Contributions of personality to social influence: Contingent associations between social network body size composition and BMI

Social Science & Medicine 224 (2019) 1–10

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Social Science & Medicine journal homepage: www.elsevier.com/locate/socscimed

Contributions of personality to social influence: Contingent associations between social network body size composition and BMI

T

Brea L. Perrya,∗, Gabriele Ciciurkaiteb a b

Department of Sociology, Network Science Institute, Indiana University, Ballantine Hall 747, 1020 Kirkwood Ave., Bloomington, IN, 47405, USA Department of Sociology, Utah State University, 0730 Old Main Hill, Logan, UT, 84322-0730, USA

A R T I C LE I N FO

A B S T R A C T

Keywords: Social networks Social influence Self-monitoring Obesity BMI

Social influence is a key determinant of health behaviors and outcomes. Research in the social network tradition emphasizes social structural mechanisms like network content (i.e., the degree to which particular attitudes, attributes, or behaviors are present in the network) and social proximity (i.e., opportunities for social interaction). In contrast, psychologists are oriented toward the individual, identifying how personality traits like selfmonitoring affect susceptibility to peer pressure. Here, we integrate social network and personality approaches, examining social influence on body size using surveys of 379 adults with dependent children. Our findings suggest that the association between social network body size composition and respondent BMI is contingent on both individual susceptibility to influence (i.e., high self-monitoring) and social proximity (i.e., opportunities for co-eating). These results indicate that individuals embedded in social networks bring unique sets of social skills and orientations to interactions, potentially influencing the flow of content across networks.

A large literature has documented the important role of relationships and social networks in health behaviors and outcomes (for reviews, see Berkman and Glass, 2000; Smith and Christakis, 2008; Umberson et al., 2010). Commonly, social influence – or the modification of attitudes and behaviors by one person in response to others – is the mechanism proposed to explain this association (Cunningham et al., 2012; Marsden and Friedkin, 1993). Through processes like behavior modeling, social control, and the development of shared social norms, individuals embedded in social networks tend to become more similar with respect to health behaviors and outcomes over time (Umberson, 1992). Some researchers have drawn parallels between social influence in complex health conditions and contagion in infectious disease, arguing that health behaviors spread through social networks from person to person (Christakis and Fowler, 2007). An important strength of the social network approach to social influence is the focus on the nature of ties between social actors. In particular, the direction of influence is theorized to depend on the attitudes, attributes, or behaviors of actors in the network. Additionally, the strength of the push toward a particular outcome is thought to be a function of social proximity (Marsden and Friedkin, 1993). That is, social influence is expected to be stronger in the context of frequent opportunities for relevant social interaction, and when the sources of influence are valued social referents. These considerations have produced unique insights about the social processes that condition social ∗

influence. At the same time, psychologists have developed a social influence literature that is oriented toward the individual in interaction. In particular, researchers have examined how personality traits like selfmonitoring affect individuals' susceptibility to social influence. High self-monitors are sensitive to social cues and tend to behave consistently with anticipated social norms in any given social context. In contrast, low self-monitors ignore or fail to perceive others’ expectations and are internally motivated (Gangestad and Snyder, 1985, 2000; Snyder, 1979, 1987). Broadly, this body of research suggests that individuals bring their own sets of social skills and orientations to interactions, potentially influencing the flow of content across a network. Integrating theories of personality into network studies of social influence could help reduce unexplained variation in social network models of health, and lead to a richer understanding of interactions between mechanisms at multiple levels. In this study, we use the case of obesity to test a model of social influence that integrates personality and social network theories. Specifically, focusing on respondent BMI, we examine interactions between social network body size composition, self-monitoring, and social proximity. To accomplish this, we developed a 7-item self-monitoring scale that targets susceptibility to social influence in eating behavior, the Self-Monitoring of Eating Scale. Using data collected from 375 individuals with dependent children, we address the following research

Corresponding author. E-mail address: [email protected] (B.L. Perry).

https://doi.org/10.1016/j.socscimed.2019.01.044 Received 12 June 2018; Received in revised form 22 January 2019; Accepted 24 January 2019 Available online 28 January 2019 0277-9536/ © 2019 Elsevier Ltd. All rights reserved.

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concordant ones are reinforced. Social influence is more likely to occur when the potential source of influence is interpersonally visible and socially relevant, and therefore a person who is a meaningful comparison point (Simmel, 1950; Merton, 1968[1949]). A unique strength of the network approach to social influence, in contrast to an individual or dyadic perspective, is the focus on social structure and group processes. Of central concern is the nature of linkages between networks of social actors, and the content that flows between them. However, theories about the role of the individual, and particularly intraindividual psychological traits, are seldom integrated into network studies of social influence in health (see Umberson et al., 2010). In particular, psychologists have studied how personality traits like self-monitoring affect individuals’ susceptibility to social influence under different social conditions (Gangestad and Snyder, 1985; Snyder, 1979, 1987). Integrating these theories could help reduce unexplained variation in social network effects on health, and lead to a richer understanding of interactions between mechanisms at multiple levels of observation.

questions: 1) Is the body size composition of social networks (i.e., partners, adult family members, and friends) associated with respondent BMI, consistent with social influence processes? 2) Do personality traits (i.e., self-monitoring) and social proximity (i.e., frequency of co-eating) moderate the association between body size composition of social networks and respondent BMI? Our findings suggest that the magnitude of social influence in body size is contingent on individual susceptibility to influence (i.e., high self-monitoring) and social proximity (i.e., opportunities for co-eating). These results have important implications for network theories of social influence, and for improving the efficacy of interventions aimed at modifying health behaviors. 1. Background 1.1. Networks, social influence, and health

