office on April 25, 2016 (Exempt #13204). Therefore, informed consent was not required for the primary data collection process.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003cp\u003eThe analysis utilizing HINTS 5 data was classified as non-human subjects research by the Johns Hopkins University School of Medicine IRB. As the study involved secondary analysis of existing, de-identified, and publicly available datasets, it was exempt from further ethical review and approval.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSample characteristics by MVPA time, sedentary time, and activity classes.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSample characteristics were described in total and by MVPA time, sedentary time, and activity classes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The current study included a total of 3370 participants, with 53.9% female, average age 73.9 years (SD\u0026thinsp;=\u0026thinsp;7.1), 21.9% had a college degree or higher, and most were Non-Hispanic White (78.0%). Over half of the participants exercised less than 150 minutes per week (63.7%) and sat for more than 6 hours per day (60.1%). The percentage of participants classified as the \u0026ldquo;Low MVPA and High Sedentary\u0026rdquo; class was 32.1%.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeighted Sociodemographic and Clinical Characteristics by PA Patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eWeekly MVPA Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eDaily Sedentary Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eActivity Classes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;3370\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;150min/wk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;150 min/wk\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;6 hrs/day\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;6 hrs/day\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLow MVPA\u003c/p\u003e\u003cp\u003eHigh Sedentary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLow MVPA Low Sedentary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eHigh MVPA High Sedentary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eHigh MVPA Low Sedentary\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2,142\u003c/p\u003e\u003cp\u003e(63.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1,228\u003c/p\u003e\u003cp\u003e(36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1,330\u003c/p\u003e\u003cp\u003e(39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2040\u003c/p\u003e\u003cp\u003e(60.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1,086\u003c/p\u003e\u003cp\u003e(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;1056\u003c/p\u003e\u003cp\u003e(31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;476\u003c/p\u003e\u003cp\u003e(13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;752\u003c/p\u003e\u003cp\u003e(22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,374 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e807 (58.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e567(41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e515 (38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e859 (61.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e411 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e396 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e229 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e338 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,675 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,142 (67.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e533 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e683 (41.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e992 (58.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e569 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e573 (35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e194 (11.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e339 (20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years) 73.9 +- 1.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75\u0026amp;above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,330 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e912 (68.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e418 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e491 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e839 (62.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e473 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e439 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e180 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e238 (17.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,040 (58.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,230 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e810 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e839 (41.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,201 (58.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e613 (30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e617 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e296 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e514 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school degree and lower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,024 (38.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e735 (70.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e414 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e610 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e368 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e367 (34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e123 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e166 (17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,036 (39.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e682 (62.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e354 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e387 (39.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e649 (61.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e361 (32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e321 (30.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e130 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e224 (23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege degree or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,237 (21.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e683 (54.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e554(45.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e503 (42.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e734 (57.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e332 (24.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e351 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e204 (15.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e350 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace and Ethnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,044 (78.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,280 (63.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e764 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e760 (38.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,284(61.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e672 (33.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e608 (29.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e285 (13.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e479 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e358 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255 (72.