Health Psychology 2015, Vol. 34, No. 10, 1004 –1012
© 2015 American Psychological Association 0278-6133/15/$12.00 http://dx.doi.org/10.1037/hea0000201
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Trajectories of Overweight and Their Association With Adolescent Depressive Symptoms Alexa Martin-Storey
Robert Crosnoe
Université de Sherbrooke
University of Texas at Austin
Objective: To explore the potential for a developmental approach to reveal new insights into the well-documented link between weight and depressive symptoms. Method: Latent class analysis identified multiple trajectories of overweight from 24 months to 15 years in the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n ⫽ 957). Structural equation models then used these classes to predict depressive symptoms at age 15. Results: Five latent classes captured continuity and change in weight from early childhood into middle adolescence. Controlling for current weight, stably overweight girls tended to have the most depressive symptoms, but popularity and positive image appeared to buffer against some of the risks that girls faced from being stably overweight or becoming overweight in early to middle childhood. Notably, boys’ longitudinal weight patterns were not associated with their depressive symptoms in adolescence. Conclusions: Weight histories, controlling for current weight, are important for understanding the psychological experience of overweight, especially when such histories are considered in relation to other aspects of psychosocial functioning. Keywords: depressive symptoms, gender differences, trajectories, weight
toms in long-term trajectories of mental health highlight adolescence as an important period for understanding this link (Adair, 2008; Crosnoe, 2010; Lee et al., 2010). In this spirit, this study examined the implications of developmental variation in weight across the early life course for adolescent depressive symptomatology in the United States. Life course theory (see Elder, 1998) underlines the importance of the timing of developmental milestones in understanding individual outcomes. Health-focused iterations of this theory assess how the timing of exposure to various health-related transitions influences health (Pearlin, Schieman, Fazio, & Meersman, 2005). Drawing on life course theory, specific patterns of continuity and change in overweight status over the first 15 years of life were captured and then used to predict depressive symptoms early in high school, in general and then for youth with diverse profiles of psychosocial functioning. The purpose in doing so was to better understand how the timing of overweight determines its implications for depressive symptoms for different groups of youth.
Obesity is increasingly viewed as a major national and international public health issue due to its role in many chronic diseases, such as cardiovascular disease, diabetes, and musculoskeletal problems (World Health Organization, 2013). Yet, obesity is more than just a physical condition; it is also a key factor in socioemotional functioning. One example is the potential for obesity to influence depressive symptomatology, an important indicator of socioemotional adjustment with consequences for current and future health and well-being (Boutelle, Hannan, Fulkerson, Crow, & Stice, 2010; Brownell, Puhl, Schwartz, & Rudd, 2005; Erickson, Robinson, Haydel, & Killen, 2000; Kandel & Davies, 1986). The early life course is a potentially critical period in which to study this link between obesity and depressive symptoms. Variation in body weight, its social penalties across childhood and adolescence and the foundational role of early experiences of depressive symp-
This article was published Online First January 19, 2015. Alexa Martin-Storey, Département de Psychoéducation, Université de Sherbrooke; Robert Crosnoe, Department of Sociology and Population Research Center, University of Texas at Austin. We acknowledge the support of a postdoctoral fellowship to the first author from Fonds Québécois de la Recherche sur la Société et la Culture, a faculty scholar award to the second author from the William T. Grant Foundation, and a grant from the National Institute of Child Health and Human Development (R24 HD42849, PI: Mark Hayward) to the Population Research Center, University of Texas at Austin. Opinions reflect those of the authors and not necessarily those of the granting agencies. Correspondence concerning this article should be addressed to Alexa Martin-Storey, Département de Psychoéducation, Université de Sherbrooke, Pavillon A7, 2500 Boulevard de l‘Université, Sherbrooke, Quebec, J1K 2R1 Canada. E-mail: [email protected]
Linking Weight to Depressive Symptoms Part of the connection between overweight status and depressive symptoms likely reflects the tendency for some to cope with emotional distress through obesogenic behaviors (e.g., eating for comfort). It also likely reflects the emotional distress that can be engendered by being overweight (Anderson, Cohen, Naumova, Jacques, & Must, 2007; Luppino et al., 2010). Distress resulting from being overweight is frequently interpreted as resulting from the stigmatization of obesity in U.S. culture (Puhl & Latner, 2007). Overweight bodies are frequently evaluated more negatively than thinner bodies, and these attitudes are linked with slighting, exclusion, harassment, victimization, and ultimately, depressive 1004
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TRAJECTORIES OF OVERWEIGHT
symptoms (Janssen, Craig, Boyce, & Pickett, 2004; Puhl & Latner, 2007; Strauss & Pollack, 2003). Any stigmatization, including weight-related stigmatization, tends to be felt more acutely among adolescents. Ongoing social and brain development leaves them more socially oriented and influenced by peer approval, and physical appearance increasingly becomes a mechanism for social sorting after puberty (Bradley et al., 2008; Crosnoe, 2011; Mustillo et al., 2003). Models of the psychosocial consequences of overweight are also more applicable to the experiences of girls (and women) compared to boys (and men). Female bodies are more objectified in the United States as gender socialization places greater emphasis on female physical appearances, and female body ideals tend to be stricter (Grabe, Hyde, & Lindberg, 2007; Keery, van den Berg, & Thompson, 2004). Thus, being overweight is likely to be more conducive to depressive symptomatology among adolescent girls than in other segments of the population. Life course theory can be leveraged to identify new angles in this oft-discussed pattern (see Elder, 1998). Specifically, it suggests the added value that can be derived from recognizing the dynamically variable nature of such a link between overweight and depressive symptoms.
Exploring Variability Weight (and being overweight) is dynamic and evolves over time among many children and youth (Bradley et al., 2008; Mustillo, Hendrix & Schafer, 2012). For example, O’Brien and colleagues (2007) used the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (SECCYD) to identify childhood weight trajectories, finding that youth with the same weight at one age often had different weight histories prior to that age. Such developmental change in weight is important given evidence of weight “memory” in adult samples, in which formerly overweight individuals report weight-related emotional distress long after they have gotten thin (Annis, Cash, & Hrabosky, 2004; Carr & Jaffe, 2012). In other words, shaking an overweight identity can be difficult, as negative social experiences in the past have scarring effects that linger. Although currently untested, this weight memory phenomenon may be particularly relevant to the depressive symptoms of young people. More so than adults, youth live in closed social systems, in which interpersonal relations are stably bound over time by an age-graded, community-based school system that can make identities stick even when they no longer apply (Crosnoe, 2011). A key question, then, is whether adolescents’ depressive symptoms are related to their weight histories, not just their current weights. The expectation was that weight history from early childhood on would qualify the general tendency of adolescents (especially girls) who are overweight to exhibit more depressive symptoms than their peers. In other words, greater length of time spent overweight should predict increased likelihood of depressive symptoms in adolescence, but the psychological benefits of not being overweight (i.e., fewer depressive symptoms) should be less pronounced among thin adolescents who were overweight in the past. This longitudinal approach has some grounding in the literature, such as a study of disadvantaged youth in the Great Smoky Mountains whose experience of major psychiatric disorders varied according to multiyear weight histories (Mustillo et al., 2003). Similarly, trajectories of Body Mass Index (BMI) through adoles-
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cence, although stable, have been previously linked to psychological distress (Kubzansky, Gilthorpe, & Goodman, 2012). Although these studies suggest that weight is associated with psychological distress, they do not permit for an understanding of how fluctuations in and out of overweight status are associated with psychological wellbeing. Turning to between-child variation, overweight status—and its social stigma— can be experienced in a variety of ways from risk to resilience. This variation can reflect youth’s everyday ecological settings as well as how they perceive these settings and evaluate themselves in them (Huh, Stice, Shaw & Boutelle, 2012; Rehkopf, Laraia, Segal, Braithwaite, & Epel, 2011). Heavier children and youth are more likely to have social experiences that are then associated with higher rates of internalizing (Crosnoe, 2011; Mustillo et al., 2012; Wang, Houshyar, & Prinstein, 2006). What matters, however, is not just what kind of body a youth has but also how she or he interprets that body and the social experience of living in it (Mond, van den Berg, Boutelle, Hannan, & NeumarkSztainer, 2011). Two youth with the same current weight and the same weight history could have different levels of depressive symptoms if one is unaware of, able to ignore, or free from the potential social costs of being overweight. Indeed, previous research suggests that experiences like victimization are more closely linked with depressive symptoms among overweight youth (Adams & Bukowski, 2008) and that body image modifies how weight is linked with depressive symptoms (Kostanski & Gullone, 1998; Pesa, Syre, & Jones, 2000). The current study had two goals. The first of these goals was to understand how trajectories of overweight from early childhood to mid adolescence were associated with depressive symptoms in mid adolescence. Using the SECCYD, a prospective longitudinal study of children sampled from 10 U.S. cities, children who had been overweight more consistently over the course of their childhoods were expected to report higher levels of depressive symptoms than children who moved into overweight latter on in time. These associations were expected to be stronger among girls than among boys. The second goal was to understand how victimization, popularity and body image moderated the association between trajectories of overweight and depressive symptoms. Having positive body image, experiencing lower levels of peer victimization, and being popular in the face of (i.e., despite) current and/or past overweight status was expected to weaken the association between weight histories and depressive symptoms (especially for girls). Exploring this psychosocial variation in the SECCYD was also anticipated to account for potentially related sources of sociodemographic variation, such as race/ethnicity and socioeconomic status.
