The Longitudinal Relationships between Social Relationships and Physical, Mental, and Cognitive Health: The Role of Frailty

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We examined whether changes in physical frailty moderated the associations between changes in social relationships and changes in health outcomes among older adults. Methods This longitudinal study is based on three waves of the FRéLE study among 1643 Canadian community-dwelling older adults aged 65 years and older over two years. We performed latent growth curve modeling to assess changes with the assumption of missing not at random, adjusting for time-invariant covariates. Social relationships were measured by social participation, social networks, and social support from social ties. Frailty was assessed using the five components of the phenotype of frailty. Results The moderation results revealed that changes toward increasing social participation, social contact with friends, social support from friends, and nuclear and extended family were associated with greater changes toward better cognitive and mental health, but not physical health, among frailer older adults in contrast with those who were more robust. These results highlight the beneficial role of social relationships on mental and cognitive health among frail older adults. Conclusions This longitudinal study suggests that social support has a protective and compensatory role in enhancing mental health among frail older adults. Further experimental studies and interventions are warranted to extend findings on the relationships between social relationships and health outcomes, targeting frail older adults. Future studies may consider other health-related risk factors that may impact the associations between social relationships and physical, mental, and cognitive health outcomes among older adults. Social Networks Social Participation Social Support Frailty Moderation Longitudinal Aging Figures Figure 1 Figure 2 Figure 3 Background Social isolation is a global public health concern with important implications for well-being in later life (1). Empirical research has indicated that social isolation is linked to poor physical, mental, and cognitive health outcomes in older age (2, 3, 4), rivaling the effects of cigarette smoking and obesity (2). These risks are represented in an underpinning theoretical model proposed by Berkman and Krishna (5) that links structural (social networks and social participation) and functional (social support) aspects of social isolation to adverse health outcomes. Previous research suggests an association between social isolation and biological processes such as frailty (6). Physical frailty is a state of increased vulnerability to external stressors due to a decline in physiological reserves across multiple organ systems (7, 8). Frailty is associated with increased risks of disability, comorbidity (7), depression (9), cognitive impairment (10), and mortality (11). Given the physiologic vulnerability inherent in physical frailty, it is plausible that the stress of isolation may result in adverse health outcomes in frail older adults compared to robust peers. The underlying mechanism is that social isolation is a stressor leading to poor health and challenges resilience, similar to the development of physical frailty due to the effect of stressors on physiological reserves (7, 12). Fried and colleagues (7) provided support for the assumption that frail older adults are at greater risk for various deleterious outcomes due to key features of frailty such as muscle weakness, decreased endurance performance, and diminished physical activity. A paucity of research has examined the interplay between structural and functional aspects of social isolation, frailty, and health outcomes, and the results appear inconsistent among studies. Some (13, 14) found that less social support and social participation were associated with frailty and falls. In a longitudinal study examining the combined effects of frailty and social isolation on health outcomes, Hoogendijk and colleagues (15) illustrated that coexisting frailty and social isolation in older adults increased the risk of mortality compared to those with one or none of these conditions. However, Malini and colleagues (16) reported contradictory results that neither social support nor frailty was linked to fear of falling. These discrepancies suggest some degrees of uncertainty about the ability of frailty to alter the relationship between social isolation and health outcomes. Therefore, the hypothesis that frail and socially isolated older adults become more vulnerable to health-related conditions than their robust and isolated peers, needs further investigation. Thus far, few studies have assessed the impact of changes in structural and functional aspects of social isolation and frailty on health outcomes. None of these studies shed light on whether changes in one’s social networks are more or less problematic than changes in social support and social participation. The general conclusion derived from the existing evidence is that structural and functional aspects of social isolation may differently impact frailty and health outcomes among older adults. To our knowledge and based on a recent scoping review (17), no studies have specifically examined the longitudinal moderating effects of frailty on the relationship between multidimensional social isolation and health outcomes. To address gaps and shortcomings in the literature, the objective of this study was to explore whether the relationship between changes in social relationships and changes in health outcomes varied based on changes in frailty among older adults. We explored two alternative hypotheses that might explain the moderating role of frailty in this relationship. H 1a : Changes toward increasing social relationships will lead to changes toward better health outcomes among robust older adults compared to frailer older adults. Rationale: Robust older adults have sufficient physiological reserves to mobilize social relationships. In contrast, the positive impact of changes in social relationships on changes in health outcomes will be small for older adults with increasing frailty because they lack the physiological reserves to benefit from social relationships. H 1b : Changes toward increasing social relationships will lead to changes toward better health outcomes among frailer older adults compared to those who are more robust. Rationale: Social relationships compensate for the lack of physiologic reserves in frailer older adults. Consequently, the beneficial effect of changes in social relationships on changes in health outcomes will occur in older adults with increasing frailty. However, the health of older adults with stable frailty or those who are more robust will be less impacted by changes in social relationships as they need fewer social relationships to maintain or enhance their health status. A null hypothesis is as follows: H 0 : Changes in frailty do not moderate the relationship between changes in social relationships and changes in health outcomes in older adults. Methods Study population We analyzed data from three waves of the FRéLE (Fragilité, une étude longitudinale de ses expressions/ Frailty: A longitudinal study of its expressions) population-based longitudinal study. The study population comprised 1643 community-dwelling older adults aged 65 and over from three areas in the province of Québec in Canada, including a metropolitan city (Montréal), a small city (Sherbrooke), and an urban-rural area (Victoriaville). The sample was stratified by age (65–74;75– 84; 85+), sex, and living areas. Twelve subgroups with an equal number of respondents were obtained. Wave 1 of the study (baseline) took place in 2010, and subsequent data were collected yearly over two longitudinal waves (2011-2012). Of the 1643 participants at baseline, 84.4% participated in the first follow-up, and 88.4% of those from the first follow-up participated in the second follow-up. Losses were either due to mortality (13% over two years) or voluntary withdrawal and inability to contact (13% over two years). The FRéLE baseline results were compared with the Canadian Community Health Survey (CCHS) in the province of Québec. The results illustrated that the sociodemographic characteristics and health status of the FRéLE participants represented some characteristics of community-dwelling older adults across Québec. For example, 56.5% of participants in the FRéLE study had an education greater than high school compared to 55.1% of CCHS older respondents in Québec. Likewise, 48.4% of the FRéLE participants had an income higher than 30,000 CAD compared to 42.3% of Québec CCHS respondents (18, 19). The full cohort profile has been described in detail elsewhere (18, 20). All FRéLE participants provided signed informed consent. The Jewish General Hospital’s Research Ethics Committee granted ethical approval for the FRéLE study. The Integrated Health and Social Services University Network for West-Central Montréal Research Ethics Board (#CODIM-MBM-17-146; 10/10/2022) and the Health Research Ethics Board of the Université de Montréal approved the ethical oversight for the present study (#17-162-CERES-D; 3/08/2022). Predictors: Social relationships According to Berkman’s (5) theory, we measured social relationships by social participation, social networks, and social support from different social ties, namely friends, nuclear family (i.e., children and spouse), and extended family (i.e., grandchildren and siblings). The Cronbach alpha coefficients of internal consistency for social variables are provided in Supplementary Table 1. The Cronbach alphas for social participation ranged from 0.69 to 0.66 across T0 to T2. The Cronbach alphas for social networks with different types of social ties ranged from 0.70 to 0.88 across three-time points. Lastly, the Cronbach alphas estimates for social support from social ties ranged from 0.70 to 0.74 across three-time points. Social participation Social participation is a 12-item measure on a five-point scale, ranging from 1 (almost every day) to 5 (never) (21). The components of this scale included membership in community organizations, involvement in religious, community-based, and family activities, volunteering, playing music, painting, shopping, and going to restaurants, libraries, sports, and recreation centers. Scores were summed and higher scores indicated lower social participation. We reversed the score so that higher values represented a higher level of participation. Social networks We measured social networks with the longitudinal International Mobility in Aging Study’s (IMIAS) social network scale (22), a validated scale among older populations. Social networks comprised a series of questions asked separately about family members, friends, and children: “How many family/friends/ living children do you have?”; “How many of them do you see at least once a month?”; “How many of them do you have a very close relationship with?”; and “How many of them do you speak to by phone at least once a month?” Social contact with a spouse was not asked due to daily contact. The items for each social tie were summed to give a related social network score. The scores ranged from 1 (never) to 5 (always), with greater scores indicating higher levels of social contact. Social support We used the IMIAS’s social support scale (22) to determine social support. The following questions were asked separately about one’s friends and members of one’s nuclear and extended family: “Do you help your family/friends/ children/ partner from time to time?”; “Do you feel that you are loved by them?”; “Do they listen to you when you need to talk about your problems?”; ” Do you feel that you play an important role in their lives?; and “Do you feel useful to them?” The scores ranged from 1 (never) to 5 (always), with greater scores suggesting higher levels of support. The absence of social ties Following the methodology proposed in the previous study (23), we created binary variables, indicating the absence of social ties. We assigned a score of zero to the participants with social ties (i.e., having friends) and a score of one to the participants without social ties (i.e., having no friends). The absence of social ties was a time-invariant variable as the number of participants’ social ties (e.g., children, siblings) did not often change in two years. In addition, we created a continuous variable for each social network and social support variable by multiplying each continuous social variable by its related-binary variable (i.e., social networks with friends × no friends). We introduced these continuous variables along with binary variables simultaneously in the equations (23). Moderator: Frailty In the FRéLE study, frailty was operationalized based on Fried’s (7) frailty criteria. The frailty scale consists of five components, including exhaustion, weight loss, low physical activity, slow gait, and low grip strength. Full details about the measurement methods for each criterion of frailty have been previously described (20). Frailty