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e103 (27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e149 (39.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e209 (60.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e121 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e134 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e43 (11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e60 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanics, Asians, and others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e486 (12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e307 (64.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e179 (35.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e236 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e250 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e119 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e188 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e63 (13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e116 (24.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Household Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 35k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,150 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e831(71.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e319 (28.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e411 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e739 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e448 (38.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e383 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e145 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e174 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e35k to less than 75k\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e964 (36.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e601 (63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e363 (36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e389 (41.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e575 (58.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e283 (30.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e318 (32.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e132 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e231 (24.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75K or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e765 (27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (51.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e366 (48.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e319 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e443 (53.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e184 (22.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e212 (29.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e130 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e236 (32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e285 ( 8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e203 (72.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (27.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e110 (38.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e175 (61.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e101 (35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e102 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e34 (11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e48 (16.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormer Smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,189 (37.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e775 (65.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e414 (34.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e420 (35.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e769 (64.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e430 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e345 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e148 (12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e266 (22.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever Smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,835 (54.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,133 (61.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e702 (38.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e792 (43.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1043 (56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e535 (28.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e598 (33.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e267 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e435 (24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBody Mass Index (BMI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI under 30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,345 (70.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,397 (60.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e948 (39.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e989 (43.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,356 (56.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e679 (28.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e718 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e339 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e609 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI 30 or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,025 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e745 (72.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e280 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e341 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e684 (68.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e407 (40.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e338 (31.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e137 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e143 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried or living with a partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,559 (58.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e916 (60.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e643 (39.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e655 (41.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e904 (58.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e425 (29.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e491 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e229 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e414 (25.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced, Widowed, Separated, or never married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,744 (41.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,191 (68.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e553 (31.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e649 (37.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1095 (62.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e641 (36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e550 (32.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e228 (13.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e325 (18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eComorbidity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOne or No Comorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,777 (56.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,044 (57.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e733 (42.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e781 (45.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e996 (55.