Abstract
Objective This article tested whether disordered eating in the spring of sixth grade can be predicted by the behaviors of fifth grade elementary school children. Method Measurements of disordered eating were collected from 1906 children (mean age = 10.86 years) at Time 1 (spring of fifth grade), Time 2 (fall of sixth grade), and Time 3 (spring of sixth grade). Results A number of fifth grade children reported disordered eating during the previous 2 weeks: 12.1% reported objective binge episodes, 4.8% reported purging food, and 9.8% reported restricting food intake. These behaviors predicted disordered eating during the spring of sixth grade. In addition, fifth grade pubertal onset predicted higher levels of restricting for girls. Conclusion A substantial number of fifth grade children reported disordered eating behaviors, and these behaviors predicted disordered eating behaviors in the spring of sixth grade. Disordered eating can be studied at least as early as fifth grade.
Keywords: adolescents, children, eating disorder, longitudinal, puberty
There is reason to believe that we should investigate disordered eating in children before puberty. Disordered eating can take many forms, including binge eating (eating an unusually large amount of food while feeling a loss of control), purging one’s food (through inducing vomiting), engaging in compensatory behaviors like diuretic use or laxative use, and restricting one’s intake in an effort to lose weight. Assessment most often occurs in early adolescence because body image, weight, and eating disturbances are substantially present for girls at the start of adolescence (Beato-Fernandez, Rodriguez-Cano, Belmonte-Llario, & Martinez-Delgado, 2004; Bryant-Waugh & Lask, 1995; Cotrufo, Cella, Cremato, & Labella, 2007; Franko & Omori, 1999; Gardner, Stark, Friedman, & Jackson, 2000; Killen et al., 1994; Smith, Simmons, Flory, Annus, & Hill, 2007). Disordered eating behaviors are also highly predictive of diagnosable eating disorders by late adolescence, including a ninefold increase in bulimia nervosa diagnosis and a 20-fold increase in anorexia nervosa diagnosis (Killen et al., 1994; Kotler, Cohen, Davies, Pine, & Walsh, 2001). Disordered eating at a young age also is associated with a broad range of physical and mental health problems during early adulthood (Johnson, Cohen, Kasen, & Brook, 2002).
Although assessment typically occurs during adolescence, there are indications that preadolescent disordered eating behavior is predictive of subsequent dysfunctional behavior and eating disorder diagnosis. Although most eating disorder research on children has, to date, focused on determining appropriate diagnostic criteria for young children and determining the prevalence of eating issues in children (Bravender et al., 2010; Nicholls, Lynn, & Viner, 2011), two prospective studies have suggested the importance of preadolescent eating and weight-related difficulties. An extensive case–control investigation found that even a single medical consultation to address weight- or eating-related problems predicted anorexia nervosa diagnosis during adolescence (Lask et al., 2005). One extensive longitudinal investigation has been conducted, and results indicated that loss of control eating in children of mean age 10 years predicted the subsequent development of partial or full-syndrome binge eating disorder, assessed later between 4 and 5 years (Tanofsky-Kraff et al., 2011). The behavior of loss of control eating proved to be the important predictor; weight, shape, eating concerns, or negative affect did not predict subsequent binge eating behavior.
We highlight two features of this existing literature on prospective prediction from childhood behavior. The first is that, in one study, a single medical consultation expressing concern predicted future dysfunction (Lask et al., 2005); in the other, the behavior of loss of control eating predicted future dysfunction (Tanofsky-Kraff et al., 2011). In neither study was it necessary for a fully diagnosable disorder to be present in children to predict adolescent disordered eating.
Second, Tanofsky-Kraff et al. (2011) found that approximately half of the children who reported loss of control eating at age 10 years continued to report that behavior 4–5 years later, and half showed a reduction in loss of control eating. This indicates that for some children, there may be a protective developmental trajectory that reduces loss of control eating. However, the children who continue to engage in loss of control eating are at the greatest risk for future disordered eating behaviors even after puberty and well into adolescence.
