Neighbourhood disadvantage, network capital and restless sleep: Is the association moderated by gender in urban-dwelling adults?

Social Science & Medicine 108 (2014) 185e193

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Neighbourhood disadvantage, network capital and restless sleep: Is the association moderated by gender in urban-dwelling adults? Emma Bassett a, Spencer Moore a, b, * a b

School of Kinesiology and Health Studies, Queen’s University, Canada Department of Community Health and Epidemiology, Queen’s University, Canada

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 August 2013 Received in revised form 17 January 2014 Accepted 18 February 2014 Available online 19 February 2014

Despite evidence suggesting that social and neighbourhood contexts may relate to sleep in adults, the underlying social and demographic mechanisms involved remain relatively unexplored. This study proposes a conceptual framework for examining the link between social environments and restless sleep, and assesses whether associations among restless sleep, social capital, and neighbourhood environments differ by gender. Data come from the 2008 Montreal Neighborhood Networks and Healthy Aging Study (n ¼ 2707). Participants self-reported restless sleep. Network and cognitive dimensions of social capital were examined. Neighbourhood disadvantage and population density were measured using 2006 Canada Census data. Multilevel logistic analyses adjusting for socio-economic and -demographic variables were used to estimate associations among study variables. The final sample size for this study was 2643 adults (nmen ¼ 930; nwomen ¼ 1713). Women were more likely to experience restless sleep than men (OR: 1.29; 95% CIs: 1.07, 1.55). Network capital increased the likelihood of restless sleep in men (OR ¼ 1.25; 95% CI: 1.04e1.50) but not women. High generalized trust decreased the odds of restless sleep in women (OR ¼ 0.75; 95% CI: 0.59e0.94); neighbourhood disadvantage increased the odds of restless sleep in women but not men (OR ¼ 1.18; 95% CI: 1.01 e1.38). The association among restless sleep, social capital, and neighbourhood environmental factors differed in male and female Montreal adults. This study contributes to a greater understanding of possible differential associations between social environments and health in men and women. Greater knowledge of the social and environmental factors that contribute to poor sleep in men and women can aid in the design of interventions to improve sleep patterns in the general population. Social and health promotion interventions might aim to improve general neighbourhood environmental conditions to improve the sleep health of women. Ó 2014 Elsevier Ltd. All rights reserved.

Keywords: Sleep Gender Social capital Neighbourhoods Social networks

1. Introduction Poor sleep quality is associated with adverse physical and mental health outcomes, including chronic disease, respiratory problems, depressive symptoms, anxiety, and poor general and self-reported health (Strine and Chapman, 2005; Foley et al., 1995; Devins et al., 1993). Restless sleep, characterized by trouble falling asleep and maintaining sleep throughout the night, is a complaint that is prevalent in about 30% of the Canadian population (Hurst, 2008). Self-reported restless sleep has been linked with chronic illness, poorer perceived health, depressive symptoms, illness

* Corresponding author. 28 Division Street, School of Kinesiology and Health Studies, Queen’s University, Kingston, ON K7L 3N6, Canada. E-mail address: [email protected] (S. Moore). http://dx.doi.org/10.1016/j.socscimed.2014.02.029 0277-9536/Ó 2014 Elsevier Ltd. All rights reserved.

comorbidities, and increased illness intrusiveness (Devins et al., 1993; Kutner et al., 2001). Although men have been shown to sleep shorter durations than women (Hurst, 2008), studies on selfreported sleep and gender have reported that women tend to have more sleep problems than men (Arber et al., 2009; Foley et al., 1995; Hurst, 2008; Nordin et al., 2005). Approximately 35% of women from a nationally representative survey had trouble falling asleep or trouble staying asleep, whereas 25% of men reported the same difficulties (Hurst, 2008). Research on sleep has conventionally focused on the physiological, behavioural, and psychological factors associated with poor sleep (Freedman and Sattler, 1982). More recently, however, social epidemiological research has begun to examine the importance of social environmental characteristics, including social relationships and neighbourhood environments, in people’s experience of poor sleep (Hill et al., 2009; Nieminen et al., 2013; Nordin et al., 2005;

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Riedel et al., 2012). The present study addresses theoretical, methodological, and substantive gaps in our knowledge of social environments and sleep. Little is known about the specific mechanisms underlying associations between neighbourhood and social environmental characteristics and restless sleep, and whether these associations differ in men and women (Arber et al., 2009). Previous research has suggested that women’s sleep may be more vulnerable to features of social and neighbourhood environments, whereas men’s sleep may be more vulnerable to economic and employment conditions. For example, studies have shown that low social support, low social network integration, and environmental noise are associated with poor sleep in women, whereas unemployment and work-related causes are associated with poor sleep in men (Li et al., 2002; Nordin et al., 2005; Urponen et al., 1988). From an epidemiological and public health perspective, social and environmental factors are potentially modifiable and amenable to population health interventions, policies, and programs. Greater knowledge of the social and environmental factors that contribute to poor sleep in men and women can aid in the design of interventions to improve sleep patterns in the general population. Given the importance of sleep for a range of physical and mental health outcomes (Strine and Chapman, 2005; Foley et al., 1995; Devins et al., 1993), such interventions could have additional benefits for broader population health. 1.1. Conceptual framework Fig. 1 presents the conceptual model underlying our analysis of gender, neighbourhood and social environments, and restless sleep. Daniel et al.’s (2008) conceptual model on the biosocial pathways and multilevel influences underlying the association between place and cardiometabolic disease guided our study. We have extracted several theoretical elements in Daniel et al.'s model and added a gender dimension so as to conceptualize more specifically the role of gender as a potential effect modifier in the associations among social, neighbourhood environments, and restless sleep. Daniel et al.’s (2008) model provided three theoretical elements: (1)

