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Activity Tracking Behavior and Engagement in Regular Physical Activity Among Older Adults and Care Partners

A peer-reviewed version of this preprint was published in:
Journal of Ageing and Longevity 2026, 6(1), 33. https://doi.org/10.3390/jal6010033

Submitted:

23 January 2026

Posted:

26 January 2026

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Abstract
Background: Activity trackers support physical activity, yet evidence on their effectiveness among older adults and care partners is limited. This study assesses the relationship between activity-tracking frequency and engagement in regular physical activity among older adults and care partners. Methods: For this cross-sectional study, 615 older adults and care partners completed online surveys assessing the frequency of activity tracking (predictor) and the regularity in physical activity engagement (outcome). Using multivariable logistic regression, we assessed the association between the predictors and the outcome across the entire population and separately among older adults (n=310) and care partners (n=305), adjusting for sociodemographic, mobility, and health-related covariates. We reported the adjusted odds ratio (aOR) and 95% confidence intervals (CI). Results: Older adult (OA) and care partner (CP) respondents were predominantly female (OA: 57%, CP: 53%) and non-Hispanic White (OA: 51%, CP: 43%). Across the entire population, frequent tracking of physical activity was associated with a 2.4-fold increase in the odds of engaging in regular physical activity (aOR: 2.40; 95% CI: 1.45 – 3.96). Older adults who frequently track their physical activity were 2.5 times more likely to engage in regular physical activity (aOR: 2.47; 95% CI: 1.08–5.64). Care partners who occasionally tracked their physical activity were 3.5 times more likely to engage in regular physical activity (aOR: 3.54; 95% CI: 1.54 – 8.11). Conclusion: Physical activity tracking is associated with greater physical activity engagement among older adults and care partners. Physical activity trackers can therefore serve as low-cost interventions to improve physical activity behavior.
Keywords: 
physical activity
;  
activity tracking
;  
older adults
;  
care partners
;  
exercise
Subject: 
Public Health and Healthcare  -   Public Health and Health Services

1. Introduction

Regular physical activity is a critical determinant of healthy aging [1,2], promoting mobility [3,4], cardiovascular function [5,6], cognitive health [7,8], and overall quality of life in older adults [9,10,11,12]. Despite these substantial benefits [13,14,15], fewer than one in four older adults in the United States [16] meet recommended physical activity guidelines, with participation declining further in the presence of chronic disease, mobility limitations, or advancing age [17,18]. Lack of regular physical activity often extends to care partners, defined as family members or friends who provide physical, emotional, or logistical support to an older adult [19], who may face similar or even heightened barriers to maintaining regular activity [20,21,22]. Time constraints, caregiving responsibilities, stress, and competing daily demands can limit opportunities for both older adults and their care partners to engage in consistent physical activity [20,21,22,23,24].
As interest grows in practical approaches to increase physical activity, digital activity trackers, including smartphones, pedometers, and wearable devices, have emerged as widely accessible tools that encourage self-monitoring and behavior change [25,26]. These devices provide real-time feedback, reinforce progress, promote awareness of daily movement, and facilitate long-term adherence to physical activity [26]. For older adults and care partners, who often navigate fluctuating schedules and complex health-related demands, activity tracking may offer a simple and adaptable strategy to support consistent engagement in physical activity [27,28].
Existing research indicates that older adults adopt wearable activity trackers at lower rates than younger populations [29,30,31]. Randomized trials have demonstrated short-term increases in daily step counts and moderate-to-vigorous physical activity among older adults using activity trackers [32,33,34,35,36,37,38,39,40,41,42,43]. While some observational studies have examined associations between wearable use and activity levels among older adults [44,45,46], the evidence base characterizing the naturally occurring frequency of wearable use and its relationship to regular physical activity behavior in real-world settings remains limited. Care partners are rarely examined as a distinct group in these experimental and observational studies [47,48], despite their distinct caregiving demands and the potential for wearable activity trackers to influence their physical activity.
The aim of this study, therefore, is to evaluate the association between frequency of activity tracking and engagement in regular physical activity among older adults and care partners. We hypothesized that more frequent tracking would be associated with higher odds of engaging in regular physical activity, with potentially distinct patterns between older adults and care partners. Understanding how tracking behaviors relate to physical activity engagement can inform low-cost, scalable strategies to promote health and mobility in aging populations and their support networks.

