Night eating: prevalence and demographic correlates
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1 Wesleyan University WesScholar Division III Faculty Publications Natural Sciences and Mathematics January 2006 Night eating: prevalence and demographic correlates R H. Striegel rstriegel@wesleyan.edu D L. Franko D Thompson S Affenito H C. Kraemer Follow this and additional works at: Part of the Psychology Commons Recommended Citation Striegel, R H.; Franko, D L.; Thompson, D; Affenito, S; and Kraemer, H C., "Night eating: prevalence and demographic correlates" (2006). Division III Faculty Publications. Paper This Article is brought to you for free and open access by the Natural Sciences and Mathematics at WesScholar. It has been accepted for inclusion in Division III Faculty Publications by an authorized administrator of WesScholar. For more information, please contact dschnaidt@wesleyan.edu, ljohnson@wesleyan.edu.
2 Night Eating: Prevalence and Demographic Correlates Ruth H. Striegel-Moore,* Debra L. Franko, Douglas Thompson, Sandra Affenito, and Helena C. Kraemer Abstract STRIEGEL-MOORE, RUTH H., DEBRA L. FRANKO, DOUGLAS THOMPSON, SANDRA AFFENITO, AND HELENA C. KRAEMER. Night eating: prevalence and demographic correlates. Obesity. 2006;14: Objective: To examine the prevalence and correlates of night eating, the core behavioral symptom of night eating syndrome among adolescents and adults, using two public access survey databases of nationally representative samples. Research Methods and Procedures: Data were extracted for individuals age 13 years or older who completed food diary data for the National Health and Nutrition Examination Survey III (N 18,407) or the Continuing Survey of Food Intakes by Individuals (N 10,741). Prevalence estimates were calculated for three commonly used definitions of night eating. Logistic regression was used to examine correlates of night eating: type of day, season, gender, age, race/ethnicity, and BMI or obesity. Results: With few exceptions, findings were similar in the two surveys. Night eating is most common during the weekend; prevalence is greatest among young adults (18 to 30 years of age) and least common among individuals age 65 years or older; and is not associated with BMI or obesity. Gender or ethnicity effects were not found to be stable across surveys. Discussion: Experts need to consider type of day, age group, and possibly gender and race/ethnicity when examining population differences in night eating. Longitudinal Received for review June 13, Accepted in final form October 21, The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. *Department of Psychology, Wesleyan University, Middletown, Connecticut; Department of Counseling and Applied Psychology, Northeastern University, Boston, Massachusetts; Maryland Medical Research Institute, Baltimore, Maryland; Department of Nutrition and Dietetics, Saint Joseph College, West Hartford, Connecticut; and Department of Psychiatry and Behavioral Science, Stanford University, Stanford, California. Address correspondence to Ruth H. Striegel-Moore, Department of Psychology, Wesleyan University, 207 High Street, Middletown, CT Copyright 2006 NAASO studies are needed to further examine the link between night eating and obesity. Key words: night eating, disordered eating, BMI, meal patterns Introduction Night eating syndrome (NES) 1 was introduced by Stunkard et al. (1) to characterize a clinical disturbance that seemed to have caused or contributed to the maintenance of obesity in a subset of some severely obese patients. The defining features of NES were evening hyperphagia or night eating, morning anorexia, and sleep disturbance. After being ignored by researchers for several decades, in recent years NES has become the focus of increasing scientific investigation. Although there is a growing literature that suggests that night eating (the core behavioral symptom of NES) is an important clinical feature among some obese patients, epidemiological studies have failed to show a correlation between night eating and obesity. As described in a comprehensive review of the literature (2), however, this research has progressed in the absence of a uniformly accepted definition of NES or its clinical features, and the conflicting findings in the literature concerning the association between night eating and obesity may, in part, be the result of differences in the definition of NES. The behavioral symptom of night eating has been operationalized based on how late in the day (evening or night) the eating episode occurred and/or the proportion of total daily calories consumed during the eating episode. As illustrated by Striegel-Moore et al. (3), who applied five different definitions of night eating to a large epidemiological sample of girls, not surprisingly the prevalence of night eating varied considerably depending 1 Nonstandard abbreviations: NES, night eating syndrome; NHANES, National Health and Nutrition Examination Survey; CSFII, Continuing Survey of Food Intakes by Individuals; CI, confidence interval. OBESITY Vol. 14 No. 1 January
