BACKGROUND: Several cross-sectional studies reported that heavier children eat breakfast less often. However, no longitudinal studies have addressed whether skipping breakfast leads to excessive weight gain.
OBJECTIVE: To investigate whether skipping breakfast was prospectively associated with changes in body fatness.
METHODS: A cohort of >14?000 boys and girls from all over the US, 9- to 14-y-old in 1996, returned annual mailed questionnaires (1996–1999) for the Growing Up Today Study. We analyzed change in body mass index (BMI; kg/m2) over three 1-y periods among children who reported breakfast frequency.
RESULTS: Children who reported that they never eat breakfast had lower energy intakes than those who eat breakfast nearly every day. Children who were more physically active reported higher energy intakes, as did those who reported more time watching television/videos and playing videogames. Like previous studies, skipping breakfast was associated with overweight, cross-sectionally. However, overweight children who never ate breakfast lost BMI over the following year compared to overweight children who ate breakfast nearly every day (boys: −0.66?kg/m2 (s.e.=0.22); girls: −0.50?kg/m2 (s.e.=0.14)). But normal weight children who never ate breakfast gained weight relative to peers who ate breakfast nearly every day (boys: +0.21?kg/m2 (s.e.=0.13); girls: +0.08?kg/m2 (s.e.=0.05)). Breakfast frequency was positively correlated with self-reported quality of schoolwork.
CONCLUSIONS: Overweight children who never eat breakfast may lose body fat, but normal weight children do not. Since numerous studies link skipping breakfast to poorer academics, children should be encouraged to eat breakfast.
Numerous studies have documented the increase over recent decades in the prevalence of childhood obesity1,2,3,4,5,6,7,8 and the associated health and social consequences.3,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25 This rapid increase implicates environmental factors.26,27,28 During these decades of rising obesity prevalence, physical activity among adolescents has declined, while time spent in sedentary activities such as watching television and playing computer games has increased.5,6 Furthermore, in nationally representative samples of US adolescents, breakfast consumption declined from 1965 to 1991,29 a concern since eating breakfast is associated with higher overall diet quality.30,31,32
Cross-sectional studies have consistently reported positive associations between measures of adiposity in children and skipping breakfast.33,34,35,36,37 A recent analysis of US adults who maintained substantial weight losses for at least 1?y suggested that eating breakfast may be an important factor in weight-loss maintenance.38 However, a randomized clinical trial of obese women found that those who stopped eating breakfast lost the most weight, although those who ate breakfast had lower dietary fat and impulsive snack intakes.39 There are no longitudinal studies of adolescents that evaluate whether, as implied by cross-sectional studies, skipping breakfast may result in excessive body weight increases over time. Using data from the Growing Up Today Study (GUTS), we prospectively analyzed the relationship between breakfast frequency and subsequent changes in body mass index (BMI).
Established in the fall of 1996, the ongoing Growing Up Today Study consists of 16?882 children, residing in 50 states, who are offspring of Nurses’ Health Study II participants.40 The Study was designed to assess prospectively the determinants of adolescent weight changes, including dietary factors, physical activity and inactivity. Human Subjects Committees at the Harvard School of Public Health and Brigham and Women's Hospital approved the Study. In 1996, letters were sent to 34?174 mothers, explaining the goals of the new study and requesting consent for their children to participate; 18?526 mothers returned the consent form, providing the name, age, gender, and mailing address of 26?765 children. Introductory letters and gender-specific questionnaires were then mailed directly to children whose mothers granted consent. These letters assured potential participants that the information they provided would remain confidential. The baseline (1996) sample included 9039 girls (68% response rate) and 7843 boys (58% response rate) who returned completed questionnaires, thereby assenting to participate. Some of these respondents were outside the 9- to 14-y age range at the time of questionnaire completion, leaving us with 8980 girls and 7791 boys at baseline. In 1997, 1998, and 1999, we sent subjects follow-up questionnaires to update all information. Response rates to at least one of these follow-up surveys were 94.1% for girls and 89.5% for boys. The present longitudinal analyses were restricted to 8128 girls and 6458 boys who were of age 9–17?y upon returning two or more consecutive annual surveys, or 1996 and 1999 surveys.
