The authors have declared that no competing interests exist.
Conceived and designed the experiments: SM LR LG. Performed the experiments: SM JL. Analyzed the data: SM JL LR LG. Wrote the paper: JL SM LR LG.
Social networks influence the spread of depression, health behaviors, and obesity. The social networks of older urban-dwelling adults were examined to assess whether physical inactivity mediated the association between social networks and obesity.
Data come from the Montreal Neighborhood Networks and Healthy Aging study (n = 2707). Self-reported height and weight were used to calculate body mass index (BMI) with obesity defined as a BMI≥30. A name generator/interpreter instrument was used to elicit participants’ core ties (i.e., alters), and assess whether alters exercised regularly and resided in participants’ neighborhoods. The International Physical Activity Questionnaire was used to measure physical inactivity. Separate multilevel logistic regression analyses were conducted for younger (18–54 years) and older (55 years plus) age groups to examine the association between the exercising behavior of alters and obesity. Ancillary analyses examined whether the residential location of alters was associated with obesity. Mediation analyses assessed whether physical inactivity mediated the association between alter exercising behavior and obesity. Models adjusted for participant socio-demographic and -economic characteristics.
Among the older age stratum (55 years plus), physically inactive individuals were more likely obese (OR 2.14; 95% CIs: 1.48–3.10); participants who had more exercising alters were less likely obese (OR: 0.85; 95% CIs: 0.72–0.99). Physical inactivity mediated the association between exercising alters and obesity. Ancillary analyses showed that having exercising alters in the neighborhood compared to other locations tended to reduce the odds of obesity.
This work demonstrates the importance of social networks among older adults in facilitating a physically active lifestyle and reducing the odds of obesity. Such findings can inform the design of public health interventions that seek to improve the environmental conditions supporting the physical activity of older adults.
Recent research on social networks and health has highlighted the importance of networks for the spread of a range of health conditions, including depression and obesity
Research on social networks and health has identified a number of pathways by which networks may ultimately impact a person’s health. These include behavioral, psychological, and physiological pathways
Studies of social networks have also shown that the impact of social relationships on health and well-being can vary across the life course. A longitudinal study on networks and aging showed declines in physical and cognitive functioning related to alterations in the size and composition of personal networks
Besides the general shrinking of social networks with age, research has also shown that older age groups may be more vulnerable than younger adults to the negative effects of neighborhood environments on health and health-related behavior
Using network data on the composition of adult social networks, the following study addresses three sets of questions:
Does the potential association between perceived exercising behavior of a person’s core network and their obesity status differ by older and younger age stratum?
Does an individual’s own physical activity level mediate the association between obesity and the perceived exercising behavior of a person’s core network?
Is the perceived exercising behavior of core members residing in the neighborhood more strongly associated with obesity compared to those residing elsewhere?
Prior to taking part in the study’s telephone interview, participants were read the study’s letter of information and consent form. If individuals gave verbal consent to participate, they were administered the questionnaire by trained interviewers. Verbal consent was recorded on the computer-administered telephone interviewing system. This interview protocol was documented in the study’s ethics application. Ethics approval for the study was given by the Committee of Scientific Evaluation and Research Ethics of the Centre de Recherche at the Centre Hospitalier de l’Université de Montréal (CHUM) in October 2007 (N.D. 07.049).
Data come from the Montreal Neighborhood Networks and Health Aging Study (MoNNET-HA). The MoNNETs-HA study used a two-stage stratified cluster sampling design. In stage one, Montreal Metropolitan Area (MMA) census tracts (N = 862) were stratified using 2001 Canada Census data into tertiles of high, medium, and low household income. One hundred census tracts were selected from each tertile (nj = 300). In stage two, potential household respondents within each tract were stratified into three age groups: 25–44 years old, 45–64, and 65 or older. Three respondents were randomly selected within each age stratum and census tract for a total of 9 respondents per tract, except for seven tracts in which four participants were selected (ni = 2707). To be selected, individuals had to 1) be non-institutionalized, 2) have resided at their current address for at least one year, and 3) able to complete the questionnaire in French or English. Random digit dialing of listed telephone numbers was used to select households and a computer-assisted telephone interviewing system guided questionnaire administration. Participants completed the telephone interview between mid June and early August 2008.