1.2. Self-monitoring and susceptibility to social influence The social network perspective is unique in that it embeds individuals and their decisions, behaviors, and outcomes in the larger social context of relationships and groups (Perry et al., 2018). Many agenda-setting reports point to social networks as the “fundamental mediators of human adaptation,” as well as the “active ingredients of environmental influences” (Shonkoff and Phillips, 2000:28). Indeed, a large body of research has established a strong link between social network characteristics and health behaviors, attitudes, and outcomes (Smith and Christakis, 2008; Umberson et al., 2010). In many cases, the proposed mechanism underlying the association between networks and health is social influence, or “the social relations that provide a basis for the alteration of an attitude or behavior by one network actor in response to another” (Marsden and Friedkin, 1993:127; also Cunningham et al., 2012). The social influence tradition has long been a mainstay in the social sciences, and can be conceptualized as a collection of theories that explain why people's attitudes, emotions, and behaviors tend to converge over time. The term encompasses a range of processes that are distinct but related – e.g., conformity, peer pressure, persuasion, socialization, and social regulation – all of which have their own scientific literatures in different disciplines. Social influence is of particular interest in the subfield of social networks and health because it provides a link between the structure and composition of networks and the individual behaviors and outcomes of actors embedded in them. In the network tradition, social influence is conceptualized as a function of two properties interacting over time, roughly corresponding to the direction and strength of the “push” toward a particular outcome (Perry et al., 2018). First, the direction of influence depends on network content, or the attitudes, attributes, or behaviors of actors in the network. For example, whether a person's network includes smokers is among the strongest and most robust predictors of whether a nonsmoker will become a smoker (Simons-Morton and Farhat, 2010). In the case of smoking, this influence might operate through active peer pressure, passive behavior modeling, or adoption of the peer group's shared norms or values around smoking. In short, social influence does not require purposive or conscious efforts to modify someone else's behavior, nor is direct contact between two actors necessary for influence to occur (Berkman and Glass, 2000). Second, the strength of the push toward similarity, and therefore the potential for social influence, is a function of social proximity (Marsden and Friedkin, 1993). Social proximity often has a structural basis, where greater proximity is reflected in the propensity for social interaction between two actors in a network (Burt, 1987). Another key element of social proximity is social comparison (Erickson, 1988). People evaluate their behaviors through comparison to reference groups of similar others, where discrepant behaviors are altered and

Psychologists have extensively studied the construct of self-monitoring, defined as sensitivity to social norms and the tendency to behave consistently with those norms in particular social contexts (Snyder, 1979, 1987). Proponents of self-monitoring theory argue that individuals differ in the extent to which they expressively control their behavior to project particular social images in public or group settings (Gangestad and Snyder, 1985, 2000). A sociologist might describe this as an individual's motivation or willingness to display normative front stage behavior in particular social contexts. Psychologists measure self-monitoring on a continuum, but often classify individuals as high or low self-monitors. High self-monitors are thought to be more sensitive to social cues about situationally appropriate behavior and tend to engage in tight regulation of their presentation of self (Snyder, 1979). In contrast, low self-monitors do not share the same concerns for situational appropriateness, and exhibit less deliberate control of their behavior. Rather, compared to high selfmonitors, the behavior of low self-monitors is typically congruent with their inner attitudes, emotions, and beliefs, and more consistent across social settings (Gangestad and Snyder, 1985, 2000). The theory of self-monitoring has important implications for individual susceptibility to social influence in health research. Among high self-monitors, who are sensitive and responsive to social cues about others' expectations, we would likely observe strong convergence between an actor's behavior or outcomes and the normative content of their personal social networks. However, this pattern of social influence might be absent or attenuated among low self-monitors. For example, Perrine and Aloise-Young (2004) demonstrated that high self-monitors who believed that cigarette smoking was normative among their peers were more likely to start smoking and continue smoking over a oneyear period, while normative beliefs had no effect on smoking uptake among low self-monitors. In sum, the theory of self-monitoring holds promise for explaining why some individuals are susceptible to social influence and readily conform to network norms for health and behavior, while others maintain patterns of behavior that are independent of network contexts. 1.3. The empirical case: networks, obesity, and self-monitoring To test the utility of integrating social network and self-monitoring theories of social influence, we draw on the case of obesity and eating behavior. Over the past decade, research has suggested that obesity spreads through social networks such that individuals with similar body mass index (BMI) cluster together and become more similar in body size over time (Christakis and Fowler, 2007). Simultaneously, scholars have noted that dyads of spouses, family members, and friends experience 2

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social influence in body size among these high self-monitors. In contrast, low self-monitors tend to behave in ways that are consistent with their own internal motivations (e.g., hunger, food preferences, dietary goals), and are therefore unlikely to be susceptible to influence in social eating situations. Specifically, we hypothesize:

body size convergence and increasing similarity around behaviors like diet and exercise (Bahr et al., 2009; Christakis and Fowler, 2007) which may be attributed to social influence. One potential mechanism of social influence in obesity outcomes is shared group norms. That is, social networks have their own set of values and practices around food and exercise, as well as distinct norms about what constitutes a healthy body. Being embedded in a particular network culture can change a person's attitudes and behaviors, including their perceptions of ideal body size and health behaviors. For example, people's body size has been linked to perceptions of their friends' beliefs about ideal body type (Krones et al., 2005; Thompson et al., 2006). However, explicit communication of norms is not necessary for social influence to occur. Simply interacting with someone who conforms to a thin body ideal may increase body dissatisfaction (Hruschka et al., 2011; Krones et al., 2005). Consistent with social network theories of social influence, this effect is particularly strong in the context of ties that are more meaningful and socially proximate, such as in romantic relationships where one partner is heavier (Markey and Markey, 2011). Another mechanism of network effects in obesity is behavior modeling, wherein people intentionally or subconsciously conform to the behavior exhibited by others in their presence (Cruwys et al., 2015). Experimental studies have consistently demonstrated that individuals adjust the amount of food they eat in congruence with the amount consumed by confederates (i.e., members of the research team pretending to be subjects of the experiment; Herman et al., 2003). Within family networks, behavior modeling operates through the development of shared eating practices. Befort et al. (2008) found that preferences for certain foods among obese women were heavily influenced by their social affiliations. Likewise, Bertoli et al. (2011) demonstrated conformity in food choices within family networks, and identified relationship strain resulting from discordant dietary choices, with similar findings extending to peers (Feunekes et al., 1998; Guidetti et al., 2012, 2014). While behavior modeling may be deliberate and even openly negotiated, as in the case of family dietary practices, research has shown that social influence in food consumption operates at least partially through unconscious mimicry. Given research reviewed here on social influence in obesity, we expect to observe similarity in body size between individuals and the networks in which they are embedded. We propose that the BMI of romantic partners and the body size composition of personal networks will reflect the direction of social influence. Specifically, we hypothesize:

H2. Self-monitoring of eating behavior will moderate the effects of partner BMI and proportion of overweight or obese persons in family and friendship networks on respondent BMI. Consistent with a social network perspective, the social proximity of individuals to members of their network is likely to moderate the strength of social influence, and therefore the degree of body size concordance. As noted above, greater social proximity is reflected in opportunities for social interaction (Burt, 1987), particularly interactions that are relevant to the behavior or outcome being studied. Some mechanisms of social influence in obesity (e.g., behavior modeling) are unlikely to occur outside of a social eating context. In other words, body size convergence should be more pronounced when a person eats regularly in the presence of their partner, family members, and friends, providing opportunities for establishing group norms and modeling others’ consumption behaviors. Consequently, we hypothesize: H3. Frequency of co-eating will moderate the effects of self-monitoring and partner BMI and proportion of overweight or obese persons in family and friendship networks on respondent BMI. In addition to opportunities for interaction, social proximity is also reflected in the relevance of social comparison (Erickson, 1988). Laboratory studies of food consumption consistently find that behavior modeling is strongest when individuals like and perceive themselves to be similar to the confederate (Cruwys et al., 2015; Stok et al., 2012; Cruwys et al., 2012; Hermans et al., 2009). In real world settings, this tendency may lead to variation in the magnitude of social influence across different types of relationships. For example, Pachucki and colleagues (2011), in their analysis of food choice concordance, found the strongest social influence effect between spouses relative to other relationship types. Similarly, Feunekes et al. (1998) identified higher resemblance in food consumption patterns between spouses and parents and children relative to friends. In contrast, Christakis and Fowler (2007) found no significant differences in the degree of obesity convergence among spouses, friends, and siblings. Given these findings, we might expect body size convergence to be especially strong in the context of networks or dyads that are closely connected and socially meaningful. In the current study, we examine three types of ties – partners, adult family members, and friends. However, given the lack of consensus in the literature about which types of relationships are most influential, we hesitate to make specific predictions about the pattern of results.

H1. Higher partner BMI and greater proportion of overweight or obese persons in family and friendship networks will be associated with higher respondent BMI. In the current paper, we test whether self-monitoring of eating behavior moderates the strength of social influence, or the association between network body size and respondent BMI. We found only two existing studies examining the role of self-monitoring in food consumption. Using an experimental design, Cavazza et al. (2011) demonstrated associations between self-monitoring and the amount of food ordered in a mock restaurant context. Specifically, the number of dishes ordered by high self-monitors was influenced by the number of co-eaters, but not by level of hunger, while the opposite was true for low self-monitors. This suggests that self-monitoring increases the degree to which individuals tailor their eating behaviors in the company of others to match implicit group norms. Likewise, in a dyadic analysis of parents' and friends’ influence on adolescent food choices, Guidetti et al. (2016) found that high self-monitors were more similar to their friends with respect to food preferences and types of food consumed in comparison to low self-monitors. Research on self-monitoring theory indicates that some individuals are particularly attuned and responsive to social cues about expectations for eating behavior. We would likely observe a high degree of

2. Data and methods We use data from the Networks and Obesity: Relationships and Mechanisms Study (NORMS), fielded in 2012 to examine the impact of obesogenic networks and community contexts on individual behaviors and outcomes. Data were collected via self-report surveys from 410 adults with children. The only inclusion criterion for participation in the study was being the parent or primary caregiver of a child between the ages of 2 and 17 years old. Participants were recruited across ten sites (e.g. child care centers, schools, churches) in Lexington, Kentucky using a sampling frame designed to maximize racial and socioeconomic heterogeneity. In these sites, all parents were first informed about the study through emails and/or personal contact from directors of these organizations and via fliers. Then, surveys were distributed to all families and were returned via mail (self-addressed, postage-paid envelopes) or using locked drop boxes in the various facilities. Across all of these sites, the response rate was 65%. These recruitment strategies yielded a sample of 410 focal 3