recognition of the role of macrosocial, multilevel environmental influences on health; (2) the situating of environmental structures and neighbourhood contexts as risk conditions that affect the expression of individual outcomes; and (3) the positing of directcontextual and indirect-cognitive pathways to explain placeepersonehealth relationships. First, recognition of the role of macrosocial and multilevel influences on health has been an essential aspect of neighbourhood health effects research. Nevertheless, researchers have suggested that there remains a lack of attention to these multilevel social influences in research on sleep (Arber et al., 2009). Second, risk conditions represent the objective and subjective properties of social and built environments that increase the underlying vulnerability of people to places. Risk conditions consist of structural (i.e., asymmetries in the production and allocation of social resources) and contextual factors (i.e., the local attributes of places). Third, Daniel et al. (2008) posit that risk conditions impact health via direct-contextual and indirect-cognitive paths. The direct-contextual path represents the non-conscious stress responses by which neighbourhood contexts directly affect important biological mediators (e.g., allostatic load) of person-health relationships. The indirect-cognitive path is predicated on the conscious perception of environmental influences along with a person’s psychosocial and behavioural responses to those influences (Daniel et al., 2008). Based on these theoretical elements, our model posits that gender acts as a potential effect modifier in the association between socio-environmental characteristics and restless sleep, whereby the way in which neighbourhood contexts and social relationships are associated with restless sleep differ in men and women. 1.2. Gender and sleep First, our model suggests that sleep behaviour and patterns differ in men and women. This is based on a large body of evidence finding gender differences in adult sleep (Arber et al., 2009; Burgard et al., 2010; Foley et al., 1995; Hurst, 2008; Nordin et al., 2005; Reyner et al., 1995). It is widely accepted in the sleep

Fig. 1. Conceptual model of the association among restless sleep, social environmental characteristics, and gender.

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literature that women have higher prevalence rates of insomnia, and are more likely to report poor sleep than men (Li et al., 2002; Reyner et al., 1995; van den Berg et al., 2009). Conversely, men may be vulnerable to other indicators of sleep disturbance. For example, men are more likely to go to bed later than women, and to sleep for shorter durations (Ohayon et al., 2004; Reyner et al., 1995). Longer sleep duration in women does not however always translate into better overall sleep quality. Burgard et al. (2010) showed that despite sleeping for longer, women reported more interrupted sleep than men. These gender differences were largely explained by women’s social roles, particularly mothers with young children, since it was this element of caregiving that contributed to interrupted sleep in women (Burgard et al., 2010). Studies that have used objective measures of sleep have also found differences in sleep among men and women (Ohayon et al., 2004; Lauderdale et al., 2006; Redline et al., 2004). These studies have confirmed that women typically have longer total sleep time than men, but that they also take longer to fall asleep (Ohayon et al., 2004; Lauderdale et al., 2006). Based on this body of research, hypothesis one states: Hypothesis 1. Women are more likely to experience restless sleep than men.

1.3. Neighbourhood disadvantage, gender, and sleep Previous research on neighbourhood environments and sleep have tended to examine sleep as either mediating or moderating the relationship between neighbourhood exposures, mainly neighbourhood disorder, and physical health or psychological distress. For example, Hale et al. (2010) reported that neighbourhood disorder was associated with poor sleep quality, and that poor sleep quality also partially mediated the relationship between neighbourhood disorder and poor physical health. In another study, Hill et al. (2009) showed that sleep quality moderated the association between neighbourhood disorder and psychological distress, with disorder more strongly associated with distress among those persons who reported poor sleep quality (Hill et al., 2009). Neighbourhood disadvantage is characterized by residential areas with high proportions of mother-only households, renters, immigrants, and unemployment (Haines et al., 2011; Kim, 2010; Ross and Mirowsky, 2001; Schneiders et al., 2003; Silver et al., 2002). While disadvantage captures socioeconomic aspects of neighbourhood environments, it also includes added dimensions of social marginalization. Previous research has shown high neighbourhood disadvantage associated with serious sleep problems in children (Singh and Kenney, 2013). Despite research on the direct association between neighbourhood conditions and sleep, little research has examined whether gender moderates the association between neighbourhood disadvantage and sleep. Nevertheless, recent studies have suggested that social and environmental characteristics might contribute differently to sleep in women compared to men (Arber et al., 2009; Burgard et al., 2010; Nordin et al., 2005; Riedel et al., 2012; Yao et al., 2008). Researchers have hypothesized that women may be more vulnerable to poor sleep due to differences in social roles, experiences, and levels of social embeddedness in their places of residence (Arber et al., 2009; Burgard et al., 2010; Sekine et al., 2006). Arber et al. (2009), for example, found that half of the selfreported sleep problems of women could be attributed to their higher likelihood of being socio-economically disadvantaged. This research leads to our second hypothesis: Hypothesis 2. Neighbourhood disadvantage is more strongly associated with restless sleep in women than men such that women

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residing in disadvantaged neighbourhoods are more likely to experience restless sleep.