refers to a clinical syndrome in the Fried (7) frailty phenotype. Unlike the frailty phenotype, we defined frailty as a marker and determinant of health outcomes based on the construct validity of frailty measurement assessed in the FRéLE study (20), which is consistent with the health-based conceptual frameworks of frailty proposed by Bergman and colleagues (24) and Gobben and colleagues (25). Accordingly, we adopted Béland and colleagues’ (20) procedure and considered frailty a continuous latent variable. Higher scores equated to a lower level of frailty. Health outcomes Cognitive health Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), which has high reliability and internal consistency (α =0.83). Scores ranged from 0 to 30, with higher scores suggesting better cognitive performance (≥25) (26). Comorbidity Comorbidity was measured with the Functional Comorbidity Index (FCI) which is a validated scale for predicting physical function among older adults (27). Diagnoses included 19 health problems (i.e., arthritis, asthma, heart disease, stroke, diabetes, visual and hearing impairment, obesity, cancer, etc.). Scores ranged from 1 to 19, with higher scores indicating comorbid conditions. We reversed this scale so that higher scores indicated less comorbidity. Depressive symptoms The 15-item Geriatric Depression Scale (GDS-15) was used to assess depressive symptoms. The scores ranged from 0 to 15, with higher scores indicating higher depressive symptoms (28). We reversed this scale so that higher scores indicated better mental health. The Cronbach alphas for the GDS were 0.75 in T0 and 0.78 in T1 and T2. Disability We measured functional disability by the Katz (29) scale of Independence in Activities of Daily Living (ADLs) and the Lawton (30) scale of Instrumental ADLs (IADLs). ADLs consisted of bathing and showering, grooming, dressing, eating, toileting, walking across a room, getting in/out of bed, getting up from a chair, and cutting nails. IADLs were as follows: preparing hot meals, telephoning, using transportation, shopping, doing errands, light and heavy housekeeping, taking medications, and handling finances. A scale ranged from 0 to 9, with higher scores indicating greater functional limitations. As suggested by Spector and Fleishman (31), we combined ADLs and IADLs items into one single scale, representing a count variable. Covariates The time-invariant covariates comprised sociodemographic and life habit variables associated longitudinally with frailty (32) and health outcomes (33), including age ( 65–98 years ), gender ( 1= female, 0= male ), education levels ( range=0-30, none-master/doctorate ), annual income ( range= 2,500– >80,000 ), smoking status ( 0=non-smoker, 1=former smoker, 2=current smoker ), alcohol consumption ( 1=yes, 0=no ), and sleeping disturbance ( 1=yes, 0=no ). Analytic strategy We employed a series of latent growth curve models (LGMs) in Mplus (34) to assess changes, adjusting for time-invariant covariates. The LGMs estimated two indicators for each time-variant variable, including the initial status at baseline (intercept) and the growth change (slope). We estimated the interactions in LGMs using the latent moderated structural equations (LMS) approach under the normality assumption (35). This approach minimizes the convergent problems and provides less biased estimates for coefficients and standard errors (35). In this study, the distributions of all change scores were almost normal (See Figures 1–3). As the central aim of this study was to examine longitudinal associations, the interactions of slopes (indicating change over time) of social relationships and frailty on slopes of health outcomes were of primary interest. Model building occurred in four steps. First, we regressed the slopes of changes in health outcomes on the interactions between slopes of changes in social relationships and changes in frailty. Second, we regressed the slope of changes in health outcomes on the interactions between the intercepts of binary indicators of social relationships and the slope of change in frailty. Third, we regressed the slopes of changes in health outcomes on the interactions between the intercepts of social relationships and frailty. Fourth, we regressed the intercepts of health outcomes on the interactions between the intercepts of social relationships and frailty. Of note, the interactions involving intercepts in the third and fourth steps were not the subject of our moderation hypotheses and were added as control variables. Among predicted variables, the growth rate for disability was low and unstable. Therefore, we examined the intercept of disability, not the slope. Estimation procedures for the interaction models are prone to convergence problems (36). To minimize convergence problems, we estimated sets of starting values for residual variances and other terms from a collection of sub-models that were together approaching a saturated model (36). In addition, convergence problems increased with an increasing number of interaction terms. Accordingly, we estimated LGMs separately for friends, nuclear family, extended family, and social participation, simultaneously entering all health outcomes into the models. We performed simple slope analyses (37) for significant interactions that depict the association between changes in social isolation and health outcomes at one standard deviation (SD) below, one SD above, and at the mean value of changes in frailty. All continuous predictors and moderators were mean-centered. To test the significance of the interaction terms, we calculated p-values of the likelihood-ratio tests and compared models without and with interactions. We also used the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and an adjusted BIC. We compared the log-likelihood, number of parameters, and BIC values in all LGMs. The lower the BIC value, the better the model (38). We estimated LGMs using the maximum likelihood estimator. The Poisson regression models were used for disability. The bootstrap procedure could not be applied in moderation analyses due to tedious computations. The estimations of interactions for social contacts with children and siblings were unstable, perhaps due to the small residual variances which were close to zero. Therefore, the findings were not reported. The number of missing data was 264 (16.1%) during the first follow-up (T1) and 421 (25%) during the second follow-up (T2). We handled missing data through a pattern mixture approach with the assumption of missing not at random (39). The statistical significance level was defined at p <.05. Results Participants Characteristics Among 1643 participants at baseline, the average age was 78.7 years (SD= 7.9), and at least half were women (50.2%). Most of the participants were either former smokers (49%) or non-smokers (44%) and consumed alcohol (71%). More than half of the participants had no sleeping problems (58%). The averages for education and income levels ranged from (10.6 ± 4.7, 4.1 ± 1.7) at baseline to (10.8 ± 4.6, 4.3 ± 1.7) at time-point 2, respectively (Supplementary Table 2). We compared participants who completed the study with those with missing values at follow-up. Those who dropped out were more likely to be women, frail, consume alcohol, and have chronic conditions and cognitive decline than those who remained in the study. Estimates of changes Table 1 presents descriptive statistics on estimates of the average initial status and the average growth rate of variables of interest at population (fixed) and individual levels (random) (40). At the population level, a variable may vary at baseline, but growth rates may or may not differ between individuals. At the individual level, baseline averages and growth rates may head toward similar or different trends. As such, high averages at baseline may be associated with downward rates of growth and low averages with upward rates of growth. At the population level, all variables varied significantly at baseline as shown by the fixed averages and standard deviations (initial status). Growth rates were positive and significant for chronic conditions and disability, indicating a selective effect, such that respondents remaining in the sample were in better physical health than those who dropped out and the deceased. However, growth rates were not significant for depressive symptoms, cognitive function, and frailty. Nonetheless, their random terms were significant, indicating changes at the individual level. Individual growth rates did not vary significantly for disability. At the population level, average growth rates for all social relationships were negative and significant except for social contact with grandchildren (positive and significant), indicating an increase in social contact with grandchildren over time. At the individual level, only growth rates for social support from friends and spouse and social contact with grandchildren were significant. Moderating effects of changes in frailty on changes in social relationships and health Multivariate LGMs revealed significant interactions between changes in social participation, contacts with friends, and support from different social ties and changes in frailty on changes in cognitive and mental health. No other moderation effects were observed. Visualizing these interactions, Figures 1–3 illustrate the simple slope analyses of the conditional effects of changes in social relationships on changes in mental and cognitive health across three levels of changes in frailty (average changes in frailty ± 1 SD). To contextualize these changes, one standard deviation (SD) above the average change in frailty refers to positive changes in frailty among older adults (robust older adults), whereas one SD below the average change in frailty refers to negative changes in frailty (frail older adults). Table 1. Parameters estimates from latent growth curve models Fixed Random Health outcomes Coef. CI0.95 Coef. CI0.95 Cognitive function Average (i) 4.822 *** 4.773 4.867 0.669 *** 0.591 0.762 Growth rate (s) 0.001 -0.016 0.019 0.033 * 0.002 0.070 i WITH s 0.054 ** 0.017 0.090 Depressive symptoms Average (i) -1.330 *** -1.402 -1.264 1.408 *** 1.229 1.600 Growth rate (s) 0.002 -0.027 0.031 0.171 *** 0.100 0.238 i WITH s -0.110 * -0.194 -0.032 Chronic conditions Average (i) -1.584 *** -1.641 -1.528 0.879 *** 0.796 0.965 Growth rate (s) -0.076 *** -0.096 -0.054 0.110 *** 0.070 0.149 i WITH s -0.008 -0.047 0.031 Disability Average (i) -0.967 *** -1.107 -0.828 2.730 *** 2.384 3.076 Growth rate (s) 0.226 *** 0.156 0.297 0.001 -0.002 0.004 i WITH s -0.045 -0.131 0.041 Moderator/mediator Frailty Average (i) 0.225 *** 0.148 0.300 1.791 *** 1.657 1.910 Growth rate (s) 0.001 -0.026 0.028 0.162 *** 0.106 0.208 i WITH s -0.005 -0.070 0.056 Predictors Social Participation Average (i) -7.022 *** -7.073 -6.971 0.669 *** 0.607 0.737 Growth rate (s) -0.059 *** -0.078 -0.040 0.014 -0.014 0.041 i WITH s -0.011 -0.042 0.019 Social Networks- Friends Average (i) 0.921 *** 0.875 0.968 0.445 *** 0.373 0.517 Growth rate (s) -0.094 *** -0.114 -0.073 0.010 -0.024 0.045 i WITH s -0.036 -0.080 0.007 Social Support- Friends Average (i) 3.316 *** 3.233 3.396 2.004 *** 1.826 2.187 Growth rate (s) -0.116 *** -0.147 -0.088 0.100 ** 0.039 0.163 i WITH s -0.031 -0.109 0.025 Social Networks-Children Average (i) 1.905 *** 1.822 1.987 2.149 *** 1.962 2.373 Growth rate (s) -0.028 *** -0.041 -0.016 0.022 -0.018 0.065 i WITH s -0.083 *** -0.142 -0.033 Social Support-Children Average (i) 3.566 *** 3.473 3.649 2.402 *** 2.215 2.617 Growth rate (s) -0.037 *** -0.054 -0.022 0.000 -0.029 0.028 i WITH s -0.005 -0.036 0.026 Social Support-Partner Average (i) 1.211 *** 1.148 1.274 1.212 *** 1.179 1.245 Growth rate (s) -0.040 *** -0.053 -0.030 0.035 *** 0.019 0.049 i WITH s -0.036 *** -0.055 -0.019 Social Networks-Grandchildren Average (i) 1.139 *** 1.1077 1.201 1.115 *** 1.007 1.223 Growth rate (s) 0.018 ** 0.005 0.032 0.037 *** 0.024 0.049 i WITH s 0.029 * 0.000 0.058 Social Networks-Siblings Average (i) 1.816 *** 1.731 1.899 1.953 *** 1.782 2.144 Growth rate (s) -0.066 *** -0.084 -0.048 0.024 -0.029 0.074 i WITH s -0.052 -0.111 0.006 Social Support-Family Average (i) 3.453 *** 3.403 3.503 0.600 *** 0.506 0.701 Growth rate (s) -0.030 ** -0.052 -0.007 0.017 -0.021 0.054 i WITH s -0.015 -0.058 0.027 Notes: Coef: coefficient, (i): intercept, (s): slope, “WITH” indicates covariance between intercept and slope, Number of Bootstrap Samples=5000, * p≤0,05, ** p≤0,01, *** p≤0,001, † p≤0.20. The models were unadjusted for covariates. Overall, 6 out of 24 interaction terms were significant after adjustment for covariates. The results of the simple slopes analyses demonstrated that greater changes toward social participation, support from friends and nuclear and extended family members, and contacts with friends were consistently and positively related to greater changes toward better mental and cognitive health among older adults with negative changes in frailty (1 SD below average) compared to those with average and positive (1 SD above average) changes in frailty (See Figures 1-3). However, the slope linking changes in social