2. Materials and Methods

Study Design and Setting
We conducted a cross-sectional online survey study to examine the relationship between activity-tracking behaviors and engagement in regular physical activity among older adults and care partners. The survey was administered as part of the pilot and survey validation of the Activity Tracking, Care Partner Co-Participation, Text Reminders, Instructional Education, Virtual Physical Therapy, and Exercise (ACTIVE) project.
Participant Recruitment
Participants were recruited nationwide through ResearchMatch, a National Institutes of Health–supported volunteer registry that connects researchers with individuals interested in participating in health studies [49]. Recruitment messages were distributed to eligible platform members, and individuals who responded were directed to an electronic consent form and the study survey. Data were collected and stored using Research Electronic Data Capture (REDCap), a secure, web-based platform for research data management [50].
Eligibility Criteria
Two groups were eligible for participation: older adults and care partners. Older adults were eligible if they were aged 65 years or older, could read English, and had internet access to complete the online survey. Care partners were eligible if they were aged 18 years or older, provided ongoing unpaid support to an older adult, and met the same language and internet-access requirements. Individuals providing paid or formal caregiving services and respondents who indicated substantial difficulty completing an online survey due to cognitive limitations were excluded.
Outcome Measure
The primary outcome was engagement in regular physical activity. Respondents were asked, “In the past week, how many days did you engage in physical activity, such as walking or light exercise?” Response was measured on an ordinal scale: 0 days, 1-2 days, 3-4 days, 5 or more days. We defined regular physical activity as engagement in physical activity for 3 or more days per week, consistent with the World Health Organization guidelines [51].
Primary Predictor Variable
The main predictor was frequency of activity tracking. Participants were asked how often they track their physical activity using any tool (e.g., smartphone application, pedometer, wearable device). Response was measured using a five-item ordinal scale: never, rarely, sometimes, often, always. We defined frequency of activity tracking as either rarely (“never” or “rare”), occasionally (“sometimes”), or frequently (“often” or “always”).
Potential Confounders
We selected sociodemographic, mobility, and health characteristics as potential confounders of the association between frequency of activity tracking and regular physical activity, based on prior literature [47,52,53,54,55,56]. Sociodemographic characteristics included age, sex, race and ethnicity, educational attainment, marital status, and living situation (living alone versus with others). Mobility-related factors included a history of a fall in the past year and functional limitations. For the mobility characteristic, respondents were asked whether they had fallen in the past 12 months (yes or no) and whether they had any health conditions that limit their ability to be physically active (yes or no). Health characteristics included the Charlson Comorbidity Index and the Clinical Frailty Scale score. The Charlson comorbidity index is a measure of the comorbidities and a predictor of 10-year mortality, computed from 16 chronic diseases [57,58]. The score is a weighted sum of the 16 chronic diseases, ranging from 0 to 24 [59,60]. We measured the Charlson comorbidity index as a continuous variable. The Clinical Frailty Scale, a measure of frailty, is a 9-item scale (Cronbach alpha: 0.76 – 0.91) that assesses an individual’s vulnerability to stressors [61,62,63]. We defined the Clinical Frailty Scale score as a three-level categorical variable – fit (scores 1 – 3), vulnerable (score of 4), and frail (score of 5 – 9), consistent with prior studies [64,65].
Missing Data
A total of 698 persons initiated the survey, and 615 (88.1%) provided complete responses. We performed listwise deletion on these 83 respondents because missingness was not at random [66].
Statistical Analysis
We reported the frequency distributions of all categorical variables. Continuous variables were summarized using means and standard deviations or medians and interquartile ranges, depending on their distribution. Bivariate associations between participant characteristics and engagement in regular physical activity were examined using chi-square tests, independent sample T-test, and Mann-Whitney U test as appropriate. To evaluate the association between activity-tracking behavior and engagement in regular physical activity, we first assessed variance inflation factors and correlation coefficients among the covariates, ensuring that no covariate exhibits a correlation greater than 0.3 and that the model’s variance inflation factor does not exceed 3.0 [67,68]. All the variables in the model met these criteria. Next, we performed a multivariable logistic regression for three analytic groups: the combined sample of older adults and care partners (N=615), older adults only (n=310), and care partners only (n=305). We reported the adjusted odds ratios (aORs) with corresponding 95% confidence intervals. All analyses were performed using Stata version 16 [69].
Ethical Considerations
This study was approved by the New York University Langone Health Institutional Review Board (IRB# i25-00450). All study participants provided electronic informed consent before access to the survey. Study procedures adhered to institutional and federal ethical guidelines for research involving human participants.