1.4. Social capital, gender, and sleep Third, our model posits gender differences in the association between social capital and restless sleep. Social capital refers to the resources to which individuals and groups have access through their social networks (Moore et al., 2009a). Recent research on social capital and health has tended to measure social capital along different dimensions, including generalized trust, social participation, and network capital (Fujiwara and Kawachi, 2008; Haines et al., 2011). Generalized trust captures cognitive aspects of social capital, whereas measures of participation and network capital capture structural and resource-related aspects of the concept. Although social capital has been shown associated with a range of health outcomes (Kawachi et al., 2008), few studies have examined whether social capital is associated with poor sleep. In a sample of Finnish adults, Nieminen et al. (2013) examined the association between sleep duration and social capital, showing that social participation, higher social support, and higher generalized trust were positively associated with adequate duration of sleep. Adequate sleep duration was defined as reporting between 7 and 8 h of sleep per 24 h. Nasermoaddeli et al. (2005) examined sleep in a sample of British and Japanese civil servants and showed that social engagement in clubs and organizations was associated with better self-reported sleep quality (Nasermoaddeli et al., 2005). To our knowledge, associations of sleep with resource-related components of network social capital have not been studied. Given the general lack of research on social capital and sleep, it may be useful to consider studies that have examined the importance of social support for sleep. Social support and social capital each emerge from social networks, although social support tends to come from a person’s strong ties (Nordin et al., 2005). Research on social support and sleep has shown that sleep quality was higher in those persons who reported closer relationships with family and friends (Yao et al., 2008). In another study, Nordin et al. (2005) found lower levels of social support and integration associated with poor sleep ratings in women but not men. Nordin et al.’s (2005) findings suggests that gender differences may also exist in the association between social capital and sleep. This research leads to our third hypothesis: Hypothesis 3. Social capital is more strongly associated with sleep in women such that women with higher social capital are less likely to experience restless sleep. 1.5. Study purpose The current study tests three hypotheses on the association among gender, neighbourhood contexts, social capital, and restless sleep. These hypotheses allow us to investigate more generally the importance of direct-contextual and indirect-cognitive paths in the link between macrosocial environmental factors and health. In so doing, our study aims to increase current knowledge on the underlying mechanisms potentially linking social and environmental factors with restless sleep. 2. Methods 2.1. Study design Data came from the 2008 Montreal Neighborhood Networks and Healthy Aging Study (MoNNET-HA), which used a two-stage

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stratified cluster sampling design. In stage one, Montreal Metropolitan Area (MMA) census tracts (N ¼ 862) were stratified using 2001 Canada Census data into tertiles of high, medium, and low household income. One hundred census tracts (CT) were selected from each tertile (nj ¼ 300). In stage two, potential respondents within each CT were stratified into three age groups: 25e44 years old, 45e64, and 65 or older. Three respondents were randomly selected within each age stratum and CT for a total of 9 respondents per tract, except for seven tracts in which four participants were selected (ni ¼ 2707). To be selected, individuals 1) were noninstitutionalized, 2) had resided at their current address for at least one year, and 3) were able to complete the questionnaire in French or English. Random digit dialling 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 midJune and early August 2008. 2.2. Measures 2.2.1. Individual-level variables 2.2.1.1. Restless sleep. Participants responded yes or no to the following item, which assessed restless sleep over the previous week: “my sleep was restless.” This item has been used to assess restless sleep previous studies, and was extracted from the Center for Epidemiologic Studies Depression (CES-D) scale (Devins et al., 1993; Kutner et al., 2001; Radloff, 1977). 2.2.2. Social capital dimensions 2.2.2.1. Network capital. Network capital was measured using a position generator. The MoNNET position generator measured access to people with 10 different occupational titles. These ten occupations were selected from a listing of 90 occupations through a stratified random selection process. The position generator captures three dimensions of social capital: (1) reachability, (2) diversity, and (3) range (Lin, 1999). Reachability represents the hierarchical dimension of social capital, and is simply the most prestigious occupation that a person can reach through their social ties. Diversity represents network size as reflected in the number of different occupations accessed. Range is the difference between the highest and lowest prestige job accessed (Haines et al., 2011). Principal components analysis was used to create a social capital score, with range (0.69) and diversity (0.21) contributing the highest coefficients. Additional information on the MoNNET-HA position generator, including the list of occupations can be found elsewhere (Moore et al., 2011). 2.2.2.2. Participation. To measure participation, participants were asked whether they had been active in any inside- or outsideneighbourhood associations as a volunteer or officer in the past five years. Respondents who participated in both neighbourhood and non-neighbourhood organizations received a score of two; those who participated in either one or the other were given a score of one; and, those who did not participate in any organizations were given a zero. 2.2.2.3. Generalized trust. To measure generalized trust, respondents were asked, “Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people?” Response options included a) most people can be trusted, b) can’t be too careful, c) depends, d) most people cannot be trusted, and e) I don’t know. “Don’t know” responses were treated as missing (n ¼ 20). Responses were reverse coded so that higher numbers indicated greater trust. This item is the most