Method Sample The SECCYD is a birth cohort study started in 1991 (NICHD Early Child Care Research Network, 2005). It had the broader goal of providing detailed information on health and human development from infancy into adolescence. Families were selected based on a conditionally random sampling plan to ensure demographic diversity. Sampling occurred in hospitals at 10 sites across the United States. The methods were approved by the ethics review
MARTIN-STOREY AND CROSNOE
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6, 7, 8; and age 15 (Bradley et al., 2008; O’Brien et al., 2007). To assess height, they stood against a measuring stick affixed to the wall, with their shoes off and feet together. Each child was measured twice. If the difference between the two measures was greater than one quarter of an inch, they were measured twice again. After children removed shoes, outer layers of clothing, and other heavy items, weight was measured twice with a regularly calibrated physician’s two-beam scale. If measures differed by more than 4 oz, they were measured twice again. These measures were used to calculate BMI percentile. Youth with a BMI at the 85th percentile or above were classified as overweight, based on Centers for Disease Control and Prevention recommendations (Barlow & the Expert Committee, 2007). The use of an 85th percentile cutoff resulted in both overweight and obese youth being included in this category. Infant weight was measured in grams as it appeared on hospital records. Psychosocial factors. To explore variation by perceptions of self and social status, several variables were created with age 15 data. Self-perceived popularity was assessed via the “what my peers think of me” measure (Cillessen & Rose, 2005), an eightitem measure in which youth assessed how many people in their grade thought that they were popular or likable on a 7-point scale (Cronbach’s alpha ⫽ .76 in in our analytical sample). For perceived victimization, youth completed the Peer Relationships Questionnaire, based on the University of Illinois Aggression Scale (Espelage & Holt, 2001). Youth were asked the number of times they were picked on, made fun of, called names, and hit or pushed in the past month (0 ⫽ never to 4 ⫽ seven or more times). Items were averaged (Cronbach’s alpha ⫽ .85 in in our analytical sample). Finally, body image was ascertained via a questionnaire drawn from the Perceived Competence Scale (Harter, 1982). It included six items pertaining to how youth felt about their body, face, and hair (1 ⫽ very unhappy to 4 ⫽ very happy). Items were summed, with higher scores indicating more positive body image (Cronbach’s alpha ⫽ .91 in our analytical sample).
boards at these hospitals as well as by the associated universities (www.nichd.nih.gov/research/supported/seccyd.cfm). In order to be eligible, mothers of newborn children had to be over the age of 17, could not be planning to move, needed to speak English, and could not have substance use problems. Additionally, families in which the newborn was hospitalized for more than seven days after birth, had a twin, or had obvious disabilities were not eligible. Data collection continued in four major phases until the children reached mid-adolescence. The four waves used in this study covered birth through age 3 (n ⫽ 1,364), age 4.5 through Grade 1 (n ⫽ 1,226), Grade 2 through Grade 6 (n ⫽ 1,061), and Grade 7 through age 15 (n ⫽ 1,009). The analytical sample for the current study included the 957 youth from the fourth wave (479 of whom were girls) who had completed the measure of depressive symptoms at age 15.
Measures Depressive symptoms. At age 15 and at Grades 5 and 6, youth completed the short form of the Child Depression Inventory (Kovacs, 1992). The age 15 measure was used as the outcome, and the Grades 5 and 6 measures were used to control for the potentially bidirectional association between overweight and depressive symptoms. It contains the 10 most reliable items from the Child Depression Inventory long form (r with long form ⫽ .89) and has good reliability (Cronbach’s alpha ⫽ .80 in in our analytical sample). Scores were created by summing the child’s responses on all items (ranging from 0 to 20), for a maximum possible score of 20, with higher scores indicating higher levels of depressive symptoms (see descriptive statistics for all variables, by gender, in Table 1). Previous research has established a clinical cutoff of 8 or higher for this instrument (Sterba, Prinstein, & Cox, 2007). Approximately 6.1% of girls and 1.7% of boys in the sample were at or above this cutoff at age 15. Body Mass Index. Children were weighed and measured using a standardized procedure at ages 2, 3, and 4.5; Grades 1, 3, 5, Table 1 Descriptive Statistics for Study Variables, by Gender (N ⫽ 957) Boys
Depressive symptoms (age 15) Depressive symptoms (Grade 6) Depressive symptoms (Grade 5) BMI percentile at age 15 Birth weight (in grams) Psychosocial moderators Self-perceived popularity Victimization Body image Covariates European (%) African American (%) Hispanic (%) Family income-to-needs ratio Maternal education Maternal depressed mood at 1 month Father presence in home at birth Maternal age at birth Note.
Mean
SD
1.48 1.33 1.21 66.57 3,576.82
2.10 1.99 1.84 28.20 512.93
5.58 .55 19.58
.91 .68 3.66
81.1 13.1 6.1 5.15 14.35 11.71 .86 29.29
6.04 2.46 9.15 .35 5.70
SD ⫽ standard deviation; Min ⫽ minimum; Max ⫽ maximum.
Girls Min .00 .00 .00 .10
2,171 1.88 .00 6.00
.07 7.00 .00 .00 18.00
Max
Mean
SD
12.00 19.00 12.00 99.90 5,028
2.52 1.48 1.34 64.98 3,423.77
2.99 2.21 2.00 25.25 491.78
7.00 4.00 24.00
5.74 .43 18.45
63.55 21.00 53.00 1.00 44.00
79.7 12.8 6.1 5.32 14.55 10.67 .87 28.98
Min
Max
.00 .00 .00 .48
18.00 14.00 15.00 99.57 5,343
.83 .57 4.06
1.75 .00 6.00
7.00 4.00 24.00
5.44 4.24 8.47 .34 8.47
.08 7.00 .00 .00 18.00
42.92 21.00 46.00 1.00 46.00
2,000
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TRAJECTORIES OF OVERWEIGHT
Covariates. The selected covariates were measured at different periods across the 15 years of data collection. Race/ethnicity was measured using parent report. Participants were asked one question about their race/ethnicity, for which their choices were Native American or Native Alaskan, Asian or Pacific Islander, African America/Black, White, or other. They were asked an additional question about being Hispanic or not. Dummy variables designating European American (81.4%), African American (12.1%), and Other (6.5%) were constructed to capture the largest groups within the sample. Additionally, 6.1% of the sample identified as Hispanic, with some overlap between this category and the European American and African American categories. Maternal education (in total years) came from the interview in the initial post birth assessment. Maternal age at the birth of the child (in years) was also assessed during the initial interview. These variables were only measured during the postbirth assessment. Family structure and maternal depressed mood were also assessed in the initial assessment to capture early care circumstances. Maternal depressed mood was measured with the Center for Epidemiological Studies Depression Scale (Radloff, 1977). Twenty items about the presence of depressive symptoms, such as “I had crying spells” and “I felt depressed” were summed (Cronbach’s alpha ⫽ .88 in the current sample). Paternal presence in the home at birth was measured with a binary variable. Family income-to-needs ratio was measured when the child was age 15 because of the concurrent association between family socioeconomic status and depressive symptoms (Eamon, 2002). A family income-to-needs ratio was calculated by dividing income from all sources by the cutoff for poverty established by the U.S. Census Bureau (DeNavas-Walt, Proctor, & Smith, 2010).
Plan of Analyses Latent class analysis is a statistical method that allows cases to be sorted into groups based on latent associations within the data (McCutcheon, 1987). A variant of this procedure, repeatedmeasures latent class analysis (RMLCA), was used here given the need to explore patterns of values on a binary variable over time (Collins & Lanza, 2010). Binary markers of overweight status across the main SECCYD data collection points provided the source for estimating trajectories in Mplus, and were the only variables included in these models (v. 7.10, Muthén & Muthén, 1998 –2010). The best fitting number of groups was determined by a series of fit indices (McCutcheon, 1987; Nylund, Asparouhov, & Muthén, 2007). Of these fit indices, the likelihood ratio chisquared statistic (referred to as G2) reflects a better fit the closer it is to zero. Parsimony indices, including the Akaike information criterion and the Bayesian information criterion reflect the efficiency of the model. They also indicate better fit the closer they are to zero, and evaluate the effect of increasing the number of classes on the overall fit of the model. Entropy indicated the certainty of classification within the groups, with values closer to one indicating greater certainty. Next, overweight trajectories from childhood into adolescence were linked to adolescent depressive symptomatology (for boys and girls separately). A total of five models were tested. The first explored the association between weight trajectory class and depressive symptoms controlling for covariates (weight a birth, weight at age 15, race/ethnicity, family income-to-needs ratio,
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maternal education, depressive symptoms at Grade 5 and 6, maternal depressed mood, and paternal presence in the home at birth). The second model presented these same associations including the main effects of the three psychosocial moderators at age 15 (e.g., body image, victimization, and popularity). The third through fifth models presented the interactions between the trajectories of overweight and each psychosocial mediator, starting with body image. The number of interactions tested reflected the number of overweight trajectory groups identified, with youth who were never overweight as a referent category. A series of linear structural equation models (with observed variables along with the latent classes) were estimated. Employing this method, the effect size of the association between two variables was calculated as the percent standard deviation change in the outcome variable based on a unit increase in the predictor variable. Mplus accounts for missing data with full information maximum likelihood, which calculates missing data according to existing correlation matrices (Allison, 2001). As is the case with all longitudinal studies, some item-level data were missing in the analytical sample. Exploratory analyses compared those who were included in the present analyses and those who were not due to study attrition. These analyses revealed lower income-to-needs ratios (F ⫽ 9.22, p ⬍ .01), higher maternal depressed mood (F ⫽ 4.53, p ⬍ .01), and lower maternal age (F ⫽ 28.10, p ⬍ .01) at birth among families who dropped out. No significant differences were observed in birth weight or early home environment. Families who left the study were also less likely to have mothers who reported being married to the father of the target child (2 ⫽ 4.51, p ⬍ .05), but they were not more likely to report being European American or African American. All other missing data among the 957 participants were estimated with full information maximum likelihood.