3. Results

The mean age was 52.8 years (SD 19.0) for the overall sample, 70.1 years (SD 4.3) for older adults, and 35.3 years (SD 10.1) for care partners. Overall, 55.0% of participants were female (older adults 57.1%, care partners 52.8%). By race/ethnicity, 42.9% were non-Hispanic White (older adults 50.7%, care partners 35.1%), 30.6% non-Hispanic Black (older adults 31.6%, care partners 29.5%), 20.7% Hispanic (older adults 11.3%, care partners 30.2%), and 5.8% other races (older adults 6.5%, care partners 5.3%).
Regular physical activity was significantly associated with several demographic and social factors. Participants with regular activity were younger, more likely to be male, and less likely to be Non-Hispanic White. Participants living with others were more likely to engage in regular activity than those living alone. Participant status was strongly associated with physical activity: older adults were significantly more likely to report regular activity than care partners.
Several covariates were independently associated with activity. Across models, race and ethnicity demonstrated consistent associations, with Non-Hispanic Black, Hispanic, and Other racial groups generally exhibiting lower odds of regular physical activity compared with Non-Hispanic White participants. Among care partners, male sex was associated with lower activity. Insurance type was significant in the older adult model, and prior-year falls were associated with higher activity levels among care partners. Frailty was inversely associated with regular activity among older adults.
After adjusting for demographic, social, mobility-related, and health characteristics, activity-tracking frequency remained independently associated with regular physical activity, with different patterns observed across analytic groups. In the combined population, both occasional and frequent tracking were associated with higher odds of regular activity compared with rare tracking (occasional aOR = 2.18; 95% CI: 1.19 – 4.01; frequent aOR = 2.40; 95% CI: 1.45 – 3.96). Among older adults, frequent physical activity tracking was associated with a 2.5-fold increase in the odds of engaging in regular physical activity (aOR = 2.47; 95% CI: 1.08 – 5.64), whereas occasional physical activity tracking was not significantly associated with increased odds of regular physical activity. Among care partners, occasional physical activity tracking was associated with 3.5-fold increased odds of regular physical activity (aOR = 3.54; 95% CI: 1.54 – 8.11), while frequent physical activity tracking was associated with a 2-foldincreased odds regular physical activity (aOR = 1.99; 95% CI: 0.98 – 4.02), although this association was marginally insignificant (p = 0.05).
Table 1. Sociodemographic, mobility, and health characteristics of the study population (N=615), including older adults (n=310) and care partners (n=305).
Table 1. Sociodemographic, mobility, and health characteristics of the study population (N=615), including older adults (n=310) and care partners (n=305).
Variables All Population Older Adults Care Partners
Sociodemographic Characteristics
Age (Mean, SD) 52.8 (19.0) 70.1 (4.3) 35.3 (10.1)
Sex
Male 277 (45.0) 133 (42.9) 144 (47.2)
Female 338 (55.0) 177 (57.1) 161 (52.8)
Race/Ethnicity
Non-Hispanic White 264 (42.9) 157 (50.7) 107 (35.1)
Non-Hispanic Black 188 (30.6) 98 (31.6) 90 (29.5)
Hispanic 127 (20.7) 35 (11.3) 92 (30.2)
Other Races 36 (5.8) 20 (6.5) 16 (5.3)
Highest Educational Degree
High School Diploma 117 (19.0) 53 (17.1) 64 (21.0)
College Degree 277 (45.1) 107 (34.5) 170 (55.7)
Graduate Degree 221 (35.9) 150 (48.4) 71 (23.3)
Marital Status
Married 453 (73.7) 214 (69.0) 239 (78.4)
Never Married 49 (8.0) 14 (4.5) 35 (11.5)
Widow/Separated/Divorced 113 (18.4) 82 (26.5) 31 (10.1)
Living Situation
Living alone 310 (50.4) 248 (80.0) 62 (20.3)
Living with others 305 (49.6) 62 (20.0) 243 (79.7)
Mobility Characteristics
Fall in the last year
Yes 479 (77.9) 234 (75.5) 245 (80.3)
No 136 (22.1) 76 (24.5) 60 (19.7)
Functional Limitations
Yes 51 (8.3) 42 (13.5) 9 (3.0)
No 564 (91.7) 268 (86.5) 296 (97.0)
Health Characteristics
Charlson Comorbidity Score
Median, IQR 0.0 (0.0 – 0.0) 0.0 (0.0 – 1.0) 0.0 (0.0 – 0.0)
Clinical Frailty Scale
Fit 559 (90.9) 276 (89.0) 283 (92.8)
Vulnerable 18 (2.9) 12 (3.9) 6 (2.0)
Frail 38 (6.2) 22 (7.1) 16 (5.2)
Predictor and Outcome Measures
Activity Tracking Behavior
Rarely 151 (24.5) 82 (26.5) 69 (22.6)
Occasional 100 (16.3) 36 (11.6) 64 (21.0)
Frequently 364 (59.2) 192 (61.9) 172 (56.4)
Physical Activity Engagement
Irregular 184 (29.9) 67 (21.6) 117 (38.4)
Regular 431 (70.1) 243 (78.4) 188 (61.6)
Table 2. Table 2. Bivariate association between engagement in physical activity and activity tracking behavior, sociodemographic, mobility, and health characteristics among the study population.
Table 2. Table 2. Bivariate association between engagement in physical activity and activity tracking behavior, sociodemographic, mobility, and health characteristics among the study population.
Variables Physical Activity Engagement p-value
Regular (n=431) Irregular (184)
Sociodemographic Characteristics
Participant Status
Older Adult 67 (36.4) 243 (56.4) <0.001
Care Partner 117 (63.6) 188 (43.6)
Age (Mean, SD) 47.5 (18.7) 55.1 (18.8) <0.001
Sex
Male 99 (53.8) 178 (41.3) 0.004
Female 85 (46.2) 253 (58.7)
Race/Ethnicity
Non-Hispanic White 45 (24.5) 219 (50.8) <0.001
Non-Hispanic Black 75 (40.7) 113 (26.2)
Hispanic 45 (24.5) 82 (19.0)
Other Races 19 (10.3) 17 (4.0)
Highest Educational Degree
High School Diploma 40 (21.7) 77 (17.8) 0.335
College Degree 85b (46.2) 192 (44.6)
Graduate Degree 59 (32.1) 162 (37.6)
Marital Status
Married 143 (77.7) 310 (71.9) 0.324
Never Married 12 (6.5) 37 (8.6)
Widow/Separated/Divorced 29 (15.8) 84 (19.5)
Living Situation
Living alone 76 (41.3) 234 (54.3) 0.003
Living with others 108 (58.7) 197 (45.7)
Mobility Characteristics
Fall in the last year
Yes 151 (82.1) 328 (76.1) 0.103
No 33 (17.9) 103 (23.9)
Functional Limitations
Yes 172 (93.5) 392 (91.0) 0.298
No 12 (6.5) 39 (9.0)
Health Characteristics
Charlson Comorbidity Score
Median (IQR) 0.0 (0.0 – 0.0) 0.0 (0.0 – 0.0) 0.880
Clinical Frailty Scale
Fit 163 (88.5) 396 (91.9) 0.385
Vulnerable 6 (3.3) 12 (2.8)
Frail 15 (8.2) 23 (5.3)
Predictor Measure
Activity Tracking Behavior
Rarely 54 (29.4) 97 (22.5) 0.190
Occasional 29 (15.7) 71 (16.5)
Frequently 101 (54.9) 263 (61.0)
Table 3. Multivariable logistic regression result of the association between engagement in physical activity and activity tracking behavior among the study population and separately among older adults and care partners.
Table 3. Multivariable logistic regression result of the association between engagement in physical activity and activity tracking behavior among the study population and separately among older adults and care partners.
Variables Physical Activity Engagement (Adjusted Odds Ratio (95% CI))
All Population Older Adults Care Partners
Activity Tracking Behavior
Rarely Ref Ref Ref
Occasional 2.18 (1.19 – 4.01) 0.86 (0.32 – 2.28) 3.54 (1.54 – 8.11)
Frequently 2.40 (1.45 – 3.96) 2.47 (1.08 – 5.64) 1.99 (0.98 – 4.02)
Demographic Characteristics
Age 1.02 (1.01 – 1.03) 1.03 (0.95 – 1.12) 0.99 (0.96 – 1.02)
Sex
Male 0.71 (0.48 – 1.04) 1.46 (0.78 – 2.73) 0.50 (0.29 – 0.87)
Female Ref Ref Ref
Race/Ethnicity
Non-Hispanic White Ref Ref Ref
Non-Hispanic Black 0.29 (0.17 – 0.49) 0.37 (0.15 – 0.94) 0.15 (0.07 – 0.34)
Hispanic 0.56 (0.31 – 0.99) 0.50 (0.16 – 1.53) 0.50 (0.23 – 1.08)
Other Races 0.19 (0.09 – 0.42) 0.13 (0.04 – 0.41) 0.20 (0.06 – 0.67)
Highest Educational Degree
High School Diploma Ref Ref Ref
College Degree 1.43 (0.82 – 2.48) 1.68 (0.67 – 4.23) 1.91 (0.86 – 4.26)
Graduate Degree 1.37 (0.74 – 2.57) 1.50 (0.61 – 3.67) 1.59 (0.59 – 4.34)
Marital Status
Married Ref Ref Ref
Never Married 2.08 (0.91 – 4.76) 0.61 (0.13 – 2.79) 2.85 (0.91 – 8.88)
Widow/Separated/Divorced 0.98 (0.55 – 1.73) 0.96 (0.40 – 2.27) 1.23 (0.46 – 3.31)
Living Situation
Living alone 0.77 (0.48 – 1.23) 0.95 (0.37 – 2.41) 1.13 (0.57 – 2.27)
Living with others Ref Ref Ref
Fall in the last year
Yes 1.43 (0.84 – 2.42) 1.07 (0.50 – 2.31) 2.68 (1.14 – 6.34)
No Ref Ref Ref
Functional Limitations
Yes 0.79 (0.35 – 1.76) 1.10 (0.42 – 2.88) 0.57 (0.10 – 3.14)
No Ref Ref Ref
Charlson Comorbidity Score 1.00 (0.81 – 1.25) 0.79 (0.59 – 1.05) 1.11 (0.79 – 1.57)
Clinical Frailty Scale
Fit Ref Ref Ref
Vulnerable 0.59 (0.19 – 1.81) 0.41 (0.10 – 1.68) 0.95 (0.12 – 7.52)
Frail 0.56 (0.26 – 1.24) 0.36 (0.11 – 1.14) 1.07 (0.28 – 4.11)
Significant relationship in bold.