commonly used measure of generalized trust within the public health literature (Grootaert et al., 2004).

2.2.3. Individual confounding variables Sociodemographic characteristics of respondents included gender, age, marital status, primary household language, and socioeconomic status (SES). Gender was self-reported. Age was measured in six categories: (1) 25e34, (2) 35e44, (3) 45e54, (4) 55e64, (5) 65e74 and (6) 75 and older. Marital status consisted of six categories: (1) married or in a common-law relationship, (2) widowed, (3) separated, (4) divorced, and (5) single. Primary household languages included French, English, or foreign languages. Participants selected their income from five categories: (a) less than $28,000, (b) $28,000e$49,000, (c) $50,000e$74,000, (d) $75,000e$100,000, and (e) more than $100,000. Missing responses to the income question were imputed using ordinal regression for 20% of the respondents using (a) questionnaire data on sociodemographic variables, including education, age, and employment status, and (b) Canada census data on median household income for the census tract in which respondents resided. Study participants were asked to select their highest level of educational attainment from seven categories: (a) no high school degree or certificate, (b) high school diploma or equivalent, (c) trade certificate or diploma, (d) college certificate or diploma below Bachelor’s degree, (e) Bachelor’s degree, (f) Master’s degree, or (g) earned doctorate, medical, or professional degree. For employment status, participants indicated if they were currently employed. The socioeconomic status (SES) score was created using principal components analysis of respondent’s income, education, and employment status. The scoring coefficients were 0.49, 0.32, and 0.24 respectively. In addition to social capital and sociodemographic characteristics, analyses adjusted for the self-rated health of respondents. Participants were asked if they would rate their health as excellent, very good, good, fair, or poor. Responses were dichotomized into excellent and less than excellent (very good, good, fair, and poor) categories. This was meant to reduce potential confounding of social capital and sleep quality associations by general health.

2.2.4. Neighbourhood variables 2.2.4.1. Neighbourhood disadvantage. The neighbourhood disadvantage measure was created using six census tract variables: unemployment rates (0.29), median household income (0.18), the percentage of immigrants (0.03), the percentage of single mothers (0.07), the percentage of renters (0.50), and the percentage of college educated residents (0.13). The scoring coefficients are provided in parentheses. Data came from the 2006 Canada Census.

2.2.4.2. Neighbourhood population density. Neighbourhood population density was based on census tract-level estimates of population size per square kilometre. This measure was log transformed in the analyses to account for its skewed distribution. Population density is a neighbourhood environmental feature that may be harmful to health due to increased the stressors associated with living in a densely populated area (Gangadharan and Valenzuela, 2001). Population density has been recognized as an important characteristic of the neighbourhood environment that must be considered in health research (Greiner et al., 2004), and may act as a potential confounder in the association between disadvantage and restless sleep.

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2.3. Statistical analysis Multilevel logistic analysis was used to account for MoNNET’s clustered sampling design, and examine the individual- and neighbourhood-level correlates of restless sleep. The estimation equations for multilevel logistic regression can be found in standard multilevel regression texts (Snijders and Bosker, 2012; RabeHesketh and Skrondal, 2012). Using interaction terms, preliminary analyses assessed whether gender moderated the association between neighbourhood disadvantage, network capital, generalized trust and restless sleep. Based on these findings, stratified analyses by gender were conducted to assess further whether associations among social capital, neighbourhood environments, and restless sleep differed in men and women. For both men and women, the results of three models are provided: (1) crude estimates of the bivariate associations between restless sleep and each variable; (2) adjusted estimates of the individual-level associations among socio-demographic and -economic, SRH, social capital variables and restless sleep; and (3) the adjusted model that added the neighbourhood-level variables of disadvantage and population density to the individual-level model. The degree to which restless sleep clustered within neighbourhoods is reported using the intraclass correlation coefficient (ICC) along with its plausible value range (PVR) (Snijders and Bosker, 2012). Given the low sample counts for men or women separately within tracts, these values are reported solely for the overall sample. Odds ratios and 95% confidence intervals are reported. Stata version 12 was used for the analyses (StataCorp, 2011). 3. Results 3.1. Sample Of the 2707 participants, data were available for 2643 participants e 930 males and 1713 females. Over half of the participants were married (57.4% males, 52.8% females), most spoke French within the household (76.7% males, 78.7% females), and less than half had a university degree or higher (43.9% males, 35.2% females). Approximately 27.5% of men and 32.8% of women reported restless sleep over the previous week. Table 1 provides further information on the sociodemographic characteristics and social capital of the sample.