In the current study, we extended this previous research by assessing several disordered eating behaviors during the spring of fifth grade (the last year of elementary school), and then twice during sixth grade or the first year of middle school. In addition to binge eating, purging, and restricting food intake, we assessed diuretic use and laxative use. We also examined another factor associated with disordered eating behavior, pubertal onset. Pubertal onset is associated with increased risk for disordered eating, even holding age constant (Klump, McGue, & Iacono, 2003).
Because of the relative paucity of research on dysfunctional eating in children this young, we were not in a position to form specific hypotheses about prospective relationships between each dysfunctional behavior and other such behaviors. We hypothesized, based on previous literature described earlier, the following: (1) sixth grade (middle school) dysfunctional eating behaviors could be predicted from fifth grade (elementary school) dysfunctional eating reports, (2) binge eating would predict current and future purging and purging would predict current and future binge eating, (3) purging would predict current and future restriction of intake (as they are both weight maintenance behaviors), and (4) early pubertal onset would predict some disordered eating behavior and predictions will differ between boys and girls. We explored the roles of diuretic and laxative use, as they are usually not reported in child disordered eating literature.
Methods
Participants
Participants for this study were part of a larger study called the Adolescent Risk Behavior Research Project, designed to assess the development of several different types of dysfunctional behaviors during the preadolescent to early adolescent years. The initial sample was made up of members of fifth grades classes in 23 public elementary schools in two counties in Kentucky: that of the research university and one immediately adjacent. The two counties include urban, suburban, and rural dwellings. A sample of 1906 children participated at three different time points: spring of fifth grade, fall of sixth grade, and spring of sixth grade. The retention rate was 96% from Time 1 to Time 2 and 92% from Time 1 to Time 3. Retained and lost participants did not differ on any study variables. As a result, expectation maximization was used to impute for all missing data at random data points. The mean age of the participants at the initiation of the study was 10.86 years, and the sample was made up of 908 (50.2%) boys and 905 (49.8%) girls. Most were European–American (60.9%), followed by African–American (18.7%); the remainder of the sample identified themselves as Hispanic (8.2%), Asian (2.9%), Middle Eastern (0.4%), or other (8.8%). African–American and Hispanic children were oversampled; hence, these groups are represented at higher rates than is characteristic of the local population.
Procedure
The questionnaires were administered in 23 public elementary schools during school hours. A passive consent procedure was used. Questionnaires were administered in school classrooms. It was made clear to the students that their responses on the questionnaire were to be kept confidential and no one outside of the research team would see them. The research team introduced the federal certificate of confidentiality for the project and emphasized that they were legally bound to keep all responses confidential. After each participant signed the assent form, the researchers then passed out packets of questionnaires. The procedure took ≤60 min. This procedure was approved by the University’s Institutional Review Board and by the participating school systems.
Measures
Demographics
Participants were asked questions pertaining to age, sex, ethnicity, and other sociocultural factors.
Pubertal Development Scale
The Pubertal Development Scale (Peterson, Crockett, Richards, & Boxer, 1988) consists of five questions for each gender. This questionnaire measure has demonstrated strong validity when used with 9–16-year-olds (Carskadon & Acebo, 1993) and correlates highly with physician ratings and other forms of self-report when administered to children of the same age as those in this study (r values ranging from 0.61 to 0.67; Brooks-Gunn, Warren, Rosso, & Gargiulo, 1987; Coleman & Coleman, 2002). The Pubertal Development Scale permits dichotomous classifications as pre- or postpubertal, which are frequently used (e.g., Culbert, Burt, McGue, Iacono, & Klump, 2009), and which we used in the current study.
Eating Disorder Examination Questionnaire
The Eating Disorder Examination Questionaire (EDE-Q; Fairburn & Beglin, 1994) is a self-report version of a semi-structured interview (EDE; Fairburn & Wilson, 1993) that assesses eating disorder symptoms. For this study, we adapted the original EDE-Q for use with a younger population by using age-appropriate wording and shortening the length of time referred to in the questions to the past 2 weeks as has been shown to be reliable and valid with children of this age in the past (Carter, Stewart, & Fairburn, 2001; Decaluwe, Braet, & Fairburn, 2003). For this study, only items pertaining to behavioral expressions of disordered eating were used.