relationships to changes in mental and cognitive health was almost flat or negative among older adults with positive changes in frailty. For example, as depicted in Figure 3-Panel A, changes toward greater support from friends were positively associated with changes toward better cognitive function among individuals with negative changes in frailty (β=2.406, 95% CI: 1.894, 2.917). However, this association was not significant for older adults with gradual and positive changes in frailty (β=0.109, 95% CI: -0.343, 0.561). Another example can be seen in Figure 3-Panel B, where changes toward greater support from children were positively associated with changes toward better cognitive function among those with negative changes in frailty (β=2.957, 95% CI: 1.932, 3.982). However, contrary to our hypotheses, changes toward greater support from children were associated with changes toward decreasing cognitive function among older adults with positive changes in frailty (β= -1.322, 95% CI: -2.215, -0.429). Of note, cases with decreasing change scores on frailty had lower scores on the frailty scales at baseline than cases with increasing change scores. [Inserts Figures 1- 2] The gray bars in Figures 1-3 show the distributions of cases according to changes in social relationships. The distributions are almost normal with medians located at no change and the number of cases is decreasing with greater changes. The conditional effects of changes in social relationships on changes in cognitive and mental health across changes in frailty appeared to be clustered among participants with decreasing loss of social relationships. In most cases, the interactions between changes in social relationships and frailty were significant in the extreme quartiles, indicating that the interaction effects were apparent for a few older adults. In particular, the effect size between changes in family support and changes in frailty was small (see Table 2). [Insert Figure 3] Moderating effects of frailty on baseline social relationships We found no interaction effects of baseline frailty and binary indicators of social relationships on baseline health outcomes, suggesting that the initial status of binary social variables was not part of the moderation terms with frailty. We found only two significant interactions involving continuous indicators of social relationships. Concordant with the second hypothesis (H 1b ), social participation at baseline was associated with changes toward increasing mental health among older adults with negative changes in frailty (frailer older adults) (β= 0.059, 95% CI: 0.003, 0.116). However, contrary to our hypotheses and similar to Figure 3-Panel B, baseline social participation was associated with changes toward declining mental health among older adults with positive changes in frailty (β= -0.056, 95% CI: -0.107, -0.004) (Supplementary Figure 1). In line with the second hypothesis (H 1b ), social support from children was related to less functional limitations among frail older adults at baseline. Table 2. The effects of frailty on the association between social relationships and health outcomes Chronic conditions slope Cognitive function slope Depressive symptoms slope Interaction effects β [95%CI] β [95%CI] β [95%CI] Social participation (T0) × Frailty (slope) -- -- -0.347 [-0.027, -0.112] Social participation (slope) × Frailty (slope) -- -- -17.577 [-21.282, -13.873] Social networks-friends (slope) × Frailty (slope) -- -- -15.022 [-21.666, -8.379] Social support-friends (slope) × Frailty (slope) -- -8.833 [-11.070, -6.596] -- Social support-children (slope) × Frailty (slope) -- -15.847 [-19.225, -12.469] -- Social support-partner (slope) × Frailty (slope) -- -- -16.639 [-20.621, -12.657] Social support-family (slope) × Frailty (slope) -- -- -25.657 [-42.045, -9.270] Notes: Significant associations are solely presented. Two hyphens (--) represent not-significant associations. All models were adjusted for covariates (age, gender, life habits, income, and education levels). Discussion The link between social relationships and health is well-established, as demonstrated through Berkman and Krishna’s ( 5 ) theory and prior studies ( 1 , 2 ). However, the biological explanatory mechanisms by which social relationships connect to health, such as frailty, remain unknown. Our findings extend the research on the interplay between social relationships, frailty, and health in later life in three ways. First and foremost, in line with our second hypothesis (H 1b ), changes in frailty moderated the associations between changes toward increasing social participation, contacts with friends, and support from different social ties with changes toward better cognitive and mental health among older adults. The underlying assumption is that robust older adults have sufficient physiological reserves and capacity to cope with challenges related to aging, respond to health stressors, and recover or maintain health status without support from others ( 41 ). Therefore, social connectedness provides fewer benefits for health status among robust older adults than frail peers. In this vein, the concept of physiological reserves buffers the positive impact of social relationships on health for robust older adults. However, social relationships compensate for age-related challenges among frail older adults who have low physiological reserves to overcome stressors. Second, we examined the distinct associations between multiple aspects of social relationships with physical, mental, and cognitive health outcomes. This examination provided insight into the impacts of social relationships on various health outcomes and whether the effects of multidimensional social relationships on health differ based on frailty. The moderation results further corroborate two key points. First, changes in frailty moderated the longitudinal relationship between social relationships and mental and cognitive health, but not physical health, among older adults. Prior studies ( 42 , 43 ) lend support to this assumption, reporting that perceived social relationships were linked to mental health rather than physical health in later life. Second, the moderation results elucidate the substantial role of social support from all types of social ties rather than social networks on the mental health of frail older adults. It is not thus the absence of social ties or frequency of social contacts – but the quality of those interactions – that has an important bearing on a person’s mental health ( 44 ). The underlying mechanism is that social support is a protective and compensatory factor against life stressors which may ameliorate vulnerability and lead to better health status among frail older adults, and a fundamental feature of frailty is physiological vulnerability to stressors ( 45 ). According to Berkman and Krishna’s ( 5 ) theory, such social ties may provide essential emotional or instrumental support and companionship during illness by helping a person to better cope and compensate for psychological stress and recover more quickly from an illness. Third, the moderation findings corroborate that higher levels of social activities at baseline and increasing changes in social participation compensated for a decline in mental health among frailer older adults over two years. This result reflects findings from a previous longitudinal study ( 46 ), indicating that social gathering at baseline predicted changes in mental health among older adults over four years. This result is concerning for age-friendly initiatives that focus predominantly on healthy individuals and leave behind people with health conditions and high-risk groups such as frail older populations ( 47 ). This study has some limitations. We could not estimate changes in disability due to the low number of changes in the disability status over the two-year panel. We faced estimation problems for social contacts with children and siblings that limited our ability to estimate changes in these variables. We were also unable to simultaneously incorporate all social isolation variables in one model due to convergence issues. Accordingly, further analysis over a longer period would be valuable to capture changes and unveil how differently these variables could be influenced by changes in frailty. Additionally, this study cannot rule out the possibility of reverse causation. The observational design of the study precludes any inference on causality, although time-varying variables were used. Future intervention research targeting contact with family members is necessary to clarify the directionality of our findings. Attrition is another limitation in the present study, resulting in a dropout rate of 25% and more healthy individuals remaining in the sample. Despite these limitations, this study has several notable strengths. In addition to examining structural and functional aspects of social isolation, we considered whether different sources of social ties showed different patterns of association with multiple health outcomes in older age. Another strength of this study is the population-based longitudinal follow-up design with moderate sample size. Additionally, we employed comprehensive and validated measurements of social relationships, frailty, and health outcomes to capture different dimensions of social relationships and health status. Conclusions In conclusion, this longitudinal study addresses one of the main components of healthy aging ( 48 ), underlining that the beneficial impact of social support and social participation on mental health mainly appeared among frail older adults over time. However, social connectedness has limited benefits on the health status of robust older adults. It is thus of utmost importance to include frail older adults with mental and cognitive conditions in social isolation interventions and programs. Given that most older adults, particularly frail older adults, have experienced social isolation and loneliness due to the COVID-19 pandemic, there is some evidence to support targeting this vulnerable population in public health policies and programs. Future studies may consider other health-related risk factors (i.e., sedentary behaviors) that may impact the relationships between social relationships and physical, mental, and cognitive health outcomes among older adults. Fundamental questions remain about how public health policies may foster social programs to enhance social support and activity, targeting frail older people. Abbreviations ADL Activities of Daily Living AIC Akaike Information Criterion BIC Bayesian Information Criterion CCHS Canadian Community Health Survey FCI Functional Comorbidity Index FRéLE Fragilité, une étude longitudinale de ses expressions/Frailty:A longitudinal study of its expressions GDS Geriatric Depression Scale IADL Instrumental Activities of Daily Living IMIAS International Mobility in Aging Study LGM Latent Growth Curve Models LMS Latent Moderated Structural Methods MoCA Montreal Cognitive Assessment SD Standard Deviation Declarations Ethics approval and consent to participate: All FRéLE participants provided signed informed consent. The Jewish General Hospital’s Research Ethics Committee granted ethical approval for the FRéLE study (12/01/2010). The Integrated Health and Social Services University Network for West-Central Montréal Research Ethics Board (#CODIM-MBM-17-146;10/10/2022) and the Health Research Ethics Board of the Université de Montréal approved the ethical oversight for the present study (#17-162-CERES-D;3/08/2022). This study was performed in accordance with the Declaration of Helsinki. Consent for publication: None applicable Availability of data and materials : The datasets used and/or analysed during the current study are available from the second author on reasonable request. Funding: This work was supported by the Canadian Institutes of Health Research [grant number 82945]. Additional funding was obtained from the Quebec’s Ministry of Health and Social Services (MSSS-23 March 2009). This work was partially supported by the University of Montreal & CIUSSS South Central Montreal’s Public Health Research Center (CReSP) Conflict of interest: None Authors' contributions: FM and FB developed the conceptual and methodological frameworks and conceived the research hypotheses. Data analysis and interpretating of the results were conducted by FB and FM. FM wrote the draft and FB contributed to writing and revised the paper. Both authors read and approved the final manuscript. Acknowledgements : We would like to thank Dr. Emiel Hoogendijk for reviewing an earlier version of this manuscript and for providing insightful feedback. We are also grateful to all participants of the FRéLE longitudinal study. References Holt-Lunstad J, Robles TF, Sbarra DA. Advancing social connection as a public health priority in the United States. Am Psychol. 2017;72(6):517. Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. 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Bauer DJ, Curran PJ. Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivar Behav Res. 2005;40(3):373–400. Muthén B, Asparouhov T. Growth mixture modeling: Analysis with non-Gaussian random effects. Longitud data Anal. 2008;143165. Muthén B, Asparouhov T, Hunter AM, Leuchter AF. Growth modeling with nonignorable dropout: alternative analyses of the STAR* D antidepressant trial. Psychol Methods. 2011;16(1):17. Muthén BO, Khoo S-T. Longitudinal studies of achievement growth using latent variable modeling. Learn individual differences. 1998;10(2):73–101. Whitson HE, Duan-Porter W, Schmader KE, Morey MC, Cohen HJ, Colón-Emeric CS. Physical resilience in older adults: systematic review and development of an emerging construct. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2016;71(4):489–95. Fiordelli M, Sak G, Guggiari B, Schulz PJ, Petrocchi S. Differentiating objective and subjective dimensions of social isolation and apprasing their relations with physical and mental health in italian older adults. BMC Geriatr. 2020;20(1):1–13. Cornwell EY, Waite LJ. Social disconnectedness, perceived isolation, and health among older adults. J Health Soc Behav. 2009;50(1):31–48. Uchino B, Ong A, Queen T, Kent de Grey R. Theories of social support in health and aging. Handbook of theories of aging. 3rd ed. New York, NY: Springer; 2016. Peek MK, Howrey BT, Ternent RS, Ray LA, Ottenbacher KJ. Social support, stressors, and frailty among older Mexican American adults. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2012;67(6):755–64. Min J, Ailshire J, Crimmins EM. Social engagement and depressive symptoms: do baseline depression status and type of social activities make a difference? Age Ageing. 2016;45(6):838–43. Buffel T, Phillipson C, Rémillard-Boilard S, Dupre D.ME, Eds. 2019:1–11. World Health Organization. World report on ageing and health. World Health Organization; 2015. p. 9241565047. Report No. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfiles.docx Cite Share Download PDF Status: Published Journal Publication published 24 Feb, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Major revision 09 Aug, 2023 Reviews received at journal 17 Jul, 2023 Reviews received at journal 12 Jul, 2023 Reviews received at journal 11 Jul, 2023 Reviewers agreed at journal 08 Jul, 2023 Reviewers agreed at journal 04 Jul, 2023 Reviewers agreed at journal 29 Jun, 2023 Reviewers invited by journal 07 Jun, 2023 Editor assigned by journal 02 Jun, 2023 Editor invited by journal 15 Apr, 2023 Submission checks completed at journal 15 Apr, 2023 First submitted to journal 09 Apr, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2795811","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":192131552,"identity":"38bafcc7-e420-47c0-b6d5-97f7e5c60247","order_by":0,"name":"Fereshteh Mehrabi","email":"data:image/png;base64,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","orcid":"","institution":"School of Public Health, Université de Montréal","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Fereshteh","middleName":"","lastName":"Mehrabi","suffix":""},{"id":192131553,"identity":"2535dcb6-f041-4c16-97b2-d8a93ca86c70","order_by":1,"name":"François Béland","email":"","orcid":"","institution":"School of Public Health, Université de Montréal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"François","middleName":"","lastName":"Béland","suffix":""}],"badges":[],"createdAt":"2023-04-09 17:44:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2795811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2795811/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-18111-x","type":"published","date":"2024-02-24T15:01:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":35965287,"identity":"95b7682b-8a63-4d4a-99c8-cfc48423927b","added_by":"auto","created_at":"2023-04-18 22:35:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":207986,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of changes in social participation and friends’ networks and depression with changes in frailty\u003c/p\u003e\n\u003cp\u003eNote: For simplicity, random terms and covariates are not shown.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2795811/v1/e2a6845c91592c37430542a2.png"},{"id":35965288,"identity":"d9454888-4977-40e9-a704-2f8e4310153b","added_by":"auto","created_at":"2023-04-18 22:35:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198093,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of changes in family and partner support and depression with changes in frailty\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2795811/v1/173c509c4f70523157cb3154.png"},{"id":35965290,"identity":"f6d0664e-b075-4dde-9b71-b52a47ba2ad6","added_by":"auto","created_at":"2023-04-18 22:35:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267189,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction of changes in friends and children support and cognitive health with changes in frailty\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2795811/v1/983db69c3cbb2bab70ad09f1.png"},{"id":51648515,"identity":"4ff41a1d-0c97-4217-bf52-3b9129e3b4a0","added_by":"auto","created_at":"2024-02-26 15:13:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1042147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2795811/v1/35b9622b-f2a4-4630-bfcd-43ae524c101a.pdf"},{"id":35965289,"identity":"6a72851d-17ef-4d08-a3c8-eb0f247e69c3","added_by":"auto","created_at":"2023-04-18 22:35:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":237945,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-2795811/v1/3cd7cac67a3a2fd452f17728.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Longitudinal Relationships between Social Relationships and Physical, Mental, and Cognitive Health: The Role of Frailty","fulltext":[{"header":"Background","content":"\u003cp\u003eSocial isolation is a global public\u0026nbsp;health concern with important implications\u0026nbsp;for well-being in later life\u0026nbsp;(1). Empirical research has indicated that social isolation is linked to poor physical, mental, and cognitive health outcomes in older age\u0026nbsp;(2, 3, 4), rivaling the effects of cigarette smoking and obesity\u0026nbsp;(2). These risks are represented in an underpinning theoretical model proposed by Berkman and Krishna\u0026nbsp;(5)\u0026nbsp;that links structural (social networks and social participation) and functional (social support) aspects of social isolation to adverse health outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious research suggests an association between social isolation and biological processes such as frailty\u0026nbsp;(6). Physical frailty is a state of increased vulnerability to external stressors due to\u0026nbsp;a decline in physiological reserves across multiple organ systems\u0026nbsp;(7, 8).\u0026nbsp;Frailty is associated with increased risks of disability, comorbidity\u0026nbsp;(7), depression\u0026nbsp;(9), cognitive impairment\u0026nbsp;(10), and mortality\u0026nbsp;(11). Given the physiologic vulnerability inherent in physical frailty, it is plausible that the stress of isolation may result in adverse health outcomes in frail older adults compared to robust peers. The underlying mechanism is that social isolation is a stressor leading to poor health and challenges resilience, similar to the development of physical frailty due to the effect of stressors on physiological reserves\u0026nbsp;(7, 12). Fried and colleagues\u0026nbsp;(7)\u0026nbsp;provided support for the assumption that frail older adults are at greater risk for various deleterious outcomes due to key features of frailty such as muscle weakness, decreased endurance performance, and diminished physical activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA paucity of research has examined the interplay between structural and functional aspects of social isolation, frailty, and health outcomes, and the results appear inconsistent among studies. Some\u0026nbsp;(13, 14)\u0026nbsp;found\u0026nbsp;that less social support and social participation were associated with frailty and\u0026nbsp;falls. In a longitudinal study examining the\u0026nbsp;combined effects of frailty and social isolation on health outcomes,\u0026nbsp;Hoogendijk and colleagues\u0026nbsp;(15)\u0026nbsp;illustrated that\u0026nbsp;coexisting frailty and social isolation in older adults increased the risk of mortality compared to those with one or none of these conditions. However, Malini and colleagues\u0026nbsp;(16)\u0026nbsp;reported contradictory results that neither social support nor frailty was linked to fear of falling. These discrepancies suggest some degrees of uncertainty about the ability of frailty to alter the relationship between social isolation and health outcomes. Therefore, the hypothesis that frail and socially isolated older adults become more vulnerable to health-related conditions than their robust and isolated peers, needs further investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus far, few studies have assessed the impact of changes in structural and functional aspects of social isolation and frailty on health outcomes.\u0026nbsp;None of these studies shed light on whether changes in one’s social networks are more or less problematic than changes in social support and social participation. The general conclusion derived from the existing evidence is that structural and functional aspects of social isolation may differently impact frailty and health outcomes among older adults. To our knowledge and based on a recent scoping review\u0026nbsp;(17), no studies have specifically examined the longitudinal moderating effects of frailty on the relationship between multidimensional social isolation and health outcomes. To address gaps and shortcomings in the literature, the objective of this study was to explore whether the relationship between changes in social relationships and changes in health outcomes varied based on changes in frailty among older adults. We explored two alternative hypotheses that might explain the moderating role of frailty in this relationship.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e1a\u003c/sub\u003e: Changes toward increasing social relationships will lead to changes toward better health outcomes among robust older adults compared to frailer older adults.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRationale: \u0026nbsp;Robust older adults have sufficient physiological reserves to mobilize social relationships. In contrast, the positive impact of changes in social relationships on changes in health outcomes will be small for older adults with increasing frailty because they lack the physiological reserves to benefit from social relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e1b\u003c/sub\u003e: Changes toward increasing social relationships will lead to changes toward better health outcomes among frailer older adults compared to those who are more robust.\u003c/p\u003e\n\u003cp\u003eRationale: Social relationships compensate for the lack of physiologic reserves in frailer older adults. Consequently, the beneficial effect of changes in social relationships on changes in health outcomes will occur in older adults with increasing frailty.\u0026nbsp;However, the health of older adults with stable frailty or those who are more robust will be less impacted by changes in social relationships as they need fewer social relationships to maintain or enhance their health status.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA null hypothesis is as follows:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH\u003csub\u003e0\u003c/sub\u003e: Changes in frailty do not moderate the relationship between changes in social relationships and changes in health outcomes in older adults.\u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed data from three waves of the FRéLE (Fragilité, une étude longitudinale de ses expressions/ Frailty: A longitudinal study of its expressions) population-based longitudinal study. The study population comprised 1643 community-dwelling older adults aged 65 and over from three areas in the province of Québec in Canada, including a metropolitan city (Montréal), a small city (Sherbrooke), and an urban-rural area (Victoriaville). The sample was stratified by age (65–74;75– 84; 85+), sex, and living areas. Twelve subgroups with an equal number of respondents were obtained. Wave 1 of the study (baseline) took place in 2010, and subsequent data were collected yearly over two longitudinal waves (2011-2012). Of the 1643 participants at baseline, 84.4% participated in the first follow-up, and 88.4% of those from the first follow-up participated in the second follow-up. Losses were either due to mortality (13% over two years) or voluntary withdrawal and inability to contact (13% over two years). The FRéLE baseline results were compared with the Canadian Community Health Survey (CCHS) in the province of Québec. The results illustrated that the sociodemographic characteristics and health status of the FRéLE participants represented some characteristics of community-dwelling older adults across Québec. For example, 56.5% of participants in the FRéLE study had an education greater than high school compared to 55.1% of CCHS older respondents in Québec. Likewise, 48.4% of the FRéLE participants had an income higher than 30,000 CAD compared to 42.3% of Québec CCHS respondents\u0026nbsp;(18, 19). The full cohort profile has been described in detail elsewhere\u0026nbsp;(18, 20). All FRéLE participants provided signed informed\u0026nbsp;consent. The Jewish General Hospital’s Research Ethics Committee granted ethical approval for the FRéLE study. The Integrated Health and Social Services University Network for West-Central Montréal Research Ethics Board (#CODIM-MBM-17-146; 10/10/2022) and the Health Research Ethics Board of the Université de Montréal approved the ethical oversight for the present study (#17-162-CERES-D; 3/08/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors: Social relationships\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Berkman’s\u0026nbsp;(5)\u0026nbsp;theory, we measured social relationships by social participation, social networks, and social support from different social ties, namely friends, nuclear family (i.e., children and spouse), and extended family (i.e., grandchildren and siblings). The Cronbach alpha coefficients of internal consistency for social variables are provided in\u0026nbsp;Supplementary Table 1.