3 on the particular cut-off points chosen for time of day or percentage of caloric intake consumed. For example, when defining night eating as eating 25% or more (1) vs. 50% or more of daily caloric intake after 7 PM (4), the prevalence in a large sample of 16-year-old girls was 40.7% and 24.5%, respectively. When defining night eating as eating very late in the day (after 11 PM), prevalence was reduced to 14.5% (3). The study did not examine whether night eating varies by day of the week or time of the year, yet it seems reasonable to assume that the behavior is more common on weekend days than during the week, and it may also be more common in the summer months when many people take vacations and high school and college students have the opportunity to stay up later. This study also found that, with increasing age, night eating, especially eating very late in the day, became considerably more common and ranged from a low of 6.5% in 9-year-olds to a high of 21% in 19-yearolds. How prevalence of night eating changes over the course of adulthood is unclear. For example, given the time demands of employment, it is possible that younger adults are more likely to eat late in the day compared with adults over 65 (who may no longer be working full-time or at all). Experts have noted that the time of day when the evening meal occurs varies among cultural groups. For example, Italians tend to eat dinner much later than Americans, a difference that has prompted Italian obesity experts to propose that a definition of night eating should use a relatively late cut-off point for time of day (5). To determine more fully the demographic correlates of night eating, studies of more diverse population samples are needed. Finally, a critical question concerns the clinical significance of the behavior of night eating. Striegel-Moore et al. (3) did not find a significant association between night eating and overweight among their sample of girls 9 to 19 years old. It is possible that the association of night eating and overweight may be observed in adults, yet we are not aware of studies that have tested this relationship in a representative community sample. The present study addressed these gaps by examining the behavior of night eating in two nationally representative samples of adolescents and adults residing in the United States. In the absence of prior data across a range of ethnic groups and age groups, we did not formulate specific hypotheses concerning ethnic or age group differences in the prevalence of night eating. Based on the well-established finding that food intake varies qualitatively and quantitatively between weekdays and weekend days (6), we hypothesized that night eating would be more common on Fridays and Saturdays compared with weekdays. We also hypothesized that night eating would occur more frequently during the summer months, compared with other times of the year. Whether the behavior of night eating is of clinical interest has not yet been established, and we did not formulate a specific hypothesis concerning the association of night eating and BMI or obesity. To answer these questions, we capitalized on the availability of two large public access databases, namely the National Health and Nutrition Examination Survey (NHANES) III (7) and the Continuing Survey of Food Intakes by Individuals (CSFII) (8). Each contains detailed data from a representative sample of noninstitutionalized, civilian U.S. residents concerning food intake, including all foods and liquids consumed over a 24-hour period, time of day of intake, and type of meal (e.g., dinner). Research Methods and Procedures Description of the Surveys The analyses used data from two surveys, the NHANES III (7) and the CSFII 1994 to 1996/1998 (8). The design of NHANES-III has been described elsewhere (7). Briefly, NHANES-III involved 33,994 respondents in 89 geographic areas in the U.S. The sample was designed to be representative of the non-institutionalized, civilian population of the U.S., covering the entire lifespan; the age range for the survey was 2 months and older. The survey data were collected over a 6-year period (1988 to 1994). Each survey participant was interviewed at home and asked to participate in a medical examination, which included measurements of height and weight, and a 24-hour dietary intake recall. Respondents reported all foods and beverages consumed except plain drinking water (i.e., not bottled) for the previous 24-hour time period (midnight to midnight). The present analysis focused on the subpopulation of NHANES- III respondents who were age 13 years or older and either or non- or. Younger children were not included because it was assumed that their eating times would be determined largely by their parents. Rates of night eating were not estimated for other ethnic groups because sample sizes were insufficient for statistically reliable estimates. A detailed description of CSFII 1994 to 1996/1998 is available elsewhere (8). Briefly, CSFII involved 21,662 respondents in 62 geographic areas in the U.S. The sample was designed to be representative of the non-institutionalized U.S. population residing in households, covering the entire lifespan. The survey consisted of a sample of individuals of all ages, collected from 1994 to 1996, and an additional sample of children (age 1 to 9 years) interviewed in Each respondent was asked to complete two food intake interviews (24-hour dietary intake recalls) conducted 3 to 10 days apart. Participants were asked to report everything eaten or drunk the previous day between midnight and midnight. To maintain comparability with the 140 OBESITY Vol. 14 No. 1 January 2006
4 NHANES-III data, the present study used only the first day, and the analysis focused on the subpopulation of CSFII respondents who were age 13 years or older and either or non- or. ethnic group, gender, and total caloric intake throughout the day. Obesity was modeled using logistic regression, and BMI was modeled using linear regression (SAS PROC SURVEYREG). Statistical Analysis Three definitions of night eating previously examined by Striegel-Moore et al. (3) were used: consuming 25% of the total daily calories between 7:00 PM and 4:59 AM, consuming 50% of the total daily calories between 7:00 PM and 4:59 AM, and consuming anything between 11:00 PM and 4:59 AM, regardless of the amount of calories consumed. Because the CSFII and NHANES-III food recalls covered a 24-hour period from 12:01 AM until the next midnight, night was defined as the union of two time segments, 12:01 AM to 4:59 AM, plus 7:00 PM or 11:00 PM (depending on the definition) to midnight. The percentage of individuals who exhibited night eating was estimated for each definition by season of the year, type of day (weekend vs. weekday), age group, gender, and race/ethnicity. Due to the complex survey designs of CSFII and NHANES-III, weighting, stratification, and clustering were taken into account in all statistical analyses. Mean and percentage estimates were computed using PROCs SURVEYMEANS and SURVEYFREQ in SAS version 9.1 (SAS Institute, Cary, NC). Logistic regression (PROC SURVEYLOGISTIC in SAS) was used to model variables associated with night eating. Separate models were constructed for each survey and for each definition of night eating. Night eating by a given definition (1 yes; 0 no) served as the outcome variable. The predictors were type of day, season, gender, age group, race/ethnicity, and all possible two-way interactions among gender, age, and race/ethnicity. Type of day was coded as weekday (Sunday through Thursday) or weekend day (Friday and Saturday). Season was coded as winter (December through February), spring (March through May), summer (June through August), and autumn (September through November). Age was coded into four categories (13 to 18, 19 to 30, 31 to 64, and 65 years old), and race/ethnicity was coded into three categories (non-, non-, and ). Obesity was defined as BMI 30 for individuals above age 18 years and at or above the age- and genderspecific 95th percentile of BMI for adolescents. Prior to analysis, all covariates were centered as recommended (9). To adjust for the large number of statistical tests, a conservative level of statistical significance was adopted (p 0.01). It has been hypothesized that night eating is associated with obesity (1). To examine this association, obesity (binary) and BMI (continuous) were modeled as a function of night eating status, controlling for age group, racial/ Results Demographic Characteristics Estimates of demographic characteristics based on the two surveys are described in Table 1. Estimates differed slightly across surveys, perhaps, in part, because they covered different timeframes. The largest