Results To determine the best number of latent classes capturing trajectories of overweight status from childhood into adolescence in RMLCA, a series of models were estimated starting with a model that specified one class and finishing with a model that specified seven classes. Similar fit indices were found for models with four, five, and six classes (see Table 2). The five-class model was selected because it had marginally better fit indices compared to the four-class model. Furthermore, although the six-class model had similar fit indices to the five-class model, one of its classes contained less than 5% of the participants. All five classes included equal numbers of boys and girls except the never overweight class, which contained significantly more girls (60%) than boys (53%). The RMLCA was run with boys and girls together to ease interpretability and because analyses run with the boys and girls separately showed extremely similar results to the model selected. Indeed, only 4% of boys and 4% of girls would have been classified differently when these analyses were performed by gender (further details available from the authors on request). The five latent classes are illustrated in Figure 1. The first was the early childhood onset class, which included 16% of the sample. It consisted of youth of whom over two thirds were overweight by Grade 1 and almost all were overweight by Grade 3. The latent class probability, or the likelihood with which each participant in this class would be correctly classified, was .87 (out of 1.00). The
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Classes
Parameters
G2
df
AIC
BIC
ᐉ
Entropy
Latent class probability
1 2 3 4 5 6 7
10 21 32 43 54 65 76
1,910.49 681.01 632.96 396.84 301.78 275.29 266.57
910 938 945 944 931 926 921
9,414.62 6,028.54 5,701.64 5,443.82 5,366.80 5,325.77 5,316.57
9,463.21 6,130.57 5,857.11 5,652.74 5,629.16 5,641.58 5,685.82
⫺4,697.31 ⫺2,993.27 ⫺2,818.82 ⫺2,678.91 ⫺2,629.40 ⫺2,597.89 ⫺2,582.29
— .94 .88 .90 .90 .91 .91
1.00 .98–.99 .94–.97 .91–.97 .87–.97 .88–.97 .87–.97
Note. RMLCA ⫽ repeated-measures latent class analysis; G2 ⫽ 2 square fit index; df ⫽ degrees of freedom; AIC ⫽ Akaike information criterion; BIC ⫽ Bayesian information criterion; ᐉ ⫽ log likelihood.
second class, the middle childhood onset class, included youth of whom slightly over half were overweight by Grade 5. They represented 10% of the sample and had an average latent class probability of .88. The third class, the stably overweight, class included roughly 9% of the sample (almost all of whom had been overweight since 36 months of age), and had an average latent class probability of .93. The fourth class was early childhood limited overweight class, included youth who had been overweight from birth until 3 years of age but were primarily not overweight by Grade 6. They represented 7% of the sample and had an average latent class probability of .93. Finally, the fifth class, the never overweight class, included 58% the sample, was made up of individuals who were consistently not overweight, and had an average latent class probability of .97. Model 1 in Table 3 presents the results from linear structural equation modeling relevant to the first study aim, which was to assess the degree to which weight status trajectories from childhood into adolescence were associated with adolescents’ depressive symptomatology. These analyses were performed separately for boys and girls (note: exploratory analyses suggested an interaction between latent class and gender in association with depressive symptoms). For girls, being in the stably overweight class (Class 3) was associated with 59% of a standard deviation increase in depressive symptoms, even after controlling for the other covariates, including depressive symptoms from Grades 5 and 6. Thus, girls who had been stably overweight since early childhood were more likely to be depressed at age 15 than girls who had never
1 Early childhood (16%)
0.9 0.8
Middle childhood (10%)
0.7
BMI percenle
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Table 2 Fit Indices for the RMLCA Models (N ⫽ 957)
0.6
Stable (9%)
0.5 Early childhood limited (7%)
0.4 0.3
Never (58%)
0.2 0.1 0
Child age
Figure 1. Trajectories of overweight status from childhood into adolescence.
been overweight. Follow up analyses suggested that girls in the stably overweight category also reported significantly higher levels of depressive symptoms than girls in the early childhood onset group but not relative to girls in the other groups. Girls in the middle childhood onset of overweight group were also more likely to experience depressive symptoms, compared with girls who were never overweight, by 36% of a standard deviation. For boys, associations between the latent weight classes and the outcome did not reach statistical significance once all covariates were included, although concurrent weight was negatively associated with depressive symptoms. Results in Models 2–5 are relevant to the second aim (also explored using structural equation modeling), which was to explore psychosocial variation in links between the weight trajectories and adolescent depressive symptomatology. To begin, the psychosocial factors, as measured at age 15, were added to the model as main effects alongside the covariates. More positive body image, less peer victimization, and greater perceived popularity were all significantly and inversely associated with depressive symptoms for both boys and girls. Their inclusion attenuated the Model 1 coefficients for the middle childhood onset of overweight class but not the coefficient for the stably overweight class among girls. Next, the interactions between the latent class variables and the psychosocial factors were iteratively included. Significant interactions were found primarily for girls—for body image (Model 3) and popularity (Model 5) but not for victimization (Model 4). For boys, interactions were found only for popularity. To interpret these significant interactions, predicted values for depressive symptoms were calculated. These values were then graphed for youth in the latent classes being compared, with high and low values of the psychosocial moderators (defined as one standard deviation above and one standard deviation below the mean) and all covariates held to the sample means/modes. Interactions revealed that, although girls in the middle childhood onset of overweight class reported less depressive symptoms than girls who were never overweight, body image was more important for understanding the depressive symptoms for the former. In other words, having a positive body image mattered more (in a good way) to their depressive symptomatology than it did for girls who had never been overweight (see Figure 2). This apparent protective factor accounted for 7% of a standard deviation change in depressive symptoms.