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Table 1 Montreal Neighborhood Networks and Healthy Aging Study (MoNNET-HA) sample characteristics, n ¼ 2643. Sample characteristics

Male (n ¼ 930) %

Female (n ¼ 1713) %

Restless sleep Age 25e34 years 35e44 years 45e54 years 55e64 years 65e74 75 þ Marital status Married Single Separated Divorced Widowed Household language French English Foreign language Educational attainment Less than high school High school or trade certificate College diploma or equivalent University degree or higher Household income Less than $28,000 $28,000e$49,000 $50,000e$74,000 $75,000e$100,000 More than $100,000 Self-reported health Excellent Less than excellent Generalized trust Most people can be trusted Less than most can be trusted

27.5

32.8

13.4 19.5 21.5 16.0 20.6 9.0

15.5 16.8 19.6 16.3 21.1 10.8

57.4 24.2 4.7 7.7 6.1

52.8 18.4 3.9 12.4 12.5

76.7 14.3 9.0

78.7 13.4 7.9

8.6 27.9

13.7 30.0

19.7

21.2

43.9

35.2

15.7 28.4 27.0 14.4 14.5

22.9 28.4 26.9 12.1 9.8

55.2 44.9

55.2 764

48.8

672

51.3

1033

Mean (Standard deviation) 0.47 (0.7)

Mean (Standard deviation) 0.47 (0.7)

0.03 (1.0) 16.8 (5.8)

0.03 (1.0) 16.1 (5.7)

Social participation Neighbourhood characteristics Concentrated disadvantage Population density

3.2. Preliminary analyses There was negligible clustering of restless sleep in the overall sample (ICC: 0.01; PVR: 0.02, 0.04). In crude and adjusted models, women were shown to more likely experience restless sleep than men (OR: 1.29; 95% CIs: 1.07, 1.55). Analyses comparing the shape of the relationship between neighbourhood disadvantage and restless sleep, and social capital and restless sleep showed differences in men and women. As a result, disadvantage and network capital were split into tertiles to assess effect modification. Medium disadvantage was used as the referent group. Separate effect modification tests showed that gender modified the association between neighbourhood disadvantage and restless sleep, and network capital and restless sleep at significance levels less than 0.05. Generalized trust did not. Based on these preliminary assessments, stratified analyses were conducted to examine further gender differences in the direct and indirect paths linking social conditions to restless sleep. 3.2.1. Men Table 2 provides the results for men. Age was shown to have a non-linear association with restless sleep. Compared to men aged

25e34, those over 55 years were less likely to report restless sleep; no differences were found in those aged 25e54. Men who lived in English-speaking households were more likely to report restless sleep than men living in French households (OR ¼ 1.89, CI ¼ 1.23e 2.91). In terms of self-reported health, men with poorer health were more likely to report restless sleep than those with better health. Network social capital was associated with restless sleep in men (OR ¼ 1.25, CI ¼ 1.04e1.50). In other words, those with higher network capital were more likely to report restless sleep than those with low levels of network social capital. In model two (neighbourhood exposures model), men living in high density neighbourhoods were more likely to report restless sleep than those from low density neighbourhoods (OR ¼ 1.04, CI ¼ 1.01e1.09).

3.2.2. Women Table 3 provides the results for women. Women in age categories over 35 years were less likely to report restless sleep than women aged 25e34. Divorced women were more likely to report restless sleep than married women (OR ¼ 1.57, CI ¼ 1.12e2.20). Women with poorer health were more likely to report restless sleep

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Table 2 Adjusted odds ratio and 95% confidence intervals of the association between social capital and neighbourhood environments with restless sleep in men, controlling for socio-demographic and economic variables, n ¼ 930. Variables Individual-level variables Age 35e44 45e54 55e64 65e74 75þ 25e34 Socioeconomic status Marital status Widowed Separated Divorced Single Married Household language English Foreign language French Self-rated health Excellent Less than excellent Network social capital Participation Generalized trust High generalized trust

Crude models OR (95% CI)

Model 1 OR (95% CI)

Model 2 OR (95% CI)

1.12 (0.67e1.87) 0.97 (0.58e1.63) 0.61 (0.34e1.07) **0.46 (027e0.81) 0.57 (0.29e1.10) 1.00

1.09 (0.65e1.85) 0.94 (0.56e1.58) *0.50 (0.28e0.90) **0.40 (0.22e0.73) *0.43 (0.20e0.92) 1.00 0.91 (0.70e1.18)

1.17 (0.69e0.98) 1.00 (0.59e1.69) *0.54 (0.30e0.97) **0.43 (0.24e0.79) *0.45 (0.21e0.95) 1.00 0.90 (0.69e1.17)