All behaviors included in the EDE-Q (e.g., binge eating, purging, laxative use, diuretic use, and restricting) were converted into dichotomous variables, as the main goal was to determine whether membership in a particular eating behavior group (disordered vs. nondisordered) predicted future disordered eating behavior; therefore, dichotomizing the variables allowed us to take full advantage of the zero-heavy data and to appropriately determine initial group level.
Binge eating was determined by positive endorsement of these two items: (1) “In the past two weeks, have there been times when you have eaten what most people would regard as an unusually large amount of food, and how many of these times did you feel you had lost control over your eating?” and (2) “Binge-eating is when you eat what most people, like your friends, would consider a very large amount of food while feeling like you can’t stop. Have you done any binge-eating in the past two weeks?” Purger status was determined by endorsement of one item: “Over the past two weeks, have you made yourself sick (vomit) as a means of controlling your weight, or because you ate a lot?” Laxative-user status was determined by responding “yes” to the following item: “Over the past two weeks, have you taken laxatives (pills or liquids that make you poop) as a means of controlling your shape or weight or because you ate a lot?” Diuretic-user status was determined by endorsement of one item: “Over the past two weeks, have you taken diuretics (pills that make you urinate or pee) as a means of controlling your shape or weight or because you ate a lot?” Restricting status was determined by responding “yes” to two items: (1) “Over the past two weeks, have you been consciously trying to cut back on the amount of food you eat to control your shape or weight?” and (2) “Over the past two weeks, have you gone for long periods of time (8 hours or more, not when you are sleeping) without eating anything in order to control your shape or weight?”.
Statistical Analyses
All analyses were conducted with SPSS software, version 18.0. Descriptive statistics and crosstabulation were used to determine the presence or absence of each eating disordered behavior at each time. Binomial logistic regression analysis was used to determine the significance of the prediction of future behavior from initial behavior and from puberty. We report odds ratios (OR) and confidence intervals from those analyses. A Bonferroni correction was used to account for multiple comparisons; all data were considered significant at p < .0007.
Results
Disordered Eating at Times 1, 2, and 3
At the end of fifth grade (Time 1), substantial disordered eating was reported. Binge eating was present in 12.1% of children, and purging was present in 4.8% of children. Approximately 9.8% of children reported restricting their food intake at Time 1. Laxative use and diuretic use were present in 2.2% of children and 1.0% of children, respectively. These data and data on rates of disordered behavior at Times 2 and 3 can be seen in Table I. Reports of each behavior dropped over time. These rates are similar to those found in other reports on childhood disordered eating behaviors (Tanofsky-Kraff et al., 2009). There were no differences found between male and female participants or between African–American and European–American participants on any study variables.
Table I.
Presence of Disordered Eating Behaviors at Times 1, 2, and 3
| Behavior | Time 1 | Time 2 | Time 3 |
|---|---|---|---|
| N (% of sample) | N (% of sample) | N (% of sample) | |
| Binge eating | 231 (12.1) | 147 (8.9) | 136 (8.4) |
| Purge | 92 (4.8) | 59 (3.4) | 45 (2.6) |
| Restrict | 186 (9.8) | 126 (7.2) | 133 (7.7) |
| Laxative | 41 (2.2) | 32 (1.8) | 27 (1.6) |
| Diuretic | 20 (1.0) | 14 (0.8) | 14 (0.8) |
Note. N = 1906. In each cell, N = the number of children reporting engagement in the behavior; % refers to the percentage of the overall sample represented by the number. All behaviors are dichotomized yielding a range of 0–1.