\u0026nbsp;The Cronbach alphas for social participation ranged from 0.69 to 0.66 across T0 to T2. The Cronbach alphas for social networks with different types of social ties ranged from 0.70 to 0.88 across three-time points. Lastly, the Cronbach alphas estimates for social support from social ties ranged from 0.70 to 0.74 across three-time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSocial participation is a 12-item measure on a five-point scale, ranging from 1 (almost every day) to 5 (never)\u0026nbsp;(21). The components of this scale included membership in community organizations, involvement in religious, community-based, and family activities, volunteering, playing music, painting, shopping, and going to restaurants, libraries, sports, and recreation centers. Scores were summed and higher scores indicated lower social participation.\u0026nbsp;We reversed the score so that higher values represented a higher level of participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial networks\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured social networks with the longitudinal International Mobility in Aging Study’s (IMIAS) social network scale\u0026nbsp;(22), a validated scale among older populations. Social networks comprised a series of questions asked separately about family members, friends, and children: “How many family/friends/ living children do you have?”; “How many of them do you see at least once a month?”; “How many of them do you have a very close relationship with?”; and “How many of them do you speak to by phone at least once a month?” Social contact with a spouse was not asked due to daily contact. The items for each social tie were summed to give a related social network score. The scores ranged from 1 (never) to 5 (always), with greater scores indicating higher levels of social contact.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial support\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the IMIAS’s social support scale\u0026nbsp;(22)\u0026nbsp;to determine social support. The following questions were asked separately about one’s friends and members of one’s nuclear and extended family: “Do you help your family/friends/ children/ partner from time to time?”; “Do you feel that you are loved by them?”; “Do they listen to you when you need to talk about your problems?”; ” Do you feel that you play an important role in their lives?; and “Do you feel useful to them?” The scores ranged from 1 (never) to 5 (always), with greater scores suggesting higher levels of support.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe absence of social ties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Following the methodology proposed in the previous study\u0026nbsp;(23), we created binary variables, indicating the absence of social ties. We assigned a score of zero to the participants with social ties (i.e., having\u0026nbsp;friends) and a score of one to the participants without social ties (i.e., having no\u0026nbsp;friends). The absence of social ties was a time-invariant variable as the number of participants’ social ties (e.g., children, siblings) did not often change in two years. In addition,\u0026nbsp;we created a continuous variable for each social network and social support variable by multiplying each continuous social variable by its related-binary variable (i.e., social networks with friends × no friends). We introduced these continuous variables along with binary variables simultaneously in the equations\u0026nbsp;(23).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerator: Frailty\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the FRéLE study, frailty was operationalized based on Fried’s\u0026nbsp;(7)\u0026nbsp;frailty criteria. The frailty scale consists of five components, including exhaustion, weight loss, low physical activity, slow gait, and low grip strength. Full details about the measurement methods for each criterion of frailty have been previously described\u0026nbsp;(20).\u0026nbsp;Frailty refers to a clinical syndrome in the Fried\u0026nbsp;(7)\u0026nbsp;frailty phenotype. Unlike the frailty phenotype, we defined frailty as a marker and determinant of health outcomes based on the construct validity of frailty measurement assessed in the FRéLE study\u0026nbsp;(20), which is consistent with the health-based conceptual frameworks of frailty proposed by Bergman and colleagues\u0026nbsp;(24)\u0026nbsp;and Gobben and colleagues\u0026nbsp;(25).\u0026nbsp;Accordingly, we adopted Béland and colleagues’\u0026nbsp;(20)\u0026nbsp;procedure and considered frailty a continuous\u0026nbsp;latent variable. Higher scores equated to a lower level of frailty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHealth outcomes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive health\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA), which has high reliability and internal consistency (α =0.83). Scores\u0026nbsp;ranged from 0 to 30, with higher scores suggesting better cognitive performance (≥25)\u0026nbsp;(26).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComorbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Comorbidity was measured with the Functional Comorbidity Index (FCI) which is a validated scale for predicting physical function among older adults\u0026nbsp;(27). Diagnoses included\u0026nbsp;19 health problems (i.e., arthritis, asthma, heart\u0026nbsp;disease, stroke, diabetes, visual and hearing impairment, obesity, cancer,\u0026nbsp;etc.).\u0026nbsp;Scores ranged from 1 to 19, with higher scores indicating comorbid conditions. We reversed this scale so that higher scores indicated less comorbidity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepressive symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 15-item Geriatric Depression Scale (GDS-15) was used to assess depressive symptoms. The scores ranged from 0 to 15, with higher scores indicating higher depressive symptoms\u0026nbsp;(28). We reversed this scale so that higher scores indicated better mental health.\u0026nbsp;The Cronbach alphas for the GDS were 0.75 in T0 and 0.78 in T1 and T2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisability \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe measured functional disability\u0026nbsp;by the Katz\u0026nbsp;(29)\u0026nbsp;scale of Independence in Activities of Daily Living (ADLs) and the Lawton\u0026nbsp;(30)\u0026nbsp;scale of Instrumental ADLs (IADLs). ADLs consisted of bathing and showering, grooming, dressing, eating, toileting, walking across a room, getting in/out of bed, getting up from a chair, and cutting nails. IADLs were as follows: preparing hot meals, telephoning, using transportation, shopping, doing errands, light and heavy housekeeping, taking medications, and handling finances. A scale ranged from 0 to 9, with higher scores indicating greater functional limitations. As suggested by Spector and Fleishman\u0026nbsp;(31), we combined ADLs and IADLs items into one single scale, representing a count variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe time-invariant covariates comprised sociodemographic and life habit variables associated longitudinally with frailty\u0026nbsp;(32)\u0026nbsp;and health outcomes\u0026nbsp;(33), including age (\u003cem\u003e65–98 years\u003c/em\u003e), gender (\u003cem\u003e1= female, 0= male\u003c/em\u003e), education levels (\u003cem\u003erange=0-30, none-master/doctorate\u003c/em\u003e),\u0026nbsp;annual income (\u003cem\u003erange= 2,500– \u0026gt;80,000\u003c/em\u003e), smoking status (\u003cem\u003e0=non-smoker, 1=former smoker, 2=current smoker\u003c/em\u003e), alcohol consumption (\u003cem\u003e1=yes, 0=no\u003c/em\u003e), and sleeping disturbance (\u003cem\u003e1=yes, 0=no\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytic strategy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe employed a series of latent growth curve models (LGMs) in Mplus\u0026nbsp;(34)\u0026nbsp;to assess changes, adjusting for time-invariant covariates. The LGMs estimated two indicators for each time-variant variable, including the initial status at baseline (intercept) and the growth change (slope). We estimated the interactions in LGMs using the latent moderated structural equations (LMS) approach under the normality assumption\u0026nbsp;(35). This approach minimizes the convergent problems and provides less biased estimates for coefficients and standard errors\u0026nbsp;(35). In this study, the distributions of all change scores were almost normal (See Figures 1–3).\u0026nbsp;As the central aim of this study was to examine longitudinal associations, the interactions of slopes (indicating change over time) of social relationships and frailty on slopes of health outcomes were of primary interest. Model building occurred in four steps. First, we regressed the slopes of changes in health outcomes on the interactions between slopes of changes in social relationships and changes in frailty. Second, we regressed the slope of changes in health outcomes on the interactions between the intercepts of binary indicators of social relationships and the slope of change in frailty. Third, we regressed the slopes of changes in health outcomes on the interactions between the intercepts of social relationships and frailty.\u0026nbsp;Fourth, we regressed the intercepts of health outcomes on the interactions between the intercepts of social relationships and frailty. Of note, the interactions involving intercepts in the third and fourth steps were not the subject of our moderation hypotheses and were added as control variables. Among predicted variables, the growth rate for disability was low and unstable. Therefore, we examined the intercept of disability, not the slope.\u003c/p\u003e\n\u003cp\u003eEstimation procedures for the interaction models are prone to convergence problems\u0026nbsp;(36). To minimize convergence problems, we estimated sets of starting values for residual variances and other terms from a collection of sub-models that were together approaching a saturated model\u0026nbsp;(36). In addition, convergence problems increased with an increasing number of interaction terms. Accordingly, we estimated LGMs separately for friends, nuclear family, extended family, and social participation, simultaneously entering all health outcomes into the models. We performed simple slope analyses\u0026nbsp;(37)\u0026nbsp;for significant interactions that depict the association between changes in social isolation and health outcomes at one standard deviation (SD) below, one SD above, and at the mean value of changes in frailty.\u0026nbsp;All continuous predictors and moderators were mean-centered. To test the significance of the interaction terms, we calculated p-values of the likelihood-ratio tests and compared models without and with interactions. We also used the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and an adjusted BIC.\u003c/p\u003e\n\u003cp\u003eWe compared the log-likelihood, number of parameters, and BIC values in all LGMs. The lower the BIC value, the better the model (38). We estimated LGMs using the maximum likelihood estimator. The Poisson regression models were used for disability. The bootstrap procedure could not be applied in moderation analyses due to tedious computations. The estimations of interactions for social contacts with children and siblings were unstable, perhaps due to the small residual variances which were close to zero. Therefore, the findings were not reported. The number of missing data was 264 (16.1%) during the first follow-up (T1) and 421 (25%) during the second follow-up (T2). We handled missing data through a pattern mixture approach with the assumption of missing not at random (39). The statistical significance level was defined at p \u0026lt;.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipants Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 1643 participants at baseline, the average age was 78.7 years (SD= 7.9), and at least half were women (50.2%).