The association between diet quality and chrononutritional patterns in young adults

Abstract

Purpose: Young adults eat erratically and later in the day which may impact weight and cardiometabolic health. This cross-sectional study examined relationships between chrononutritional patterns and diet quality in two young adult populations: a university and community sample.

Methods: Three days of dietary data were collected including food images captured using wearable cameras. Chrononutritional variables were extracted: time of first and last eating occasions, caloric midpoint (time at which 50% of daily energy was consumed), number of eating occasions per day, eating window, day-to-day variability of the above metrics, and evening eating (≥20:00h). The Healthy Eating Index for Australian Adults scored diet quality. Statistical analyses controlled for gender, body mass index, and socio-economic status.

Results: No significant associations between chrononutritional patterns and diet quality were found for all participants (n = 95). However, differences in diet quality were found between university (n = 54) and community (n = 41) samples with average diet quality scores of 59.1 (SD 9.7) and 47.3 (SD 14.4), respectively. Of those who extended eating ≥20:00 h, university participants had better diet quality (62.9±SE 2.5 vs. 44.3±SE 2.3, p < 0.001) and discretionary scores (7.9±SE 0.9 vs. 1.6±SE 0.6, p < 0.001) than community participants. University participants consumed predominately healthful dinners and fruit ≥20:00h whereas community participants consumed predominately discretionary foods.

Conclusion: For the general young adult population, meal timing needs to be considered. Food choices made by this cohort may be poorer during evenings when the desire for energy-dense nutrient-poor foods is stronger. However, meal timing may be less relevant for young adults who already engage in healthy dietary patterns.

Keywords: Chrononutrition; Diet quality; Evening eating; Meal timing; Variability; Young adults.

Fig. 1

Stacked bar chart showing the percentage of participants in the university sample and community sample that fell into the low, moderate, high, and very high meal timing variability categories for each metric over the 3 days of data collection