Body mass index
Children self-reported their height and weight annually on our questionnaire which provided specific measuring instructions but suggested they ask someone to help; since their mothers are nurses, assistance is available. A previous study reported high validity for self-reported heights and weights in adolescents.41 We assessed adiposity by computing BMI (=weight/height2 (kg/m2)). The International Obesity Task Force supports the use of BMI to assess fatness in children and adolescents.42 Roche et al43 demonstrated that childhood BMI is related to other measures of adiposity that were not feasible to collect on our cohort. A recent study44 supported the validity of BMI computed from self-reported height and weight, with a correlation of 0.92 between BMI computed from youth (grades 7–12) self-reports and measured values.
We estimated our primary outcome, 1?y change in adiposity, by BMI1997−BMI1996, BMI1998−BMI1997, and BMI1999−BMI1998, dividing each by the exact time interval between the pair of measurements. We also estimated 3-y change in adiposity, BMI1999−BMI1996, divided by the exact time interval.
Before computing BMIs, we excluded any height that was more than 3 standard deviations (s.d.) beyond the gender–age-specific mean height (0.46% of heights excluded), and any 1?y height change that declined by more than 1?in or increased by >3?s.d. above the mean change (1.94% excluded). We further excluded any BMI <12.0?kg/m2 as a biological lower limit (clinical opinion), and any BMI >3s.d. above or below the gender- and age-specific mean (in log (BMI) scale) (0.89% excluded). As all BMI changes computed from the remaining data represented realistic weight changes, there were no further exclusions: 7545 girls and 5962 boys provided annual BMI-change data.
We grouped together children, using their BMI at the earlier year of each 1-y interval, who were above the Center for Disease Prevention and Health Promotion (CDC) gender- and age-specific (to the month) 85th percentile for BMI; children above the 85% were at risk of overweight (85–95%) or were overweight (>95th percentile).45 Similarly, we grouped together those below the 10th percentile. We refer to children whose BMI exceeded the 85th as ‘overweight’, those below the 10th as ‘lean’, and those between the 10th and 85th as ‘normal weight’. The CDC standards also provided BMI Z-scores.
In 1996, 1997, and 1998, we asked ‘How many times each week (including weekdays and weekends) do you eat breakfast?’ Response categories were never (or almost never), 1–2 times per week, 3–4 times per week, and five or more times per week.
We developed a physical activity questionnaire for youth that asked the participants to recall the typical number of hours per week, within each season over the past year, during which they participated in 17 activities and team sports, outside of gym class. Response categories ranged from 0 to 10+ h/week. We computed each child's typical hours per day of physical activity over the entire year. Assessments of an earlier nonseasonal version of this instrument found that estimates of total activity were moderately reproducible and reasonably correlated with cardio-respiratory fitness,46 and another validation study reported a correlation of r=0.80 between self-reports and 24-h recalls.47 We developed the seasonal version used in this paper to further improve reliability and validity.48
Estimates of total physical activity that exceeded 40?h/week were deemed implausible and excluded (3.60%) from our longitudinal analyses.
Another series of questions were designed to measure weekly hours of recreational inactivity: ‘watching TV’, ‘watching videos or VCR’, and ‘Nintendo/Sega/computer games (not homework)’. For each of these three items, children reported their usual number of h/week, separate for weekdays and for weekends, from options ranging from zero to 31+ h. From this information, we computed each child's hours of recreational inactivity per day. Moderate validity has been reported for recalled total inactivity from a similar instrument.47 We excluded totals exceeding 80?h/week (0.89%).
Members of our group designed a self-administered semiquantitative food frequency questionnaire (FFQ) for older children and adolescents which is inexpensive and easy to administer to large populations.49 This FFQ for youth has been shown to be valid50 and reproducible,49 comparable in performance to a similar adult FFQ. It included questions regarding frequency of intake of 132 specific food items over the past year. Using nutrient composition databases, food portion sizes specific to this age range, and each child's reported intake of each food and fat level of relevant food items (such as milk), we estimated total energy intake (kcal/day). We excluded intakes <500 or >5000?kcal/day (0.53% excluded). Some of our models adjusted for total energy intake and cereal intake (hot and cold), reported at the same time as breakfast frequency, since cereal (per calorie and per cost) may provide a healthy breakfast.51
At baseline, children reported their race/ethnic group by marking all that apply from six options. We assigned each child to one of five race/ethnic groups following US Census definitions, except that we retained Asians as a separate group rather than pooled with ‘Other’.1
Tanner stage, menarche, age
Each year children reported their Tanner maturation stage, a validated self-rating52 of sexual maturity with five stages of pubic hair development, and girls reported whether/when their menstrual periods began. We derived a menstrual history variable having three categories: premenarche before and after the 1-y BMI change, periods began during the interval, and postmenarche both years. We computed each child's age from birthdate and date of questionnaire return.
