Social capital predicts accelerometry-measured physical activity among older adults in the U.S.: a cross-sectional study in the National Social Life, Health, and Aging Project

It has been well established, both conceptually and empirically, that more social capital (i.e., the relationships between people and the potential resources represented by these relationships) later in life is linked to a wide range of better health outcomes important to older adults. Key measures of social capital include 1) social network structure (i.e., the size and composition of one’s social network) and 2) levels of social engagement, (i.e., participation in social and community activities) [1]. Seminal research published by Berkman and Syme in 1979 [2] showed that the extent of one’s social network predicted 9-year mortality in a prospective study of adults in Alameda County, California, with the most isolated group being over two-times as likely to die during the follow-up period than the most socially connected group. Follow up work by House, et al. in 1982 [3] using data from the Tecumseh Community Health Study added that, in addition to one’s social network, increased participation in various social and community activities also predicted lower mortality in men over a 9–12 year follow-up period. Since then, a myriad of older-adult focused studies examining social capital have linked social networks and social engagement to a variety of key physical and mental health benefits in older adults, including protection against dementia [4, 5] and disability [6], higher levels of life satisfaction and perceived quality of life [7, 8], better self-rated health [9], and overall lower mortality [10].

While the mechanisms behind these beneficial associations are not yet fully understood [11], a leading theory is that social networks alter social and interpersonal behaviors, like physical activity (PA) which, in turn, influence health [12]. Physical activity has well established beneficial effects, both immediate and long-term, on the health of older adults [13]. Immediate benefits include improved sleep, insulin sensitivity, and reduced blood pressure [13]. In the long term, activity lowers risk for coronary heart disease, diabetes, colon and breast cancers, fractures and falls, dementia, and depression and anxiety [14]. Physical activity promotes feelings of well-being and delays disability and frailty for older adults, both of which are crucial for maintaining independent living [15].

Previous studies have suggested some aspects of social capital may be related to physical activity, though few studies focus specifically on older adults and almost all use self-reported physical activity measures. In particular, social network composition, network size and social engagement may be related self-reported PA. Older adults reporting “diverse” or largely “friend”-based networks report higher PA levels than those who report primarily “family”-based or “restricted” networks [16‐18]. In fact, Loprinzi, Joyner et al. in 2016 found a dose-related relationship such that the greater the number of close friends reported in an older adult’s network, the greater their self-reported physical activity participation [19]. Social engagement with friends and family, whether in formal or informal groups, has repeatedly been seen to predict self-reported physical activity levels in middle-aged and older adults [20‐24]. Conversely, those with greater social isolation, defined by a lack of social network ties and social integration, are the least physically active [25, 26]. One significant drawback of the above studies is that they rely on self-reported PA measures, which are subject to recall bias [27] and tend to overestimate true PA levels when compared to physical activity measured by accelerometry [27, 28].

To the best of our knowledge, no studies have directly related objective PA levels to social network and social engagement measures in older adults. Loprinzi, Crush et al. in 2018 related social support network measures to accelerometry-measured sedentary behavior, rather than physical activity, and found spousal social support for men and larger household sizes to be associated with lower levels of sedentary behavior [29]. Carlson et al. reported an association between accelerometry-measured PA and social support related specifically to PA (such as friends or family walking with, exercising with, or encouraging the participant to exercise) but did not study other aspects of social capital such as social network size or composition or social engagement [30].

To extend the existing research, the aim of this study was to relate an objective measurement of physical activity, wrist accelerometry [28, 31], to social capital measures (social networks and social engagement) in a nationally representative dataset of older adults in the United States. We analyzed social capital measures pertaining to social network structure and levels of engagement in social and community activities, all of which are unique and independently important aspects of social health that are modifiable by physicians, caretakers, and policy makers through targeted public health initiatives. We hypothesized that older adults with larger network sizes, a friend-focused network composition, and higher levels of social engagement would exhibit higher accelerometer-measured physical activity levels.

Descriptive demographic, health and activity characteristics of the study sample are listed in Table 1. Of the 738 participants in the accelerometry sub-study, 673 had the complete set of accelerometry, demographic, and health data required to be included in the study sample. The mean age was 71.9 years (SD: 7.2 years), 54.0% were female, and 83.9% were White/Caucasian. A majority (62.9%) was married and 24.8% was still working. One in five participants rated their physical health as poor or fair. Some degree of disability was common in this sample: 31.0 and 38.9% of the sample reported having difficulty performing at least one ADL and at least one IADL, respectively.