Hypothesis 1: Behaviors Are Stable Across Time
In Tables II–VI, we present findings indicating the degree to which each eating disordered behavior in the spring of sixth grade was predictable from the same behavior measured in the spring of fifth grade. These data indicate that engagement in any form of eating disordered behavior in the spring of fifth grade predicted continued engagement in that behavior during the first year of middle school. Children who report binge eating in fifth grade are 7.6 times more likely to binge eat in the fall of sixth grade and 6.1 times more likely to binge eat at the end of sixth grade. Children who begin to restrict their intake in fifth grade are 3.2 times more likely to do so at the beginning of sixth grade and 2.9 times more likely to be doing so at the end of sixth grade. Patterns are similar for other behaviors including purging, diuretic use, and laxative use.
Table II.
Impact of Time 1 Binge Status on Disordered Eating Behavior at Times 2 and 3
| Time 1 binge status | |||
|---|---|---|---|
| Behavior at each time point | Yes (12.1%) | No (87.9%) | OR (CI) |
| Time 1 | |||
| Purge | 13.4 | 3.6 | 4.10* (2.60–6.47) |
| Diuretic drugs | 4.8 | 0.5 | 9.26* (3.79–22.59) |
| Laxative | 7.8 | 1.4 | 6.07* (3.22–11.43) |
| Restrict | 26.4 | 7.5 | 4.45* (3.15–6.28) |
| Time 2 | |||
| Binge | 32.0 | 5.8 | 7.58* (5.22–11.00) |
| Purge | 6.3 | 3.0 | 2.16 (1.14–4.06) |
| Diuretic drugs | 1.4 | 2.7 | 2.00 (0.55–7.23) |
| Laxative | 3.8 | 1.6 | 2.54 (1.12–5.72) |
| Restrict | 14.1 | 6.3 | 2.42* (1.56–3.78) |
| Time 3 | |||
| Binge | 27.3 | 5.8 | 6.11* (4.15–8.98) |
| Purge | 4.3 | 2.4 | 1.85 (0.88–3.90) |
| Diuretic drugs | 2.5 | 0.6 | 4.19 (1.39–12.61) |
| Laxative | 4.0 | 1.2 | 3.32 (1.43–7.68) |
| Restrict | 13.9 | 6.8 | 2.21* (1.43–3.44) |
Note. OR = odds ratio; CI = confidence interval.
N = 1906. “Yes” and “No” refer to the percentage of participants who reported binge eating at Time 1. All numbers in the first two columns are percentages that reflect the portion of participants that demonstrate each respective behavior. For example, of the children who did report binge eating at Time 1, 13.4% reported purging at Time 1. ORs are results from binary logistic regression analysis; each Time 1 behavior corrected for other Time 1 behaviors.
*p < .0007, the Bonferroni-corrected significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Table III.
Impact of Time 1 Purge Status on Disordered Eating Behavior at Times 2 and 3
| Time 1 purge status | |||
|---|---|---|---|
| Behavior at each time point | Yes (4.8%) | No (95.2%) | OR (CI) |
| Time 1 | |||
| Binge | 33.7 | 11.0 | 4.10* (2.60–6.47) |
| Diuretic drugs | 9.8 | 0.6 | 17.77* (7.17–44.07) |
| Laxative | 17.4 | 1.4 | 15.07* (7.72–29.39) |
| Restrict | 32.6 | 8.6 | 5.14* (3.23–8.19) |
| Time 2 | |||
| Binge | 22.4 | 8.3 | 3.20* (1.81–5.65) |
| Purge | 22.6 | 2.4 | 11.75* (6.45–21.41) |
| Diuretic drugs | 4.8 | 0.6 | 8.29* (2.55–27.03) |
| Laxative | 10.7 | 1.4 | 8.59* (3.84–19.21) |
| Restrict | 21.7 | 6.5 | 3.97* (2.27–6.93) |
| Time 3 | |||
| Binge | 20.0 | 7.8 | 2.97* (1.66–5.30) |
| Purge | 15.1 | 2.0 | 8.94* (4.50–17.75) |
| Diuretic drugs | 3.6 | 0.7 | 5.44 (1.49–19.90) |
| Laxative | 10.8 | 1.1 | 10.96* (4.76–25.22) |
| Restrict | 20.9 | 7.0 | 3.53* (2.03–6.13) |
Note. OR = odds ratio; CI = confidence interval.