On the 1996–1999 questionnaires, we included questions adapted from the Harter Self-Perception Profile for Children.53 We asked the participants how much this statement describes them ‘Some kids feel like they are very good at their school work’, to which they responded ‘Really true for me’, ‘Sort of true for me’, or ‘Not true for me’. About 55% responded that it was ‘Really true’ for them. As very few (4%) said it was ‘Not true’, we combined the two categories ‘Sort of true’ and ‘Not true’.
To assess the potential for bias, we compared the baseline (1996) values of age, BMI, breakfast frequency, and energy intake of those children who returned surveys in consecutive follow-up years with those who did not. The differences were small (see Results).
The cross-sectional association between baseline breakfast frequency and BMI was summarized by tabulating the percent of children in each breakfast frequency group who were lean, normal, or overweight. We fitted a regression model of baseline energy intake on breakfast frequency and body weight, with age, Tanner stage, race, menarche, physical activity and inactivity included.
To assess prospectively the effect of breakfast frequency at time t on change in BMI from time t to t+1 years, we used mixed linear regression models,54 estimated by SAS proc mixed,55 with annual change in BMI as the continuous outcome variable. We used mixed models because each child had one, two, or three BMI changes; the assumption of independent observations required by ordinary linear regression models was thus not satisfied. To account for changes in BMI that typically occur during growth and maturation, all models included height growth from year t to t+1, menstrual history for years t and t+1, Tanner stage, age, and BMI Z-score at year t.26,56,57,58,59 We used continuous exact age with linear and quadratic terms. All models included race, as well as activity and inactivity during the year of BMI change.60 Some models further included energy intake and (hot and cold) cereal intake. All models obtained separate estimates of breakfast effects for lean, normal, and overweight children. The effects of change in breakfast frequency were estimated from a model with an interaction between breakfast frequency at time t and time t+1. A separate analysis related 3-y (1996–1999) change in BMI to mean (3-y) breakfast frequency, adjusting for 3-y height growth, means and changes in activity, in inactivity, and in energy intake.
Logistic models, estimated using generalized estimating equations61 and SAS proc genmod55 to account for up to three observations per child, related academic performance (very good at schoolwork, or not) at year t+1 to breakfast frequency at years t and t+1, adjusting for race, age, Tanner stage, and academic performance at year t.
The participants were mostly white children (94.7% white, not Hispanic), with only 0.9% black children (not Hispanic), 1.5% Hispanic, 1.5% Asian, and 1.4% other (including Native American); this distribution reflects the small minority representation of their mothers who are participants in the Nurses’ Health Study II.40
Children not included in our longitudinal analyses were slightly older (girls by 0.3?y; boys by 0.46?y), and at baseline ate breakfast slightly less often (girls by 0.39 days/week; boys by 0.18 days/week) (age-adjusted P<0.05), but they were similar in age-adjusted BMI and total energy intake.
At baseline, 23.2% of the boys and 17.4% of the girls were overweight (>85th percentile BMI), while 7.2% of the boys and 8.6% of the girls were very lean (<10th percentile). Children who never ate breakfast were heavier (26.4% of boy never-eaters were overweight and 25.3% of girl never-eaters were overweight) than those who ate breakfast nearly every day (21.2% of boys and 15.8% of girls were overweight) (Table 1). As suggested by the mean energy intakes in Table 1, normal weight and overweight children who never ate breakfast reported lower (by 1.3?MJ) energy intakes than those (of similar weight) who ate breakfast daily (P<0.0001, adjusted for age, Tanner stage, race, menarche, physical activity and inactivity). More physically active children also reported higher energy intakes: 0.49 (s.e.=0.03) MJ/day for boys, 0.32 (s.e.=0.02) MJ/day for girls per hour daily activity. Contrary to our expectations, more inactivity (TV/videos/videogames) time was also associated with higher energy intakes: 0.08 (0.02) MJ/day for boys and 0.12 (0.01) MJ/day for girls, per hour inactivity.
Our prospective model, which related BMI changes over 1?y to the breakfast frequency at the start of each year, provided contrasting findings for children who were overweight vs those who were normal weight (Table 2). For overweight adolescents, those who never ate breakfast had smaller BMI increases over the subsequent year relative to those who ate breakfast daily (−0.66?kg/m2 for boys and −0.50?kg/m2 for girls). The linear trend for breakfast frequency was significant in overweight girls (P=0.049). For normal weight boys and girls, there was a suggestion, although not statistically significant, that those who never ate breakfast gained weight relative to daily eaters. When we further adjusted for daily cereal (hot and cold) intake and total energy intake, the estimated effects for breakfast were hardly changed (Table 2). However, we do not know when the cereal was consumed, and some children who never ate breakfast ate as many as four bowls daily.