MoNNET-HA had a response rate of 38.7%. Chi-square analyses showed the MoNNET-HA sample to over represent 1) older adults (by sampling design), 2) individuals with an income less than 50,000 per year, 3) persons who lived in their places of residence for more than five years, 4) females, and 5) those with more than a high school degree.
Participants reported their height in meters and weight in kilograms. This information was used to estimate body mass index (BMI) (kg/m2) of each participant. Obesity status was defined as a BMI>30 kg/m2.
Participants reported their physical activity levels using the short-form version of the International Physical Activity Questionnaire (IPAQ). The IPAQ incorporates questions about the total volume of physical activity and the number of days during a week that such activity was conducted to calculate the energy costs of activity as the metabolic equivalent of task (MET). For example, participants would be asked “
To identify respondents’ core social networks, MoNNET-HA asked participants to name up to three people (i.e., their alters) with whom they had discussed important matters in the past six months. A name interpreter instrument followed the name generator question. The name interpreter consisted of a series of questions asking about those alters. One question asked participants whether their alters did or did not exercise regularly. The “exercising alters” variable represented the number of alters that a participant reported as exercising regularly. A second question asked participants if their alters resided in their i) household, ii) neighborhood, iii) in the MMA, or iv) outside the MMA.
To adjust for the potential confounding effects of age on network characteristics, physical inactivity, and obesity, analyses were stratified into younger (25–54 years) and older (55+ years) age groups. Within each age stratum, analyses also adjusted for the following age categories: 25–34, 35–44, 45–54, 55–64, 65–74, and 75 or older. Analyses also adjusted for gender, socioeconomic status, marital status, and household language. Socioeconomic status was based on a composite factor score consisting of educational attainment, household income, and employment status. Marital status was classified as married/common-law relationship, single, divorced, and widowed. Household language was grouped into French, English, or other.
Separate multilevel logistic regression analyses were conducted for younger and older age strata. Multilevel methods were used to account for MoNNET’s clustered sampling design, and assess the associations among obesity, physical inactivity, network characteristics, and potential confounders. The following series of analyses were conducted. First, in model one, obesity was regressed on participant socioeconomic and demographic characteristics and the number of alters who exercise. Second, obesity was regressed on participant socioeconomic and demographic characteristics and the number of alters who exercised in the household, neighborhood, MMA, and outside the MMA. Third, obesity was regressed on previous variables and participant physical inactivity levels. Formal mediation analyses were then conducted to assess whether a participant’s physical inactivity behavior mediated the association of obesity and exercising alters. Observations were excluded if they were missing information on any study variables.
Multilevel regression and the three-step mediation analysis procedures recommended by Krull and MacKinnon were followed
Variable | Younger Stratum, n = 1329 | Older Stratum, n = 1229 |
Obese | 14.5% | 16.0% |
Physically inactive | 12.6% | 21.9% |