4. Discussion

In this cross-sectional study of 615 older adults and care partners, we found that more frequent activity tracking was associated with significantly greater engagement in regular physical activity. Specifically, older adults who frequently tracked their activity were more likely to engage in regular physical activity, whereas those who tracked occasionally did not show a similar effect. Among care partners, occasional tracking—rather than frequent tracking—was significantly associated with regular physical activity, with frequent tracking approaching statistical significance.
Activity tracking has been consistently linked to higher physical activity levels through mechanisms rooted in self-regulation and behavior-change theories [71,72,73,74]. Self-monitoring increases awareness of daily movement, strengthens accountability, and enables individuals to set and evaluate progress toward personalized goals [75,76,77]. Prior studies have shown that tracking reinforces positive feedback loops that motivate continued activity and can reduce sedentary time [73,78]. For older adults in particular, tracking may provide a compensatory structure that supports habit formation and offers real-time validation of effort [27,79]. Among care partners, activity tracking may also serve as a self-care tool, providing a structured way to maintain health behaviors amid caregiving responsibilities [22,47].
We report that older adults who frequently tracked their activity had substantially higher odds of engaging in regular physical activity, consistent with earlier studies [80,81]. A plausible explanation may be that older adults who track frequently may have integrated activity tracking into their daily routines, using feedback to maintain accountability and motivation [27,82,83]. Yet, older adults’ adoption and continued use of physical activity trackers may be influenced by personal and social factors, as well as smartphone quality. Jin et al [82] reported factors that inform activity self-tracking behavior, including self-efficacy, technology affinity, device quality and design, perceived ease of use, perceived usefulness, perceived device value, social influence, and financial incentive. Hence, interventions aimed at improving physical activity through the use of physical activity trackers should be designed to minimize user burden, leverage intuitive and reliable technologies, and incorporate behavioral and social supports, such as goal setting, feedback, and caregiver or peer involvement, to promote sustained engagement and maximize potential benefits among older adults.
In contrast to older adults, care partners who occasionally tracked their physical activity had higher odds of engaging in regular physical activity. Although care partners who frequently tracked also showed higher odds of activity engagement, this association did not reach statistical significance. A possible explanation for the observed difference among care partners who occasionally and frequently track their physical activity may relate to the burden of caregiving they provide. Care partners often juggle multiple responsibilities, including work, caregiving, and household tasks [84,85,86], leaving less time for structured self-monitoring. Occasional tracking of physical activity may therefore represent a practical strategy for care partners with competing demands [87], in which tracking is used selectively to support motivation or goal setting without requiring sustained daily attention. In contrast, frequent physical activity tracking among care partners may reflect habitual self-monitoring behavior that persists despite time constraints and competing caregiving demands [86,88], such that high tracking frequency does not necessarily translate into greater opportunities to engage in physical activity.
Our findings have several implications for practice, program development, and future research. Activity tracking is a low-cost, accessible tool that may support regular physical activity among both older adults and care partners [27,81,83]. Hence, interventions aimed at increasing physical activity should use activity tracking as a supportive tool, recognizing that older adults and care partners may differ in how frequently or consistently they track their activity. Programs should emphasize achieving recommended weekly exercise goals [89,90], rather than strict adherence to daily tracking, so that tracking supports meaningful improvements in physical activity. In addition, providers should encourage coordinated and mutually reinforcing physical activity tracking behaviors between older adults and their care partners to strengthen motivation and promote shared participation in physical activity. At the community and organizational levels, senior centers, caregiving programs, and health-promotion initiatives could integrate easy-to-use physical activity-tracking devices into existing wellness activities and chronic disease management. Policy initiatives that expand access to digital activity-tracking devices through insurance coverage, community-based aging services, or fall-prevention programs may increase device adoption and reduce sedentary behavior. Finally, these results underscore the need for further research examining longitudinal patterns of activity tracking, dyadic influences on health behaviors, and the mechanisms through which tracking frequency affects physical activity among older adults and care partners.
This study has several limitations. First, its cross-sectional design precludes causal inference. Second, the observed association between physical activity tracking and engagement in regular physical activity may be bidirectional, as individuals who are already more active may be more likely to track their activity. Third, physical activity and tracking behaviors were self-reported, which may introduce recall or social desirability bias [91,92]. Fourth, our study participants were recruited online, which may overrepresent individuals with higher digital literacy or greater interest in health research, potentially limiting generalizability to the larger population of older adults and care partners. Finally, although we controlled for sociodemographic and health characteristics, unmeasured factors, such as broader social networks, environmental supports, or access to caregiving resources, may influence physical activity engagement or activity tracking behaviors. Despite these limitations, this study is among the few to examine both US older adults’ and care partners’ activity tracking and physical activity behaviors, providing insights into how tracking frequency relates to engagement in physical activities within this population. Our findings highlight practical, low-cost strategies that promote physical activity and provide information to inform interventions, programs, and policies aimed at improving physical activity among older adults and care partners.

5. Conclusions

Activity tracking is a low-cost, accessible tool that may support regular physical activity among both older adults and their care partners. Our study demonstrates that tracking frequency is associated with higher physical activity engagement, though patterns differ slightly between older adults and care partners. Interventions, programs, and policies that seek to improve physical activity among older adults and care partners can therefore leverage activity trackers while focusing on achieving recommended weekly exercise goals rather than strict daily adherence.