racial/ethnic group was non-, and the largest age group was adults age 31 to 64 years. Rates of Night Eating for Each Definition Estimates of the percentage of individuals exhibiting night eating for each definition, by survey, are provided in Table 2. Night eating by the first definition (25% kcal after 7 PM) was estimated to be present in more than onethird of the population. Night eating by the other two definitions (50% kcal after 7 PM and any eating after 11 PM) was less common; the estimates indicated that fewer than 13% met these definitions of night eating, regardless of survey. For all definitions of night eating, estimated rates were lower in CSFII than in NHANES-III. Correlates of Night Eating Weekdays vs. Weekend Days. Compared with CSFII, the food diaries in NHANES-III more often described eating on weekend days (36.1% of food diaries in NHANES-III vs. 30.7% in CSFII; Table 3), possibly resulting in the aforementioned greater rates of night eating in NHANES- III. In both surveys, there was a main effect of type of day for the definitions involving consuming 25% and 50% of the daily calories after 7 PM [Wald 2 (1) 6.76 to 20.29, p 0.01], consistent with the results shown in Table 3, after adjusting for type of day, season, gender, age group, race/ethnicity, and all possible two-way interactions among gender, age, and race/ethnicity. Night eating was 1.2 to 1.4 times more likely to occur on weekends, depending on the definition and survey. Type of day was not associated with eating after 11 PM in either survey (p 0.05). Season effects were not significant for any definition or survey (p 0.05). Demographic Characteristics. Tables 4 (NHANES-III) and 5 (CSFII) show estimated rates of night eating for each definition by gender, age group, and racial/ethnic group. For each definition, in each survey, the log odds of night eating were modeled as a function of type of day, season, gender, age group, race/ethnicity, and all possible two-way interactions among the latter three variables. The main effects of gender and race/ethnicity differed across surveys. No main effects of gender and race/ethnicity OBESITY Vol. 14 No. 1 January
5 Table 1. Number of respondents and percentage [95% confidence interval (CI)] with selected demographic characteristics by survey* NHANES-III CSFII Demographic characteristics No. Percentage (95% CI) No. Percentage (95% CI) Gender Men (46.7 to 48.4) (47.2 to 49.2) Women (51.6 to 53.3) (50.8 to 52.8) Racial/ethnic group (75.6 to 80.4) (74.2 to 81.8) (10.4 to 13.3) (9.9 to 14.4) (8.4 to 11.9) (6.5 to 13.2) Age group Adolescents (13 to 17 years old) (8.1 to 9.7) (8.6 to 9.7) Young adults (18 to 30 years old) (22.8 to 26.5) (20.9 to 24.1) Adults (31 to 64 years old) (50.1 to 53.0) (51.6 to 54.8) Elderly (65 years) (13.2 to 16.6) (14.0 to 16.3) * Respondents were included in the analysis if they were ages 13 or older and or non- or, participated in the Mobile Exam Centers exam, and provided complete, reliable food diary data (NHANES-III) or provided food diary data during Day 1 (CSFII). Number of respondents (not weighted). Percentage estimates and 95% CI are weighted and take stratification and clustering into account. were significant in CSFII (p 0.03). In contrast, in NHANES-III, men were 1.2 and 1.4 times more likely than women to exhibit night eating for the definitions involving consuming 25% of the daily calories after 7 PM and any eating after 11 PM, respectively [Wald 2 (1) 10.61/13.25, p 0.002]. Furthermore, in NHANES-III, by all definitions of night eating, respondents were 1.3 to 1.6 times more likely than respondents in the other racial/ethnic groups to exhibit night eating by all definitions [Wald 2 (1) to 35.51, p ]. In both surveys, young adults were 1.2 and 1.6 times more likely than respondents in the other age groups to exhibit night eating by the definitions involving consuming 25% of the daily calories after 7 PM and any eating after Table 2. Number of respondents and percentage (95% CI) with selected demographic characteristics by survey* NHANES-III CSFII Definition of night eating Number Percentage (95% CI) No. Percentage (95% CI) 25% kcal after 7 PM (34.3 to 37.2) (29.9 to 32.8) 50% kcal after 7 PM (11.5 to 13.4) (10.0 to 11.7) Any eating after 11 PM (11.1 to 13.3) (8.3 to 10.4) * Respondents were included in the analysis if they were ages 13 or older and or non- or, participated in the Mobile Exam Center exam, and provided complete, reliable food diary data (NHANES-III) or provided food diary data during Day 1 (CSFII). Number of respondents (not weighted). Percentage estimates and 95% CI are weighted and take stratification and clustering into account. 142 OBESITY Vol. 14 No. 1 January 2006