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e482 (26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e562 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e243 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e490 (28.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultiple Comorbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,423 (43.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e988 (71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e435 (28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e480 (33.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e943 (67.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e550 (40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e438 (31.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e206 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e229 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"13\" nameend=\"c13\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMental health Depression \u0026amp; Anxiety\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo or Mild\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1876 (62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1132 (37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1224 (41.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1784 (58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e925 (31.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e951 (31.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e427 (14.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e705 (23.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate or Severe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e266 (75.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96 (24.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e106 (29.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e256 (70.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e161 (39.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e105 (35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e49 (12.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e47 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"13\"\u003eWeekly MVPA minutes mean 152.3; sd: 269.0. Daily sedentary hours mean:6.5; sd: 3.6. Row percentages were shown in this table.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSocial factors associated with MVPA time, sedentary time, and activity classes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociations between Social Factors and Physical Activity Patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial Factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eWeekly MVPA Minutes\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2430\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eDaily Sedentary Hours\u003c/p\u003e\u003cp\u003en\u0026thinsp;=\u0026thinsp;2218\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eHigh MVPA time\u003c/p\u003e\u003cp\u003eLow sedentary time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eHigh MVPA time\u003c/p\u003e\u003cp\u003eHigh sedentary time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eLow MVPA time\u003c/p\u003e\u003cp\u003eLow sedentary time\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eCoef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCoef\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003eAOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-55.79*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-101.05, -10.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.90, 0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.57, 1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.48, 1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.86, 1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge Group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e65\u0026ndash;74 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75 and older\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-51.47*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-98.90, -4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.35, 0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.46, 1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.51, 1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.82, 1.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace/Ethnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic White (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-31.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.81, 68.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.63*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.18, -0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.44, 1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.44, 2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.72, 1.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic, Asian, and Others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.98, 117.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.72*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.31, -0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.79, 2.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.72, 2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.06**8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.35, 3.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Household Income\u003c/b\u003e\u0026nbsp;(US Dollars)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow: \u0026lt;35,000 (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle: 35,000 to 75,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.81, 68.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.25, 0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.77**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.17, 2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.52, 1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.75, 1.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh: \u0026gt;75,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60.90*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.98, 117.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.13, 0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26, 3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.85, 3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.78, 2.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or less (ref)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome college\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-19.52, 62.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.19, 0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.76, 1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.71, 1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.76, 1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-38.87, 67.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.37, 0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.65*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.08, 2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.71*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.05, 2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.92, 1.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eAOR: Adjusted Odds Ratio. Reference group: Low MVPA time \u0026amp; High sedentary time.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003e* 0.01\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** 0.001\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eThis table presents weighted associations between Social Factors and Physical Activity (MVPA time, Sedentary time, and Activity classes)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eAll results were adjusted for sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, bmi, comorbidity, and mental health condition.