TRAJECTORIES OF OVERWEIGHT
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Table 3 Standardized Results From Models Predicting Depressive Symptoms for Boys and Girls Class by Weight Status and Psychosocial and Contextual Variables (N ⫽ 957) Model 1
BMI at age 15 Birth weight (in grams) Weight status latent class Early childhood onset of overweight Middle childhood onset of overweight Stably overweight Early childhood limited overweight Never overweight (referent) Sociodemographic variables European American African American Family income-to-needs ratio Maternal education Individual covariates Depressive symptoms at Grade 6 Depressive symptoms at Grade 5 Maternal depressed mood 1 month Father presence in home at birth Maternal age at birth Psychosocial variables Body image Peer victimization Popularity Psychosocial Interactions Moderator ⫻ Early Childhood Onset Moderator ⫻ Middle Childhood Onset Moderator ⫻ Stably Overweight Moderator ⫻ Early Childhood Limited 2 R
⫺.16 .03
ⴱ
.11 .05 .06 .01
Girls ⫺.02 ⫺.03 .07 .11ⴱ .17ⴱⴱ .08
Model 3 (Perceived body)
Model 4 (Victimization) Boys
Boys
Girls
Boys
Girls
ⴱ
ⴱ
ⴱ
⫺.12 .01
ⴱ
⫺.12 .02
.07 .08 .10ⴱ .07
.06 .17 .08 .29
.05 ⫺.30 .26ⴱ .13
.01 .03 .03 .00
⫺.12 .02 .00 .03 .01 ⫺.01
.13 ⫺.01
ⴱⴱ
⫺.13 .00
Girls ⴱⴱ
⫺.12 ⫺.01
.07 .13ⴱ .04 .08
Model 5 (Popularity) Boys ⴱⴱ
⫺.13 .01
⫺.10 .16 .01 .51ⴱ
Girls ⫺.12ⴱ ⫺.02 .37 .20 .75ⴱⴱ .84ⴱⴱ
⫺.01 .00 ⫺.07 .09
⫺.08 ⫺.17ⴱ ⫺.01 .04
⫺.01 .10 ⫺.07 .11ⴱ
⫺.04 ⫺.03 .00 .06
⫺.01 .10 ⫺.07 .11ⴱ
⫺.03 ⫺.03 .00 .07
⫺.01 .10 ⫺.07 .11ⴱ
⫺.04 ⫺.03 .01 .06
⫺.02 .09 ⫺.07 .10ⴱ
⫺.06 ⫺.05 .00 .06
.27ⴱⴱ .10ⴱ .03 ⫺.10ⴱ ⫺.05
.28ⴱⴱ .11ⴱⴱ ⫺.06 ⫺.03 .02
.12ⴱⴱ .05 .00 ⫺.08 ⫺.05
.14ⴱⴱ .04 ⫺.04 ⫺.09 .01
.13ⴱ .05 .00 ⫺.08 ⫺.05
.13ⴱⴱ .05 ⫺.05 ⫺.10 .00
.12ⴱⴱ .05 .00 ⫺.08 ⫺.05
.12ⴱⴱ .05 ⫺.04 ⫺.09ⴱ .01
.13ⴱⴱ .05 ⫺.01 ⫺.07 ⫺.05
.13ⴱⴱ .03 ⫺.05 ⫺.08 .01
⫺.33ⴱⴱ .15ⴱⴱ ⫺.25ⴱⴱ
⫺.42ⴱⴱ .16ⴱⴱ ⫺.19ⴱⴱ
⫺.30ⴱⴱ .16 ⫺.25ⴱⴱ
⫺.44ⴱⴱ .15ⴱⴱ ⫺.19ⴱⴱ
⫺.33ⴱⴱ .18ⴱⴱ ⫺.25ⴱⴱ
⫺.42ⴱⴱ .16ⴱⴱ ⫺.19ⴱⴱ
⫺.33ⴱⴱ .17ⴱⴱ ⫺.23ⴱⴱ
⫺.41ⴱⴱ .15ⴱⴱ ⫺.11ⴱ
.42ⴱⴱ
⫺.06 ⫺.15 ⫺.07 ⫺.07 .39ⴱⴱ
.03 .40ⴱⴱ ⫺.17 ⫺.06 .43ⴱⴱ
⫺.02 .01 ⫺.05 ⫺.01 .39ⴱⴱ
.00 ⫺.07 .09 ⫺.02 .43ⴱⴱ
.11 ⫺.13 .00 ⫺.53ⴱ .40ⴱⴱ
⫺.30 ⫺.11 ⫺.66ⴱⴱ ⫺.78ⴱⴱ .44ⴱⴱ
.13ⴱⴱ
.17ⴱⴱ
.39ⴱⴱ
Note. BMI ⫽ Body Mass Index; AIC ⫽ Akaike information criterion; BIC ⫽ Bayesian information criterion. Model 1: AIC ⫽ 42,058.41, BIC ⫽ 42,571.52. Model 2: AIC ⫽ 50,786.05, BIC ⫽ 51,492.56. Model 3: AIC ⫽ 62,682.61, BIC ⫽ 63,691.94. Model 4: AIC ⫽ 49,258.41, BIC ⫽ 50,267.73. Model 5: AIC ⫽ 51,470.35, BIC ⫽ 52,479.68. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.
As noted previously, no significant interactions were observed for victimization. Perceived popularity, however, significantly interacted with two latent classes among girls and one among boys: stably overweight (girls only) and early childhood limited over15 14 13
Depressive Symptoms
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Boys
Model 2
12 11 Low Body image
10 High Body image
9 8
weight (boys and girls). The interaction with stably overweight girls accounted for 42% of a standard deviation change in depressive symptoms, and being in the early childhood limited group accounted for 52% of a standard deviation change among girls and 37% of a standard deviation change among boys. Figure 3 presents the results for the stably overweight versus never overweight comparison, with the findings being graphically similar when the early childhood limited class replaced the former in the comparison. Feeling popular made no difference among youth who had never been overweight, but it did make a difference (apparently reducing depressive symptoms) among girls who were in the stably overweight class and boys and girls who were in the early childhood limited overweight class. Again, popularity appeared to be a protective factor only for youth with some history of overweight.
7
Discussion
6 5
Not Early Onset of Overweight Early Onset of Overweight
Figure 2. Associations between body image and adolescent depressive symptomatology among girls in the never overweight class and the middle childhood onset of overweight class.
The stigma that heavier bodies engender has many implications that undermine individual wellbeing (Puhl & Latner, 2007). Understanding this stigma is important as it has implications that can also prevent the pursuit of healthy weight loss strategies and contribute to patterns of behavior likely to result in further weight
MARTIN-STOREY AND CROSNOE
1010 10 9.5
Deppressive Symptoms
9 8.5 8 Low Popularity
7.5 High Popularity
7 6.5 6
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5.5 5
Never Overweight Class
Stably Overweight Class
Figure 3. Associations between popularity and adolescent depressive symptomatology among girls in the stably overweight class and the never overweight class.
gain (Lillis, Levin, & Hayes, 2011). These associations have consequences for public health, which is one reason that they have been studied so much in recent years. Psychologists have contributed to this literature in the past and can take this area of research in new directions in the future. Rather than treating weight statically, weight status was treated dynamically in this study in line with life course theory as well as a few developmental studies (e.g., Kubzansky et al., 2012; O’Brien et al., 2007; Mustillo et al., 2003). Although the majority of young people were consistently not overweight, those who experienced being overweight did so in diverse ways. This dynamic approach revealed a cumulative disadvantage of being overweight for girls, for whom physical attractiveness is often more of a harsh standard of evaluation in American society (Phares, Steinberg, & Thompson, 2004). Most at risk were girls who had been stably overweight from early childhood, more so than other currently overweight girls. Continuously experiencing the stressors associated with this stigmatized identity may make girls vulnerable (Lee, Sohn, Lee, & Lee, 2004; Mustillo et al., 2003). Following a classic understanding of the role of stigma and stress, the longer an individual experiences a stigmatized identity, the more likely it is that this identity will undermine wellbeing (Goffman, 1963). Furthermore, rather than treating weight as having uniform implications for youth, the potential for weight to be differentially experienced by youth with varying levels of psychosocial functioning was examined. Perceptions of popularity appeared to moderate the association between weight trajectories and depressive symptoms, such that popularity was not associated with significant differences in depressive symptoms among youth who were never overweight. These perceptions were, however, protective for girls with either stable histories of overweight or boys and girls with childhood-limited histories of overweight. These findings support past work suggesting that positive peer experiences— or, at least, perceiving peer experiences to be positive, whether they are or not—may protect youth from negative psychosocial outcomes (La Greca & Harrison, 2005). For girls in the middle childhood onset of overweight class, perceptions of body image moderated the link between their weight trajectory and depressive symptoms compared to girls who were never overweight. The effect size was
fairly small but nonetheless suggested that, despite the overall lower levels of depressive symptoms for this group, selfperceptions may be protective for depressive outcomes. These two sets of results point to new avenues of inquiry. The value of examining weight status with a simultaneous focus on dynamism and variability could be realized in research on a diverse set of outcomes, not just depressive symptoms, and on a diverse set of risks, not just overweight status. They also suggest that further attention needs to be paid to the timing of weight gain during the transition to adolescence as well as the changes in individual psychosocial contexts that precede and follow this period. The timing of weight gain may be important for individual functioning. Indeed, the weight gain occurring at the onset of puberty has been associated with an increased vulnerability to depressive symptoms, particularly among girls (Ge, Elder, Regnerus & Cox, 2001; Vogt Yuan, 2010). Future research may clarify the association between trajectories of overweight and depressive symptoms by understanding these weight changes within the context of pubertal timing. Finally, understanding how self-concept and peer relations change in response to weight loss and weight gain may clarify the mechanisms by which weight alters the individual’s internal functioning and external contexts. Ultimately, the identification of these mechanisms may explain how weight trajectories are associated with depressive symptoms. Of course, these suggestions for future research come with the caveat that some limitations of this work need to be addressed. For example, approximately 80% of the participants in the SECCYD were White. Given that many of the latent class groups of overweight were quite small, this sample provides limited opportunities to explore how class membership was differently associated with depressive symptoms by race/ethnicity. Similar analyses employing a larger and more diverse sample could be used to address this problem in the future. The current study focused on the longitudinal outcomes of weight, despite evidence suggesting that the association between weight and depressive symptoms are likely bidirectional (Felton, Cole, Tilghman-Osborne, & Maxwell, 2010). Although we considered using depressive symptoms as a time varying covariate, we rejected this approach for several reasons. First, this approach would have significantly limited the developmental scope of the paper, forcing us to focus exclusively on adolescence. Second, as we were particularly interested in trajectories of overweight, the use of a dichotomous outcome variable would have required an analytic strategy that precluded the use of time varying covariates. The present study also employed a community sample, explaining the low numbers of children who were obese or had levels of depressive symptoms above the clinical cutoff. The low number of children with BMIs above the 95th percentile was a major factor in choosing to focus on children with BMIs above the 85th percentile, but may have implications for the current findings. Finally, families who left the study had mothers who reported higher levels of depressive symptoms, contributing to increased homogeneity of the sample across time. Still, the findings discussed here are intriguing in a preliminary way and need to be built upon in the future. The high potential costs of the ongoing obesity epidemic mean that weight status will continue to be studied, and developmental insights need to help guide this research.