0.95 (0.49e1.83) 1.01 (0.47e2.15) 0.86 (0.47e1.58) 1.39 (0.96e2.01) 1.00

1.27 (0.62e2.61) 1.10 (0.51e2.36) 0.88 (0.47e1.66) 1.21 (0.81e1.80) 1.00

1.30 (0.64e2.67) 1.03 (0.48e2.23) 0.83 (0.44e1.57) 1.10 (0.74e1.65) 1.00

**1.78 (1.16e2.67) 0.94 (0.54e1.64) 1.00

**1.91 (1.24e2.94) 0.85 (0.48e1.51) 1.00

**1.89 (1.23e2.91) 0.82 (0.47e1.45) 1.00

***0.57 (0.42e0.78) 1.00 *1.19 (1.01e1.41) 0.91 (0.73e1.14)

***0.52 (0.38e0.72) 1.00 *1.24 (1.04e1.49) 0.90 (0.71e1.14)

***0.53 (0.38e0.72) 1.00 *1.25 (1.04e1.50) 0.90 (0.71e1.15)

0.97 (0.72e1.32) 1.00

0.96 (0.69e1.32) 1.00

0.94 (0.68e1.29) 1.00

e

0.92 (0.72e1.17) *1.04 (1.01e1.09)

Low generalized trust Neighbourhood-level variables Neighbourhood disadvantage 1.12 (0.95e1.32) Census tract *1.04 density (1.01e1.07)

e

Table 3 Adjusted odds ratio and 95% confidence intervals of the association between social capital and neighbourhood environments with restless sleep in women, controlling for socio-demographic and economic variables, n ¼ 1713. Variables Individual-level variables Age 35e44 45e54 55e64 65e74 75þ

Crude models OR (95% CI)

Model 1 OR (95% CI)

Model 2 OR (95% CI)

**0.62 (0.44e0.88) *0.69 (0.49e0.96) **0.59 (0.41e0.84) ***0.51 (0.36e0.71) ***0.48 (0.32e0.73)

**0.57 (0.40e0.82) **0.58 (0.41e0.82) ***0.46 (0.31e0.66) ***0.34 (0.23e0.51) ***0.30 (0.18e0.49) 1.00 0.88 (0.74e1.04)

**0.60 (0.42e0.86) **0.61 (0.43e0.86) ***0.48 (0.33e0.71) ***0.37 (0.25e0.55) ***0.32 (0.19e0.52) 1.00 0.92 (0.77e1.10)

0.91 (0.66e1.26) 1.31 (0.78e2.21) **1.56 (1.14e2.12) 1.20 (0.91e1.57) 1.00

1.12 (0.76e1.67) 1.42 (0.83e2.42) **1.61 (1.15e2.24) 1.01 (0.76e1.34) 1.00

1.13 (0.76e1.68) 1.38 (0.81e2.36) **1.57 (1.12e2.20) 0.97 (0.73e1.30) 1.00

1.00 (0.74e1.36) 1.16 (0.80e1.67) 1.00

0.97 (0.71e1.32) 0.82 (0.56e1.22) 1.00

0.98 (0.72e1.34) 0.77 (0.52e1.15) 1.00

***0.57 (0.46e0.69) 1.00 1.05 (0.95e1.17) 0.97 (0.83e1.13)

***0.55 (0.44e0.69) 1.00 1.10 (0.98e1.23) 1.04 (0.88e1.22)

***0.56 (0.45e0.69) 1.00 1.10 (0.98e1.24) 1.04 (0.88e1.22)

**0.73 (0.59e0.91) 1.00

*0.75 (0.59e0.94) 1.00

*0.75 (0.59e0.94) 1.00

e

*1.18 (1.01e1.38) 0.99 (0.97e1.02)

25e34 Socioeconomic status Marital status Widowed Separated Divorced Single Married Household language English Foreign language French Self-rated health Excellent Less than excellent Network social capital Participation Generalized trust High generalized trust

Low generalized trust Neighbourhood-level variables Neighbourhood disadvantage ***1.22 (1.10e1.36) Census tract density *1.02 (1.00e1.04)

e

*p < 0.05, **p < 0.01, ***p < 0.001.

*p < 0.05, **p < 0.01, ***p < 0.001.

than those with higher SRH. Network social capital was not associated with restless sleep in women. Generalized trust was associated with restless sleep in women, such that those women with higher levels of trust in others were less likely to report restless sleep (OR ¼ 0.75, CI ¼ 0.59e0.94). In model two, neighbourhood disadvantage increased the likelihood of women reporting restless sleep (OR ¼ 1.18, CI ¼ 1.01e1.38).

whether gender moderated the association between neighbourhood disadvantage and restless sleep, showing that disadvantage was more strongly associated with restless sleep in women than men. Finally, our study examined whether social capital was more strongly associated with restless sleep in women. In contrast to research on social support and sleep, our study found that network capital increased the odds of restless sleep in men but not women and generalized trust decreased the odds of restless sleep in women.