The group wore the wrist accelerometers for an average of 36.3 waking hours (SD: 7.9 h) over the 72-h wear period; 18.4% wore the accelerometer on one weekend day and 22.1% wore the accelerometer over both weekend days. The mean accelerometer-measured activity level was 222.9 counts-per-minute (SD: 88.6 counts-per-minute).

Social relationship characteristics are described in Table 2. The mean network size was 3.9 persons (SD: 1.3 persons) and 47.6% of the sample reported having 5 people (the maximum number recorded) in their network. The mean network proportion consisting of friends was 0.27 (SD: 0.29). The reported frequencies of visiting with neighbors, attending a community group meeting, attending a religious service, and volunteering were relatively equally distributed across response options; however, 53.3% of respondents reported socializing with friends or relatives ‘often’ and 57.0% of respondents did not volunteer in the past year.

Multivariate linear regression models (Table 3 and Additional file 1), each consisting of mean daily counts as the outcome variable and a single social capital variable (e.g., network size) plus covariates as the independent variables, revealed that a number of social capital measures were significantly associated with accelerometry-assessed activity. When adjusting for accelerometer-wear covariates only (Table 3: Minimally Adjusted Regression Models adjusted for accelerometer number of hours worn, number of weekend days worn, and month of wear), all hypothesized social capital measures were significantly associated with physical activity: larger network size (β = 6.16, p = 0.033), higher network proportion friends (β = 36.91, p = 0.029), more frequent socializing (β = 12.35, p = 0.006) and higher levels of community involvement (β = 4.5 p = 0.033) all predicted higher PA levels. After additionally controlling for demographic, health, and accelerometry covariates (Table 3: Fully Adjusted Regression Models), network size (β = 4.77, p = 0.042), network proportion friends (β = 35.81, p = 0.013) and frequency of socializing (β = 8.73, p = 0.028) persisted as significant predictors of activity.

Additional regression analyses were conducted on the individual social activities comprising the composite social engagement measures to assess whether the effects of certain activities predominated in their associations with physical activity. For socializing (Table 3: model 3), the frequency of visiting with neighbors (Table 3: model 3b, fully adjusted, β = 14.62, p = 0.009) was found to largely account for the predictive value of socializing as a composite measure. To illustrate, after adjusting for all other covariates, older adults who reported ‘often’ visiting with neighbors had a predicted mean activity level that was 13.5% higher than those who reported ‘never or rarely’ visiting with their neighbors, 245.6 cpm (95% CI: 230.2–261.0) vs. 216.3 cpm (95% CI: 204.6–228.0) (Fig. 1). In contrast, the frequency of socializing with friends/relatives (Table 3: model 3a) did not significantly predict physical activity, even prior to full adjustment with covariates. For community involvement (Table 3: model 4), attendance at organized group meetings showed a positive association with PA that persisted after adjustment for demographic and health covariates (Table 3: model 4a, fully-adjusted, β = 9.04, p = 0.035). Frequency of volunteering was significantly associated with PA when adjusting for accelerometry covariates only (Table 3: model 4c, minimally adjusted, β = 10.39, p = 0.041) and lost significance as a predictor of physical activity after controlling for demographic variables, notably gender and assets. No significant relationship was found between physical activity and frequency of attending religious services (Table 3: model 4b).

We are among the first to use an objective measure of physical activity to explore how social capital is related to physical activity in a nationally representative sample of U.S. older adults. We found significant associations between social capital and accelerometry-measured physical activity, even after adjusting for potential confounders. Most notably, larger-sized networks, networks with higher proportion of friends, and more frequent socialization with neighbors predicted higher PA levels.

The positive association between total network size and physical activity agreed with findings from previous research studies using self-reported physical activity data [2, 16, 18, 19]. One potential explanation is that larger social networks protect against the detrimental psychological and physiological effects of social isolation [2, 25]. The network size captured by NSHAP, which includes people that the respondent feels close enough with to discuss important matters, indicates the extent to which the respondent has strong, frequently accessed relationships through which support and resources can flow [32]. Conversely, respondents in NSHAP with small to nonexistent network are likely to feel socially isolated, a major risk factor for and/or consequence of various negative health behaviors and outcomes, including decreased physical activity levels and increased sedentary time [34, 35].

We also found that study participants reporting networks with higher proportions of friends also had higher levels of accelerometry-measured PA. Prior research characterizing friendships in older adults has found that interactions with friends are more likely to be dictated by pleasure and enjoyment whereas family interactions often involve daily needs and routine tasks. Moreover, older adults frequently name friends as the network members with whom they enjoy spending their time and engaging in leisure activities [43], which substantiate our findings that older adults with relatively more friend-focused networks also had higher PA levels. Our findings add to the growing evidence that maintaining friendships later in life may benefit health and/or reflect better health in ways that having relationships with family members cannot [19, 43‐46].