Author Contributions

Conceptualization, O.A; Methodology, O.A.; Software, O.A., J.C.; Formal Analysis, O.A.; Data Curation, O.A.; Writing – Original Draft Preparation, O.A.; Writing – Review & Editing, O.A., D.B, J.C.; Visualization, O.A.; Supervision, J.C., D.B.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was reviewed and approved by the NYU Langone Health Institutional Review Board (IRB#: i25-00450).

Data Availability Statement

The original data presented in the study are openly available in FigShare at 10.6084/m9.figshare.30929594.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ACTIVE Activity Tracking, Care Partner Co-Participation, Text Reminders, Instructional Education, Virtual Physical Therapy, and Exercise
REDCap Research Electronic Data Capture

References

  1. Szychowska, A.; Drygas, W. Physical activity as a determinant of successful aging: a narrative review article. Aging Clin Exp Res 2022, 34, 1209-1214. [CrossRef]
  2. WHO. Physical activity. Available online: https://www.who.int/news-room/fact-sheets/detail/physical-activity (accessed on.
  3. CDC. Benefits of Physical Activity. Available online: https://www.cdc.gov/physical-activity-basics/benefits/index.html (accessed on.
  4. Ungvari, Z.; Fazekas-Pongor, V.; Csiszar, A.; Kunutsor, S.K. The multifaceted benefits of walking for healthy aging: from Blue Zones to molecular mechanisms. Geroscience 2023, 45, 3211-3239. [CrossRef]
  5. Nystoriak, M.A.; Bhatnagar, A. Cardiovascular Effects and Benefits of Exercise. Front Cardiovasc Med 2018, 5, 135. [CrossRef]
  6. Drakopoulou, M.; Vlachakis, P.K.; Apostolos, A.; Tsioufis, K.; Toutouzas, K. How does regular exercise improve cardiovascular function: Congenital heart disease and beyond. Int J Cardiol Congenit Heart Dis 2025, 19, 100542. [CrossRef]
  7. Mandolesi, L.; Polverino, A.; Montuori, S.; Foti, F.; Ferraioli, G.; Sorrentino, P.; Sorrentino, G. Effects of Physical Exercise on Cognitive Functioning and Wellbeing: Biological and Psychological Benefits. Front Psychol 2018, 9, 509. [CrossRef]
  8. Cui, L.; Xing, Y.; Zhou, H.; Qian, J.; Li, J.; Shen, F.; Bu, Y. Physical activity promotes the development of cognitive ability in adolescents: the chain mediating role based on self-education expectations and learning behaviors. Frontiers in Psychology 2024, Volume 15 - 2024. [CrossRef]
  9. Langhammer, B.; Bergland, A.; Rydwik, E. The Importance of Physical Activity Exercise among Older People. Biomed Res Int 2018, 2018, 7856823. [CrossRef]
  10. Marquez, D.X.; Aguiñaga, S.; Vásquez, P.M.; Conroy, D.E.; Erickson, K.I.; Hillman, C.; Stillman, C.M.; Ballard, R.M.; Sheppard, B.B.; Petruzzello, S.J.; et al. A systematic review of physical activity and quality of life and well-being. Transl Behav Med 2020, 10, 1098-1109. [CrossRef]
  11. Barcelos, A.; Lopes, D.G.; Mazeda, C.; Canhão, H.; da Cunha Branco, J.; Rodrigues, A.M. Regular physical activity improves physical function and health-related quality of life among middle-aged and older women who suffered a fragility fracture-a population-based cohort. Osteoporos Int 2024, 35, 2203-2213. [CrossRef]
  12. Ruegsegger, G.N.; Booth, F.W. Health Benefits of Exercise. Cold Spring Harb Perspect Med 2018, 8. [CrossRef]
  13. Blackwell, D.; Clarke, T. State Variation in Meeting the 2008 Federal Guidelines for Both Aerobic and Muscle-strengthening Activities Through Leisure-time Physical Activity Among Adults Aged 18–64: United States, 2010–2015; U.S. Department of Health and Human Services: 2018.
  14. Elgaddal N, K.E. Characteristics of Older Adults Who Met Federal Physical Activity Guidelines for Americans: United States, 2022.; National Health Statistics Reports [Internet]: 2024.
  15. Adeyemi, O.; Chippendale, T.; Ogedegbe, O.; Boatright, D.; Chodosh, J. Activity Intensity and All-Cause Mortality Following Fall Injury Among Older Adults: Results from a 12-Year National Survey. Healthcare 2025, 13, 2530. [CrossRef]
  16. Bureau, U.S.C. 2010 US Census summary file 1: Singe years of age and sex 2010. 2014.
  17. Meredith, S.J.; Cox, N.J.; Ibrahim, K.; Higson, J.; McNiff, J.; Mitchell, S.; Rutherford, M.; Wijayendran, A.; Shenkin, S.D.; Kilgour, A.H.M.; et al. Factors that influence older adults’ participation in physical activity: a systematic review of qualitative studies. Age Ageing 2023, 52. [CrossRef]
  18. CDC. Report: Adults 50 and Older Need More Physical Activity. Available online: https://www.cdc.gov/physical-activity/php/reports/adults-50-and-older.html (accessed on.
  19. Schulz, R.; Beach, S.R.; Czaja, S.J.; Martire, L.M.; Monin, J.K. Family Caregiving for Older Adults. Annu Rev Psychol 2020, 71, 635-659. [CrossRef]
  20. Committee on Family Caregiving for Older Adults; Board on Health Care Services; Health and Medicine Division; National Academies of Sciences, E., and Medicine. Families Caring for an Aging America. Family Caregiving Roles and Impacts. 2016.
  21. Stanfors, M.; Jacobs, J. Unpaid caregiving and stress among older working-age men and women in Sweden. SSM - population health 2023, 23, 101458. [CrossRef]
  22. Boon, J.T.; Maxwell, C.A.; Layne, D.M. Caring While Moving: Case Studies on Physical Activity and Dementia Caregiver Well-Being. Healthcare (Basel) 2025, 13. [CrossRef]
