Associations Between Body Composition And Sleep Health In Young Adults

Evidence has consistently demonstrated an association between BMI and sleep duration such that short sleepers are at increased risk for being overweight/obese. However, reliance on BMI and self-reported measures of sleep duration has limited our understanding of the relationship between body composition and sleep health. PURPOSE: Determine if body composition (body fat percentage, fat-free mass, and bone mineral density (BMD)) associates with actigraphy and electroencephalogram (EEG) measures of sleep health. METHODS: Young adults (n = 36, 18-26 y, 66.7% female) with non-disordered sleep patterns were recruited from a large public university. Participants completed several surveys at baseline, including the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), completed a twice-daily sleep diary and wore an actigraph on their non-dominant wrist 24/7 for two weeks, and wore an at-home EEG device for one night. Participants were instructed to sleep as they normally would during the study. Body composition was measured via dual-energy x-ray absorptiometry (DXA) scan. Pearson’s r correlations were used to determine associations between body composition and sleep variables. RESULTS: Participants averaged 6.88 ± 0.96 h sleep/night during the two-week study and slept 6.24 ± 1.37 h during the at-home EEG nocturnal assessment. Higher body fat percentage associated with less wake after sleep onset (r = -0.41, p = 0.04) and less non-rapid eye movement (NREM) stage 2 sleep (r = -0.41, p = 0.04). Higher fat-free mass associated with shorter sleep duration (r = -0.47, p = 0.02), less total sleep time (r = -0.54, p = 0.007), lower sleep efficiency (r = -0.41, p = 0.045), and greater sleep fragmentation (r = 0.50, p = 0.013). Higher BMD associated with lower sleep efficiency (r = -0.37, p = 0.08) and greater sleep fragmentation (r = 0.55, p = 0.006). Body composition variables were not associated with subjective sleep quality or daytime sleepiness (PSQI global score, ESS total; p > 0.05). CONCLUSION: Individuals with greater fat-free mass and BMD exhibited shorter sleep duration with greater fragmentation; however, they did not report poorer sleep quality or greater daytime sleepiness. Future research is needed to further investigate the relationship between body composition and components of sleep health.

Authors

Institutions

Publication Details

Journal
Medicine & Science in Sports & Exercise
Published
2024-09-16
DOI
https://doi.org/10.1249/01.mss.0001054860.86039.4c
Primary Topic
Nutrition and Health in Aging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Associations Between Body Composition And Sleep Health In Young Adults

Andrea M. Spaeth, Anthony J. Sochan, Taylor McCoy, S. Sreekanth
Medicine & Science in Sports & Exercise
Nutrition and Health in Aging
article

Associations Between Body Composition And Sleep Health In Young Adults

Andrea M. Spaeth, Anthony J. Sochan, Taylor McCoy, S. Sreekanth
article en

Abstract

Evidence has consistently demonstrated an association between BMI and sleep duration such that short sleepers are at increased risk for being overweight/obese. However, reliance on BMI and self-reported measures of sleep duration has limited our understanding of the relationship between body composition and sleep health. PURPOSE: Determine if body composition (body fat percentage, fat-free mass, and bone mineral density (BMD)) associates with actigraphy and electroencephalogram (EEG) measures of sleep health. METHODS: Young adults (n = 36, 18-26 y, 66.7% female) with non-disordered sleep patterns were recruited from a large public university. Participants completed several surveys at baseline, including the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS), completed a twice-daily sleep diary and wore an actigraph on their non-dominant wrist 24/7 for two weeks, and wore an at-home EEG device for one night. Participants were instructed to sleep as they normally would during the study. Body composition was measured via dual-energy x-ray absorptiometry (DXA) scan. Pearson’s r correlations were used to determine associations between body composition and sleep variables. RESULTS: Participants averaged 6.88 ± 0.96 h sleep/night during the two-week study and slept 6.24 ± 1.37 h during the at-home EEG nocturnal assessment. Higher body fat percentage associated with less wake after sleep onset (r = -0.41, p = 0.04) and less non-rapid eye movement (NREM) stage 2 sleep (r = -0.41, p = 0.04). Higher fat-free mass associated with shorter sleep duration (r = -0.47, p = 0.02), less total sleep time (r = -0.54, p = 0.007), lower sleep efficiency (r = -0.41, p = 0.045), and greater sleep fragmentation (r = 0.50, p = 0.013). Higher BMD associated with lower sleep efficiency (r = -0.37, p = 0.08) and greater sleep fragmentation (r = 0.55, p = 0.006). Body composition variables were not associated with subjective sleep quality or daytime sleepiness (PSQI global score, ESS total; p > 0.05). CONCLUSION: Individuals with greater fat-free mass and BMD exhibited shorter sleep duration with greater fragmentation; however, they did not report poorer sleep quality or greater daytime sleepiness. Future research is needed to further investigate the relationship between body composition and components of sleep health.

Medicine & Science in Sports & ExerciseVol. 56(10S)
Rutgers, The State University of New Jersey (US), Stony Brook University (US)
Gender equality
Openalex Percentile: Top 78%
Nutrition and Health in Aging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.