\u0026nbsp;Most of the participants were either former smokers (49%) or non-smokers (44%) and consumed alcohol (71%). More than half of the participants had no sleeping problems (58%). The averages for education and income levels ranged\u0026nbsp;from (10.6 \u0026plusmn; 4.7, 4.1 \u0026plusmn; 1.7) at baseline to (10.8 \u0026plusmn; 4.6, 4.3 \u0026plusmn; 1.7) at time-point 2, respectively (Supplementary Table 2).\u0026nbsp;We compared participants who completed the study with those with missing values at follow-up. Those who dropped out were more likely to be women, frail, consume alcohol, and have chronic conditions and cognitive decline than those who remained in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimates of changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1\u0026nbsp;presents descriptive statistics on estimates of the average initial status and the average growth rate of variables of interest\u0026nbsp;at population (fixed) and individual levels (random)\u0026nbsp;(40). At the population level, a variable may vary at baseline, but growth rates may or may not differ between individuals. At the individual level, baseline averages and growth rates may head toward similar or different trends. As such, high averages at baseline may be associated with downward rates of growth and low averages with upward rates of growth. At the population level, all variables varied significantly at baseline as shown by the fixed averages and standard deviations (initial status). Growth rates were positive and significant for chronic conditions and disability, indicating a selective effect, such that respondents remaining in the sample were in better physical health than those who dropped out and the deceased.\u0026nbsp;However, growth rates were not significant for depressive symptoms, cognitive function, and frailty. Nonetheless, their random terms were significant, indicating changes at the individual level. Individual growth rates did not vary significantly for disability. At the population level, average growth rates for all social relationships were negative and significant except for social contact with grandchildren (positive and significant), indicating an increase in social contact with grandchildren over time. At the individual level, only growth rates for social support from friends and spouse and social contact with grandchildren were significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerating effects of changes in frailty on changes in social relationships and health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariate LGMs revealed significant interactions between changes in social participation, contacts with friends, and support from different social ties and changes in frailty on changes in cognitive and mental health. No other moderation effects were observed. Visualizing these interactions,\u0026nbsp;Figures 1\u0026ndash;3\u0026nbsp;illustrate the simple slope analyses of the conditional effects of changes in social relationships on changes in mental and cognitive health across three levels of changes in frailty (average changes in frailty \u0026plusmn; 1 SD). To contextualize these changes, one standard deviation (SD) above the average change in frailty refers to positive changes in frailty among older adults (robust older adults), whereas one SD below the average change in frailty refers to negative changes in frailty (frail older adults).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 1. Parameters estimates from latent growth curve models\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" width=\"29.464285714285715%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFixed\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" width=\"29.761904761904763%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRandom\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u0026lt;0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u0026gt;0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u0026lt;0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u0026gt;0.95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eCognitive function\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e4.822\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e4.773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e4.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.669\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.033\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.054\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eDepressive symptoms\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-1.330\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-1.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-1.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e1.408\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e1.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.171\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.110\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eChronic conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-1.584\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-1.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-1.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.879\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.965\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.076\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.110\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eDisability\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.967\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-1.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e2.730\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e2.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e3.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.226 \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerator/mediator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eFrailty\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.225\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e1.791\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e1.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.162\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-7.022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-7.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-6.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.669\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.059\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Networks- Friends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.921\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.445\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.094\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Support- Friends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e3.316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e3.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e2.004\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e2.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.116\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.100\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;Social Networks-Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e1.905\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e1.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e2.149\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e2.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.028\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.083\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Support-Children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e3.566\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e3.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e2.402\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e2.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e2.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Support-Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e1.211\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e1.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e1.212\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e1.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.040\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.035\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.036\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Networks-Grandchildren\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e1.139\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e1.1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e1.115\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e0.018\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.037\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.029\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Networks-Siblings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e1.816\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e1.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e1.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e1.953\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e1.782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e2.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.066\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003eSocial Support-Family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eAverage (i)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e3.453\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e3.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e3.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.600\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e0.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003eGrowth rate (s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e-0.030\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"26.785714285714285%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.988095238095237%\"\u003e\n \u003cp\u003ei WITH s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"9.970238095238095%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"11.160714285714286%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.821428571428571%\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.755952380952381%\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.18452380952381%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Coef: coefficient, (i): intercept, (s): slope, \u0026ldquo;WITH\u0026rdquo; indicates covariance between intercept and slope, Number of Bootstrap Samples=5000, \u003csup\u003e*\u003c/sup\u003e p\u0026le;0,05, \u003csup\u003e**\u003c/sup\u003e p\u0026le;0,01, \u003csup\u003e***\u003c/sup\u003e p\u0026le;0,001, \u003csup\u003e\u0026dagger;\u003c/sup\u003ep\u0026le;0.20. The models were unadjusted for covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, 6 out of 24 interaction terms were significant after adjustment for covariates. The results of the simple slopes analyses demonstrated that greater changes toward social participation, support from friends and nuclear and extended family members, and contacts with friends were consistently and positively related to greater changes toward better mental and cognitive health among older adults with negative changes in frailty (1 SD below average) compared to those with average and positive (1 SD above average) changes in frailty (See Figures 1-3). However, the slope linking changes in social relationships to changes in mental and cognitive health was almost flat or negative among older adults with positive changes in frailty. For example, as depicted in Figure 3-Panel A, changes toward greater support from friends were positively associated with changes toward better cognitive function among individuals with negative changes in frailty (\u0026beta;=2.406, 95% CI: 1.894, 2.917). However, this association was not significant for older adults with gradual and positive changes in frailty (\u0026beta;=0.109, 95% CI: -0.343, 0.561). Another example can be seen in Figure 3-Panel B, where changes toward greater support from children were positively associated with changes toward better cognitive function among those with negative changes in frailty (\u0026beta;=2.957, 95% CI: 1.932, 3.982). However, contrary to our hypotheses, changes toward greater support from children were associated with changes toward decreasing cognitive function among older adults with positive changes in frailty (\u0026beta;= -1.322, 95% CI: -2.215, -0.429). Of note, cases with decreasing change scores on frailty had lower scores on the frailty scales at baseline than cases with increasing change scores.