Correlates of Sleep Quality and Dietary Intake among Young Adults

Background. Sleep is a critical component of adequate health. Young adults are particularly susceptible to sleep deprivation due to changing lifestyle, biological, and environmental factors. Previous studies suggest that diet may impact sleep by influencing neurotransmitters involved in the sleep-wake cycle. Based upon previous work presented in the ICTHP Section of APHA at the 2015 Meeting in New Orleans, the purpose of our study was to determine if nutritional intake differed among participants categorized as good or poor quality sleepers. 

Methods. Our team is comprised of a second-year doctoral student (principal investigator), a master’s student (co-investigator), and an assistant professor (faculty mentor). We recruited a convenience sample of 300 undergraduate students from a large southeastern university. We collected data in May 2015, after the call for abstracts in this section, making the results late-breaking. We used the Pittsburg Sleep Quality Index (PSQI) to measure sleep quality. The PSQI measures sleep quality during the past 30 days. We used a modified version of the School Physical Activity and Nutrition Project (SPAN) survey to measure dietary intake. The modified SPAN measures nutritional intake in the past 30 days. We grouped nutritional variables into protein, dairy, grains, vegetables, fruits, empty calories, and beverage intake categories. We classified these nutritional categories as either nutritious or non-nutritious. We calculated one-way analyses of variance (ANOVAs) tests to evaluate differences between the nutritional intake of good and poor quality sleepers. We set our hypotheses a priori at p < 0.05.

Results.  We found 138 participants qualified as good quality sleepers while 132 participants qualified as poor quality sleepers. Concerning nutritional categories, we detected statistically significant differences between good and poor quality sleepers for consumption of non-nutritious grains such as sugar-sweetened cereals and French fries (F(1, 268)=7.539, p=0.006). Good sleepers ate less non-nutritious grains (M=10.57; SD=3.44) than poor sleepers (M=11.77; SD=3.73). We also detected significant differences between good and poor quality sleepers for consumption of nutritious vegetables such as carrots and green beans (F(1, 268)= 4.504, p=0.035). Good sleepers ate more nutritious vegetables (M=16.07; SD=4.78) than poor sleepers (M=14.86; SD=4.54). We further detected significant differences between good and poor quality sleepers for consumption of non-nutritious beverages such as regular soda and alcohol (F(1, 268)= 4.040, p=0.045). Good sleepers consumed less non-nutritious beverages (M=15.94; SD=5.02) than poor sleepers (M=17.26; SD=5.77). Finally, we detected significant differences between good and poor quality sleepers for consumption of empty calories such as ice cream and fast food (F(1, 267)= 6.445 p=0.012). Good sleepers consumed less empty calories (M=16.11; SD=4.51) than poor sleepers (M=17.66; SD=5.45). The remaining nutritional categories were similar between both good and poor sleepers (p<0.05).

Conclusions. Our study suggests differences exist between the diets of good and poor quality sleepers. Meeting healthy dietary guidelines may positively impact sleep quality of young adults. As we only examined cross-sectional differences between consumption patterns and sleep quality, we are unable to directly link nutritional patterns to sleep quality. Future studies should use longitudinal designs to investigate possible causal connections between sleep quality and nutrition.