TRAJECTORIES OF OVERWEIGHT
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Received June 10, 2014 Revision received November 12, 2014 Accepted November 20, 2014 䡲
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Trajectories of Overweight and Their Association With Adolescent Depressive Symptoms Alexa Martin-Storey
Robert Crosnoe
Université de Sherbrooke
University of Texas at Austin
Objective: To explore the potential for a developmental approach to reveal new insights into the well-documented link between weight and depressive symptoms. Method: Latent class analysis identified multiple trajectories of overweight from 24 months to 15 years in the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n ⫽ 957). Structural equation models then used these classes to predict depressive symptoms at age 15. Results: Five latent classes captured continuity and change in weight from early childhood into middle adolescence. Controlling for current weight, stably overweight girls tended to have the most depressive symptoms, but popularity and positive image appeared to buffer against some of the risks that girls faced from being stably overweight or becoming overweight in early to middle childhood. Notably, boys’ longitudinal weight patterns were not associated with their depressive symptoms in adolescence. Conclusions: Weight histories, controlling for current weight, are important for understanding the psychological experience of overweight, especially when such histories are considered in relation to other aspects of psychosocial functioning. Keywords: depressive symptoms, gender differences, trajectories, weight
toms in long-term trajectories of mental health highlight adolescence as an important period for understanding this link (Adair, 2008; Crosnoe, 2010; Lee et al., 2010). In this spirit, this study examined the implications of developmental variation in weight across the early life course for adolescent depressive symptomatology in the United States. Life course theory (see Elder, 1998) underlines the importance of the timing of developmental milestones in understanding individual outcomes. Health-focused iterations of this theory assess how the timing of exposure to various health-related transitions influences health (Pearlin, Schieman, Fazio, & Meersman, 2005). Drawing on life course theory, specific patterns of continuity and change in overweight status over the first 15 years of life were captured and then used to predict depressive symptoms early in high school, in general and then for youth with diverse profiles of psychosocial functioning. The purpose in doing so was to better understand how the timing of overweight determines its implications for depressive symptoms for different groups of youth.
Obesity is increasingly viewed as a major national and international public health issue due to its role in many chronic diseases, such as cardiovascular disease, diabetes, and musculoskeletal problems (World Health Organization, 2013). Yet, obesity is more than just a physical condition; it is also a key factor in socioemotional functioning. One example is the potential for obesity to influence depressive symptomatology, an important indicator of socioemotional adjustment with consequences for current and future health and well-being (Boutelle, Hannan, Fulkerson, Crow, & Stice, 2010; Brownell, Puhl, Schwartz, & Rudd, 2005; Erickson, Robinson, Haydel, & Killen, 2000; Kandel & Davies, 1986). The early life course is a potentially critical period in which to study this link between obesity and depressive symptoms. Variation in body weight, its social penalties across childhood and adolescence and the foundational role of early experiences of depressive symp-
This article was published Online First January 19, 2015. Alexa Martin-Storey, Département de Psychoéducation, Université de Sherbrooke; Robert Crosnoe, Department of Sociology and Population Research Center, University of Texas at Austin. We acknowledge the support of a postdoctoral fellowship to the first author from Fonds Québécois de la Recherche sur la Société et la Culture, a faculty scholar award to the second author from the William T. Grant Foundation, and a grant from the National Institute of Child Health and Human Development (R24 HD42849, PI: Mark Hayward) to the Population Research Center, University of Texas at Austin. Opinions reflect those of the authors and not necessarily those of the granting agencies. Correspondence concerning this article should be addressed to Alexa Martin-Storey, Département de Psychoéducation, Université de Sherbrooke, Pavillon A7, 2500 Boulevard de l‘Université, Sherbrooke, Quebec, J1K 2R1 Canada. E-mail: [email protected]
Linking Weight to Depressive Symptoms Part of the connection between overweight status and depressive symptoms likely reflects the tendency for some to cope with emotional distress through obesogenic behaviors (e.g., eating for comfort). It also likely reflects the emotional distress that can be engendered by being overweight (Anderson, Cohen, Naumova, Jacques, & Must, 2007; Luppino et al., 2010). Distress resulting from being overweight is frequently interpreted as resulting from the stigmatization of obesity in U.S. culture (Puhl & Latner, 2007). Overweight bodies are frequently evaluated more negatively than thinner bodies, and these attitudes are linked with slighting, exclusion, harassment, victimization, and ultimately, depressive 1004
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TRAJECTORIES OF OVERWEIGHT
symptoms (Janssen, Craig, Boyce, & Pickett, 2004; Puhl & Latner, 2007; Strauss & Pollack, 2003). Any stigmatization, including weight-related stigmatization, tends to be felt more acutely among adolescents. Ongoing social and brain development leaves them more socially oriented and influenced by peer approval, and physical appearance increasingly becomes a mechanism for social sorting after puberty (Bradley et al., 2008; Crosnoe, 2011; Mustillo et al., 2003). Models of the psychosocial consequences of overweight are also more applicable to the experiences of girls (and women) compared to boys (and men). Female bodies are more objectified in the United States as gender socialization places greater emphasis on female physical appearances, and female body ideals tend to be stricter (Grabe, Hyde, & Lindberg, 2007; Keery, van den Berg, & Thompson, 2004). Thus, being overweight is likely to be more conducive to depressive symptomatology among adolescent girls than in other segments of the population. Life course theory can be leveraged to identify new angles in this oft-discussed pattern (see Elder, 1998). Specifically, it suggests the added value that can be derived from recognizing the dynamically variable nature of such a link between overweight and depressive symptoms.
Exploring Variability Weight (and being overweight) is dynamic and evolves over time among many children and youth (Bradley et al., 2008; Mustillo, Hendrix & Schafer, 2012). For example, O’Brien and colleagues (2007) used the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (SECCYD) to identify childhood weight trajectories, finding that youth with the same weight at one age often had different weight histories prior to that age. Such developmental change in weight is important given evidence of weight “memory” in adult samples, in which formerly overweight individuals report weight-related emotional distress long after they have gotten thin (Annis, Cash, & Hrabosky, 2004; Carr & Jaffe, 2012). In other words, shaking an overweight identity can be difficult, as negative social experiences in the past have scarring effects that linger. Although currently untested, this weight memory phenomenon may be particularly relevant to the depressive symptoms of young people. More so than adults, youth live in closed social systems, in which interpersonal relations are stably bound over time by an age-graded, community-based school system that can make identities stick even when they no longer apply (Crosnoe, 2011). A key question, then, is whether adolescents’ depressive symptoms are related to their weight histories, not just their current weights. The expectation was that weight history from early childhood on would qualify the general tendency of adolescents (especially girls) who are overweight to exhibit more depressive symptoms than their peers. In other words, greater length of time spent overweight should predict increased likelihood of depressive symptoms in adolescence, but the psychological benefits of not being overweight (i.e., fewer depressive symptoms) should be less pronounced among thin adolescents who were overweight in the past. This longitudinal approach has some grounding in the literature, such as a study of disadvantaged youth in the Great Smoky Mountains whose experience of major psychiatric disorders varied according to multiyear weight histories (Mustillo et al., 2003). Similarly, trajectories of Body Mass Index (BMI) through adoles-
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cence, although stable, have been previously linked to psychological distress (Kubzansky, Gilthorpe, & Goodman, 2012). Although these studies suggest that weight is associated with psychological distress, they do not permit for an understanding of how fluctuations in and out of overweight status are associated with psychological wellbeing. Turning to between-child variation, overweight status—and its social stigma— can be experienced in a variety of ways from risk to resilience. This variation can reflect youth’s everyday ecological settings as well as how they perceive these settings and evaluate themselves in them (Huh, Stice, Shaw & Boutelle, 2012; Rehkopf, Laraia, Segal, Braithwaite, & Epel, 2011). Heavier children and youth are more likely to have social experiences that are then associated with higher rates of internalizing (Crosnoe, 2011; Mustillo et al., 2012; Wang, Houshyar, & Prinstein, 2006). What matters, however, is not just what kind of body a youth has but also how she or he interprets that body and the social experience of living in it (Mond, van den Berg, Boutelle, Hannan, & NeumarkSztainer, 2011). Two youth with the same current weight and the same weight history could have different levels of depressive symptoms if one is unaware of, able to ignore, or free from the potential social costs of being overweight. Indeed, previous research suggests that experiences like victimization are more closely linked with depressive symptoms among overweight youth (Adams & Bukowski, 2008) and that body image modifies how weight is linked with depressive symptoms (Kostanski & Gullone, 1998; Pesa, Syre, & Jones, 2000). The current study had two goals. The first of these goals was to understand how trajectories of overweight from early childhood to mid adolescence were associated with depressive symptoms in mid adolescence. Using the SECCYD, a prospective longitudinal study of children sampled from 10 U.S. cities, children who had been overweight more consistently over the course of their childhoods were expected to report higher levels of depressive symptoms than children who moved into overweight latter on in time. These associations were expected to be stronger among girls than among boys. The second goal was to understand how victimization, popularity and body image moderated the association between trajectories of overweight and depressive symptoms. Having positive body image, experiencing lower levels of peer victimization, and being popular in the face of (i.e., despite) current and/or past overweight status was expected to weaken the association between weight histories and depressive symptoms (especially for girls). Exploring this psychosocial variation in the SECCYD was also anticipated to account for potentially related sources of sociodemographic variation, such as race/ethnicity and socioeconomic status.