N = 1906. “Yes” and “No” refer to the percentage of participants who reported purging at Time 1. All numbers in the first two columns are percentages that reflect the portion of participants that demonstrate each respective behavior. For example, of the children who did report purging at Time 1, 33.7% reported binge eating at Time 1. ORs are results from binary logistic regression analysis; each Time 1 behavior corrected for other Time 1 behaviors.
*p < .0007, the Bonferroni-corrected significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Table IV.
Impact of Time 1 Diuretic Use Status on Disordered Eating Behavior at Times 2 and 3
| Time 1 diuretic status | |||
|---|---|---|---|
| Behavior at each time point | Yes (1.0%) | No (99.0%) | OR (CI) |
| Time 1 | |||
| Binge | 55.0 | 11.7 | 9.26* (3.79–22.59) |
| Purge | 45.0 | 4.4 | 17.77* (7.17–44.07) |
| Laxative | 50.0 | 1.6 | 59.84* (23.24–154.06) |
| Restrict | 20.0 | 9.7 | 2.34 (0.77–7.08) |
| Time 2 | |||
| Binge | 33.3 | 8.6 | 5.29* (1.96–14.30) |
| Purge | 16.7 | 3.3 | 5.92 (1.67–21.04) |
| Diuretic drugs | 12.5 | 0.7 | 20.27* (4.15–99.09) |
| Laxative | 16.7 | 1.7 | 11.77* (3.23–42.89) |
| Restrict | 38.9 | 6.9 | 8.56* (3.26–22.49) |
| Time 3 | |||
| Binge | 26.7 | 8.2 | 4.07 (1.28–12.96) |
| Purge | 11.1 | 2.5 | 4.83 (1.08–21.68) |
| Diuretic drugs | 17.6 | 0.6 | 32.81* (8.25–130.49) |
| Laxative | 6.3 | 1.5 | 4.31 (0.55–33.85) |
| Restrict | 11.1 | 7.6 | 1.51 (0.34–6.65) |
Note. OR = odds ratio; CI = confidence interval.
N = 1906. “Yes” and “No” refer to the percentage of participants who reported diuretic use at Time 1. All numbers in the first two columns are percentages that reflect the portion of participants that demonstrate each respective behavior. For example, of the children who did report diuretic use at Time 1, 55.0% reported binge eating at Time 1. ORs are results from binary logistic regression analysis; each Time 1 behavior corrected for other Time 1 behaviors.
*p < .0007, the Bonferroni-corrected significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Table V.
Impact of Time 1 Laxative Use Status on Disordered Eating Behavior at Times 2 and 3
| Time 1 laxative status | |||
|---|---|---|---|
| Behavior at each time point | Yes (2.2%) | No (97.8%) | OR (CI) |
| Time 1 | |||
| Binge | 43.9 | 11.4 | 6.07* (3.22–11.43) |
| Purge | 39.0 | 4.1 | 15.07* (7.72–29.39) |
| Diuretic drugs | 24.4 | 0.5 | 59.84* (23.24–154.06) |
| Restrict | 31.7 | 9.3 | 4.54* (2.31–8.93) |
| Time 2 | |||
| Binge | 23.5 | 8.6 | 3.27 (1.45–7.36) |
| Purge | 18.4 | 3.1 | 7.13* (3.00–16.94) |
| Diuretic drugs | 5.3 | 0.7 | 7.78 (1.68–36.05) |
| Laxative | 26.3 | 1.3 | 27.50* (11.92–63.42) |
| Restrict | 18.4 | 7.0 | 3.00 (1.29–6.96) |
| Time 3 | |||
| Binge | 34.3 | 7.8 | 6.17* (3.00–12.69) |
| Purge | 7.9 | 2.5 | 3.36 (0.99–11.34) |
| Diuretic drugs | 11.4 | 0.6 | 21.51* (6.40–72.33) |
| Laxative | 19.4 | 1.2 | 20.12* (7.90–51.29) |
| Restrict | 15.8 | 7.5 | 2.32 (0.95–5.64) |
Note. OR = odds ratio; CI = confidence interval.