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CI: .220-.300), where data were collected (KDPH, 2018). Self-Monitoring of Eating Scale. The key independent variable was the Self-Monitoring of Eating Scale (SMES), which was developed for this research. This scale measured the degree to which respondents reported modifying their own eating behaviors as a function of the eating behaviors and characteristics of others. The original scale consisted of nine items, but was reduced to seven items. Respondents were asked: “Please think back to meals or snacks that you have eaten in the company of others in the past year. To what extent do you agree or disagree with the following statements?” Examples included, “I feel hesitant to overeat or eat unhealthy foods around people who are physically fit.” Potential response categories were “Strongly agree,” “Agree,” “Disagree,” and “Strongly disagree.” Full wording for all items is provided in Table 2. The original nine items were subjected to exploratory factor analysis. A Kaiser-Meyer-Olkin (KMO) test suggested that the data were well-suited for exploratory factor analysis. The minimum recommended KMO is 0.50, and the KMO for these data was 0.83 (Ferguson and Cox, 1993). We conducted an iterated principal factors model with oblique promax rotation. Eigenvalues and a scree plot clearly indicated a onefactor solution fitted to seven items (see Table 2). The eigenvalue for the single factor was 2.60, and it explained 63% of the variance. The scale reliability coefficient alpha was 0.77, and was calculated using the mean of non-missing items. The final scale ranged from 1.00 to 3.86 (higher values = higher self-monitoring) and was normally distributed. The pairwise correlation between the SMES and respondent BMI was 0.18 (p < .001). Body size of network members. The body size of network members was hypothesized to interact with self-monitoring of eating behavior. Partner body size was measured using BMI calculated from height and weight, as reported by the respondent. Prevalence of partner obesity in the sample (0.369; CI: 0.307–0.431), was significantly higher (p < .05) than the estimated rate of obesity in the county where data were collected (0.260; CI: .220-.300; KDPH, 2018). Because respondents may have erred in their reporting of partner height or weight, we conducted sensitivity analyses using an ordinal measure of subjective partner weight status. The pattern of findings was consistent. However, the interaction between social eating and partner weight status was marginally significant (p = .06), probably because of the limited variation in the smaller ordinal scale relative to BMI. This finding reduced concerns about effects of respondent error in reporting of partner BMI on results. Body size of adult family and friendship networks was calculated using two items asking, “Among the groups of people you know listed below, please estimate the percent of each group that is overweight or obese… Your adult extended family members and in-laws (e.g., parents,

respondents. About 80% of the full sample was women, 56% was white, 60% was married, and 69% worked fulltime. Mean years of education was 15.75. The median household income was $62,500, and 21% of families fell below the federal poverty line. Cases were dropped from the analysis at three different stages. First, 15 respondents did not complete the survey and were missing data on multiple study variables, and 20 were missing data on the key variable of interest – the Self-Monitoring of Eating Scale. The final sample used in psychometric analyses of this scale was n = 375. There were no significant differences between this analysis sample and the full sample of 410 on any characteristics or variables used here with the exception of years of education. Specifically, respondents dropped at this analysis stage had a mean education of 20.00 years, compared to 15. 58 years among those retained (t = -6.40; p < .001). For regression analyses, we imputed data on 28 cases with missing values, while four cases were dropped because there was insufficient data for imputation. Data were imputed using univariate imputation sampling based on multiple regression. Neither the significance nor the magnitude of results varied from listwise deletion. This process resulted in a final regression analysis sample of 371. Finally, we examined the effects of self-monitoring of eating in the context of obese partners, and for this analysis respondents without a current partner were dropped, yielding an analysis sample of 231 partnered respondents. Relative to the full regression sample (n = 371), partnered respondents were significantly more likely to be white, higher income, and college educated (see Table 1). The survey instrument was designed to measure health beliefs, behaviors, obesity outcomes, and related health problems. A unique feature of this survey was the focus on social networks and relationships. For example, respondents were asked to estimate the prevalence of obesity in different parts of their social networks, identify factors that motivated the consumption decisions of their partner, and assess the amount of time spent eating in the presence of others. Additionally, several items were developed specifically for this study, most notably a series of items about self-monitoring of eating behavior. 2.1. Measures Dependent variable. The dependent variable for all regression analyses was respondent BMI. This was calculated using self-reported height and weight. Continuous BMI, ranging from 16 to 52, was used in the final models. Because results using categories and logged BMI (to correct a slightly positive skew) did not differ substantively from those presented here, raw BMI was retained for ease of interpretation. Prevalence of self-reported obesity in the sample (0.338; CI: 0.289–0.387), calculated using BMI, was not significantly different from the estimated rate of obesity in Fayette County, Kentucky (0.260; Table 1 Sample demographic characteristics, NORMS. Full regression sample (n = 371)

% (n) Female White Education (years) Employed fulltime HH income ($1000's) Currently married Respondent BMI Self-monitoring Reg ate w/partner Reg ate w/family Reg ate w/friends Partner BMI % family obese % friends obese ∗∗∗

Partnered regression sample (n = 231)

¯ (s) X

% (n)

83.02 (308) 56.33 (209)

¯ (s) X

83.55 (193) 69.70 (161) 15.59 (2.69)

16.27 (2.69)

68.73 (255)

71.86 (166) 65.86 (46.64)

84.13 (42.38)

60.11 (233)

85.28 (197) 28.00 (6.87) 2.28 (0.47)

27.68 (6.68) 2.29 (0.46)

N/A 55.43 (204) 65.22 (240)

87.45 (202) 56.52 (130) 66.81 (153) N/A 30.75 (13.09) 26.86 (10.04)

29.13 (5.66) 31.00 (12.76) 26.82 (10.02)

p < .001. 4

X2 / t 0.03 39.82*** 6.58∗∗∗ 2.79 11.21∗∗∗ N/A 1.22 0.72 N/A 0.29 0.68 N/A 0.46 0.10

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Table 2 Exploratory factor analysis of Self-Monitoring of Eating Scale (n = 375). Factor loadingsa a. When I go out to lunch or dinner with someone who is overweight, I feel free to eat more b. When I am eating in the company of family or friends, we often discuss our food choices c. If I am eating with someone at a restaurant and they order dessert, I am more likely to do the same d. I tend to talk about food more often with people who are overweight e. The closer I am to someone, the more comfortable I feel eating what I want to in their presence f. I am not more likely to make unhealthy choices when I eat with someone who is overweight g. Thinking back to meals I've eaten with others, I tend to stop eating when others stop h. I feel hesitant to overeat or eat unhealthy foods around people who are physically fit i. The less I know the person I am eating with, the more pressure I feel to imitate their eating behaviors a

0.60 0.33 0.41 0.51 0.56 −0.09 0.62 0.72 0.69

Bold numbers indicate factor loadings > 0.35.