Age Group | ||
25–34 | 28.3% | – |
35–44 | 33.4% | – |
45–54 | 38.3% | – |
55–64 | – | 33.7% |
65–74 | – | 44.3% |
75+ | – | 22.0% |
No exercising alters | 20.7% | 33.5% |
Exercising alters (at least one) | ||
in Household | 20.5% | 11.1% |
in Neighborhood | 30.2% | 27.2% |
in MMA | 26.3% | 20.7% |
outside MMA | 39.3% | 36.2% |
Female | 62.9% | 65.3% |
Educational attainment | ||
No degree | 4.7% | 19.9% |
High school/Trade | 24.7% | 33.5% |
College | 25.8% | 15.1% |
University | 44.8% | 31.4% |
Household Income | ||
<28,000 | 11.9% | 29.3% |
28,000–49,000 | 26.8% | 29.9% |
50,000–74,000 | 28.5% | 25.3% |
75,000–100,000 | 16.0% | 9.4% |
>100,000 | 16.9% | 6.1% |
Employed | 83.2% | 23.7% |
Marital status | ||
Married/Common Law | 61.0% | 47.0% |
Single | 26.7% | 13.4% |
Separated | 4.5% | 3.8% |
Divorced | 6.7% | 15.2% |
Widowed | 1.1% | 20.5% |
Household Language | ||
French | 75.9% | 81.1% |
English | 13.5% | 13.4% |
Other (Bilingual/Other) | 10.7% | 5.5% |
Variable | Younger Age Stratum, n = 1329 | ||
Model 1 | Model 2 | Model 3 | |
|
|||
25–34 yrs (ref.) | 1.00 | 1.00 | 1.00 |
35–44 yrs | 1.78 (1.14–2.78) | 1.76 (1.13–2.74) | 1.73 (1.11–2.70) |
45–54 yrs. | 2.24 (1.45–3.48) | 2.18 (1.41–3.36) | 2.13 (1.38–3.29) |
|
– | 1.03 (0.88–1.20) | 1.04 (0.89–1.22) |
in household | 0.92 (0.62–1.36) | – | – |
in neighborhood | 1.16 (0.93–1.45) | – | – |
in MMA | 0.79 (0.58–1.06) | – | – |
outside MMA | 1.08 (0.87–1.34) | – | – |
|
– | – | 1.54 (1.01–2.35) |
|
|||
Female | 0.63 (0.46–0.87) | 0.64 (0.47–0.88) | 0.64 (0.46–0.88) |
Male (ref.) | 1.00 | 1.00 | 1.00 |
|
0.58 (0.45–0.73) | 0.57 (0.45–0.72) | 0.58 (0.46–0.74) |
|
|||
Single | 0.89 (0.61–1.32) | 0.90 (0.62–1.31) | 0.90 (0.62–1.32) |
Separated | 0.74 (0.34–1.64) | 0.75 (0.34–1.65) | 0.78 (0.35–1.72) |
Divorced | 0.64 (0.33–1.25) | 0.63 (0.32–1.23) | 0.63 (0.32–1.23) |
Widowed | 1.25 (0.37–4.17) | 1.43 (0.44–4.72) | 1.41 (0.43–4.63) |
Married/Common Law(ref.) | 1.00 | 1.00 | 1.00 |
|
|||
English | 1.35 (0.87–2.10) | 1.33 (0.86–2.07) | 1.33 (0.85–2.07) |
Foreign Language | 0.93 (0.54–1.61) | 0.91 (0.53–1.56) | 0.88 (0.51–1.51) |
French (ref.) | 1.00 | 1.00 | 1.00 |
Variable | Older Age Stratum, n = 1229 | ||
Model 1 | Model 2 | Model 3 | |
|
|||
55–64 yrs. | 1.32 (0.80–2.18) | 1.31 (0.80–2.16) | 1.41 (0.84–2.34) |
65–74 yrs. | 1.51 (0.98–2.35) | 1.50 (0.97–2.31) | 1.54 (0.98–2.40) |
75 & older (ref.) | 1.00 | 1.00 | 1.00 |
|
– | 0.82 (0.70–0.95) | 0.85 (0.72–0.99) |
in household | 0.72 (0.42–1.22) | – | – |
in neighborhood | 0.74 (0.57–0.97) | – | – |
in MMA | 0.78 (0.57–1.07) | – | – |
outside MMA | 0.87 (0.70–1.07) | – | – |
|
– | – | 2.14 (1.48–3.10) |
|
|||
Female | 0.91 (0.65–1.28) | 0.90 (0.64–1.26) | 0.82 (0.58–1.16) |
Male (ref.) | 1.00 | 1.00 | 1.00 |
|
0.83 (0.65–1.07) | 0.82 (0.64–1.06) | 0.88 (0.68–1.13) |
|
|||
Single | 1.15 (0.71–1.87) | 1.17 (0.72–1.88) | 1.13 (0.69–1.84) |
Separated | 1.01 (0.44–2.29) | 1.03 (0.45–2.34) | 1.08 (0.47–2.49) |
Divorced | 0.89 (0.55–1.46) | 0.91 (0.56–1.47) | 0.97 (0.59–1.60) |
Widowed | 0.97 (0.61–1.55) | 0.99 (0.62–1.57) | 0.98 (0.61–1.57) |
Married/Common Law(ref.) | 1.00 | 1.00 | 1.00 |
|
|||
English | 1.15 (0.72–1.83) | 1.13 (0.71–1.80) | 1.07 (0.67–1.72) |
Foreign Language | 1.05 (0.54–2.06) | 1.03 (0.53–2.02) | 0.94 (0.47–1.88) |
French (ref.) | 1.00 | 1.00 | 1.00 |
Mediation test | a(standard error) | b(standard error) | ab(standard error) | 95% Confidence Intervals | Waldstatistic |
1. Younger age stratum (25–54 yrs. old), n = 1329 | −0.33 |
0.43 |
−0.14(0.08) | −0.30, 0.02 | 3.12 |
2. Older age stratum (55 yrs. and older), n = 1229 | −0.30 |
0.76 |
−0.23 |
−0.38, −0.08 | 8.66 |
p<0.05,
p<0.01,
p<0.001.