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSocial factors associated with MVPA time.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows factors associated with MVPA time and sedentary time using multivariate linear regression. Older adults who were female (b=-55.79, 95% Confidence Interval [CI]: -101.5, -10.52), aged 75 years and above (b=-51.47, 95% CI:-98.90, -4.04) had significantly less weekly MVPA time compared to their counterparts. Older adults who had an annual household income of over 75000 US Dollars (b\u0026thinsp;=\u0026thinsp;60.90, 95% CI: 3.98, 117.81) had significantly more weekly MVPA time compared to those with lower annual household income.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSocial factors associated with sedentary time.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eNon-Hispanic Black older adults had significantly fewer daily sedentary time (hours) than Non-Hispanic Whites (Coefficient: -0.63, 95% CI: -1.18, -0.08, p\u0026thinsp;=\u0026thinsp;0.026). Similarly, Hispanic, Asian, and other ethnic groups exhibited significantly lower sedentary hours (Coefficient: -0.72, 95% CI: -1.31, -0.13, p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eSocial factors associated with activity class.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eOlder adults with higher annual household income was significantly associated with increased odds of falling into the\u0026ldquo;High MVPA time \u0026amp; Low sedentary time\u0026rdquo;activity class, with middle-income (AOR: 1.77, 95% CI: 1.17, 2.66, p\u0026thinsp;=\u0026thinsp;0.007) and high-income groups (AOR: 2.16, 95% CI: 1.26, 3.68, p\u0026thinsp;=\u0026thinsp;0.005) showing higher odds of falling in this class compared with the reference class (\u0026ldquo;Low MVPA time \u0026amp; High sedentary time\u0026rdquo;).\u003c/p\u003e\u003cp\u003eIndividuals with college or higher education had higher odds of falling into the \u0026ldquo;High MVPA time \u0026amp; Low sedentary time\u0026rdquo; (AOR: 1.65, 95% CI: 1.08, 2.53, p\u0026thinsp;=\u0026thinsp;0.022) and \u0026ldquo;High MVPA time \u0026amp; High sedentary time\u0026rdquo; (AOR: 1.71, 95% CI: 1.05, 2.78, p\u0026thinsp;=\u0026thinsp;0.033) classes.\u003c/p\u003e\u003cp\u003eCompared with the reference class, older adults who were Hispanic, Asian, and other ethnic groups had significantly higher odds of falling into the class \u0026ldquo;Low MVPA time \u0026amp; Low sedentary time\u0026rdquo; compared to Non-Hispanic Whites (AOR: 2.06, 95% CI: 1.35, 3.13, p\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFrequent WAT Use was associated with PA Patterns\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the associations between WAT use frequency and older adults\u0026rsquo; physical activity adjusting for covariates (sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, BMI, comorbidity, and mental health condition). We found that older adults who frequently used WATs (i.e., using a WAT \u0026ldquo;Every day\u0026rdquo; or \u0026ldquo;Almost every day\u0026rdquo; in the past month) had significantly longer weekly MVPA times (b\u0026thinsp;=\u0026thinsp;58.54, 95%CI: 4.73, 112.34), and had higher odds of falling in the activity class of \u0026ldquo;High MVPA time \u0026amp; Low sedentary time\u0026rdquo; (AOR\u0026thinsp;=\u0026thinsp;1.92, 95%CI: 1.10, 3.34) compared to the reference group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWeighted Adjusted Associations between WAT Use Pattern and Physical Activity Patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eWAT Use\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eWeekly MVPA Time\u003c/p\u003e\u003cp\u003e(Minutes)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c5\" namest=\"c4\" rowspan=\"2\"\u003e\u003cp\u003eDaily Sedentary Time\u003c/p\u003e\u003cp\u003e(Hours)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003eActivity Classes\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eHigh MVPA time\u003c/p\u003e\u003cp\u003eLow sedentary time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eHigh MVPA time\u003c/p\u003e\u003cp\u003eHigh sedentary time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eLow MVPA time\u003c/p\u003e\u003cp\u003eLow sedentary time\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eAOR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNo Use (ref)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInfrequent Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-36.52\u003c/p\u003e\u003cp\u003e(-79.17, 6.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003cp\u003e(-0.46, 1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003cp\u003e(0.40, 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003cp\u003e( 0.31, 3.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003cp\u003e( 0.43, 2.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrequent Use\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.54\u003c/p\u003e\u003cp\u003e(4.73, 112.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003cp\u003e( -1.02, 0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003cp\u003e(1.10, 3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003cp\u003e(0.78, 3.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.6718\u003c/p\u003e\u003cp\u003e(0.40, 1.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eAOR: Adjusted Odds Ratio. Activity Classes reference group: Low MVPA time \u0026amp; High sedentary time.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eAll results were adjusted for sex, age group, race and ethnicity, annual household income, education, smoking status, marital status, bmi, comorbidity, and mental health condition.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWAT use significantly mediates the associations between income and MVPA time.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the mediating effects of WAT use in the associations between income, education and older adults\u0026rsquo; physical activity patterns. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIncome, education and weekly MVPA time\u003c/span\u003e: WAT use explained 21% of the association (IE: 4.08, 95% CI 0.04, 8.13) between income and weekly MVPA time. No mediation effect of WAT use was detected in the association between education and weekly MVPA time. \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eIncome, education and daily sedentary time\u003c/span\u003e: No mediation effect of WAT use was detected in the associations between SES and daily sedentary time in the study sample.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation Analysis: Adjusted Direct and Indirect Association with MVPA and Sedentary Time\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eWeekly MVPA Time\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eDaily Sedentary Time\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoefficients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWith Income via WAT use frequency\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eWith Income via WAT use frequency\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.17, 42.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.04, .48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04, 8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.05, 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10, 0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.03, .49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.082\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediated Proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eNo Mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWith Education via WAT use frequency\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eWith Education via WAT use frequency\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.89, 40.