Our analysis relating change in breakfast frequency to BMI change did not include BMI changes from 1998 to 1999, because the breakfast question was dropped from our survey in 1999. Normal weight boys who never ate breakfast, either before or after a change in BMI, had significantly larger (by +0.62?kg/m2) BMI gains than those who ate breakfast daily both years, but there were no consistent patterns for normal weight children (Table 3). Overweight boys and girls who never ate breakfast (before or after) had significantly smaller BMI increases (boys −1.12?kg/m2; girls −0.67?kg/m2) relative to overweight boys and girls who ate breakfast daily both years.
Our analysis of 3-y BMI change (from 1996 to 1999) suggested that normal weight girls (1996) who ate breakfast 1–2 days/week (on average during follow-up) gained more weight (+0.072 (0.037) kg/m2) than peers who ate daily. However, overweight boys and girls who skipped breakfast gained less weight than daily eaters (boys never-eaters: −0.425 (0.234), 3–4 days/week: −0.139 (0.071); girls who ate 1–2 days/week: −0.114 (0.067) and 3–4 days/week: −0.177 (0.056)).
For these analyses, we defined overweight as any BMI above the 85th percentile,45 the widely accepted cutpoint. We were concerned that for children below the 85th percentile, the effect of skipping breakfast on weight change might be more similar to overweight than to normal weight children, for whom we observed opposite effects. So we estimated the effects of skipping breakfast separately for smaller groups of children (75–80%/80–85%/85–90%, etc), which confirmed that the 85th percentile was an appropriate cutpoint for this work.
The prospective effect of skipping breakfast on self-reported academic performance 1?y later is shown in Figure 1. Boys who never ate breakfast were less likely (RR=0.68; 95% CI: 0.53–0.89) to report a year later that they did very well at their schoolwork, and similarly for girls (RR=0.73; 95% CI: 0.62–0.86). Since considerable research demonstrates that the beneficial effects of eating breakfast are quite immediate,62,63,64,65 we then further included in the model breakfast reported the same time as schoolwork. The previous-year breakfast was no longer important and, instead, current-year breakfast was significantly beneficial (RR=0.66 (CI: 0.48–0.91), trend P=0.0004 for boys; RR=0.55 (CI: 0.44–0.67), trend P=0.0001 for girls).
Earlier cross-sectional studies reported that heavier children eat breakfast less frequently than do leaner children, and when they do eat breakfast, heavier children, compared to leaner children, have lower energy intakes and fewer nutrients supplied by it.33,34,35,36,37 Children who skipped breakfast had higher daily percentage of energy from fat and lower intakes of energy, protein, vitamins, and minerals,32 consumed snacks that were higher in fat, and had higher plasma total cholesterol levels.31 Also, adolescents who ate less cereal and milk had higher percentage of body fat.66 Thus, it has been widely assumed that skipping breakfast may lead to excessive weight gains. A cross-sectional analysis of our data similarly found that children who skipped breakfast were heavier. However, our longitudinal analyses of overweight adolescents found that skipping breakfast was instead associated with relative BMI declines over the following year. In contrast, among normal weight adolescents there was a tendency, although not statistically significant, to gain weight after skipping breakfast. Including total energy and cereal (hot and cold) in our models produced little change in estimated effects of breakfast on weight change. This may suggest some alternate pathway between breakfast and weight change, rather than through total energy or healthy breakfast foods, or may simply reflect the difficulty of reporting dietary intakes compared to reporting breakfast frequency. It is not apparent why normal weight and overweight children might respond differently to skipping breakfast. Differences in resting energy expenditure (REE) in overweight and normal weight children may be involved, and some adult studies suggest that REE declines in individuals as they lose weight.