N = 1906. “Yes” and “No” refer to the percentage of participants who reported laxative use at Time 1. All numbers in the first two columns are percentages that reflect the portion of participants that demonstrate each respective behavior. For example, of the children who did report laxative use at Time 1, 43.9% reported binge eating at Time 1. ORs are results from binary logistic regression analysis; each Time 1 behavior corrected for other Time 1 behaviors.
*p < .0007, the Bonferroni-corrected significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Table VI.
Impact of Time 1 Restricting Status on Disordered Eating Behavior at Times 2 and 3
| Time 1 restrict status | |||
|---|---|---|---|
| Behavior at each time point | Yes (9.8%) | No (90.2%) | OR (CI) |
| Time 1 | |||
| Binge | 32.8 | 9.9 | 4.45* (3.15–6.28) |
| Purge | 16.1 | 3.6 | 5.14* (3.23–8.19) |
| Diuretic drugs | 2.2 | 0.9 | 2.34 (0.77–7.08) |
| Laxative | 7.0 | 1.6 | 4.54* (2.31–8.93) |
| Time 2 | |||
| Binge | 21.3 | 7.5 | 3.33* (2.19–5.07) |
| Purge | 10.7 | 2.6 | 4.46* (2.50–7.95) |
| Diuretic drugs | 1.2 | 0.8 | 1.59 (0.35–7.16) |
| Laxative | 3.5 | 1.6 | 2.15 (0.87–5.30) |
| Restrict | 27.8 | 5.0 | 7.27* (4.84–10.90) |
| Time 3 | |||
| Binge | 13.5 | 7.8 | 1.83 (1.11–3.01) |
| Purge | 7.1 | 2.1 | 3.53* (1.78–6.96) |
| Diuretic drugs | 1.2 | 0.8 | 1.60 (0.36–7.22) |
| Laxative | 4.2 | 1.3 | 3.38 (1.41–8.13) |
| Restrict | 23.2 | 6.0 | 4.73* (3.13–7.16) |
Note. OR = odds ratio; CI = confidence interval.
N = 1906. “Yes” and “No” refer to the percentage of participants who reported restricting at Time 1. All numbers in the first two columns are percentages that reflect the portion of participants that demonstrate each respective behavior. For example, of the children who did report restricting at Time 1, 32.8% reported binge eating at Time 1. ORs are results from binary logistic regression analysis; each Time 1 behavior corrected for other Time 1 behaviors.
*p < .0007, the Bonferroni-corrected significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Hypothesis 2: Binge Eating Predicts Purging and Purging Predicts Binge Eating
Table II provides results regarding whether binge eating predicts purging over time; children who binge eat in fifth grade are 4.1 times more likely to also purge at the same time point, but are not significantly more likely to purge at the beginning or the end of sixth grade. Table III provides results predicting behaviors from purging at Time 1. Children who purge in fifth grade (the last year of elementary school) are 4.1 times more likely to binge eat at Time 1, 3.2 times more likely to binge eat at Time 2 (fall of sixth grade, start of middle school), and 3.0 times more likely to binge eat at the end of sixth grade.
Hypothesis 3: Purging Predicts Current and Future Restricting
As seen in Table III, purging in fifth grade predicts current restricting (OR: 5.1), restricting at Time 2 (OR: 4.0), and restricting at Time 3 (OR: 3.5). As seen in Table VI, the inverse is also true. Children who restrict in fifth grade are 5.1 times more likely to purge in fifth grade, 4.5 times more likely to purge in the fall of sixth grade, and 3.5 times more likely to purge by the end of sixth grade.