  23. Kim, E.; Ullrich-French, S.; Bolkan, C.; Hill, L.G. The Role of Caregivers in Physical Activity for Older Adults With Alzheimer’s Disease. American Journal of Alzheimer’s Disease & Other Dementias® 2018, 33, 122-130. [CrossRef]
  24. Bethancourt, H.J.; Rosenberg, D.E.; Beatty, T.; Arterburn, D.E. Barriers to and facilitators of physical activity program use among older adults. Clin Med Res 2014, 12, 10-20. [CrossRef]
  25. Wu, S.; Li, G.; Du, L.; Chen, S.; Zhang, X.; He, Q. The effectiveness of wearable activity trackers for increasing physical activity and reducing sedentary time in older adults: A systematic review and meta-analysis. DIGITAL HEALTH 2023, 9, 205520762311767. [CrossRef]
  26. Laffi, A.; Persiani, M.; Piras, A.; Meoni, A.; Raffi, M. Effectiveness of Wearable Technologies in Supporting Physical Activity and Metabolic Health in Adults with Type 2 Diabetes: A Systematic-Narrative Hybrid Review. Healthcare (Basel) 2025, 13. [CrossRef]
  27. Kononova, A.; Li, L.; Kamp, K.; Bowen, M.; Rikard, R.V.; Cotten, S.; Peng, W. The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change. JMIR Mhealth Uhealth 2019, 7, e9832. [CrossRef]
  28. Canali, S.; Ferretti, A.; Schiaffonati, V.; Blasimme, A. Wearable Technologies for Healthy Ageing: Prospects, Challenges, and Ethical Considerations. The Journal of Frailty & Aging 2024, 13, 149-156. [CrossRef]
  29. Friemel, T.N. The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society 2016, 18, 313-331. [CrossRef]
  30. Kim, S.; Choudhury, A. Comparison of Older and Younger Adults’ Attitudes Toward the Adoption and Use of Activity Trackers. JMIR mHealth and uHealth 2020, 8, e18312. [CrossRef]
  31. Kekade, S.; Hseieh, C.H.; Islam, M.M.; Atique, S.; Mohammed Khalfan, A.; Li, Y.C.; Abdul, S.S. The usefulness and actual use of wearable devices among the elderly population. Comput Methods Programs Biomed 2018, 153, 137-159. [CrossRef]
  32. Brickwood, K.J.; Ahuja, K.D.K.; Watson, G.; O’Brien, J.A.; Williams, A.D. Effects of Activity Tracker Use With Health Professional Support or Telephone Counseling on Maintenance of Physical Activity and Health Outcomes in Older Adults: Randomized Controlled Trial. JMIR mHealth and uHealth 2021, 9, e18686. [CrossRef]
  33. Cadmus-Bertram, L.A.; Marcus, B.H.; Patterson, R.E.; Parker, B.A.; Morey, B.L. Randomized Trial of a Fitbit-Based Physical Activity Intervention for Women. Am J Prev Med 2015, 49, 414-418. [CrossRef]
  34. Croteau, K.A.; Richeson, N.E.; Farmer, B.C.; Jones, D.B. Effect of a pedometer-based intervention on daily step counts of community-dwelling older adults. Res Q Exerc Sport 2007, 78, 401-406. [CrossRef]
  35. Duscha, B.D.; Piner, L.W.; Patel, M.P.; Craig, K.P.; Brady, M.; McGarrah, R.W., 3rd; Chen, C.; Kraus, W.E. Effects of a 12-week mHealth program on peak VO(2) and physical activity patterns after completing cardiac rehabilitation: A randomized controlled trial. Am Heart J 2018, 199, 105-114. [CrossRef]
  36. Harris, T.; Kerry, S.M.; Limb, E.S.; Victor, C.R.; Iliffe, S.; Ussher, M.; Whincup, P.H.; Ekelund, U.; Fox-Rushby, J.; Furness, C.; et al. Effect of a Primary Care Walking Intervention with and without Nurse Support on Physical Activity Levels in 45- to 75-Year-Olds: The Pedometer And Consultation Evaluation (PACE-UP) Cluster Randomised Clinical Trial. PLoS Med 2017, 14, e1002210. [CrossRef]
  37. Kenfield, S.A.; Van Blarigan, E.L.; Ameli, N.; Lavaki, E.; Cedars, B.; Paciorek, A.T.; Monroy, C.; Tantum, L.K.; Newton, R.U.; Signorell, C.; et al. Feasibility, Acceptability, and Behavioral Outcomes from a Technology-enhanced Behavioral Change Intervention (Prostate 8): A Pilot Randomized Controlled Trial in Men with Prostate Cancer. Eur Urol 2019, 75, 950-958. [CrossRef]
  38. Kim, B.H.; Glanz, K. Text messaging to motivate walking in older African Americans: a randomized controlled trial. Am J Prev Med 2013, 44, 71-75. [CrossRef]
  39. Lewis, Z.H.; Ottenbacher, K.J.; Fisher, S.R.; Jennings, K.; Brown, A.F.; Swartz, M.C.; Martinez, E.; Lyons, E.J. Effect of Electronic Activity Monitors and Pedometers on Health: Results from the TAME Health Pilot Randomized Pragmatic Trial. Int J Environ Res Public Health 2020, 17. [CrossRef]
  40. Lyons, E.J.; Swartz, M.C.; Lewis, Z.H.; Martinez, E.; Jennings, K. Feasibility and Acceptability of a Wearable Technology Physical Activity Intervention With Telephone Counseling for Mid-Aged and Older Adults: A Randomized Controlled Pilot Trial. JMIR mHealth and uHealth 2017, 5, e28. [CrossRef]
  41. Rowley, T.W.; Lenz, E.K.; Swartz, A.M.; Miller, N.E.; Maeda, H.; Strath, S.J. Efficacy of an Individually Tailored, Internet-Mediated Physical Activity Intervention in Older Adults: A Randomized Controlled Trial. J Appl Gerontol 2019, 38, 1011-1022. [CrossRef]
  42. Suboc, T.B.; Strath, S.J.; Dharmashankar, K.; Coulliard, A.; Miller, N.; Wang, J.; Tanner, M.J.; Widlansky, M.E. Relative importance of step count, intensity, and duration on physical activity’s impact on vascular structure and function in previously sedentary older adults. J Am Heart Assoc 2014, 3, e000702. [CrossRef]
  43. Talbot, L.A.; Gaines, J.M.; Huynh, T.N.; Metter, E.J. A home-based pedometer-driven walking program to increase physical activity in older adults with osteoarthritis of the knee: a preliminary study. J Am Geriatr Soc 2003, 51, 387-392. [CrossRef]