We are not aware of other longitudinal studies of breakfast and weight change in children. A randomized clinical trial of 52 obese women reported that women who initially ate breakfast lost more weight by subsequently skipping breakfast rather than continuing to eat breakfast, but the opposite was found among those who initially skipped breakfast.39
Our children who reported never eating breakfast also reported lower daily energy intakes. Higher energy intakes were related to more time in physical activity, and also to more inactivity time such as TV viewing. Thus, the role of inactivity in promoting obesity appears to involve increased energy intake as well as reduced energy expenditure.67,68 A recent study found that computer use may be displacing leisure-time physical activity in students.69
Our children who skipped breakfast were also less likely to report that they were doing very well in school, either at the same time or a year later. This is consistent with a large educational literature (including randomized controlled trials and assessments of the School Breakfast Program) on the benefits of breakfast, which include increases in standardized test scores and reductions in psychosocial problems, absenteeism, and tardiness.62,63,64,65,70,71,72,73
Among US adults, nearly as many reported eating high-fat desserts (15.1%) and eggs (15.3%) for breakfast as hot or cold cereals (21.8%).74 We did attempt to perform an in-depth analysis of specific breakfast foods, but because some children who never ate breakfast reported eating over four servings per day of traditional breakfast foods (such as cereal, bagels, English muffins), and some who ate breakfast daily reported none, it was not feasible to address the quality of the foods consumed at breakfast.
A major strength of our analysis was the longitudinal design, which allowed us to study changes over time in breakfast frequency and in BMI, while accounting for growth and maturation. BMI typically goes up from year to year among children in this age range, and we took these changes into account. Although our observational study cannot prove causation, evidence that links breakfast frequency to change over time in BMI is stronger than evidence from cross-sectional studies. However, residual and unmeasured confounding are still possible in spite of our extensive control for many important factors. Another limitation of our study was the necessity to collect data on youth by self-report on mailed questionnaires, but with our large geographically dispersed cohort, alternatives were not feasible. The impact of the resulting measurement error, if random, should be to bias estimates of effects toward the null, explaining why many of our estimates, although statistically significant, were quite small. Even if heavier children tended to under-report their weights or their energy intakes, because our analyses utilize 1-y BMI changes and compares overweight children to other overweight children (of different breakfast frequencies), the impact of any such biases should be diminished. Although our assessment of adiposity, using only height and weight, was crude, more reliable body composition techniques75 are not feasible for large epidemiologic studies. Although self-reported BMI has been validated among adolescents,44 we are not aware of any validation studies of change over time in youth self-reported BMI. Furthermore, our assessment of physical activity, inactivity, and energy intake may be sufficiently imprecise that these factors, even though our models controlled for them, could still be responsible for the apparent effects of skipping breakfast. We have not attempted to estimate duration or intensity of each physical activity session, which may be related to weight change, and the wide range of food portion sizes hinders accurate estimation of total energy intake.
Although we cannot claim that our cohort of nurses’ offspring is representative of US children, the associations among factors within our cohort should still be internally valid. However, the frequency of eating breakfast in our cohort was reasonably consistent with national data. In the Continuing Survey of Food Intakes by Individuals,76 1989–1991, 92.8% of 6- to 11-y-old males and 95.6% of females reported eating breakfast the previous day, and 79.4% of 12- to 19-y old males and 73.8% of females did so, mirroring the sex-and-age trends in our study. By eighth grade, 85% of a cohort in Minnesota ate breakfast, down from 99% in third grade.77 But 16?y olds in a New York City high school with a large minority population skipped breakfast (at least 3 days in past week) more often (58%) than our 16?y olds.37 Among New Orleans ninth graders providing a single 24-h recall, 23% of females and 14% of males skipped breakfast.32 In a study of 13?y olds in Australia, the most common reasons provided (telephone surveys) for skipping breakfast were lack of time or not being hungry in the morning.78
The Council on Scientific Affairs from the American Medical Association supports prevention of obesity as critical for adults, and for weight loss they support a nutritionally balanced, low-energy diet while increasing energy expenditure through regular physical exercise.79 Similar recommendations have been made for children.80 A review of studies of weight loss and prevention of abnormal weight gain in youth did note some strategies that provided long-term successes.7 Our data suggested that children who skipped breakfast had lower daily energy intakes. However, normal weight children who never ate breakfast tended to gain weight. Overweight children who skip breakfast might lose weight, but alternative methods of reducing energy intake are preferred given the well-documented adverse effects of skipping breakfast on academic performance.
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We are grateful to Karen Corsano, Gary Chase, Gideon Aweh, and our other colleagues in the Growing Up Today Study Research Group. We are especially grateful to the children (and their mothers) for careful completion of the questionnaires.Funded by Grant DK46834 from NIH, an SIPII grant from CDC, a grant from the Boston Obesity Nutrition Research Center (P30 DK46200), research grant, 43-3AEM-0-80074 from the Economic Research Service, US Department of Agriculture, and in part by Kelloggs.
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