Changes in eating patterns in response to chronic insufficient sleep and their associations with diet quality: a randomized trial

STUDY OBJECTIVES

Insufficient sleep leads to overconsumption, but the factors contributing to this effect are poorly understood. Therefore, we assessed the influence of prolonged curtailment of sleep on free-living eating patterns linked with overconsumption and explored associations of these eating patterns with diet quality under different sleep conditions.

METHODS

Sixty-five adults (47 females) participated in outpatient randomized crossover studies with two 6-week conditions: adequate sleep (7-9 h/night) and sleep restriction (-1.5 h/night relative to screening). Food records were collected over 3 nonconsecutive days, from which we ascertained data on eating frequency, midpoint, and window and intakes of energy and nutrients. Linear mixed models were used to assess the impact of sleep condition on change in eating pattern (sleep × week interaction) and the relation between eating patterns and dietary intakes (sleep × eating pattern interaction).

RESULTS

Sleep condition impacted the change in eating frequency across weeks, with eating frequency increasing in sleep restriction relative to adequate sleep (β = 0.3 ± 0.1; P = .046). Across conditions, eating more frequently tended to relate to higher energy intakes (β = 60.5 ± 34.6; P = .082). Sleep also influenced the relation of variability in eating midpoint with intakes of saturated fat (β = 6.0 ± 2.1; P = .005), polyunsaturated fat (β = -3.9 ± 2.0; P = .051), and added sugar (β = 17.3 ± 6.2; P = .006), with greater midpoint variability associated with more adverse changes in these diet quality components in sleep restriction vs adequate sleep.

CONCLUSIONS

Chronic short sleep increases eating frequency and adversely influences associations of variability in meal timing with components of diet quality. These findings help to explain how short sleep leads to overconsumption and obesity.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: Impact of Sleep Restriction in Women; URL: https://clinicaltrials.gov/ct2/show/NCT02835261; Identifier: NCT02835261 and Name: Impact of Sleep Restriction on Performance in Adults; URL: https://clinicaltrials.gov/ct2/show/NCT02960776; Identifier: NCT02960776.

CITATION

Barragán R, Zuraikat FM, Tam V, RoyChoudhury A, St-Onge M-P. Changes in eating patterns in response to chronic insufficient sleep and their associations with diet quality: a randomized trial. J Clin Sleep Med. 2023;19(11):1867-1875.

Differences in Dietary Intake, Eating Occasion Timings and Eating Windows between Chronotypes in Adults Living with Type 2 Diabetes Mellitus

Chronotype studies investigating dietary intake, eating occasions (EO) and eating windows (EW) are sparse in people with type 2 Diabetes mellitus (T2DM). This analysis reports data from the CODEC study. The Morningness-Eveningness questionnaire (MEQ) assessed chronotype preference. Diet diaries assessed dietary intake and temporal distribution. Regression analysis assessed whether dietary intake, EW, or EO differed by chronotype. 411 participants were included in this analysis. There were no differences in energy, macronutrient intake or EW between chronotypes. Compared to evening chronotypes, morning and intermediate chronotypes consumed 36.8 (95% CI: 11.1, 62.5) and 20.9 (95% CI: -2.1, 44.1) fewer milligrams of caffeine per day, respectively. Evening chronotypes woke up over an hour and a half later than morning (01:36 95% CI: 01:09, 02:03) and over half an hour later than intermediate chronotypes (00:45 95% CI: 00:21; 01:09. Evening chronotypes went to sleep over an hour and a half later than morning (01:48 95% CI: 01:23; 02:13) and an hour later than intermediate chronotypes (01:07 95% CI: 00:45; 01:30). Evening chronotypes' EOs and last caffeine intake occurred later but relative to their sleep timings. Future research should investigate the impact of chronotype and dietary temporal distribution on glucose control to optimise T2DM interventions.

Wearable Cameras Reveal Large Intra-Individual Variability in Timing of Eating among Young Adults

Studies have shown that young adults follow less structured eating patterns compared with older cohorts. This may have implications for dietary assessment methods which rely on memory and structured meal patterns. Our aim was to describe the intra-individual variation of eating times in young adults aged 18−30 years. Participants (n = 41) wore an Autographer camera that captured first-person perspective images every 30 s for three consecutive days. All images were timestamped and those showing food consumption were used to extract data such as the timing of the first and last eating occasions (EOs), number of EOs per day, and length of eating window. Intra-individual variability was calculated from these data using composite phase deviation (CPD) and coefficient of variation (CV). The number of individuals with high or very high variability was 28 and 18 for timing of first and last EOs, respectively (CPD > 1.70), and 27 and 17 for number of EOs and eating window, respectively (CV > 20%). In this sample of young adults, the lack of regularity in eating patterns should be considered when selecting a dietary assessment method.

Do Temporal Eating Patterns Differ in Healthy versus Unhealthy Overweight/Obese Individuals?

This study examined whether the temporal patterns of energy and macronutrient intake in early and late eating windows were associated with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) among non-shift workers. A total of 299 overweight/obese non-shift workers (Age: 40.3 ± 6.9 years; 73.6% women; BMI: 31.7 ± 5.0 kg/m²) were recruited in the Klang Valley area of Malaysia. The biochemical parameters were determined from fasting blood samples, whereas information on dietary intake and timing was obtained from a 7-day diet history questionnaire. The midpoint of eating was used to determine the early and late windows. Compared to MHO non-shift workers (n = 173), MUO non-shift workers (n = 126) had lower energy intake from carbohydrates and protein during the early window. In contrast, MUO participants had greater energy intake from carbohydrates and fat during the late window. Participants with unhealthy metabolic status (regardless of their chronotypes) had similar temporal patterns of energy intake characterized by smaller energy intake during the early window and greater energy intake during the late window compared with participants with healthier metabolic status. Overall, the lowest percentile of energy intake during the early window was associated with an increased risk of MUO, after adjustment for potential confounders [odds ratio (OR) = 4.30, 95% confidence interval (CI) 1.41-13.11]. The greater the energy intake during the late window, the greater the risk of MUO (OR = 2.38, 95% CI 1.11-5.13) (OR = 2.33, 95% CI 1.03-5.32) (OR = 4.45, 95% CI 1.71-11.56). In summary, consuming less energy earlier in the day and more energy and carbohydrate later in the day was associated with a greater risk of MUO. Thus, a prospective study is needed to explore the potential role of chrono-nutrition practices in modifying risk factors to delay the transition of MHO to MUO.