Model 3 added three-way interactions between the SMES, network body size, and regularly eats with network members. Plots of predicted values were presented to aid interpretation of interactions. In these, the SMES was held equal to the mean and 1.5 standard deviations above and below the mean.

siblings, aunts, etc.)…Your adult friends.” Response categories included: “None (0%),” “A few (5–20%),” “Some (25–40%),” “About half (45–55%),” “Most (60–75%),” “Nearly all (80–95%),” and “All (100%).” These were coded to the midpoint and converted to ten percent increments. Respondents may have reported percent obese (rather than “overweight or obese”) since people tend to underestimate overweight (Goodman et al., 2000). Moreover, the prevalence rate of obesity among friends (0.261; CI: 0.242–0.279) was not significantly different from the estimated rate of obesity in Fayette County, Kentucky (0.260; CI: .220-.300; KDPH, 2018), while the reported prevalence rate among adult family members (0.331; CI: 0.307–0.355) was slightly higher than the county rate (p < .05). Opportunity for social influence. The extent of opportunity for social influence through behavior modeling was also hypothesized to interact with self-monitoring of eating behavior. This was measured using variables asking how often respondents ate snacks or meals with their partner, adult family members, and friends. Response categories included: “Several times a day,” “Every day”, “Almost every day,” “2–3 times a week,” “Once a week,” “2–3 times a month,” “Once a month,” and “Less than once a month.” For ease of interpretation of interaction models, these were converted to binary variables cut to the most common midpoint across relationship types (i.e., 1 = Weekly or more, else 0). Sociodemographic variables. Sociodemographic variables included gender (1 = women; 0 = men) and race (1 = white; 0 = nonwhite). Racial and ethnic minorities were collapsed into one category due to insufficient cell sizes. Education was measured in years of schooling, and household income was measured in thousands of dollars (income ranges coded to midpoints). Marital status was indexed using a binary variable (1 = currently married; 0 = not currently married). Work status was also binary (1 = working fulltime; 0 = working parttime or not working).

3. Results Basic descriptive statistics (see Table 1) reflected a slightly overweight sample (Mean BMI = 28.00). The mean value on the SMES was 2.28, with a potential range of 1–4, indicating a moderate degree of self-monitoring of eating. About 87%, 55%, and 65% of respondents ate meals with their partners, family members, and friends weekly or more, respectively. Mean partner BMI was 29.10, and the average estimated percent of adult family and friends who were overweight or obese was 31% and 27%. 3.1. Partners Table 3 presents results from the regression of respondent BMI on partner BMI. As shown in Model 1, partner BMI was positively associated with respondent BMI, consistent with research on body size convergence in couples (Perry et al., 2016). Each one-unit increase in partner BMI predicted a 0.38-unit increase in respondent BMI (p < .001). Also, higher levels of self-monitoring of eating behavior were positively associated with BMI (b = 1.89; p < .05). As shown in Model 2 (see Table 3), self-monitoring significantly moderated the influence of partner BMI on respondent BMI (b = 0.31; p < .05). Specifically, the association between partner BMI and one's own BMI was 0.16 (p = 0.20) among those who reported low levels of self-monitoring (i.e., 1.5 SDs below the mean), compared to 0.38 (p < .001) and 0.60 (p < .001), respectively, among those with mean and high (i.e., 1.5 SDs above the mean) levels of self-monitoring. Fig. 1 depicts this interaction, showing that the association between partner and respondent BMI was strongest when the respondent reported high values on the SMES. However, there was no significant two-way (not shown) or three-way (see Model 3) interaction between partner BMI and eating regularly with one's partner.

2.2. Analysis A series of linear OLS regression models in Stata 15 was used to examine the role of self-monitoring, social network body size composition, and co-eating in the weight status of respondents (StataCorp, 2017). Three sets of regressions were modeled to capture the role of a spouse or partner, adult family networks, and friendship networks, respectively. Preliminary regression models also examined identical models focusing on more peripheral community members (“coworkers, neighbors, acquaintances, etc.”). These results were omitted because the obesity composition of this network had no association with respondent BMI in main effects models, in contrast to the pattern of findings among partners, family members, and friends. For each type of tie, Model 1 included main effects of sociodemographic controls, the SMES, frequency of co-eating, and body size of network members. In Models 2 and 3, interactions were modeled using pooled regressions with multiplicative interaction terms. Model 2 added two-way interactions between the SMES and network body size.

3.2. Adult family members Table 4 contains results from the regression of respondent BMI on the body size composition of the family network. As shown in Model 1, each ten-percent increase in the percent of adult family members who were obese was associated with a 0.54-unit increase in respondent BMI (p < .001), all else equal. There was also a positive association between self-monitoring and respondent BMI such that a one-unit increase on the SMES was associated with 2.73-unit increase in BMI (p < .001). However, there was no direct effect of regularly eating with adult family members. Models 2 and 3 in Table 4 present results from interaction models. 5

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Table 3 OLS regression of respondent BMI on self-monitoring of eating and partner characteristics, NORMS (n = 231).