4. Discussion 4.1. Neighbourhood environmental characteristics, gender and sleep The different associations that emerged among restless sleep, social capital, and neighbourhood environmental characteristics in men and women highlight the complex role that social influences play in the health of men and women. Our study tested three hypotheses. Our first hypothesis that women were more likely to experience restless sleep than men was confirmed, thus supporting previous research on gender and sleep. Based on these findings, our study examined

Few studies have examined whether neighbourhood conditions are related to sleep. Our study showed that neighbourhood disadvantage was associated with restless sleep in women; women who lived in areas of greater disadvantage were more likely to report restless sleep. Associations between restless sleep, gender, and neighbourhood disadvantage have not been previously

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investigated in adults. One previous study in children found children living in neighbourhoods high in disadvantage were more likely to experience serious sleep problems (Singh and Kenney, 2013). In that study however, differences between school-aged boys and girls were not found (Singh and Kenney, 2013). Yet, our findings are in line with other work that has suggested adult women’s greater vulnerability to neighbourhood disadvantage. This research has shown neighbourhood disadvantage to be associated with poor health, including lower self-rated health, higher proportions of chronic conditions, and worse mental health (Haines et al., 2011; Kim, 2010; Ross and Mirowsky, 2001). Neighbourhood disadvantage may impact women’s sleep through various psychological and social mechanisms (Haines et al., 2011; Stafford et al., 2005). Stafford et al. (2005) have suggested that women’s health may be more vulnerable to the negative effects of neighbourhood disadvantage than men’s due to the differences in how women perceive their environments, the types of stressors women face on a daily basis, and the social roles that they occupy. Thus, women’s social roles and greater investment of time within the neighbourhood may increase their vulnerabilities to health consequences as a function of disadvantaged neighbourhoods (Matheson et al., 2006). Neighbourhood population density not disadvantage was shown to increase the likelihood of restless sleep in men. No research has examined the relationship between neighbourhood density and restless sleep, so results from alternate studies are not directly comparable. However, Ohayon and Lemoine (2004) showed that higher neighbourhood density decreased sleep duration in both women and men. Levy and Herzog (1974) showed that the association of population density with health differed in men and women, with higher population density associated with increased deaths due to heart disease in men, but not women. With sleep disturbances also linked with stress (Akerstedt, 2006), similar mechanisms may be operating in the current study to explain associations of population density with increased restless sleep in men. Future research may choose to assess further the potential role of population density, stress, and restless sleep among adults. 4.2. Social capital e restless sleep associations differ in men and women Little research has examined the association between restless sleep and social capital. Even less has examined whether these associations differ in men and women. Research has noted that men and women have been shown to have different social network structures (Antonucci and Akiyama, 1987) with these structures possibly having diverse implications for health. With regard to our hypothesis that social capital was more strongly associated with restless sleep, our findings were equivocal. First, our study showed that network capital was more strongly associated with restless sleep in men than women, and, instead of reducing the chances of restless sleep, network capital increased those chances in men. While this is not the first instance in which social capital has been associated with adverse health in adults (Carpiano, 2007; Moore et al., 2009b), little is actually known about the potential mechanisms underlying negative associations between social capital and better health. Portes (1998) has suggested that the negative consequences of social capital result from social networks placing restrictions on individual opportunities and freedoms, downlevelling pressures, or making excessive claims and obligations on a person. Although having high network social capital is often considered beneficial, bridging capital may place additional cognitive and physical demands on individuals (Cornwell, 2009). Increased cognitive demand also relates to sleep patterns (Alhola and Polo-Kantola, 2007), and may impact men and women’s sleep differently since cognitive functioning has been shown to

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vary by gender (Downing et al., 2008; Hantsoo et al., 2013). Although the current study’s finding is novel, restless sleep in men with high network social capital may be attributed to increased cognitive demand associated with maintaining a diverse social network. Further research is required to examine cognitive demand as mediating the association between network capital and restless sleep. Yet, it may also be possible that men with high network capital are not necessarily more likely to experience restless sleep, but that they are more likely to report restless sleep. Boasting the need for less sleep is one of the ways that the working male may express dominance in the workplace (Courtenay, 2000). Thus, network social capital may be positively associated with restless sleep in the men of our study because of increased stress and social pressure that are perpetuated by men’s social network structures and/or the pervasive demands of maintaining socially constructed masculine stereotypes. Second, our study did however show that more cognitive aspects of social capital, i.e., generalized trust, reduced women’s odds of restless sleep. Although the stratified analyses suggest that men and women differ in the importance of generalized trust, preliminary analyses using interaction terms did not support effect modification by gender. As a result, we would caution against the conclusion that generalized trust may not be important in men. Instead, we would only highlight the finding that women with high generalized trust were less likely to experience restless sleep. Previous research has also shown generalized trust associated with adequate sleep duration in adults (Nieminen et al., 2013). Researchers have suggested that higher generalized trust may lead to better health by reducing the negative physical and emotional consequences associated with social anxiety and stress (Abbott and Freeth, 2008). 4.3. Limitations There are a number of limitations to be considered. First, our measure of restless sleep was limited to one question item, thus limiting the outcome to one dimension of sleep quality. Moreover, participants may have differed in their interpretation of restless sleep given that they were not provided with a definition. However, this item has been used in previous published studies examining sleep in different populations (Devins et al., 1993; Kutner et al., 2001). With sleep restlessness as only one of several common complaints of poor sleep, future research might examine a wider range of poor sleep symptoms or conditions, including insomnia and apnea. Second, this study used cross-sectional data and was therefore unable to establish causal relationships. In women for example, it is unclear whether decreased generalized trust causes women to experience restless sleep, or whether restless sleep decreases trust. Longitudinal study designs would aid in better understanding the role that gender plays in the links between social capital, neighbourhood environments, and restless sleep. Third, neighbourhoods were defined according to administrative census tract boundaries. Administratively-defined neighbourhood units can be imprecise measures of neighbourhood boundaries since they may not match people’s perceptions of those borders. Fourth, the study focused exclusively on neighbourhood disadvantage and population density as indicators of the neighbourhood environment. Research examining neighbourhood contexts and sleep has also measured other contextual characteristics of the neighbourhood including neighbourhood disorder, noise, and physical conditions of the built environment (Hill et al., 2009; Kruger et al., 2007; Pirerra et al., 2010). These particular measures were unavailable for our study. As a result, there may be residual confounding at the neighbourhood level for which our models did not