Hypothesis 4: Puberty Predicts Different Behaviors for Boys and Girls
To address the hypothesis that puberty has a differential impact for the levels of disordered eating behaviors across time for boys and girls, we conducted binomial logistic regressions analysis. We controlled for age, as not all participants were the same age in fifth grade, and boys and girls enter puberty at different ages (Wang, 2002). We also conducted separate analyses for boys and girls rather than controlling for gender so that we could probe the nature of the differences. The results of these analyses are seen in Table VII; girls who reported pubertal onset by fifth grade were 2.3 times as likely to restrict in fifth grade. Boys who reported early pubertal onset were no more likely than those who did not to engage in disordered behaviors.
Table VII.
Impact of Early Pubertal Onset on Disordered Eating Behavior at Times 1, 2 and 3
| Fifth grade pubertal onset | |||
|---|---|---|---|
| Behavior at each time point | Girls (n = 905) | Boys (n = 908) | |
| OR (CI) | OR (CI) | ||
| Time 1 | |||
| Binge | 1.12 (0.72–1.76) | 1.70 (1.10–2.62) | |
| Purge | 1.28 (0.72–2.26) | 3.06 (1.48–6.33) | |
| Diuretic drugs | 1.14 (0.28–4.76) | 0.33 (0.04–2.70) | |
| Laxative | 2.15 (0.98–4.65) | 3.38 (0.95–12.08) | |
| Restrict | 2.29* (1.47–3.58) | 1.99 (1.23–3.24) | |
| Time 2 | |||
| Binge | 1.61 (0.96–2.70) | 1.61 (0.92–2.83) | |
| Purge | 2.52 (1.24–5.15) | 1.06 (0.38–3.01) | |
| Diuretic drugs | 1.35 (0.21–8.58) | 1.27 (0.24–6.74) | |
| Laxative | 1.23 (0.46–3.33) | 1.52 (0.44–5.22) | |
| Restrict | 1.61 (0.96–2.71) | 1.71 (0.91–3.20) | |
| Time 3 | |||
| Binge | 1.38 (0.83–2.30) | 1.35 (0.71–2.58) | |
| Purge | 2.34 (1.08–5.09) | 1.50 (0.45–5.04) | |
| Diuretic drugs | 1.39 (0.25–7.80) | 1.44 (0.14–14.48) | |
| Laxative | 3.19 (1.07–9.51) | 0.46 (0.06–3.83) | |
| Restrict | 1.86 (1.13–3.05) | 1.62 (0.85–3.10) | |
Note. OR = odds ratio; CI = confidence interval.
Results are from logistic regression predicting membership in groups at different time points. Each regression was corrected for age.
*p < .0007, the significance level needed to correct for multiple comparisons. We used a 95% significance level for CIs; they can be used to ascertain significance at the more liberal p < .05.
Other Findings
As seen in Table III, those who purge at Time 1 are more likely to engage in nearly every studied behavior at Times 2 and 3. As seen in Table IV, diuretic use predicts binge eating at Times 1 (OR: 9.3) and 2 (OR: 5.3). Also, those who use diuretic drugs at Time 1 are 8.6 times as likely to restrict in the fall of sixth grade, but this finding was no longer significant by the spring of sixth grade. Laxative use at Time 1 is also highly predictive of engagement in other behaviors at Times 2 and 3, as seen in Table V.
Discussion
Disordered eating behaviors at the start of adolescence/middle school are highly predictive of subsequent diagnosable disorders and health problems. Anorexia nervosa and bulimia nervosa symptoms at the beginning of adolescence are correlated greater than r = 0.40 with eating disorder symptoms during adulthood (Johnson et al., 2002), and diagnosable bulimia nervosa at the beginning of adolescence is associated with a ninefold increase in bulimia nervosa and a 20-fold increase in anorexia nervosa during late adolescence (Kotler et al., 2001). In the current study, fifth grade children reported a substantial amount of disordered eating behavior, and that behavior was predictive of disordered behavior during the first year of middle school or sixth grade. This is the first study that has shown, longitudinally, the predictive impact of engaging in a wide range of disordered eating behaviors in elementary school.