6 Table 3. Number and percentage (95% CI) of respondents exhibiting night eating by each definition, by time when the food diaries were completed (season and weekday vs. weekend), and by survey* NHANES-III CSFII Season Type of day No. 25% kcal after 7 PM 50% kcal after 7 PM Any eating after 11 PM No. 25% kcal after 7 PM 50% kcal after 7 PM Any eating after 11 PM Winter Weekdays (32.0 to 42.2) 11.3 (8.5 to 14.0) 11.5 (8.3 to 14.7) (24.6 to 30.9) 8.2 (6.3 to 10.1) 10.2 (7.2 to 13.2) Spring (26.9 to 35.5) 10.3 (7.8 to 12.9) 9.9 (7.3 to 12.5) (25.7 to 31.6) 9.9 (8.1 to 11.6) 8.0 (6.5 to 9.6) Summer (31.4 to 38.7) 12.2 (10.2 to 14.2) 12.9 (10.7 to 15.1) (30.9 to 35.8) 11.2 (9.4 to 12.9) 9.2 (7.3 to 11.2) Autumn (27.9 to 32.4) 9.2 (7.3 to 11.2) 11.6 (9.1 to 14.1) (26.2 to 32.5) 9.4 (7.9 to 10.9) 8.3 (6.5 to 10.2) Winter Weekends (38.6 to 47.2) 16.6 (13.6 to 19.5) 12.7 (10.1 to 15.3) (32.9 to 43.1) 14.4 (11.2 to 17.6) 13.0 (9.8 to 16.1) Spring (34.3 to 41.5) 16.6 (13.0 to 20.2) 11.4 (7.6 to 15.2) (27.2 to 36.5) 12.8 (10.0 to 15.5) 10.3 (7.8 to 12.7) Summer (36.2 to 43.9) 14.3 (12.7 to 15.9) 15.7 (12.8 to 18.7) (30.0 to 39.6) 15.3 (11.8 to 18.7) 9.5 (7.0 to 12.1) Autumn (33.5 to 44.4) 13.8 (10.9 to 16.7) 11.8 (9.2 to 14.3) (29.8 to 38.3) 12.3 (9.6 to 14.9) 9.4 (7.0 to 11.9) * Respondents were included in the analysis if they were ages 13 or older and or non- or, participated in the Mobile Exam Center exam, and provided complete, reliable food diary data (NHANES-III) or provided food diary data during Day 1 (CSFII). Number of respondents (not weighted). Percentage estimates and 95% CI are weighted and take stratification and clustering into account. OBESITY Vol. 14 No. 1 January
7 Table 4. NHANES-III: estimated percentage (95% CI) of individuals exhibiting night eating based on consuming 25% of their total calories between 7:00 PM and 4:59 AM, by gender, age group, and race/ethnicity* 25% kcal after 7 PM Gender Age group Men Adolescents (13 to 17 years old) 45.0 (36.9 to 53.1) 48.3 (42.5 to 54.0) 33.1 (23.6 to 42.5) Young adults (18 to 30 years old) 45.4 (40.0 to 50.9) 55.3 (52.1 to 58.5) 40.7 (35.0 to 46.5) Adults (31 to 64 years old) 36.4 (33.0 to 39.8) 50.2 (46.7 to 53.6) 40.2 (34.3 to 46.0) Elderly (65 years) 19.5 (16.1 to 22.9) 24.1 (18.9 to 29.2) 19.7 (7.1 to 32.4) Women Adolescents (13 to 17 years old) 41.9 (34.7 to 49.0) 49.7 (42.6 to 56.8) 39.0 (25.0 to 53.0) Young adults (18 to 30 years old) 39.7 (36.4 to 42.9) 46.8 (42.1 to 51.5) 36.6 (28.6 to 44.6) Adults (31 to 64 years old) 33.2 (30.2 to 36.2) 43.0 (39.6 to 46.5) 34.5 (29.0 to 39.9) Elderly (65 years) 15.0 (13.1 to 16.9) 20.5 (16.3 to 24.8) 15.0 (7.0 to 22.9) All estimates are weighted and take account of stratification and clustering in the survey design. * Respondents were included in the analysis if they were ages 13 or older and or non- or, participated in the Mobile Exam Center exam, and provided complete, reliable food diary data (NHANES-III). Estimate may not be statistically reliable based on sample size, size of percentage estimate, and average design effects. 11 PM, respectively [Wald 2 (1) 8.51 to 16.78, p 0.004]. Also in both surveys, for all three definitions of night eating, elderly individuals were 2.7 to 3.6 times less likely to exhibit night eating, compared with individuals in the other age groups [Wald 2 (1) to , p ]. In NHANES-III, and elderly individuals exhibited especially large decreases in the probability of eating after 11 PM compared with the other age groups, as exhibited by significant elderly-by- and elderly-by- interactions [Wald 2 (1) 9.65/21.38, p ]. There were no main effects of season nor were there any other significant interactions in either survey. Table 5. CSFII: Estimated percentage (95% CI) of individuals exhibiting night eating based on consuming 25% of their total calories between 7:00 PM and 4:59 AM, by gender, age group, and race/ethnicity* 25% kcal after 7 PM Gender Age group Men Adolescents (13 to 17 years old) 32.9 (28.8 to 37.0) 34.3 (20.0 to 48.7) 35.8 (20.7 to 51.0) Young adults (18 to 30 years old) 44.3 (40.5 to 48.2) 45.9 (35.4 to 56.3) 39.5 (30.7 to 48.3) Adults (31 to 64 years old) 34.2 (31.4 to 37.0) 36.8 (27.0 to 46.6) 36.6 (27.9 to 45.4) Elderly (65 years) 14.4 (12.2 to 16.6) 15.4 (7.8 to 23.0) 12.4 (0.5 to 24.2) Women Adolescents (13 to 17 years old) 31.1 (26.4 to 35.8) 39.8 (29.3 to 50.4) 33.7 (22.7 to 44.7) Young adults (18 to 30 years old) 39.3 (35.7 to 42.9) 44.9 (30.9 to 58.9) 31.1 (23.5 to 38.8) Adults (31 to 64 years old) 28.4 (26.3 to 30.6) 31.3 (27.1 to 35.5) 27.6 (20.6 to 34.6) Elderly (65 years) 13.9 (11.5 to 16.3) 15.1 (6.9 to 23.4) 13.1 (4.8 to 21.5) All estimates are weighted and take account of stratification and clustering in the survey design. * Respondents were included in the analysis if they were ages 13 or older and or non- or, participated in the Mobile Exam Center exam, and provided food diary data during Day 1 (CSFII). Estimate may not be statistically reliable based on sample size, size of estimate, and average design effects. 144 OBESITY Vol. 14 No. 1 January 2006