Method Sample The SECCYD is a birth cohort study started in 1991 (NICHD Early Child Care Research Network, 2005). It had the broader goal of providing detailed information on health and human development from infancy into adolescence. Families were selected based on a conditionally random sampling plan to ensure demographic diversity. Sampling occurred in hospitals at 10 sites across the United States. The methods were approved by the ethics review
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6, 7, 8; and age 15 (Bradley et al., 2008; O’Brien et al., 2007). To assess height, they stood against a measuring stick affixed to the wall, with their shoes off and feet together. Each child was measured twice. If the difference between the two measures was greater than one quarter of an inch, they were measured twice again. After children removed shoes, outer layers of clothing, and other heavy items, weight was measured twice with a regularly calibrated physician’s two-beam scale. If measures differed by more than 4 oz, they were measured twice again. These measures were used to calculate BMI percentile. Youth with a BMI at the 85th percentile or above were classified as overweight, based on Centers for Disease Control and Prevention recommendations (Barlow & the Expert Committee, 2007). The use of an 85th percentile cutoff resulted in both overweight and obese youth being included in this category. Infant weight was measured in grams as it appeared on hospital records. Psychosocial factors. To explore variation by perceptions of self and social status, several variables were created with age 15 data. Self-perceived popularity was assessed via the “what my peers think of me” measure (Cillessen & Rose, 2005), an eightitem measure in which youth assessed how many people in their grade thought that they were popular or likable on a 7-point scale (Cronbach’s alpha ⫽ .76 in in our analytical sample). For perceived victimization, youth completed the Peer Relationships Questionnaire, based on the University of Illinois Aggression Scale (Espelage & Holt, 2001). Youth were asked the number of times they were picked on, made fun of, called names, and hit or pushed in the past month (0 ⫽ never to 4 ⫽ seven or more times). Items were averaged (Cronbach’s alpha ⫽ .85 in in our analytical sample). Finally, body image was ascertained via a questionnaire drawn from the Perceived Competence Scale (Harter, 1982). It included six items pertaining to how youth felt about their body, face, and hair (1 ⫽ very unhappy to 4 ⫽ very happy). Items were summed, with higher scores indicating more positive body image (Cronbach’s alpha ⫽ .91 in our analytical sample).
boards at these hospitals as well as by the associated universities (www.nichd.nih.gov/research/supported/seccyd.cfm). In order to be eligible, mothers of newborn children had to be over the age of 17, could not be planning to move, needed to speak English, and could not have substance use problems. Additionally, families in which the newborn was hospitalized for more than seven days after birth, had a twin, or had obvious disabilities were not eligible. Data collection continued in four major phases until the children reached mid-adolescence. The four waves used in this study covered birth through age 3 (n ⫽ 1,364), age 4.5 through Grade 1 (n ⫽ 1,226), Grade 2 through Grade 6 (n ⫽ 1,061), and Grade 7 through age 15 (n ⫽ 1,009). The analytical sample for the current study included the 957 youth from the fourth wave (479 of whom were girls) who had completed the measure of depressive symptoms at age 15.
Measures Depressive symptoms. At age 15 and at Grades 5 and 6, youth completed the short form of the Child Depression Inventory (Kovacs, 1992). The age 15 measure was used as the outcome, and the Grades 5 and 6 measures were used to control for the potentially bidirectional association between overweight and depressive symptoms. It contains the 10 most reliable items from the Child Depression Inventory long form (r with long form ⫽ .89) and has good reliability (Cronbach’s alpha ⫽ .80 in in our analytical sample). Scores were created by summing the child’s responses on all items (ranging from 0 to 20), for a maximum possible score of 20, with higher scores indicating higher levels of depressive symptoms (see descriptive statistics for all variables, by gender, in Table 1). Previous research has established a clinical cutoff of 8 or higher for this instrument (Sterba, Prinstein, & Cox, 2007). Approximately 6.1% of girls and 1.7% of boys in the sample were at or above this cutoff at age 15. Body Mass Index. Children were weighed and measured using a standardized procedure at ages 2, 3, and 4.5; Grades 1, 3, 5, Table 1 Descriptive Statistics for Study Variables, by Gender (N ⫽ 957) Boys
Depressive symptoms (age 15) Depressive symptoms (Grade 6) Depressive symptoms (Grade 5) BMI percentile at age 15 Birth weight (in grams) Psychosocial moderators Self-perceived popularity Victimization Body image Covariates European (%) African American (%) Hispanic (%) Family income-to-needs ratio Maternal education Maternal depressed mood at 1 month Father presence in home at birth Maternal age at birth Note.
Mean
SD
1.48 1.33 1.21 66.57 3,576.82
2.10 1.99 1.84 28.20 512.93
5.58 .55 19.58
.91 .68 3.66
81.1 13.1 6.1 5.15 14.35 11.71 .86 29.29
6.04 2.46 9.15 .35 5.70
SD ⫽ standard deviation; Min ⫽ minimum; Max ⫽ maximum.
Girls Min .00 .00 .00 .10
2,171 1.88 .00 6.00
.07 7.00 .00 .00 18.00
Max
Mean
SD
12.00 19.00 12.00 99.90 5,028
2.52 1.48 1.34 64.98 3,423.77
2.99 2.21 2.00 25.25 491.78
7.00 4.00 24.00
5.74 .43 18.45
63.55 21.00 53.00 1.00 44.00
79.7 12.8 6.1 5.32 14.55 10.67 .87 28.98
Min
Max
.00 .00 .00 .48
18.00 14.00 15.00 99.57 5,343
.83 .57 4.06
1.75 .00 6.00
7.00 4.00 24.00
5.44 4.24 8.47 .34 8.47
.08 7.00 .00 .00 18.00
42.92 21.00 46.00 1.00 46.00
2,000
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TRAJECTORIES OF OVERWEIGHT
Covariates. The selected covariates were measured at different periods across the 15 years of data collection. Race/ethnicity was measured using parent report. Participants were asked one question about their race/ethnicity, for which their choices were Native American or Native Alaskan, Asian or Pacific Islander, African America/Black, White, or other. They were asked an additional question about being Hispanic or not. Dummy variables designating European American (81.4%), African American (12.1%), and Other (6.5%) were constructed to capture the largest groups within the sample. Additionally, 6.1% of the sample identified as Hispanic, with some overlap between this category and the European American and African American categories. Maternal education (in total years) came from the interview in the initial post birth assessment. Maternal age at the birth of the child (in years) was also assessed during the initial interview. These variables were only measured during the postbirth assessment. Family structure and maternal depressed mood were also assessed in the initial assessment to capture early care circumstances. Maternal depressed mood was measured with the Center for Epidemiological Studies Depression Scale (Radloff, 1977). Twenty items about the presence of depressive symptoms, such as “I had crying spells” and “I felt depressed” were summed (Cronbach’s alpha ⫽ .88 in the current sample). Paternal presence in the home at birth was measured with a binary variable. Family income-to-needs ratio was measured when the child was age 15 because of the concurrent association between family socioeconomic status and depressive symptoms (Eamon, 2002). A family income-to-needs ratio was calculated by dividing income from all sources by the cutoff for poverty established by the U.S. Census Bureau (DeNavas-Walt, Proctor, & Smith, 2010).
Plan of Analyses Latent class analysis is a statistical method that allows cases to be sorted into groups based on latent associations within the data (McCutcheon, 1987). A variant of this procedure, repeatedmeasures latent class analysis (RMLCA), was used here given the need to explore patterns of values on a binary variable over time (Collins & Lanza, 2010). Binary markers of overweight status across the main SECCYD data collection points provided the source for estimating trajectories in Mplus, and were the only variables included in these models (v. 7.10, Muthén & Muthén, 1998 –2010). The best fitting number of groups was determined by a series of fit indices (McCutcheon, 1987; Nylund, Asparouhov, & Muthén, 2007). Of these fit indices, the likelihood ratio chisquared statistic (referred to as G2) reflects a better fit the closer it is to zero. Parsimony indices, including the Akaike information criterion and the Bayesian information criterion reflect the efficiency of the model. They also indicate better fit the closer they are to zero, and evaluate the effect of increasing the number of classes on the overall fit of the model. Entropy indicated the certainty of classification within the groups, with values closer to one indicating greater certainty. Next, overweight trajectories from childhood into adolescence were linked to adolescent depressive symptomatology (for boys and girls separately). A total of five models were tested. The first explored the association between weight trajectory class and depressive symptoms controlling for covariates (weight a birth, weight at age 15, race/ethnicity, family income-to-needs ratio,
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maternal education, depressive symptoms at Grade 5 and 6, maternal depressed mood, and paternal presence in the home at birth). The second model presented these same associations including the main effects of the three psychosocial moderators at age 15 (e.g., body image, victimization, and popularity). The third through fifth models presented the interactions between the trajectories of overweight and each psychosocial mediator, starting with body image. The number of interactions tested reflected the number of overweight trajectory groups identified, with youth who were never overweight as a referent category. A series of linear structural equation models (with observed variables along with the latent classes) were estimated. Employing this method, the effect size of the association between two variables was calculated as the percent standard deviation change in the outcome variable based on a unit increase in the predictor variable. Mplus accounts for missing data with full information maximum likelihood, which calculates missing data according to existing correlation matrices (Allison, 2001). As is the case with all longitudinal studies, some item-level data were missing in the analytical sample. Exploratory analyses compared those who were included in the present analyses and those who were not due to study attrition. These analyses revealed lower income-to-needs ratios (F ⫽ 9.22, p ⬍ .01), higher maternal depressed mood (F ⫽ 4.53, p ⬍ .01), and lower maternal age (F ⫽ 28.10, p ⬍ .01) at birth among families who dropped out. No significant differences were observed in birth weight or early home environment. Families who left the study were also less likely to have mothers who reported being married to the father of the target child (2 ⫽ 4.51, p ⬍ .05), but they were not more likely to report being European American or African American. All other missing data among the 957 participants were estimated with full information maximum likelihood.