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.07, 0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.157\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.57, 3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.02, 0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.33, 39.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDirect Effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.07, 0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMediated Proportion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNo Mediation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eNo Mediation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study described U.S. older adults\u0026rsquo; PA patterns and illustrated the associations among social factors, WAT use, and PA patterns. We found that over half of study sample reported less weekly MVPA time than the WHO recommendation (at least 150 minutes of moderate-intensity physical activity), and daily sedentary time more than 6 hours. Sex, age, and income were significantly associated with MVPA time, while race and ethnicity were significantly associated with sedentary time. We also learned that frequent use of WATs was associated with more MVPA but not less sedentary behavior.\u003c/p\u003e\u003cp\u003eOur finding that sex, age, and income were social factors significantly associated with older adults\u0026rsquo; physical MVPA time is consistent with previous literature. Previous research consistently showed gender differences in PA, with males generally engaging more PA than females across different age groups (Azevedo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hamrani et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The association between younger age and increased PA may be attributed to the natural decline physiological function with age, which leads to reductions in MVPA (McPhee et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, higher income has been linked to higher leisure time PA or exercises (Kari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zapata-Lamana et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we also examined social factors associated with older adults\u0026rsquo; sedentary behavior and found that ethnic minority older adults (Non-Hispanic Black Americans, Hispanic, Asian and other race and ethnicity) were less sedentary than Non-Hispanic White older adults. There are currently mixed findings in the literature regarding race and ethnicity differences in sedentary behavior. For example, one study examining low-income African American and White adults in the Southeastern U.S. found minimal differences in sedentary behavior between the two groups (Cohen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Whereas other studies reported a higher prevalence of sedentary behavior among racial/ethnic minority groups compared to Non-Hispanic Whites, particularly in adolescents (Kenney et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and in women (Seguin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Given the limited and inconsistent evidence regarding racial and ethnic differences in sedentary behavior among U.S. older adults, further research is warranted, with a particular emphasis on ethnic minority populations.\u003c/p\u003e\u003cp\u003eIn addition to investigating social factors associated with MVPA and sedentary behavior, this study created four activity classes to capture older adults\u0026rsquo; PA and sedentary time synergistically. Notably, older adults with intermediate or high annual household income were more likely to belong to the \u0026ldquo;High MVPA \u0026amp; Low Sedentary time\u0026rdquo; class compared to the reference class (\u0026ldquo;Low MVPA \u0026amp; High Sedentary time\u0026rdquo;). Previous studies with similar findings have suggested that higher income affords more resources to engage in physical activity and allows more time to engage in activities that reduce sedentary hours (Alobaid et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Da Silva et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zapata-Lamana et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We observed a similar pattern among older adults with college degree or higher in the HINTS sample, consistent with literature that identified education as an important predictor of both MVPA and sedentary behavior (Kari et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Prince et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zapata-Lamana et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings may inform the design of future interventions targeting populations at risk for insufficient PA or high sedentary behavior.\u003c/p\u003e\u003cp\u003eThe present study is among the first to illustrate associations between the frequency of WAT use and older adults\u0026rsquo; self-reported PA patterns. While previous literature has largely focused on determinants of long-term use and adherence to WATs among older adults (Hermsen et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Paolillo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), limited attention has been given to WAT use patterns or frequency in the general adult population (Brickwood et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and specifically among aging adults (Li, Huang, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given that only 65.5% of older adult WAT users reported frequent use (daily or almost daily) in 2019 and 2020 (Li, Huang, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), it was especially important to examine how WAT use frequency may influence PA patterns. The findings of this research highlight the value of not only long-term WAT use but also frequent WAT use in promoting PA. Importantly, frequent WAT use was found to partially mediate the association between income and weekly MVPA time, supporting the study\u0026rsquo;s hypothesis. This suggests that older adults with higher annual household income may be better positioned to afford and maintain regular WAT use, which in turn might further promote their PA. Interestingly, this mediation effect was not observed in the association between income and sedentary behavior, possibly due to the design of WATs, which tend to emphasize features like steps monitoring and caloric expenditures aimed at increasing MVPA, rather than reducing sedentary behavior. Although some WATs offer sedentary behavior prevention functions, such as reminders to stand up and walk around, these features may not be widely adopted or effective. To the authors\u0026rsquo; knowledge, few studies have examined how WAT use interacts with SES to influence PA in older adults. The findings of this study suggest that enhanced access to WATs could be a key mechanism through which SES influences PA in the aging population (Gidlow et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral limitations existed in the present study. In the HINTS survey, all items were self-reported by the participants, therefore the data were prone to have recall bias and social-desirability bias. Second, the cross-sectional study design limited the power of the mediation analysis and does not present a causal relationship between the exposure, mediator, and outcome. Additionally, the frequency of WAT use was developed based on two questionnaire items which assess participants' WAT user frequency in the past months while not evaluating prolonged behavior of WAT use.