\u003c/p\u003e\n\u003cp\u003e[Inserts\u0026nbsp;Figures 1- 2]\u003c/p\u003e\n\u003cp\u003eThe gray bars in\u0026nbsp;Figures 1-3\u0026nbsp;show the distributions of cases according to changes in social relationships. The distributions are almost normal with medians located at no change and the number of cases is decreasing with greater changes. The conditional effects of changes in social relationships on changes in cognitive and mental health across changes in frailty appeared to be clustered among participants with decreasing loss of social relationships. In most cases, the interactions between changes in social relationships and frailty were significant in the extreme quartiles, indicating that the interaction effects were apparent for a few older adults. In particular, the effect size between changes in family support and changes in frailty was small (see\u0026nbsp;Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[Insert\u0026nbsp;Figure 3]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerating effects of frailty on baseline social relationships\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found no interaction effects of baseline frailty and binary indicators of social relationships on baseline health outcomes, suggesting that the initial status of binary social variables was not part of the moderation terms with frailty. We found only two significant interactions involving continuous indicators of social relationships. Concordant\u0026nbsp;with the second hypothesis (H\u003csub\u003e1b\u003c/sub\u003e), social participation at baseline was associated with changes toward increasing mental health among older adults with negative changes in frailty (frailer older adults) (\u0026beta;= 0.059, 95% CI: 0.003, 0.116). However, contrary to our hypotheses and similar to Figure 3-Panel B, baseline social participation was associated with changes toward declining mental health among older adults with positive changes in frailty (\u0026beta;= -0.056, 95% CI: -0.107, -0.004) (Supplementary Figure 1). \u0026nbsp;In line with the second hypothesis (H\u003csub\u003e1b\u003c/sub\u003e), social support from children was related to less functional limitations among frail older adults at baseline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2. The effects of frailty on the association between social relationships and health outcomes\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003eChronic conditions\u003c/p\u003e\n \u003cp\u003eslope\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003cp\u003eslope\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003eDepressive symptoms\u003c/p\u003e\n \u003cp\u003eslope\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eInteraction effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e\u0026beta; [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e\u0026beta; [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e\u0026beta; [95%CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial participation (T0) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e-0.347 [-0.027, -0.112]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial participation (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e-17.577 [-21.282, -13.873]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial networks-friends (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e-15.022 [-21.666, -8.379]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial support-friends (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e-8.833 [-11.070, -6.596]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial support-children (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e-15.847 [-19.225, -12.469]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial support-partner (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e-16.639 [-20.621, -12.657]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.159609120521175%\"\u003e\n \u003cp\u003eSocial support-family (slope) \u0026times; Frailty (slope)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.309446254071661%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.03257328990228%\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.498371335504885%\"\u003e\n \u003cp\u003e-25.657 [-42.045, -9.270]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: Significant associations are solely presented. Two hyphens (--) represent not-significant associations. All models were adjusted for covariates (age, gender, life habits, income, and education levels).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe link between social relationships and health is well-established, as demonstrated through Berkman and Krishna\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) theory and prior studies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the biological explanatory mechanisms by which social relationships connect to health, such as frailty, remain unknown. Our findings extend the research on the interplay between social relationships, frailty, and health in later life in three ways. First and foremost, in line with our second hypothesis (H\u003csub\u003e1b\u003c/sub\u003e), changes in frailty moderated the associations between changes toward increasing social participation, contacts with friends, and support from different social ties with changes toward better cognitive and mental health among older adults. The underlying assumption is that robust older adults have sufficient physiological reserves and capacity to cope with challenges related to aging, respond to health stressors, and recover or maintain health status without support from others (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Therefore, social connectedness provides fewer benefits for health status among robust older adults than frail peers. In this vein, the concept of physiological reserves buffers the positive impact of social relationships on health for robust older adults. However, social relationships compensate for age-related challenges among frail older adults who have low physiological reserves to overcome stressors.\u003c/p\u003e \u003cp\u003eSecond, we examined the distinct associations between multiple aspects of social relationships with physical, mental, and cognitive health outcomes. This examination provided insight into the impacts of social relationships on various health outcomes and whether the effects of multidimensional social relationships on health differ based on frailty. The moderation results further corroborate two key points. First, changes in frailty moderated the longitudinal relationship between social relationships and mental and cognitive health, but not physical health, among older adults. Prior studies (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) lend support to this assumption, reporting that perceived social relationships were linked to mental health rather than physical health in later life. Second, the moderation results elucidate the substantial role of social support from all types of social ties rather than social networks on the mental health of frail older adults. It is not thus the absence of social ties or frequency of social contacts \u0026ndash; but the quality of those interactions \u0026ndash; that has an important bearing on a person\u0026rsquo;s mental health (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The underlying mechanism is that social support is a protective and compensatory factor against life stressors which may ameliorate vulnerability and lead to better health status among frail older adults, and a fundamental feature of frailty is physiological vulnerability to stressors (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). According to Berkman and Krishna\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) theory, such social ties may provide essential emotional or instrumental support and companionship during illness by helping a person to better cope and compensate for psychological stress and recover more quickly from an illness.\u003c/p\u003e \u003cp\u003eThird, the moderation findings corroborate that higher levels of social activities at baseline and increasing changes in social participation compensated for a decline in mental health among frailer older adults over two years. This result reflects findings from a previous longitudinal study (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), indicating that social gathering at baseline predicted changes in mental health among older adults over four years. This result is concerning for age-friendly initiatives that focus predominantly on healthy individuals and leave behind people with health conditions and high-risk groups such as frail older populations (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has some limitations. We could not estimate changes in disability due to the low number of changes in the disability status over the two-year panel. We faced estimation problems for social contacts with children and siblings that limited our ability to estimate changes in these variables. We were also unable to simultaneously incorporate all social isolation variables in one model due to convergence issues. Accordingly, further analysis over a longer period would be valuable to capture changes and unveil how differently these variables could be influenced by changes in frailty. Additionally, this study cannot rule out the possibility of reverse causation. The observational design of the study precludes any inference on causality, although time-varying variables were used. Future intervention research targeting contact with family members is necessary to clarify the directionality of our findings. Attrition is another limitation in the present study, resulting in a dropout rate of 25% and more healthy individuals remaining in the sample. Despite these limitations, this study has several notable strengths. In addition to examining structural and functional aspects of social isolation, we considered whether different sources of social ties showed different patterns of association with multiple health outcomes in older age. Another strength of this study is the population-based longitudinal follow-up design with moderate sample size. Additionally, we employed comprehensive and validated measurements of social relationships, frailty, and health outcomes to capture different dimensions of social relationships and health status.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this longitudinal study addresses one of the main components of healthy aging (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), underlining that the beneficial impact of social support and social participation on mental health mainly appeared among frail older adults over time. However, social connectedness has limited benefits on the health status of robust older adults. It is thus of utmost importance to include frail older adults with mental and cognitive conditions in social isolation interventions and programs. Given that most older adults, particularly frail older adults, have experienced social isolation and loneliness due to the COVID-19 pandemic, there is some evidence to support targeting this vulnerable population in public health policies and programs. Future studies may consider other health-related risk factors (i.e., sedentary behaviors) that may impact the relationships between social relationships and physical, mental, and cognitive health outcomes among older adults. Fundamental questions remain about how public health policies may foster social programs to enhance social support and activity, targeting frail older people.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eActivities of Daily Living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCanadian Community Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFunctional Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFR\u0026eacute;LE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFragilit\u0026eacute;, une \u0026eacute;tude longitudinale de ses expressions/Frailty:A longitudinal study of its expressions\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeriatric Depression Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIADL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrumental Activities of Daily Living\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMIAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Mobility in Aging Study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent Growth Curve Models\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLatent Moderated Structural Methods\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e All FRéLE participants provided signed informed\u0026nbsp;consent. The Jewish General Hospital’s Research Ethics Committee granted ethical approval for the FRéLE study (12/01/2010). The Integrated Health and Social Services University Network for West-Central Montréal Research Ethics Board (#CODIM-MBM-17-146;10/10/2022) and the Health Research Ethics Board of the Université de Montréal approved the ethical oversight for the present study (#17-162-CERES-D;3/08/2022). This study was performed in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e None applicable\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the second author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Canadian Institutes of Health Research [grant number 82945]. Additional funding was obtained from the Quebec’s Ministry of Health and Social Services (MSSS-23 March 2009). This work was partially supported by the University of Montreal \u0026amp; CIUSSS South Central Montreal’s Public Health Research Center (CReSP)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003eFM and FB developed the conceptual and methodological frameworks and conceived the research hypotheses. Data analysis and interpretating of the results were conducted by FB and FM.