Beyond Sleep Hygiene: A Multidimensional Clinical Profile for Precision CBT-I in Distressed University Students: Multidimensional Sleep Disturbance in University Students

Manahl Imran; Irzah Farooq

doi:10.54393/tt.v6i3.282

Authors

Manahl Imran Department of Statistical Science, University of the Punjab, Lahore, Pakistan
Irzah Farooq Department of Public Health, University of the Punjab, Lahore, Pakistan

DOI:

https://doi.org/10.54393/tt.v6i3.282

Keywords:

Sleep Dysfunction, Cognitive-Behavioral Therapy for Insomnia, Nocturnal Hyperarousal, Digital Stimulation, Therapeutic Assessment

Abstract

Sleep disturbance is a core transdiagnostic factor in mental health, intricately linked to the onset and maintenance of depression. Objectives: To identify the underlying clinical dimensions of sleep disturbance in university students and explore their behavioral and cognitive predictors. Methods: This cross-sectional study of 151 students from the University of the Punjab was conducted using a comprehensive sleep questionnaire. Data were analyzed using exploratory factor analysis to identify latent constructs of sleep disturbance, with group differences examined via Mann-Whitney U and Kruskal-Wallis tests. Regression analyses identified key behavioral and cognitive predictors of adverse sleep outcomes. Results: Factor analysis revealed eight distinct clinical dimensions of sleep disturbance: Daytime Impairment, Clinical Sleep Disorders, Perceived Sleep Quality, Sleep Hygiene, Sleep Fragmentation, Pre-sleep Stimulation, Nocturnal Hyperarousal, and Autonomic Symptoms. Clinically significant group differences emerged: females reported greater daytime functional impairment (p=0.009), while males endorsed poorer perceived sleep quality (p=0.027). Lower family income was uniquely associated with increased sleep fragmentation (p=0.034). Critically, regression models identified pre-bed screen use as a significant predictor of reduced total sleep duration (p=0.007) and nocturnal cognitive hyperarousal (worry) as a predictor of prolonged sleep onset latency (p=0.036). Conclusions: Sleep disturbances in university students comprise multiple, co-occurring dimensions rather than a single deficit. Personalized strategies combining stimulus control and cognitive techniques can directly address the core drivers of sleep dysfunction in this population.

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