Worse sleep health predicts less frequent breakfast consumption among adolescents in a micro-longitudinal analysis

Abstract

Background: Poor self-reported sleep health has been linked to not consuming breakfast in adolescents, but it is unknown whether poor sleep measured objectively predicts next-day breakfast consumption within adolescents. We investigated within- and between-person associations of objectively measured sleep dimensions and subjective sleep quality with adolescent breakfast consumption.

Methods: Data were collected from a micro-longitudinal substudy of the Year 15 wave of the Fragile Families and Child Wellbeing Study (n = 590). Adolescents wore an actigraphy device and completed daily diaries for ~ 1 week (M ± SD = 5.6 ± 1.4 nights per adolescent, range: 3-9), where they rated their sleep quality and reported whether they had eaten breakfast that day, with no specific definition of breakfast provided (M ± SD = 5.5 ± 1.4 days per adolescent, range: 3-9). Separate mixed models assessed whether actigraphy-measured sleep duration (linear and quadratic, sleep duration x sleep duration), timing, maintenance efficiency, and subjective quality predicted odds of breakfast consumption both within and between adolescents. Variability of sleep duration and timing (standard deviation per person), sleep regularity index (SRI), and social jetlag were tested as additional between-person predictors. Analyses with predictors other than sleep duration were adjusted for sleep duration.

Results: Following nights when adolescents had shorter or longer sleep duration (p = .005; curvilinear association), later sleep onset, or later sleep midpoint (both p = .025) than their own usual, they had lower odds of consuming breakfast the next day (within-person associations). Adolescents who on average had later sleep onset (p = .013) or midpoint (p = .013) or who reported lower sleep quality (p = .011) had lower average odds of consuming breakfast (between-person associations). Adolescents with greater variability of sleep duration (p = .005), midpoint (p = .004), or offset (p < .001) had lower average odds of consuming breakfast (between-person associations). Sleep maintenance efficiency (within or between adolescents), SRI, and social jetlag were not associated with breakfast consumption (all p > .10).

Conclusions: Multiple dimensions of sleep health are associated with breakfast consumption, both within and between adolescents. Poor sleep and dietary behaviors in adolescence may negatively impact future metabolic health.

Keywords: Actigraphy; Adolescence; Breakfast; Diary; Sleep duration; Sleep maintenance efficiency; Sleep timing; Sleep variability; Social jetlag; Subjective sleep quality.

Fig. 1

Associations of deviation from adolescent’s mean sleep duration (A), sleep onset (B), and sleep midpoint (C) (each in hours) and probability of next-day breakfast consumption (within-person associations) in three separate mixed models. Sleep duration model includes both linear (sleep duration) and quadratic (sleep duration²) effects (only the significant quadratic effect is depicted). Sleep onset and sleep midpoint models adjust for sleep duration (linear and quadratic, sleep duration²). The mean number of valid actigraphy nights was 5.6 ± 1.4 (range: 3–9; IQR 5–7) and the mean number of breakfast reports was 5.5 ± 1.4 (range: 3–9; IQR 4–7) per adolescent. All models adjust for demographic/household covariates: school day, boredom, loneliness, happiness, birth sex, race/ethnicity, household income, body mass index percentile, and depressive symptoms. Shaded bands depict 95% confidence interval of probability of breakfast consumption predicted from each sleep measure

Fig. 2

Associations of average sleep onset in hours (A), sleep midpoint in hours (B), and subjective sleep quality (C) per adolescent with average probability of breakfast consumption (between-person associations) in three separate linear mixed models. Each effect is represented by the adolescent’s mean across all time points. Sleep onset and midpoint were centered around midnight (0:00). Subjective sleep quality ranges from 0 (very bad) to 3 (very good). The mean number of valid actigraphy nights was 5.6 ± 1.4 (range: 3–9; IQR 5–7) and the mean number of breakfast reports was 5.5 ± 1.4 (range: 3–9; IQR 4–7) per adolescent. All models adjust for sleep duration (linear and quadratic, sleep duration²) and demographic/household covariates: school day, boredom, loneliness, happiness, birth sex, race/ethnicity, household income, body mass index percentile, and depressive symptoms. Shaded bands depict 95% confidence interval of probability of breakfast consumption predicted from each sleep measure

Fig. 3

Associations of variability in sleep duration (A), sleep midpoint (B), and sleep offset (C) (calculated as standard deviation, SD, of hours) per adolescent with average probability of breakfast consumption (between-person associations) in three separate linear mixed models. Each predictor is represented by each adolescent’s SD across all time points. The mean number of valid actigraphy nights was 5.6 ± 1.4 (range: 3–9; IQR 5–7) and the mean number of breakfast reports was 5.5 ± 1.4 (range: 3–9; IQR 4–7) per adolescent. All models adjust for sleep duration (linear and quadratic, sleep duration²) and demographic/household covariates: school day, boredom, loneliness, happiness, birth sex, race/ethnicity, household income, body mass index percentile, and depressive symptoms. Shaded bands depict 95% confidence interval of probability of breakfast consumption predicted from each sleep measure