8 Table 4. (continued) 50% kcal after 7 PM Any eating after 11 PM 15.3 (10.7 to 19.8) 20.0 (14.9 to 25.1) 8.0 (5.2 to 10.7) 13.8 (8.4 to 19.2) 16.2 (12.0 to 20.4) 11.7 (4.2 to 19.2) 17.2 (13.4 to 21.1) 19.7 (16.0 to 23.4) 14.4 (11.2 to 17.6) 19.9 (15.3 to 24.4) 27.3 (22.8 to 31.9) 15.9 (11.5 to 20.2) 12.8 (11.0 to 14.6) 20.8 (17.4 to 24.2) 15.0 (10.3 to 19.6) 14.4 (11.6 to 17.1) 20.2 (16.8 to 23.6) 11.4 (7.9 to 14.8) 4.8 (2.9 to 6.6) 6.8 (4.6 to 9.1) 2.1 (0.5 to 3.7) 8.2 (6.2 to 10.3) 5.9 (3.4 to 8.3) 1.3 (0.3 to 2.2) 12.6 (8.2 to 16.9) 21.4 (15.5 to 27.2) 9.8 (3.0 to 16.6) 10.7 (5.4 to 15.9) 13.5 (8.9 to 18.0) 8.9 (1.3 to 16.5) 13.4 (10.8 to 16.0) 18.0 (15.5 to 20.4) 14.9 (8.0 to 21.8) 11.2 (8.5 to 13.9) 15.2 (12.5 to 17.9) 11.1 (7.8 to 14.3) 12.1 (10.0 to 14.1) 15.6 (13.7 to 17.6) 11.1 (8.1 to 14.1) 8.9 (7.0 to 10.8) 15.7 (13.2 to 18.2) 9.2 (5.7 to 12.6) 2.9 (1.8 to 3.9) 5.8 (3.4 to 8.2) 5.2 (0.0 to 10.7) 5.6 (4.0 to 7.2) 5.7 (3.0 to 8.4) 2.1 (0.7 to 3.5) Night Eating and BMI/Obesity For each definition of night eating, BMI was modeled as a function of night eating, adjusted to total daily caloric intake (throughout the entire day), gender, age group, race/ ethnicity, and all possible two-way interactions among the latter three variables. In only one case, specifically for the definition involving consuming 25% of the daily calories after 7 PM in NHANES-III, was night eating associated with BMI [t(49) 3.35, p 0.002], but the association was weak and in the direction opposite to that hypothesized: Table 5. (continued) 50% kcal after 7 PM Any eating after 11 PM 11.7 (7.9 to 15.4) 12.6 (4.2 to 20.9) 11.1 (3.7 to 18.4) 9.0 (5.8 to 12.2) 5.6 (0.0 to 11.3) 5.4 (0.0 to 11.4) 16.2 (13.1 to 19.3) 15.6 (8.0 to 23.3) 14.4 (7.9 to 20.8) 18.4 (14.8 to 22.0) 20.2 (12.3 to 28.2) 15.8 (3.3 to 28.2) 12.9 (11.4 to 14.4) 12.4 (8.6 to 16.3) 14.6 (8.8 to 20.5) 11.5 (9.7 to 13.4) 11.4 (4.6 to 18.3) 9.6 (6.4 to 12.7) 3.2 (2.2 to 4.3) 3.5 (0.0 to 7.3) 6.7 (0.0 to 16.3) 3.2 (1.5 to 4.8) 6.8 (0.3 to 13.3) 3.3 (0.5 to 6.1) 10.3 (6.8 to 13.8) 11.7 (3.9 to 19.5) 9.2 (2.4 to 16.0) 7.4 (4.9 to 9.9) 7.9 (0.0 to 15.7) 7.8 (2.3 to 13.3) 14.2 (10.6 to 17.7) 20.5 (12.4 to 28.5) 8.9 (3.4 to 14.4) 10.2 (7.7 to 12.7) 11.9 (5.5 to 18.2) 7.0 (2.8 to 11.3) 9.0 (7.6 to 10.3) 15.2 (10.1 to 20.3) 8.8 (5.0 to 12.5) 6.9 (5.3 to 8.5) 10.5 (4.0 to 17.0) 4.7 (1.6 to 7.9) 2.3 (1.2 to 3.5) 1.3 (0.0 to 3.2) 7.5 (0.5 to 14.4) 4.3 (2.8 to 5.7) 0.7 (0.0 to 1.9) 3.2 (0.0 to 7.4) OBESITY Vol. 14 No. 1 January
9 predicted BMI for night eaters was 0.44 less than that for non-night eaters (data available on request). BMI was not associated with night eating by any other definition in either survey (p 0.02). Similar models predicting the log odds of obesity revealed no association with night eating by any definition, regardless of survey (p 0.06) (data available on request). Discussion Night eating represents a necessary (but not sufficient) behavioral symptom of NES, yet, to date, no uniform operational definition has been adopted in the literature. The present study used three definitions commonly found in the literature on NES to explore demographic correlates of this behavior and its possible relation with BMI or obesity. To our knowledge, this represents the first attempt to examine the prevalence and demographic correlates and association with BMI or obesity of night eating in a representative sample of U.S. adolescents and adults. We found three main findings across the two surveys. One, as expected, the most inclusive definition (consuming 25% or more after 7 PM) produced the highest prevalence. Two, for the first two definitions (which differed by the amount of calories consumed after 7 PM, namely 25% vs. 50%), but not the third definition (which involved eating very late during the night), day of the week mattered. And three, prevalence estimates varied considerably by age group, with adolescents being most likely and elderly individuals least likely to meet the criteria for night eating. In NHANES, but not CSFII, race/ethnicity moderated the relationship between age and