\u003c/p\u003e\u003cp\u003eDespite the limitations, the findings of the present study filled in scientific gaps and provided important implications for future research, policy, and clinical practice focused on WATs and physical activity patterns in older adults. First, inequities exist among older adults with different levels of social backgrounds and result in various PA patterns. Efforts should be focused on eliminating health inequities through future research and policy revision. Second, this research shed light on the potential of using WATs to promote older adults\u0026rsquo; physical activity patterns and the importance of emphasizing adherence to both length and frequency of WAT use. Meanwhile, it is a priority to ensure equal access to WATs for older adults with lower social-economic positions to avoid the risk of digital technologies becoming another social determinant of health (Sieck et al., 2021). Future WAT-based interventions should focus on the inclusion of low-SES older adults and tackle health disparities among older Americans.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis present study found that U.S. older adults reported overall low PA and frequent WAT use was significantly associated with improved PA patterns. Social disparities existed in PA patterns, and WAT use frequency was found to partially mediate the associations between Income and PA patterns. There is still an urgent need to promote PA patterns in U.S. older adults especially in socially and economically disadvantaged older adults. Frequent WATs may improve PA patterns and should be encouraged in older adults. In addition, increasing adoption of WATs among socially and economically disadvantaged older adults may help better promote PA in this population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgment\u003c/h2\u003e\n\u003cp\u003eWe thank all the participants for their participation. The authors declared that they have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the\u0026nbsp;\u003cu\u003e[blinded for review]\u003c/u\u003e University Center for Equity in Aging Pilot Grant, the Shaanxi Provincial Department of Science and Technology -Key R\u0026amp;D Program - Social Development Field (2025SF-YBXM-236), the Shaanxi Post-doctoral Research Fund (2024BSHSDZZ044).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo identifying images or other personal or clinical details of participants were presented that compromise anonymity. Therefore, consent for publication is not applicable for this study.\u003c/p\u003e\n\u003ch2\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/h2\u003e\n\u003cp\u003eDuring the preparation of this work the authors did not used any AI or AI-assisted technology. The authors take full responsibility for the content of the published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHINTS data are available publicly at the following URL: https://hints.cancer.gov/data/download-data.aspx. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlobaid A, Syed W, Al-Rawi M. Factors Associated with Sedentary Behavior and Physical Activity Among People Living in Saudi Arabia \u0026ndash; A Cross-Sectional Study. 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BMC Geriatr. 2022;22(1):231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-022-02931-w\u003c/span\u003e\u003cspan address=\"10.1186/s12877-022-02931-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social Factor, Wearable Activity Tracker, Older Adult, Physical Activity, Social Determinants of Health","lastPublishedDoi":"10.21203/rs.3.rs-8217228/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8217228/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFew research have investigated wearable activity tracker (WAT) use frequency and physical activity (PA) patterns. The present study aims to describe PA patterns (moderate-vigorous physical activity (MVPA) and sedentary behavior) and to examine the association of social factors and WAT use frequency with PA patterns among U.S. older adults.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe used cross-sectional data from 3,370 older adults from the Health Information National Trends Survey. Linear regression models and multinomial logistic regression models were used to identify associations among social factors, WAT use, and PA patterns. Baron and Kenney\u0026rsquo;s approach was used to examine the mediation effect of wearable activity tracker use on socioeconomic status and activity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eParticipants were 53.9% female and aged 73.9 years (SD\u0026thinsp;=\u0026thinsp;7.1) on average. Over half of the participants reported less than 150 minutes of weekly MVPA time (63.7%) and over 6 hours of daily sedentary time (60.1%). Older adults who were female and aged 75 and older reported significantly less weekly MVPA time compared to male and 65-to-74 years old. Older adults with high annual household income, compared with those with low income reported longer MVPA time. Frequent use of WATs was significantly associated with longer weekly MVPA time. WAT use mediated the associations between income and weekly MVPA time.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSocial disparities existed in PA patterns, but WAT use was found to partially mediate the associations between income and MVPA time. There is still an urgent need to promote PA patterns especially in socially and economically disadvantaged older adults.\u003c/p\u003e","manuscriptTitle":"Social Factors, Wearable Activity Tracker Use Frequency, and Physical Activity Patterns Among U.S. Older Adults: Findings from a National Cross-Sectional Survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 16:35:20","doi":"10.21203/rs.3.rs-8217228/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-11T08:05:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-28T04:20:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1916189408016563253816547617759132702","date":"2026-01-26T08:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150190223414930508656632011195188539448","date":"2026-01-24T03:27:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T23:46:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315592259268237120716490408250331201867","date":"2025-12-27T06:59:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135563542335211667379076939100927191614","date":"2025-12-26T09:19:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78431827044921143836058877765830173504","date":"2025-12-23T13:32:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-10T00:45:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T00:39:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-09T13:27:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T10:02:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-12-08T09:47:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f4fea957-3b16-4001-810c-517c0919d7b0","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:00:25+00:00","versionOfRecord":{"articleIdentity":"rs-8217228","link":"https://doi.org/10.1186/s12877-026-07528-1","journal":{"identity":"bmc-geriatrics","isVorOnly":false,"title":"BMC Geriatrics"},"publishedOn":"2026-04-23 15:57:06","publishedOnDateReadable":"April 23rd, 2026"},"versionCreatedAt":"2025-12-15 16:35:20","video":"","vorDoi":"10.1186/s12877-026-07528-1","vorDoiUrl":"https://doi.org/10.1186/s12877-026-07528-1","workflowStages":[]},"version":"v1","identity":"rs-8217228","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8217228","identity":"rs-8217228","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}