  44. Jiang, Y.; Zeng, K.; Yang, R. Wearable device use in older adults associated with physical activity guideline recommendations: Empirical research quantitative. J Clin Nurs 2023, 32, 6374-6383. [CrossRef]
  45. Dhingra, L.S.; Aminorroaya, A.; Oikonomou, E.K.; Nargesi, A.A.; Wilson, F.P.; Krumholz, H.M.; Khera, R. Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020. JAMA network open 2023, 6, e2316634. [CrossRef]
  46. Chandrasekaran, R.; Katthula, V.; Moustakas, E. Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey. J Med Internet Res 2020, 22, e22443. [CrossRef]
  47. Mahmood, A.; Kim, H.; Kedia, S.; Dillon, P. Wearable Activity Tracker Use and Physical Activity Among Informal Caregivers in the United States: Quantitative Study. JMIR Mhealth Uhealth 2022, 10, e40391. [CrossRef]
  48. Aparicio, A.; Inostroza-Correa, M.A.; Miranda, P.; Salinas, C.; Herskovic, V.; Chackiel, C.; Alarcón, C. The impact of technology use for care by informal female caregivers on their well-being: a scoping review. Syst Rev 2025, 14, 89. [CrossRef]
  49. ResearchMatch. What is ResearchMatch? Available online: https://www.researchmatch.org/ (accessed on 11/22/2025).
  50. Harris, P. Research Electronic Data Capture (REDCap). Journal of the Medical Library Association 2018. [CrossRef]
  51. WHO. Guidelines on Physical Activity and Sedentary Behaviour. Available online: https://www.ncbi.nlm.nih.gov/books/NBK566046/ (accessed on.
  52. Adeyemi, O.; Chippendale, T.; Ogedegbe, O.; Boatright, D.; Chodosh, J. Activity Intensity and All-Cause Mortality Following Fall Injury Among Older Adults: Results from a 12-Year National Survey. Healthcare 2025, 13. [CrossRef]
  53. Chippendale, T.; Albert, S.M.; Mahmood, A. Efficacy of the Stroll Safe Outdoor Fall Prevention Program: A Randomized Controlled Trial. The Gerontologist 2023, 63, 1556-1565. [CrossRef]
  54. Rotenberg, S.; Fritz, H.; Chippendale, T.; Lysack, C.; Cutchin, M. Out-of-Home Activities and Health in Older Adults: A Scoping Review. OTJR (Thorofare N J) 2021, 41, 285-298. [CrossRef]
  55. Ferguson, T.; Olds, T.; Curtis, R.; Blake, H.; Crozier, A.J.; Dankiw, K.; Dumuid, D.; Kasai, D.; O’Connor, E.; Virgara, R.; et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. The Lancet Digital Health 2022, 4, e615-e626. [CrossRef]
  56. Zahrt, O.H.; Evans, K.; Murnane, E.; Santoro, E.; Baiocchi, M.; Landay, J.; Delp, S.; Crum, A. Effects of Wearable Fitness Trackers and Activity Adequacy Mindsets on Affect, Behavior, and Health: Longitudinal Randomized Controlled Trial. J Med Internet Res 2023, 25, e40529. [CrossRef]
  57. Charlson, M.; Szatrowski, T.P.; Peterson, J.; Gold, J. Validation of a combined comorbidity index. Journal of Clinical Epidemiology 1994, 47, 1245-1251. [CrossRef]
  58. Charlson, M.E.; Carrozzino, D.; Guidi, J.; Patierno, C. Charlson Comorbidity Index: A Critical Review of Clinimetric Properties. Psychotherapy and psychosomatics 2022, 91, 8-35. [CrossRef]
  59. Adeyemi, O.J.; Meltzer-Bruhn, A.; Esper, G.; DiMaggio, C.; Grudzen, C.; Chodosh, J.; Konda, S. Crosswalk between Charlson Comorbidity Index and the American Society of Anesthesiologists Physical Status Score for Geriatric Trauma Assessment. Healthcare 2023, 11. [CrossRef]
  60. Konda, S.R.; Dedhia, N.; Ranson, R.A.; Tong, Y.; Ganta, A.; Egol, K.A. Loss of Ambulatory Level and Activities of Daily Living at 1 Year Following Hip Fracture: Can We Identify Patients at Risk? Geriatric Orthopaedic Surgery and Rehabilitation 2021, 12. [CrossRef]
  61. Pulok, M.H.; Theou, O.; van der Valk, A.M.; Rockwood, K. The role of illness acuity on the association between frailty and mortality in emergency department patients referred to internal medicine. Age Ageing 2020, 49, 1071-1079. [CrossRef]
  62. Rockwood, K.; Song, X.; MacKnight, C.; Bergman, H.; Hogan, D.B.; McDowell, I.; Mitnitski, A. A global clinical measure of fitness and frailty in elderly people. Cmaj 2005, 173, 489-495. [CrossRef]
  63. Leshabari, K.M. Reliability and Validity of Clinicopathological Features Associated with Frailty Syndrome in Elderly Population. In Frailty in the Elderly - Understanding and Managing Complexity, Palermo, S., Ed.; IntechOpen: London, 2021.
  64. Ekerstad, N.; Javadzadeh, D.; Alexander, K.P.; Bergström, O.; Eurenius, L.; Fredrikson, M.; Gudnadottir, G.; Held, C.; Ängerud, K.H.; Jahjah, R.; et al. Clinical Frailty Scale classes are independently associated with 6-month mortality for patients after acute myocardial infarction. Eur Heart J Acute Cardiovasc Care 2022, 11, 89-98. [CrossRef]
  65. Zhou, Q.; Deng, W.; Zhao, M.; Zhou, W.; Chen, D.; Wang, H.; Wang, S.; Yang, H.; Yin, Y. The clinical frailty scale is associated with an increased risk of postoperative complications and the development of post-traumatic osteoarthritis in elderly patients with trimalleolar ankle fractures - a retrospective study. Journal of orthopaedic surgery and research 2025, 20, 120. [CrossRef]
  66. Fox-Wasylyshyn, S.M.; El-Masri, M.M. Handling missing data in self-report measures. Res Nurs Health 2005, 28, 488-495. [CrossRef]
  67. Kim, J.H. Multicollinearity and misleading statistical results. Korean J Anesthesiol 2019, 72, 558-569. [CrossRef]