\u0026nbsp;FM wrote the draft and FB contributed to writing and revised the paper. Both authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: We would like to thank Dr. Emiel Hoogendijk for reviewing an earlier version of this manuscript and for providing insightful feedback. We are also grateful to all participants of the FRéLE longitudinal study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eHolt-Lunstad J, Robles TF, Sbarra DA. Advancing social connection as a public health priority in the United States. Am Psychol. 2017;72(6):517.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHolt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10(2):227\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSantini ZI, Fiori KL, Feeney J, Tyrovolas S, Haro JM, Koyanagi A. Social relationships, loneliness, and mental health among older men and women in Ireland: A prospective community-based study. 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J Frailty Aging. 2021:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eHoogendijk EO, Smit AP, van Dam C, Schuster NA, de Breij S, Holwerda TJ, et al. Frailty combined with loneliness or social isolation: an elevated risk for mortality in later life. J Am Geriatr Soc. 2020;68(11):2587\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMalini FM, Louren\u0026ccedil;o RA, Lopes CS. Prevalence of fear of falling in older adults, and its associations with clinical, functional and psychosocial factors: The Frailty in Brazilian Older People-Rio de Janeiro Study. Geriatr Gerontol Int. 2016;16(3):336\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMehrabi F, B\u0026eacute;land F. Effects of social isolation, loneliness and frailty on health outcomes and their possible mediators and moderators in community-dwelling older adults: A scoping review. Arch Gerontol Geriatr. 2020;90:104119.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eB\u0026eacute;land F, Julien D, Bier N, Desrosiers J, Kergoat M-J, Demers L. Association between cognitive function and life-space mobility in older adults: results from the FR\u0026eacute;LE longitudinal study. BMC Geriatr. 2018;18(1):227.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eProvencher V, B\u0026eacute;land F, Demers L, Desrosiers J, Bier N, \u0026Aacute;vila-Funes JA, et al. Are frailty components associated with disability in specific activities of daily living in community-dwelling older adults? A multicenter Canadian study. Arch Gerontol Geriatr. 2017;73:187\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eB\u0026eacute;land F, Julien D, Wolfson C, Bergman H, Gaudreau P, Galand C, et al. Revisiting the hypothesis of syndromic frailty: a cross-sectional study of the structural validity of the frailty phenotype. BMC Geriatr. 2020;20(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eStatistics Canada. Canadian Community Health Survey (CCHS)-Healthy Aging questionnaire (2008\u0026ndash;2009). 2010:117 \u0026ndash; 20.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eAhmed T, Belanger E, Vafaei A, Kon\u0026eacute; GK, Alvarado B, B\u0026eacute;land F, et al. Validation of a social networks and support measurement tool for use in international aging research: The International Mobility in Aging Study. J Cross-Cult Gerontol. 2018;33(1):101\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eB\u0026eacute;land F, Zunzunegui M-V, Alvarado B, Otero A, Del Ser T. Trajectories of cognitive decline and social relations. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2005;60(6):P320\u0026ndash;P30.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBergman H, B\u0026eacute;land F, Karunananthan S, Hummel S, Hogan D, Wolfson C. Developing a Working Framework for Understanding Frailty Howard Bergman, MD. G\u0026eacute;rontologie et soci\u0026eacute;t\u0026eacute;. 2004;109:15\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGobbens R, Luijkx K, Wijnen-Sponselee MT, Schols J. Towards an integral conceptual model of frailty. J Nutr Health Aging. 2010;14(3):175\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNasreddine ZS, Phillips NA, B\u0026eacute;dirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGroll DL, To T, Bombardier C, Wright JG. The development of a comorbidity index with physical function as the outcome. J Clin Epidemiol. 2005;58(6):595\u0026ndash;602.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSheikh JI, Yesavage JA. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontologist: J Aging Mental Health. 1986.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKatz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. JAMA. 1963;185(12):914\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3Part1):179\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSpector WD, Fleishman JA. Combining activities of daily living with instrumental activities of daily living to measure functional disability. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 1998;53(1):46\u0026ndash;S57.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eGil-Salcedo A, Dugravot A, Fayosse A, Dumurgier J, Bouillon K, Schnitzler A, et al. Healthy behaviors at age 50 years and frailty at older ages in a 20-year follow-up of the UK Whitehall II cohort: A longitudinal study. PLoS Med. 2020;17(7):e1003147.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKobayashi LC, Steptoe A. Social isolation, loneliness, and health behaviors at older ages: longitudinal cohort study. Ann Behav Med. 2018;52(7):582\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n LK, Muthen B. Mplus user\u0026apos;s guide: Statistical analysis with latent variables, user\u0026apos;s guide. Muth\u0026eacute;n \u0026amp; Muth\u0026eacute;n; 2017.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWen Z, Marsh HW, Hau K-T, Wu Y, Liu H, Morin AJ. Interaction effects in latent growth models: Evaluation of alternative estimation approaches. Struct Equation Modeling: Multidisciplinary J. 2014;21(3):361\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKim M, Hsu H-Y, Kwok O-m, Seo S. The optimal starting model to search for the accurate growth trajectory in Latent Growth Models. Front Psychol. 2018;9:349.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBauer DJ, Curran PJ. Probing interactions in fixed and multilevel regression: Inferential and graphical techniques. Multivar Behav Res. 2005;40(3):373\u0026ndash;400.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n B, Asparouhov T. Growth mixture modeling: Analysis with non-Gaussian random effects. Longitud data Anal. 2008;143165.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n B, Asparouhov T, Hunter AM, Leuchter AF. Growth modeling with nonignorable dropout: alternative analyses of the STAR* D antidepressant trial. Psychol Methods. 2011;16(1):17.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMuth\u0026eacute;n BO, Khoo S-T. Longitudinal studies of achievement growth using latent variable modeling. Learn individual differences. 1998;10(2):73\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWhitson HE, Duan-Porter W, Schmader KE, Morey MC, Cohen HJ, Col\u0026oacute;n-Emeric CS. Physical resilience in older adults: systematic review and development of an emerging construct. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2016;71(4):489\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFiordelli M, Sak G, Guggiari B, Schulz PJ, Petrocchi S. Differentiating objective and subjective dimensions of social isolation and apprasing their relations with physical and mental health in italian older adults. BMC Geriatr. 2020;20(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCornwell EY, Waite LJ. Social disconnectedness, perceived isolation, and health among older adults. J Health Soc Behav. 2009;50(1):31\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eUchino B, Ong A, Queen T, Kent de Grey R. Theories of social support in health and aging. Handbook of theories of aging. 3rd ed. New York, NY: Springer; 2016.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePeek MK, Howrey BT, Ternent RS, Ray LA, Ottenbacher KJ. Social support, stressors, and frailty among older Mexican American adults. Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2012;67(6):755\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMin J, Ailshire J, Crimmins EM. Social engagement and depressive symptoms: do baseline depression status and type of social activities make a difference? Age Ageing. 2016;45(6):838\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBuffel T, Phillipson C, R\u0026eacute;millard-Boilard S, Dupre D.ME, Eds. 2019:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWorld Health Organization. World report on ageing and health. World Health Organization; 2015. p. 9241565047. Report No.\u003c/span\u003e\u003c/li\u003e\n\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Social Networks, Social Participation, Social Support, Frailty, Moderation, Longitudinal, Aging","lastPublishedDoi":"10.21203/rs.3.rs-2795811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2795811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSocially isolated older adults incur increased risks of adverse health outcomes, though the strength of this association is unclear. We examined whether changes in physical frailty moderated the associations between changes in social relationships and changes in health outcomes among older adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis longitudinal study is based on three waves of the FR\u0026eacute;LE study among 1643 Canadian community-dwelling older adults aged 65 years and older over two years. We performed latent growth curve modeling to assess changes with the assumption of missing not at random, adjusting for time-invariant covariates. Social relationships were measured by social participation, social networks, and social support from social ties. Frailty was assessed using the five components of the phenotype of frailty.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe moderation results revealed that changes toward increasing social participation, social contact with friends, social support from friends, and nuclear and extended family were associated with greater changes toward better cognitive and mental health, but not physical health, among frailer older adults in contrast with those who were more robust. These results highlight the beneficial role of social relationships on mental and cognitive health among frail older adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis longitudinal study suggests that social support has a protective and compensatory role in enhancing mental health among frail older adults. Further experimental studies and interventions are warranted to extend findings on the relationships between social relationships and health outcomes, targeting frail older adults. Future studies may consider other health-related risk factors that may impact the associations between social relationships and physical, mental, and cognitive health outcomes among older adults.\u003c/p\u003e","manuscriptTitle":"The Longitudinal Relationships between Social Relationships and Physical, Mental, and Cognitive Health: The Role of Frailty","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-04-18 22:35:00","doi":"10.21203/rs.3.rs-2795811/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-09T14:33:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-17T17:52:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-13T02:13:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-07-11T17:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d87fea44-cddc-48f0-b6de-8ed1ddd3a942","date":"2023-07-09T02:18:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25c4e208-ffa3-4325-ad4a-896012730ae1","date":"2023-07-04T18:17:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62f02fd6-a7f0-463d-8fe0-99007725e3eb_SNPRID","date":"2023-06-29T13:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-06-07T12:21:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-02T09:52:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-04-15T14:05:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-04-15T14:03:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2023-04-09T17:29:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efb2147a-c552-44fc-bd2f-7670394cf005","owner":[],"postedDate":"April 18th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-02-26T15:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-2795811","link":"https://doi.org/10.1186/s12889-024-18111-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-02-24 15:01:59","publishedOnDateReadable":"February 24th, 2024"},"versionCreatedAt":"2023-04-18 22:35:00","video":"","vorDoi":"10.1186/s12889-024-18111-x","vorDoiUrl":"https://doi.org/10.1186/s12889-024-18111-x","workflowStages":[]},"version":"v1","identity":"rs-2795811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2795811","identity":"rs-2795811","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}