Female White Education (years) Employed fulltime Household income ($1000's) Currently married Self-monitoring Regularly ate with partner Partner BMI

Model 1

Model 2

Model 3

Model 1

Model 2

Model 3

b (SE)

b (SE)

b (SE)

b (SE)

b (SE)

b (SE)

−2.70∗ (1.10) −1.07 (0.94) −0.27 (0.17) 0.91 (0.92) −0.02∗ (0.01) 1.58 (1.31) 1.89∗ (0.91) −1.28 (1.25) 0.38∗∗∗ (0.07)

−2.71∗ (1.09) −1.49 (0.96) −0.25 (0.17) 0.89 (0.91) −0.02 (0.01) 1.55 (1.30) −7.02 (4.28) −1.05 (1.24) −0.32 (0.34)

−2.37∗ (1.09) −1.62 (0.96) −0.30 (0.17) 0.82 (0.91) −0.02 (0.01) 1.41 (1.30) −22.42 (9.29) −53.59∗ (24.66) −1.76∗ (0.83)

−0.44 (0.91) −1.73∗ (0.75) −0.29 (0.16) 2.55∗∗∗ (0.76) −0.03∗∗∗ (0.01) 1.07 (0.95) 2.73∗∗∗ (0.72) −0.72 (0.68) 0.54∗∗∗ (0.14)

−0.44 (0.91) −1.72∗ (0.75) −0.29 (0.16) 2.54∗∗∗ (0.76) −0.03∗∗ (0.01) 1.06 (0.95) 2.84∗ (1.28) −0.72 (0.68) 0.60 (0.64)

−0.35 (0.90) −1.81∗ (0.74) −0.32 (0.16) 2.57∗∗∗ (0.75) −0.02∗ (0.01) 0.85 (0.94) 4.20∗ (1.84) 7.91 (5.87) 2.32∗∗ (0.87)

0.31∗ (0.14)

0.84∗ (0.35) 19.97 (10.51) 1.79 (0.91) −0.67 (0.38)

−0.02 (0.27)

−0.69 (0.37) −3.28 (2.57) −3.84∗∗ (1.28) 1.51∗∗ (0.55)

25.48 0.16 6.69∗∗∗

24.24 0.19 6.25∗∗∗

Interaction terms Self-monitoring*Partner BMI Self-monitoring*Ate w/partner Ate w/partner*Partner BMI Self-monitoring*Ate w/partner*Partner BMI

Constant R2 F ∗

p < .05;

20.68 0.23 7.31∗∗∗ ∗∗∗

Table 4 OLS regression of respondent BMI on self-monitoring of eating and family network characteristics, NORMS (n = 368).

40.58 0.25 7.14∗∗∗

Female White Education (years) Employed fulltime Household income ($1000's) Currently married Self-monitoring Regularly ate with family Percent family members obese Interaction terms Self-monitoring*% family obese Self-monitoring*Ate w/family Ate w/family*% family obese Self-monitoring*Ate w/family*% family obese

82.53 0.26 5.97∗∗∗

Constant R2 F ∗

p < .001.

p < .05;

25.73 0.16 7.45∗∗∗ ∗∗

p < .01;

∗∗∗

p < .001.

family body size composition and respondent BMI was contingent on both the level of self-monitoring and opportunities for eating with family (see Model 3). Specifically, as shown in Fig. 2, moderation of percent of family members who were obese by self-monitoring mirrored

Two-way interactions between family body size and self-monitoring (see Model 2) and eating regularly with family (not shown) did not achieve statistical significance. However, we identified a three-way interaction (b = 1.51; p < .01) wherein the association between

Fig. 1. Predicted value of respondent BMI as a function of self-monitoring of eating and partner BMI, NORMS (n = 231). Note: Based on Model 2 in Table 3. 6

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Fig. 2. Predicted value of respondent BMI as a function of self-monitoring of eating and percent of adult family members who are obese among respondents who regularly ate meals with family members, NORMS (n = 368). Note: Based on Model 3 in Table 4.

the pattern observed for partner BMI, but only among those who ate regularly with family members. For this group, the association between family body size composition and BMI among those who reported low and mean levels of self-monitoring was −0.28 and 0.29 (p = .39 and p = .14), respectively, compared to 0.89 (p < .01) among those with high levels of self-monitoring. In contrast, for those who ate less regularly with adult family, there was no significant moderation of family body size by self-monitoring.

Table 5 OLS regression of respondent BMI on self-monitoring of eating and friendship network characteristics, NORMS (n = 367).

Female White

3.3. Friends

Education (years)

Table 5 contains results from the regression of respondent BMI on the body size composition of friendship networks. On average, every ten-percent increase in the percent of friends who were overweight or obese was associated with a 0.51-unit increase in respondent BMI (p < .01), according to Model 1. There was also a positive association between self-monitoring and BMI such that a one-unit increase on the SMES was associated with 2.55-unit increase in BMI (p < .001). However, there was no significant influence of regularly eating with friends. In contrast to findings on partners and adult family members, we found no evidence of significant two-way or three-way interactions when examining friendship networks (see Models 2 and 3 in Table 5). That is, the association between the body size composition of friendship networks and respondent BMI was not moderated by self-monitoring, nor by opportunities for modeling eating behavior.