  68. Marcoulides, K.M.; Raykov, T. Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods. Educ Psychol Meas 2019, 79, 874-882. [CrossRef]
  69. StataCorp Stata Statistical Software: Release 16, StataCorp LLC: College Station, TX, 2017.
  70. Keadle, S.K.; McKinnon, R.; Graubard, B.I.; Troiano, R.P. Prevalence and trends in physical activity among older adults in the United States: A comparison across three national surveys. Prev Med 2016, 89, 37-43. [CrossRef]
  71. Lin, S.; Ling, R. Motivating Physical Activity with Fitness Tracking and the Interpersonal Context. International Journal of Human-Computer Interaction 2023. [CrossRef]
  72. Hoj, T.H.; Covey, E.L.; Jones, A.C.; Haines, A.C.; Hall, P.C.; Crookston, B.T.; West, J.H. How Do Apps Work? An Analysis of Physical Activity App Users’ Perceptions of Behavior Change Mechanisms. JMIR Mhealth Uhealth 2017, 5, e114. [CrossRef]
  73. Asimakopoulos, S.; Asimakopoulos, G.; Spillers, F. Motivation and User Engagement in Fitness Tracking: Heuristics for Mobile Healthcare Wearables. Informatics 2017, 4, 5. [CrossRef]
  74. Chia, G.L.C.; Anderson, A.; McLean, L.A. Behavior Change Techniques Incorporated in Fitness Trackers: Content Analysis. JMIR Mhealth Uhealth 2019, 7, e12768. [CrossRef]
  75. IAT. The Role of Self-Monitoring in Developing Independence. Available online: https://www.intellistarsaba.com/blog/the-role-of-self-monitoring-in-developing-independence (accessed on.
  76. Associates, M.E.T. The Power of Self-Monitoring: A Path to Personal Growth. Available online: https://metaoh.org/the-power-of-self-monitoring-a-path-to-personal-growth/ (accessed on.
  77. Page, E.J.; Massey, A.S.; Prado-Romero, P.N.; Albadawi, S. The Use of Self-Monitoring and Technology to Increase Physical Activity: A Review of the Literature. Perspect Behav Sci 2020, 43, 501-514. [CrossRef]
  78. Sullivan, A.N.; Lachman, M.E. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Front Public Health 2016, 4, 289. [CrossRef]
  79. Peng, W.; Li, L.; Kononova, A.; Cotten, S.; Kamp, K.; Bowen, M. Habit Formation in Wearable Activity Tracker Use Among Older Adults: Qualitative Study. JMIR Mhealth Uhealth 2021, 9, e22488. [CrossRef]
  80. Wu, S.; Li, G.; Du, L.; Chen, S.; Zhang, X.; He, Q. The effectiveness of wearable activity trackers for increasing physical activity and reducing sedentary time in older adults: A systematic review and meta-analysis. Digit Health 2023, 9, 20552076231176705. [CrossRef]
  81. Li, R.; Li, Y.; Wang, L.; Li, L.; Fu, C.; Hu, D.; Wei, Q. Wearable Activity Tracker–Based Interventions for Physical Activity, Body Composition, and Physical Function Among Community-Dwelling Older Adults: Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Med Internet Res 2025, 27, e59507. [CrossRef]
  82. Jin, D.; Halvari, H.; Maehle, N.; Olafsen, A.H. Self-tracking behaviour in physical activity: a systematic review of drivers and outcomes of fitness tracking. Behaviour & Information Technology 2022, 41, 242-261. [CrossRef]
  83. Hvalič-Touzery, S.; Šetinc, M.; Dolničar, V. Benefits of a Wearable Activity Tracker with Safety Features for Older Adults: An Intervention Study. International Journal of Environmental Research and Public Health 2022, 19, 15723. [CrossRef]
  84. Honda, A.; Ono, M.; Nishida, T.; Honda, S. Balancing and Conflict Between Work and Family Life of Sandwiched Caregivers: A Scoping Review. Safety and Health at Work 2025, 16, 156-163. [CrossRef]
  85. Kokorelias, K.M.; Nadesar, N.; Bak, K.; Boger, J.; Nygård, L.; Mäki-Petäjä-Leinonen, A.; Nedlund, A.-C.; Astell, A. The impact on employment and education of caregiving for a family member with young onset dementia: A scoping review. Dementia 2024, 23, 850-881. [CrossRef]
  86. Queen, T.L.; Butner, J.; Berg, C.A.; Smith, J. Activity Engagement Among Older Adult Spousal Caregivers. J Gerontol B Psychol Sci Soc Sci 2019, 74, 1278-1282. [CrossRef]
  87. Committee on Family Caregiving for Older Adults; Board on Health Care Services; Health and Medicine Division; National Academies of Sciences, E., and Medicine. Families Caring for an Aging America. Available online: https://www.ncbi.nlm.nih.gov/books/NBK396398/ (accessed on.
  88. Kuzmik, A.; Rodriguez, M.; Hannan, J.; Boltz, M. Designing a mobile application to promote physical activity in spousal care partners of persons living with dementia and their care-recipient. Dementia (London) 2025, 24, 408-423. [CrossRef]
  89. HHS. Physical Activity Guidelines for Americans. Available online: https://odphp.health.gov/sites/default/files/2019-09/Physical_Activity_Guidelines_2nd_edition.pdf (accessed on.
  90. Association, A.H. American Heart Association Recommendations for Physical Activity in Adults and Kids. Available online: https://www.heart.org/en/healthy-living/fitness/fitness-basics/aha-recs-for-physical-activity-in-adults (accessed on.
  91. Rojas-Saunero, L.P.; Glymour, M.M.; Mayeda, E.R. Selection Bias in Health Research: Quantifying, Eliminating, or Exacerbating Health Disparities? Curr Epidemiol Rep 2024, 11, 63-72. [CrossRef]
  92. Van de Mortel, T.F. Faking it: social desirability response bias in self-report research. Australian Journal of Advanced Nursing, The 2008, 25, 40.
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