night eating, with and elderly individuals being especially unlikely to meet criteria compared with the other age groups. Finally, gender and race/ethnicity effects were observed only in NHANES but not in CSFII; men were more likely than women and Americans more likely than other racial/ethnic groups to meet the criteria for night eating. Our data show that, in the U.S., eating 25% or more of one s daily caloric intake after 7 PM is common. Quite possibly, in cultures where eating the evening meal occurs later in the day than in the U.S., the prevalence of night eating by this criterion would be far greater still. One approach to making the definition more applicable across different cultures is to use a time of day that is so late that in most cultures, it can be reasonably expected to be statistically abnormal to still be eating (e.g., eating after 11 PM). Another strategy that has been employed is to define night eating as eating more than a certain proportion of one s daily intake after the evening meal (for review, see ref. 2), but this approach has its own disadvantage in that the meaning of the evening meal may not be the same for different people and that some may eat a considerable amount of food over a long period of time (grazing) in the late evening or at night, rather than consuming an evening meal. As expected, eating a considerable proportion of one s daily intake after 7 PM was more commonly reported on weekends, rather than during weekdays, likely reflecting culturally normative practices of cooking more elaborate dinners for family or friends or going out to eat on the weekend, and this may mean eating later or eating more than usual. Eating out, in particular, has been shown to be correlated with eating more than when eating at home (10 12). On the other hand, eating after 11 PM was not found to be associated with day of the week, suggesting that cultural practices play less of a role in eating very late in the evening. One obvious methodological implication of this finding is that studies comparing various population groups need to ensure that comparable days are used when measuring night eating. Regarding the clinical assessment of frequency of night eating, similar to the instructions employed in the assessment of overeating, where episodes of overeating are not counted when they are culturally normative (e.g., the Thanksgiving meal), we recommend that contextual variables such as day of the week be considered. Our results regarding age effects extend earlier work with a sample of adolescents, where we found that with increasing age, night eating (by any definition) became increasingly more common (3). In the present study, night eating was most common among young adults and least common among the elderly. Pathology of a behavior is often defined in relative terms; what is developmentally expected in one group (e.g., going out late is more common in teenagers or young adults than in elderly adults) may be unusual (i.e., statistically abnormal) in another group. Whether the pathology of a behavior is clinically significant depends on its association with a meaningful validator. Different prevalence estimates by gender or ethnic group were found in only one survey (NHANES-III) and, therefore, need to be interpreted cautiously. These differences may reflect different subcultural practices such as employment patterns (e.g., working multiple jobs, shift work, etc.) or different social norms about eating (e.g., weight concerns may prompt women not to eat late in the day). Information about education and employment status is needed to better account for different prevalences. Because NHANES and CSFII differed in many ways, it is impossible to pinpoint why inconsistent results were found across the two surveys concerning gender and race/ethnicity. The regression coefficients for the gender effect were similar across surveys but did not reach significance in CSFII due to larger variance estimates (standard errors), which might be due, in part, to the smaller sample size in CSFII relative to NHANES-III. However, the regression coefficients for the effect of being in CSFII did not even approach the magnitude in NHANES-III, suggesting that factors other than sample size 146 OBESITY Vol. 14 No. 1 January 2006