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adjust. Finally, the MoNNET sample is restricted to adults in one major North American city. Future research is needed to assess whether the results are generalizable to other urban areas or rural settings. 4.4. Conclusion This study uses an innovative conceptual model to assess the links among gender, neighbourhood environments, social capital and restless sleep. Little research has investigated the complex social environmental mechanisms underlying gender differences in sleep. Furthermore, our study uses unique network measures of social capital and compares those to cognitive dimensions of social capital to assess the direct and indirect paths by which social relationships may be associated with sleep. Future research may wish to build upon study findings by examining mediating pathways between social capital, neighbourhoods, and restless sleep among men and women. For example, future studies might explore stress as a potential mediator between social and neighbourhood environments and restless sleep, or cognitive demand as mediating the association between network capital and sleep in men. Finally, findings may contribute to designing interventions that effectively target the particular social mechanisms by which social capital and neighbourhood environments relate to restless sleep in men and women. Gender differences in the association between neighbourhood environments and restless sleep suggest that health interventions should include multilevel and multifold strategies to improve the places where people reside. Acknowledgements This study was funded by an operating grant from the Canadian Institutes of Health Research (MOP 84584). At the time of the research and analysis, SM held a New Investigator Award from the Canadian Institutes of Health Research e Institute of Aging. References Abbott, S., Freeth, D., 2008. Social capital and health: starting to make sense of the role of generalized trust and reciprocity. Journal of Health Psychology 13 (7), 874e883. Akerstedt, T., 2006. Psychosocial stress and impaired sleep. Scandinavian Journal of Work Environment & Health 32 (6), 493e501. Alhola, P., Polo-Kantola, P., 2007. Sleep deprivation: impact on cognitive performance. Journal of Neuropsychiatric Disease and Treatment 3 (5), 553e567. Antonucci, T.C., Akiyama, H., 1987. An examination of sex differences in social support among older men and women. Sex Roles 17, 737e749. Arber, S., Bote, M., Meadows, R., 2009. Gender and socio-economic patterning of self- reported sleep problems in Britain. Social Science & Medicine 68 (2), 281e 289. Burgard, S.A., Ailshire, J.A., Hughes, N.M., 2010. Gender and Sleep Duration Among American Adults. Population Studies Center Research Report, 09e693. Carpiano, R.M., 2007. Neighborhood social capital and adult health: an empirical test of a Bourdieu-based model. Health & Place 13 (3), 639e655. Cornwell, B., 2009. Good health and the bridging of structural holes. Social Networks 31 (1), 92e103. Courtenay, W.H., 2000. Constructions of masculinity and their influence on men’s well-being: a theory of gender and health. Social Science & Medicine 50, 1385e 1401. Daniel, M., Moore, S., Kestens, Y., 2008. Framing the biosocial pathways underlying associations between place and cardiometabolic disease. Health & Place 14 (2), 117e132. Devins, G.M., Edworth, S.M., Paul, L.C., Mandin, H., Seland, T.P., Klein, G., et al., 1993. Restless sleep, illness intrusiveness, and depressive symptoms in three chronic illness conditions: rheumatoid arthritis, end-stage renal disease, and multiple sclerosis. Journal of Psychosomatic Research 37 (2), 163e170. Downing, K., Chah, S.W., Downing, W.K., Kwong, T., Lam, T.F., 2008. Measuring gender differences in cognitive functioning. Multicultural Education & Technology Journal 2 (1), 4e18. Foley, D.J., Monjan, A.A., Brown, L.S., Simonsick, E.M., et al., 1995. Sleep complaints among elderly persons: an epidemiologic study of three communities. Journal of Sleep Research & Sleep Medicine 13 (6), 425e432.

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