Results To determine the best number of latent classes capturing trajectories of overweight status from childhood into adolescence in RMLCA, a series of models were estimated starting with a model that specified one class and finishing with a model that specified seven classes. Similar fit indices were found for models with four, five, and six classes (see Table 2). The five-class model was selected because it had marginally better fit indices compared to the four-class model. Furthermore, although the six-class model had similar fit indices to the five-class model, one of its classes contained less than 5% of the participants. All five classes included equal numbers of boys and girls except the never overweight class, which contained significantly more girls (60%) than boys (53%). The RMLCA was run with boys and girls together to ease interpretability and because analyses run with the boys and girls separately showed extremely similar results to the model selected. Indeed, only 4% of boys and 4% of girls would have been classified differently when these analyses were performed by gender (further details available from the authors on request). The five latent classes are illustrated in Figure 1. The first was the early childhood onset class, which included 16% of the sample. It consisted of youth of whom over two thirds were overweight by Grade 1 and almost all were overweight by Grade 3. The latent class probability, or the likelihood with which each participant in this class would be correctly classified, was .87 (out of 1.00). The
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Classes
Parameters
G2
df
AIC
BIC
ᐉ
Entropy
Latent class probability
1 2 3 4 5 6 7
10 21 32 43 54 65 76
1,910.49 681.01 632.96 396.84 301.78 275.29 266.57
910 938 945 944 931 926 921
9,414.62 6,028.54 5,701.64 5,443.82 5,366.80 5,325.77 5,316.57
9,463.21 6,130.57 5,857.11 5,652.74 5,629.16 5,641.58 5,685.82
⫺4,697.31 ⫺2,993.27 ⫺2,818.82 ⫺2,678.91 ⫺2,629.40 ⫺2,597.89 ⫺2,582.29
— .94 .88 .90 .90 .91 .91
1.00 .98–.99 .94–.97 .91–.97 .87–.97 .88–.97 .87–.97
Note. RMLCA ⫽ repeated-measures latent class analysis; G2 ⫽ 2 square fit index; df ⫽ degrees of freedom; AIC ⫽ Akaike information criterion; BIC ⫽ Bayesian information criterion; ᐉ ⫽ log likelihood.
second class, the middle childhood onset class, included youth of whom slightly over half were overweight by Grade 5. They represented 10% of the sample and had an average latent class probability of .88. The third class, the stably overweight, class included roughly 9% of the sample (almost all of whom had been overweight since 36 months of age), and had an average latent class probability of .93. The fourth class was early childhood limited overweight class, included youth who had been overweight from birth until 3 years of age but were primarily not overweight by Grade 6. They represented 7% of the sample and had an average latent class probability of .93. Finally, the fifth class, the never overweight class, included 58% the sample, was made up of individuals who were consistently not overweight, and had an average latent class probability of .97. Model 1 in Table 3 presents the results from linear structural equation modeling relevant to the first study aim, which was to assess the degree to which weight status trajectories from childhood into adolescence were associated with adolescents’ depressive symptomatology. These analyses were performed separately for boys and girls (note: exploratory analyses suggested an interaction between latent class and gender in association with depressive symptoms). For girls, being in the stably overweight class (Class 3) was associated with 59% of a standard deviation increase in depressive symptoms, even after controlling for the other covariates, including depressive symptoms from Grades 5 and 6. Thus, girls who had been stably overweight since early childhood were more likely to be depressed at age 15 than girls who had never
1 Early childhood (16%)
0.9 0.8
Middle childhood (10%)
0.7
BMI percenle
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Table 2 Fit Indices for the RMLCA Models (N ⫽ 957)
0.6
Stable (9%)
0.5 Early childhood limited (7%)
0.4 0.3
Never (58%)
0.2 0.1 0
Child age
Figure 1. Trajectories of overweight status from childhood into adolescence.
been overweight. Follow up analyses suggested that girls in the stably overweight category also reported significantly higher levels of depressive symptoms than girls in the early childhood onset group but not relative to girls in the other groups. Girls in the middle childhood onset of overweight group were also more likely to experience depressive symptoms, compared with girls who were never overweight, by 36% of a standard deviation. For boys, associations between the latent weight classes and the outcome did not reach statistical significance once all covariates were included, although concurrent weight was negatively associated with depressive symptoms. Results in Models 2–5 are relevant to the second aim (also explored using structural equation modeling), which was to explore psychosocial variation in links between the weight trajectories and adolescent depressive symptomatology. To begin, the psychosocial factors, as measured at age 15, were added to the model as main effects alongside the covariates. More positive body image, less peer victimization, and greater perceived popularity were all significantly and inversely associated with depressive symptoms for both boys and girls. Their inclusion attenuated the Model 1 coefficients for the middle childhood onset of overweight class but not the coefficient for the stably overweight class among girls. Next, the interactions between the latent class variables and the psychosocial factors were iteratively included. Significant interactions were found primarily for girls—for body image (Model 3) and popularity (Model 5) but not for victimization (Model 4). For boys, interactions were found only for popularity. To interpret these significant interactions, predicted values for depressive symptoms were calculated. These values were then graphed for youth in the latent classes being compared, with high and low values of the psychosocial moderators (defined as one standard deviation above and one standard deviation below the mean) and all covariates held to the sample means/modes. Interactions revealed that, although girls in the middle childhood onset of overweight class reported less depressive symptoms than girls who were never overweight, body image was more important for understanding the depressive symptoms for the former. In other words, having a positive body image mattered more (in a good way) to their depressive symptomatology than it did for girls who had never been overweight (see Figure 2). This apparent protective factor accounted for 7% of a standard deviation change in depressive symptoms.