We found the predictive value of frequency of socializing seen in our study to be driven by a strong association between visiting with neighbors and physical activity. Our analyses showed that, all else being equal, an older adult who often visited with their neighbors was predicted to have a daily physical activity level that was 13.5% higher than an older adult who never or rarely visited with their neighbors (Fig. 1). Recent studies have concluded that even small increases in activity level (or decreases in sedentary time) are associated with meaningful health benefits [47, 48]; likewise, the 13.5% increase associated with visiting neighbors seen in this study suggests a clinically meaningful increase in activity, especially for older adults who spend the majority of their time completely inactive. Moreover, due to the wording of this question (“How often do you and people in this area visit in each other’s homes or when you meet on the street?”), responses may have reflected characteristics of the respondent’s neighborhood and built environment as much as the respondent’s own social behaviors and preferences. For instance, Van Holle et al. [20] found that higher frequencies of talking with neighbors were related to higher levels of self-reported walking for recreation or transport in a sample of community-dwelling Belgian older adults. Our finding, along with other studies observing the impact of neighborhood walkability, aesthetics, accessibility to parks, and neighborhood safety on self-reported physical activity levels [20, 49‐51], points to the potential physical and social health importance of the neighborhood and environment in which an older adult lives.

In contrast, frequencies of socializing with friends/relatives and religious service attendance were not related to physical activity levels. This may in part be explained by the nature of the activities themselves and whether they inherently require some minimum level of physical activity to participate. For example, compared to “get togethers” with friends and family that often occur seated inside an older adult’s home or a religious service during which participants are seated for the majority of the service duration, visiting with neighbors in each other’s homes or on the streets, participating in an exercise group, or volunteering generally require more physical activity. This suggests that not all social activities are equal in their relevance to PA. Future research should seek to replicate our findings and examine other specific types of social activities that may be relevant for PA and could be targets for interventions.

Our study has several limitations worth noting. This was a cross-sectional analysis and, therefore, we were only able to test for associations. It should be noted, though, that study participants were surveyed about their social capital prior to participating in the accelerometry sub-study. As longitudinal data become available in NSHAP, it will be possible to test causal models linking changes in social activity with subsequent changes in physical activity. Missing social data reduced our sample size, but the application of survey weights ensured the sample analyzed remained nationally representative, which is one of the notable strengths of this study [33, 52‐54]. Finally, the accelerometer used in this study has several limitations. The hip has traditionally been the preferred location for activity monitoring and activity intensity assessment [55]. The NSHAP used a wrist worn activity device to maximize adherence and to simultaneously evaluate sleep [35]. Prior studies have shown that output from the Actiwatch Spectrum is strongly associated with important health indicators in older adults, such as disability [56, 57], supporting its utility as a measure of activity. The Actiwatch Spectrum does not have established cut-points distinguishing different intensities of activity as other accelerometers have [58]. However, accelerometer count cut-points have variable accuracy in predicting energy expenditure among older adults [59]. The variable accuracy may be due, in part, to common age-related conditions that slow speed yet increase energy costs of mobility such as gait impairment [60]. We alternatively used mean daily activity counts to give an estimate of average activity volume. The short wear time protocol (72 h) is not ideal; however, prior work has found average daily activity assessed over 2 days versus 7 days is highly correlated (Lin’s concordance correlation coefficient > 0.9) among U.S. older adults [38]. Furthermore, the shortened wear time protocol facilitates adherence allowing inclusion of a more representative sample of older adults [38]. Despite these limitations, our use of wrist accelerometry extends prior work utilizing self-reported PA data. Wrist accelerometry is being deployed in each successive wave of NSHAP data to facilitate longitudinal analyses involving PA in older American adults.

This study extends prior research finding an association between aspects of social capital and self-reported physical activity by demonstrating associations between specific aspects of social capital and an objective accelerometry-based measure of physical activity. Although the causal direction of these associations remains to be determined, this study highlights a need to emphasize the social dimension of older adult health in the medical and public health systems to consider how social capital can be promoted to improve older adult health. Only 6–20% of older adults over 60 years of age reach the recommended activity targets [58, 61], and most spend the large majority of their waking hours completely sedentary [62, 63]. Social health interventions on an individual or community level could be used to motivate older adults to become more physically active. Intervention efforts will benefit from future work that explores the mechanisms by which social capital may promote physical activity among older adults.