Our hypotheses were generally supported. For each behavior in fifth grade, reporting a behavior significantly increased the likelihood that the child would continue reporting that behavior for the next two time points. Those who reported purging at Time 1 were more likely to report binge eating at subsequent time points, although the inverse was not true; those who reported purging also reported restricting at subsequent time points and vice versa; girls who entered early puberty were more likely to report restricting behaviors, although these relationships disappeared over time. A surprising finding was the extent to which use of laxative and diuretic drugs was reported in this particular population; perhaps a useful step toward addressing this issue would be limiting the access to these substances for children of this age.
A major and unexpected finding for this particular population was the lack of support for the prediction of binge eating increasing purging over time; instead, we see that binge eating predicts purging at Time 1, but this relationship subsequently disappears. We also see that reports of purging at Time 1 predicts reports of binge eating over time. Conceptually, we may often think of purging following and even being solely predictable by binge eating, but these findings suggest otherwise. It is possible that developing purging behaviors predicts binge eating and not the inverse because children may feel less inhibited about their eating when they have already developed compensatory purging behaviors. It is also possible that this evidence for binge eating without subsequent purging provides greater support for the potential diagnosis of binge eating disorder, which is currently a disorder for further research in the Diagnostic and Statistical Manual—4th Edition (DSM-IV; American Psychiatric Association, 2000), in children of this age.
Interestingly, there were mean declines in binge eating, purging, and restricting food intake from the spring of fifth grade to the spring of sixth grade. This finding is consistent with Tanofsky-Kraff et al.’s (2011) observation of a decline in loss of control eating from childhood to adolescence. The mean declines were in the context of fluctuation in behavior across the longitudinal period. This drop is perhaps to be expected, as children may learn to better handle eating without experiencing the loss of control that is more common (and perhaps normative) for younger children. Nonetheless, there is reason to be concerned about any such disordered eating reports in children this young, and reason to be particularly concerned about the apparent stability of the disordered eating over time and the ability of the symptoms at such a young age to predict future eating problems (Lask et al., 2005; Tanofsky-Kraff et al., 2011). Within the context of behavioral change, and across this important developmental transition, fifth grade behavior was highly predictive of behavior at the end of the first year of middle school.
The primary limitation of this study is the reliance on self-report questionnaire assessments of disordered behavior; interview assessments might have provided opportunities to clarify questions or responses. This is especially true for the puberty questionnaire, which may be unreliable in children (especially in overweight children; Kaplowitz & Oberfield, 1999). However, the self-report questionnaire method of gathering data has been validated in relation to interview data; hence, there is some evidence to suggest that this method is valid and a reasonable way of acquiring this type of information (Wilfley, Schwartz, Spurrell, & Fairburn, 1997). It is also true that one does not know the impact of assessment using face-to-face interviews, as is often done by health care professionals. Perhaps children can complete intake assessments by questionnaires that include inquiry into disordered eating behavior; alternatively, data can be corroborated by observer reports of behavioral expressions of disordered eating in children (body weight change, binge eating, purging, etc.; Bravender et al., 2010).
To know that fifth grade children report engaging in a variety of disordered eating behaviors, and that those reports predict middle school involvement in those behaviors, is valuable new knowledge. These findings combine with other findings to suggest that the risk process seems to be underway before the adolescent period that many researchers and clinicians emphasize. The trajectory of risk begins at least as early as fifth grade and continues on through puberty into early adolescence and young adulthood, implying that targeted early prevention efforts may be worthwhile. Researchers have already shown that a single eating disordered behavior or weight consultation with a family practitioner can predict early onset disordered eating behavior (Lask et al., 2005), suggesting the possible value in universally screening for disordered eating. However, there is the risk of false positives with a universal screening strategy, and the potential harms of early weight control efforts need to be investigated. It is of course true that some subclinical disordered eating behavior may never develop into the following diagnosable eating disorder: research designed to determine when early, subclinical disordered eating behavior resolves, continues as subclinical dysfunction (itself distressing), or progresses into diagnosable disorders is an important next step. The crucial first step toward effective early intervention/prevention must be to give children the chance to report these problematic behaviors.
Funding
This research was funded by NIAAA grant ROIAA016166 to Gregory T. Smith, PhD.
Conflicts of interest: None declared.
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