Employed fulltime Household income ($1000's) Currently married Self-monitoring Regularly ate with friends Percent friends obese

Model 1

Model 2

Model 3

b (SE)

b (SE)

b (SE)

−0.20 (0.93) −2.17∗∗ (0.75) −0.20 (0.16) 2.13∗∗ (0.76) −0.03∗ (0.01) 1.11 (0.95) 2.55∗∗∗ (0.74) −0.09 (0.71) 0.51∗∗ (0.19)

−0.22 (0.93) −2.16∗∗ (0.75) −0.20 (0.16) 2.15∗∗ (0.76) −0.03∗ (0.01) 1.08 (0.96) 3.41∗ (1.35) −0.11 (0.71) 1.15 (0.86)

−0.25 (0.94) −2.17∗∗ (0.76) −0.21 (0.16) 2.11∗∗ (0.77) −0.03∗ (0.01) 1.09 (0.96) 3.12 (2.14) −1.76 (6.25) 0.49 (1.50)

−0.28 (0.36)

−0.08 (0.62) 0.40 (2.75) 0.89 (1.85) −0.26 (0.77) 24.48 0.14 4.46∗∗∗

Interaction terms Self-monitoring*% friends obese Self-monitoring*Ate w/friends Ate w/friends*% friends obese Self-monitoring*Ate w/friends*% friends obese Constant 2 R F

4. Discussion Focusing on the case of obesity and eating behaviors, the goal of this study was to test a model of social influence that integrates personality and social network theories. Broadly, we found associations between social network body size composition and self-monitoring of eating behaviors on individuals’ body mass index, consistent with sociological and psychological approaches to social influence. That is, weight status of partners, family members, and friends were directly and positive related to respondent BMI. At the same time, greater self-monitoring of eating behavior was also directly associated with higher BMI,

∗

p < .05;

∗∗

p < .01;

∗∗∗

25.03 0.14 6.37∗∗∗

23.14 0.14 5.78∗∗∗

p < .001.

suggesting that internal motivation to maintain particular dietary habits or other healthy lifestyle behaviors may have a protective influence on weight status (Elfhag and Rossner, 2005). Findings from interaction models were particularly instructive for 7

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associations between network composition and one's own BMI are quite large in magnitude among high self-monitors and essentially null among low self-monitors. In other words, our findings are consistent with the theory that individuals bring distinct social orientations to their interactions, potentially influencing the flow of content across a network. Integrating theories of personality into network studies of social influence reduces unexplained variation in social network models of health, and leads to a richer understanding of interactions between mechanisms at the individual, dyadic, and network levels of observation. Of course, a major barrier to integrating additional measures at the individual level is the already high cost and respondent burden associated with network data collection. To increase the feasibility of including data on personality, we have developed and preliminarily validated the brief Self-Monitoring of Eating Scale. These items could easily be adapted to other health behaviors, and the scale should be subjected to more rigorous validation using different populations. Nonetheless, our findings point to the utility of brief personality measures for explaining individual susceptibility to social influence.

theories of social influence. That is, we identified significant moderation of the magnitude of social influence by personality and social proximity for adult family networks. Specifically, the association between reporting obese family networks and respondent BMI was largest among high self-monitors who regularly ate in the presence of network members. In contrast, family networks had little influence among low self-monitors. Similarly, we found a stronger correlation between partner and respondent BMI at high levels of self-monitoring. However, there were no significant moderations of social influence in friendship networks. We cannot determine using these data why the moderating effects of self-monitoring varied across types of network ties. However, this pattern may be related to social comparison aspects of social proximity. Multiple studies have found that convergence over time in patterns of consumption are stronger among partners and close family members compared to friends (Feunekes et al., 1998; Pachucki et al., 2011). Friends may not be socially proximate enough, as a group, to serve as meaningful social referents, and therefore do not trigger self-monitoring. This explanation is consistent with our findings on more peripheral network members (i.e., coworkers, neighbors, other acquaintances; results not shown), where there was also no significant moderation of body size composition by self-monitoring. Alternatively, people may be more likely to self-monitor among friends than in the company of partners and close family members. Therefore, there may be too little variation in the degree of self-monitoring of eating behaviors among friends to detect significant moderation. In the case of partners, frequency of co-eating did not moderate the effect of social influence or self-monitoring, perhaps because partner influence on body size is not restricted to mealtimes. Because partners share frequent opportunities for behavior modeling and exert social regulation related to health behaviors of all kinds, co-eating may not adequately capture variation in social proximity to partners. A measure of relationship quality (unavailable in these data) might be a better indicator of proximity in couples. In general, the absence of direct effects of co-eating is noteworthy. Research suggests that eating behavior varies in the presence of others relative to eating alone, and so-called “social facilitation” of overeating has been documented in multiple studies (Herman, 2015). However, the impact of co-eating is likely to be contingent on a host of factors, including individual attributes like self-monitoring, as well as shared cultural expectations and environmental cues. Null findings on coeating may also indicate something about the mechanisms underlying social influence. That is, it may not be necessary to eat in the immediate presence of some types of network ties (e.g., partners or friends) for social influence to occur. Rather, shared implicit social norms or food preferences may be driving these effects rather than behavior modeling or social feedback.

