BACKGROUND:Poor sleep health is strongly associated with depression, anxiety, and stress among college students. This study used latent profile analysis (LPA) to identify sleep profiles and examine their associations with mental health.
METHODS:College students (N = 276) from two U.S. universities completed 14 days of sleep diaries between March and December 2022. Six sleep health dimensions (regularity, satisfaction, alertness, timing, efficiency, and duration) were derived. Depression, anxiety, and stress were assessed daily. LPA was performed in R, and one-way ANOVAs tested differences in mental health across profiles.
RESULTS:Five sleep profiles emerged: exhausted (n = 17), irregular (n = 13), inefficient (n = 27), typical (n = 162), and good sleepers (n = 57). ANOVAs revealed significant group differences for depression (F[4271] = 31.81, p < 0.001, η2 = 0.32), anxiety (F[4271] = 17.30, p < 0.001, η2 = 0.20), and stress (F[4271] = 14.83, p < 0.001, η2 = 0.18) severities. Exhausted sleepers had the highest mean levels of depression, anxiety, and stress, while good sleepers reported the lowest. There were also significant profile differences in variability of depression (F[4271] = 12.14, p < 0.001, η2 = 0.15) and anxiety (F[4271] = 11.82, p < 0.001, η2 = 0.15) but not stress (F[4271] = 2.15, p = 0.08, η2 = 0.03).
CONCLUSIONS:Distinct sleep profiles are associated with both average and day-to-day variability in mental health among college students. Fatigue and irregularity appear particularly detrimental, highlighting the importance of screening for problematic sleep patterns and implementing targeted interventions to support student well-being.