(1)
(2)
(3)
Among adults between 25–54 years old, adults within the age categories of 35–44 years (OR: 1.73; 95% CIs: 1.11–2.70) and 45–54 years (OR: 2.13; 95% CIs: 1.38–3.29) were more likely to be obese than 25–34 years old. Females (OR: 0.64; 95% CIs: 0.46–0.88) were less likely to be obese than males. Higher SES persons (OR: 0.58; 95% CIs: 0.46–0.74) were less likely to be obese. Neither the number nor the residential location of perceived exercising alters was associated with obesity. Physical inactivity (OR: 1.54; 95% CIs: 1.01–2.35) increased the odds of obesity. Having a higher number of perceived exercising alters reduced the likelihood of physical inactivity among younger adults (
Among adults older than 55 years, there were no differences among age categories in the risk of being obese. Having a higher number of exercising alters reduced an older adult’s chances of being obese (OR: 0.85; 95% CIs: 0.72–0.99). Physical inactivity increased the likelihood of obesity (OR: 2.14; 95% CIs: 1.48–3.10). Mediation analyses showed that physical inactivity partially mediated the association between exercising alters and obesity (
Our study addressed three questions regarding the association of obesity, physical inactivity, and social networks in urban-dwelling adults. First, the study found that the association between a person’s obesity status and the perceived exercising behavior of their core network did differ by older and younger age stratum. Although no association was found in younger adults, there was an association shown between obesity and the social network characteristics of older adults. In contrast, SES was a significant factor for obesity among younger and not older adults. Such findings may highlight the relatively increased importance of network resources in the health status of older age groups. Second, in each age stratum, there was an association observed between the perceived number of exercising alters and the participant’s physical inactivity level. Yet, only among older adults did physical inactivity partially mediate the association between obesity and the exercising behavior of alters. As such, it is important to note, firstly, that physical inactivity explained most of the association between social networks and obesity among older adults. Nevertheless, there was some remaining association between networks and obesity, suggesting that having alters who exercise may confer obesity-prevention advantages beyond those of physical activity (e.g., healthy dietary advice). The lack of mediation in younger adults may be reflective of broader network influences (i.e., beyond core ties) at play in younger age groups, their greater exposure to a range of social contexts, or a stronger role for other environmental or behavioral correlates of obesity. Third, the ancillary analyses showed that among older adults, the strongest association between obesity and exercising alters was found for those whose exercising alters resided in the participants’ neighborhoods. The lack of an association in the other residential categories (e.g., outside Montreal) suggests that neighborhood-dwelling alters may have a relatively greater role to play in the physical activity behavior and obesity status of older adults. Recent research on neighborhoods, health, and older adults has emphasized the potential importance of residential settings for the physical, social and emotional health of older adults
This study does have limitations. First, the study’s cross-sectional nature prohibits causal inferences from being drawn. In terms of network analysis, this precludes the opportunity to disentangle social selection from influence. In other words, rather than assuming that the exercising alters somehow altered participant physical activity levels, it may be that participants sought out alters who tended to be like themselves in terms of physical activity patterns, a process often referred to as homophily
Despite these limitations, this study expands on research examining the spread of health behaviors and outcomes through social networks in several meaningful ways. Our analysis offers a unique perspective on the potential pathways linking networks to obesity among older adults. More specifically, we highlight the potential importance of behavioral pathways, particularly physical inactivity, in the link between networks and obesity in older adults. An increasingly sedentary lifestyle and expanding aging population carries widespread implications for an already over-burdened healthcare system, and creates an urgency to investigate the complex interplay between psychosocial and environmental factors which contribute to physical activity behavior