10 may play a role. Effects of both race/ethnicity and gender warrant further investigation. Importantly, our results suggest that, for other correlates (e.g., age, time of day, or season), findings were quite similar across the two surveys. Unexpectedly, we found a significant inverse association between night eating and BMI for the definition of eating 25% or more after 7 PM. Individuals who reported night eating by this definition were slightly thinner than those who did not meet this night eating criterion. The analysis was adjusted for total caloric intake; hence, the difference is not due to night eaters consuming fewer calories in general. However, this effect was very small and was found in only one of the two surveys. This adds to the growing evidence that, in non-clinical samples (for review, see ref. 2), night eaters are less likely than others are to be obese. Several limitations need to be considered. Because neither survey collected food diaries for consecutive days, night eating was defined using single day records. Hence, it was not possible to use a more restrictive definition of night eating, requiring the behavior to occur on a recurrent basis. We cannot rule out that a more regular pattern of night eating might be associated with elevated BMI or obesity. Also, the two surveys differ in several respects, suggesting that greater confidence should be placed in the results that were consistent across surveys. The survey data did not permit examining specifically whether participants experienced nocturnal eating episodes involving awakening during the night and eating before returning to sleep, nor did they include information about the participants sleeping patterns or sleep disorder symptoms. A study comparing a small sample of obese individuals with NES and obese individuals without NES found that nocturnal eating was unique to NES and concluded that NES involved disturbances of sleep and eating (13). Finally, the data are crosssectional, and prospective studies are needed to further determine the clinical significance of night eating. In conclusion, our findings suggest that researchers need to consider day of the week, age, and possibly gender and race/ethnicity when examining population differences in night eating. Studies are needed to identify critical thresholds (which may well vary by culture) for determining clinical significance of night eating. Our cross-sectional data do not support a link between the behavior of night eating and obesity in community samples. Future studies need to explore whether adding a frequency criterion (e.g., twice weekly or more) or other behavioral or affective components to identify a syndrome of NES will result in a significant association with obesity and, if so, will identify the mechanisms that mediate this association. Acknowledgment This work was supported, in part, by the National Heart, Lung, and Blood Institute (Grant R01-HL/DK71122). References 1. Stunkard AJ, Grace WJ, Wolff HG. The night-eating syndrome; a pattern of food intake among certain obese patients. Am J Med. 1955;19: de Zwaan M, Burgard M, Schenck C, Mitchell J. Night time eating: a review of the literature. Eur Eat Disord Rev. 2003;11: Striegel-Moore RH, Thompson D, Franko DL, et al. Definitions of night eating in adolescent girls. Obes Res. 2004; 12: Stunkard A, Berkowitz R, Wadden T, et al. Binge eating disorder and the night-eating syndrome. Int J Obes Relat Metab Disord. 1996;20: Adami GF, Meneghelli A, Scopinaro N. Night eating and binge eating disorder in obese patients. Int J Eat Disord. 1999;25: Haines PS, Hama MY, Guilkey DK, Popkin BM. Weekend eating in the United States is linked with greater energy, fat, and alcohol intake. Obes Res. 2003;11: National Center for Health Statistics. Plan and operation of the Third National Health and Nutrition Examination Survey, Series 1: programs and collection procedures. Vital Health Stat ;Jul: United States Department of Agriculture Continuing Survey of Food Intakes by Individuals and Diet and Health Knowledge Survey. CD-ROM including survey documentation. Available from National Technical Information Service, Springfield, VA (NTIS Accession No. PB ); Kraemer HC, Blasey CM. Centring in regression analyses: a strategy to prevent errors in statistical inference. Int J Methods Psychiatr Res. 2004;13: Kant AK, Graubard BI. Eating out in America, : trends and nutritional correlates. Prev Med. 2004;38: Thompson OM, Ballew C, Resnicow K, et al. Food purchased away from home as a predictor of change in BMI z-score among girls. Int J Obes Relat Metab Disord. 2004;28: Diliberti N, Bordi PL, Conklin MT, Roe LS, Rolls BJ. Increased portion size leads to increased energy intake in a restaurant meal. Obes Res. 2004;12: Reardon JP, Ringel BL, Dinges DF, et al. Circadian eating and sleeping patterns in the night eating syndrome. Obes Res. 2004;12: OBESITY Vol. 14 No. 1 January
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