4.2. Implications for prevention and intervention Recently there has been a recognition of the need to incorporate peer, family and community influences into obesity prevention and intervention programs (Bahr et al., 2009; Valente et al., 2007). The results of this study further emphasize the importance of social context and the need to incorporate information about the social environment in which individuals are embedded when developing obesity intervention programs. Recent studies have demonstrated dyadic dependence in the ability of coupled persons to achieve and maintain weight loss, with partners helping in some cases and hindering in others (Cornelius et al., 2016; Gorin et al., 2017). Commitment and early participation from all members of a social group or dyad may be necessary to cultivate an environment that is emotionally and instrumentally supportive of weight loss. At the same time, our finding that psychosocial characteristics, i.e., self-monitoring, may condition the magnitude of social influence on behavior and outcomes is relevant for both the development and evaluation of obesity prevention efforts. Despite considerable resources devoted to addressing obesity over the past several decades, long-term weight loss intervention efforts have been largely unsuccessful. One reason may be because interventions and policies are seldom tailored to individual traits and motivations, on the one hand, or to unique social environments, on the other. Such an approach could provide novel strategies for capitalizing on people's strengths and removing barriers to long-term behavior change. In particular, it is critical to consider factors like self-monitoring, internal motivation to lose weight, and self-efficacy alongside assessments of the eating environment and what kinds of relationships and supports would be most helpful (e.g., whether to enlist the spouse, a best friend, or another partner in a weight loss intervention; Elfhag and Rossner, 2005; Gorin et al., 2017). While our findings underscore the challenges of future work for health practitioners and policy makers, programs rooted in theory-based models of personality and social influence may have the most potential to contribute to reducing rates of obesity and related co-morbidities.

4.1. Implications for theory This research has important implications for theories of social influence. In the social network tradition, social influence is conceptualized and measured at the network level. That is, how does the type of content flowing through networks interact with structural opportunities for diffusion and behavior modeling? This perspective often treats individual actors in similar kinds of networks and network positions as “blank slates” that are equally receptive to influence. In contrast, theories of personality emphasize individual variation, examining how sensitivity to social cues and self-regulation influence behavioral convergence, often among dyads and in experimental settings (Snyder, 1979). The broader social structural context in which individuals are embedded is largely ignored. This research underscores the need to more fully integrate theories of personality and other individual differences into social network research on social influence. Our findings indicate that personality has a strong moderating relationship in social influence processes such that

4.3. Limitations We have largely framed the association between network member body size and respondent BMI as social influence, consistent with theories of self-monitoring. However, there is no way to determine using these cross-sectional data whether body size similarity reflects social influence or homophily (i.e., preference for similar others). In other words, body size similarity could be driving relationship preferences, reflecting reverse causation. This explanation also has face validity in the context of the observed interactions. That is, overweight individuals 8

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social influence in health. The present research suggests that characteristics of our proximal social environments and personality traits may interact to shape individual health behaviors and outcomes, leading to a richer understanding of complex processes that condition health. Specifically, by integrating social network and self-monitoring theories of social influence in the case of eating behavior and BMI, we found that the magnitude of social influence in body size was contingent on both social proximity (i.e., opportunities for co-eating) and susceptibility to social influence (i.e., high self-monitoring). Importantly, these interactions between the individual and network levels would have been obscured if either personality or social network approaches had been utilized independently. Broadly, our results indicate that the unique sets of social skills and orientations that individuals bring to interactions merit additional consideration by sociologists since they may influence the flow of content across social networks.

who are high in self-monitoring may be especially likely to select into overweight networks, or to prefer eating regularly with overweight friends and family members. Additional research using longitudinal data is needed to verify the direction of causation in relationships between these variables. However, the inconsistent patterns across types of ties provide some evidence for social influence. Specifically, we identified no significant moderation of body size composition in friendship networks or more peripheral networks of coworkers, neighbors, and other acquaintances (results not shown) by self-monitoring or opportunities for modeling eating behavior, but did observe these contingencies for partners and family members. Since friendship networks are elective and family networks are not, we would expect to see stronger interaction results in the former set of models if social selection were the dominant process at work. Moreover, even if the findings are a reflection of some degree of social selection, this explanation still has important public health implications. Namely, body size homophily could reinforce unhealthy behaviors and would likely shape norms around what constitutes an ideal or healthy body size (Markey and Markey, 2011; Perry et al., 2016). Also, our research relies on self-reported height and weight, which are susceptible to error. For instance, studies using nationally representative data have demonstrated systematic differences between self-reported and measured weight and height values, especially at the low and high ends of the BMI scale (Stommel and Schoenborn, 2009). Similarly, bias in partner weight perceptions has been documented among men and women, consistent with observed errors in self reports (Christensen, 2012). Notably, however, Stommel and Schoenborn (2009) found that 80% of deviations between self-reported BMI did not exceed ± 2 units from the objectively measured BMI, and health risk estimates associated with BMI were very similar whether self-reported or objectively measured data were used. We conducted sensitivity analyses using a subjective ordinal indicator of partner weight and achieved very similar results. Moreover, respondent-reported rates of obesity for self, partner, family, and friends are similar to estimates for Fayette County, Kentucky, suggesting that obesity data in this sample may be fairly reliable. Finally, it is worth noting some potentially relevant features of the population from which our sample was drawn. A goal of the broader data collection effort was to understand how obesogenic family environments affect children's behaviors and outcomes. Therefore, our study targeted parents with one or more school-aged children in the home. Parents with minor children may differ systematically from nonparents or older empty-nesters, particularly with respect to social network composition and influence. Specifically, compared to adults without children, parents tend to have less contact with network members overall, and their networks are composed disproportionately of kin and friends who are linked through their children (Hammer et al., 1982). Moreover, research suggests that parenthood constrains and induces health behaviors (e.g., opportunities for exercise, types of foods prepared in the home) in ways that accelerate weight gain over the life course relative to non-parents (Umberson et al., 2011). These patterns suggest that the magnitude of findings on family body size and respondent BMI may be overestimated in this sample relative to the general population since kin networks may have an outsized influence in this demographic group. At the same time, because parents' health behaviors are perhaps more affected by time constraints and children's preferences than by broader social influences, the magnitude of effects may be underestimated. Additional research using a sample that includes a representative number of non-parents is needed before results can be generalized beyond parents of school-aged children.

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5. Conclusions Limitations notwithstanding, our research underscores the importance of integrating theories of personality into network studies of 9

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