TRAJECTORIES OF OVERWEIGHT
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Table 3 Standardized Results From Models Predicting Depressive Symptoms for Boys and Girls Class by Weight Status and Psychosocial and Contextual Variables (N ⫽ 957) Model 1
BMI at age 15 Birth weight (in grams) Weight status latent class Early childhood onset of overweight Middle childhood onset of overweight Stably overweight Early childhood limited overweight Never overweight (referent) Sociodemographic variables European American African American Family income-to-needs ratio Maternal education Individual covariates Depressive symptoms at Grade 6 Depressive symptoms at Grade 5 Maternal depressed mood 1 month Father presence in home at birth Maternal age at birth Psychosocial variables Body image Peer victimization Popularity Psychosocial Interactions Moderator ⫻ Early Childhood Onset Moderator ⫻ Middle Childhood Onset Moderator ⫻ Stably Overweight Moderator ⫻ Early Childhood Limited 2 R
⫺.16 .03
ⴱ
.11 .05 .06 .01
Girls ⫺.02 ⫺.03 .07 .11ⴱ .17ⴱⴱ .08
Model 3 (Perceived body)
Model 4 (Victimization) Boys
Boys
Girls
Boys
Girls
ⴱ
ⴱ
ⴱ
⫺.12 .01
ⴱ
⫺.12 .02
.07 .08 .10ⴱ .07
.06 .17 .08 .29
.05 ⫺.30 .26ⴱ .13
.01 .03 .03 .00
⫺.12 .02 .00 .03 .01 ⫺.01
.13 ⫺.01
ⴱⴱ
⫺.13 .00
Girls ⴱⴱ
⫺.12 ⫺.01
.07 .13ⴱ .04 .08
Model 5 (Popularity) Boys ⴱⴱ
⫺.13 .01
⫺.10 .16 .01 .51ⴱ
Girls ⫺.12ⴱ ⫺.02 .37 .20 .75ⴱⴱ .84ⴱⴱ
⫺.01 .00 ⫺.07 .09
⫺.08 ⫺.17ⴱ ⫺.01 .04
⫺.01 .10 ⫺.07 .11ⴱ
⫺.04 ⫺.03 .00 .06
⫺.01 .10 ⫺.07 .11ⴱ
⫺.03 ⫺.03 .00 .07
⫺.01 .10 ⫺.07 .11ⴱ
⫺.04 ⫺.03 .01 .06
⫺.02 .09 ⫺.07 .10ⴱ
⫺.06 ⫺.05 .00 .06
.27ⴱⴱ .10ⴱ .03 ⫺.10ⴱ ⫺.05
.28ⴱⴱ .11ⴱⴱ ⫺.06 ⫺.03 .02
.12ⴱⴱ .05 .00 ⫺.08 ⫺.05
.14ⴱⴱ .04 ⫺.04 ⫺.09 .01
.13ⴱ .05 .00 ⫺.08 ⫺.05
.13ⴱⴱ .05 ⫺.05 ⫺.10 .00
.12ⴱⴱ .05 .00 ⫺.08 ⫺.05
.12ⴱⴱ .05 ⫺.04 ⫺.09ⴱ .01
.13ⴱⴱ .05 ⫺.01 ⫺.07 ⫺.05
.13ⴱⴱ .03 ⫺.05 ⫺.08 .01
⫺.33ⴱⴱ .15ⴱⴱ ⫺.25ⴱⴱ
⫺.42ⴱⴱ .16ⴱⴱ ⫺.19ⴱⴱ
⫺.30ⴱⴱ .16 ⫺.25ⴱⴱ
⫺.44ⴱⴱ .15ⴱⴱ ⫺.19ⴱⴱ
⫺.33ⴱⴱ .18ⴱⴱ ⫺.25ⴱⴱ
⫺.42ⴱⴱ .16ⴱⴱ ⫺.19ⴱⴱ
⫺.33ⴱⴱ .17ⴱⴱ ⫺.23ⴱⴱ
⫺.41ⴱⴱ .15ⴱⴱ ⫺.11ⴱ
.42ⴱⴱ
⫺.06 ⫺.15 ⫺.07 ⫺.07 .39ⴱⴱ
.03 .40ⴱⴱ ⫺.17 ⫺.06 .43ⴱⴱ
⫺.02 .01 ⫺.05 ⫺.01 .39ⴱⴱ
.00 ⫺.07 .09 ⫺.02 .43ⴱⴱ
.11 ⫺.13 .00 ⫺.53ⴱ .40ⴱⴱ
⫺.30 ⫺.11 ⫺.66ⴱⴱ ⫺.78ⴱⴱ .44ⴱⴱ
.13ⴱⴱ
.17ⴱⴱ
.39ⴱⴱ
Note. BMI ⫽ Body Mass Index; AIC ⫽ Akaike information criterion; BIC ⫽ Bayesian information criterion. Model 1: AIC ⫽ 42,058.41, BIC ⫽ 42,571.52. Model 2: AIC ⫽ 50,786.05, BIC ⫽ 51,492.56. Model 3: AIC ⫽ 62,682.61, BIC ⫽ 63,691.94. Model 4: AIC ⫽ 49,258.41, BIC ⫽ 50,267.73. Model 5: AIC ⫽ 51,470.35, BIC ⫽ 52,479.68. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01.
As noted previously, no significant interactions were observed for victimization. Perceived popularity, however, significantly interacted with two latent classes among girls and one among boys: stably overweight (girls only) and early childhood limited over15 14 13
Depressive Symptoms
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Boys
Model 2
12 11 Low Body image
10 High Body image
9 8
weight (boys and girls). The interaction with stably overweight girls accounted for 42% of a standard deviation change in depressive symptoms, and being in the early childhood limited group accounted for 52% of a standard deviation change among girls and 37% of a standard deviation change among boys. Figure 3 presents the results for the stably overweight versus never overweight comparison, with the findings being graphically similar when the early childhood limited class replaced the former in the comparison. Feeling popular made no difference among youth who had never been overweight, but it did make a difference (apparently reducing depressive symptoms) among girls who were in the stably overweight class and boys and girls who were in the early childhood limited overweight class. Again, popularity appeared to be a protective factor only for youth with some history of overweight.
7
Discussion
6 5
Not Early Onset of Overweight Early Onset of Overweight
Figure 2. Associations between body image and adolescent depressive symptomatology among girls in the never overweight class and the middle childhood onset of overweight class.
The stigma that heavier bodies engender has many implications that undermine individual wellbeing (Puhl & Latner, 2007). Understanding this stigma is important as it has implications that can also prevent the pursuit of healthy weight loss strategies and contribute to patterns of behavior likely to result in further weight
MARTIN-STOREY AND CROSNOE
1010 10 9.5
Deppressive Symptoms
9 8.5 8 Low Popularity
7.5 High Popularity
7 6.5 6
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5.5 5
Never Overweight Class
Stably Overweight Class
Figure 3. Associations between popularity and adolescent depressive symptomatology among girls in the stably overweight class and the never overweight class.
gain (Lillis, Levin, & Hayes, 2011). These associations have consequences for public health, which is one reason that they have been studied so much in recent years. Psychologists have contributed to this literature in the past and can take this area of research in new directions in the future. Rather than treating weight statically, weight status was treated dynamically in this study in line with life course theory as well as a few developmental studies (e.g., Kubzansky et al., 2012; O’Brien et al., 2007; Mustillo et al., 2003). Although the majority of young people were consistently not overweight, those who experienced being overweight did so in diverse ways. This dynamic approach revealed a cumulative disadvantage of being overweight for girls, for whom physical attractiveness is often more of a harsh standard of evaluation in American society (Phares, Steinberg, & Thompson, 2004). Most at risk were girls who had been stably overweight from early childhood, more so than other currently overweight girls. Continuously experiencing the stressors associated with this stigmatized identity may make girls vulnerable (Lee, Sohn, Lee, & Lee, 2004; Mustillo et al., 2003). Following a classic understanding of the role of stigma and stress, the longer an individual experiences a stigmatized identity, the more likely it is that this identity will undermine wellbeing (Goffman, 1963). Furthermore, rather than treating weight as having uniform implications for youth, the potential for weight to be differentially experienced by youth with varying levels of psychosocial functioning was examined. Perceptions of popularity appeared to moderate the association between weight trajectories and depressive symptoms, such that popularity was not associated with significant differences in depressive symptoms among youth who were never overweight. These perceptions were, however, protective for girls with either stable histories of overweight or boys and girls with childhood-limited histories of overweight. These findings support past work suggesting that positive peer experiences— or, at least, perceiving peer experiences to be positive, whether they are or not—may protect youth from negative psychosocial outcomes (La Greca & Harrison, 2005). For girls in the middle childhood onset of overweight class, perceptions of body image moderated the link between their weight trajectory and depressive symptoms compared to girls who were never overweight. The effect size was
fairly small but nonetheless suggested that, despite the overall lower levels of depressive symptoms for this group, selfperceptions may be protective for depressive outcomes. These two sets of results point to new avenues of inquiry. The value of examining weight status with a simultaneous focus on dynamism and variability could be realized in research on a diverse set of outcomes, not just depressive symptoms, and on a diverse set of risks, not just overweight status. They also suggest that further attention needs to be paid to the timing of weight gain during the transition to adolescence as well as the changes in individual psychosocial contexts that precede and follow this period. The timing of weight gain may be important for individual functioning. Indeed, the weight gain occurring at the onset of puberty has been associated with an increased vulnerability to depressive symptoms, particularly among girls (Ge, Elder, Regnerus & Cox, 2001; Vogt Yuan, 2010). Future research may clarify the association between trajectories of overweight and depressive symptoms by understanding these weight changes within the context of pubertal timing. Finally, understanding how self-concept and peer relations change in response to weight loss and weight gain may clarify the mechanisms by which weight alters the individual’s internal functioning and external contexts. Ultimately, the identification of these mechanisms may explain how weight trajectories are associated with depressive symptoms. Of course, these suggestions for future research come with the caveat that some limitations of this work need to be addressed. For example, approximately 80% of the participants in the SECCYD were White. Given that many of the latent class groups of overweight were quite small, this sample provides limited opportunities to explore how class membership was differently associated with depressive symptoms by race/ethnicity. Similar analyses employing a larger and more diverse sample could be used to address this problem in the future. The current study focused on the longitudinal outcomes of weight, despite evidence suggesting that the association between weight and depressive symptoms are likely bidirectional (Felton, Cole, Tilghman-Osborne, & Maxwell, 2010). Although we considered using depressive symptoms as a time varying covariate, we rejected this approach for several reasons. First, this approach would have significantly limited the developmental scope of the paper, forcing us to focus exclusively on adolescence. Second, as we were particularly interested in trajectories of overweight, the use of a dichotomous outcome variable would have required an analytic strategy that precluded the use of time varying covariates. The present study also employed a community sample, explaining the low numbers of children who were obese or had levels of depressive symptoms above the clinical cutoff. The low number of children with BMIs above the 95th percentile was a major factor in choosing to focus on children with BMIs above the 85th percentile, but may have implications for the current findings. Finally, families who left the study had mothers who reported higher levels of depressive symptoms, contributing to increased homogeneity of the sample across time. Still, the findings discussed here are intriguing in a preliminary way and need to be built upon in the future. The high potential costs of the ongoing obesity epidemic mean that weight status will continue to be studied, and developmental insights need to help guide this research.
TRAJECTORIES OF OVERWEIGHT
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Received June 10, 2014 Revision received November 12, 2014 Accepted November 20, 2014 䡲
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