Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jun;25(2):1119-1126.
doi: 10.1007/s11325-020-02150-w. Epub 2020 Jul 23.

Predictors of sleep quality among university students: the use of advanced machine learning techniques

Alia A Alghwiri  1 , Fidaa Almomani  2 , Alaa A Alghwiri  3 , Susan L Whitney  4
Affiliations

Predictors of sleep quality among university students: the use of advanced machine learning techniques

Alia A Alghwiri et al. Sleep Breath. 2021 Jun.

Abstract

Purpose: To assess the prevalence of sleep disturbances among university students and investigate potential correlated factors and their relative importance in quantifying sleep quality using advanced machine learning techniques.

Methods: A total of 1600 university students participated in this cross-sectional study. Sociodemographic information was collected, and the Pittsburgh Sleep Quality Index (PSQI) was administered to assess sleep quality among university students. Study variables were evaluated using logistic regression and advanced machine learning techniques. Study variables that were significant in the logistic regression and had high mean decrease in model accuracy in the machine learning technique were considered important predictors of sleep quality.

Results: The mean (SD) age of the sample was 26.65 (6.38) and 57% of them were females. The prevalence of poor sleep quality in our sample was 70%. The most accurate and balanced predictive model was the random forest model with a 74% accuracy and a 95% specificity. Age and number of cups of tea per day were identified as protective factors for a better sleep quality, while electronics usage hours, headache, other systematic diseases, and neck pain were found risk factors for poor sleep quality.

Conclusions: Six predictors of poor sleep quality were identified in university students in which 2 of them were protective and 3 were risk factors. The results of this study can be used to promote health and well-being in university students, improve their academic performance, and assist in developing appropriate interventions.

Keywords: Logistic regression; Machine learning techniques; Sleep quality; University students.

PubMed Disclaimer

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Barnes CM, Drake CL (2015) Prioritizing sleep health: public health policy recommendations. Perspect Psychol Sci 10:733–737 - PubMed
    1. Lund HG, Reider BD, Whiting AB, Prichard JR (2010) Sleep patterns and predictors of disturbed sleep in a large population of college students. J Adolesc Health 46:124–132 - PubMed
    1. Smith MT, Wickwire EM, Grace EG, Edwards RR, Buenaver LF, Peterson S, Klick B, Haythornthwaite JA (2009) Sleep disorders and their association with laboratory pain sensitivity in temporomandibular joint disorder. Sleep 32:779–790 - PubMed - PMC
    1. Lee G, Baek S, Park HW, Kang EK (2018) Sleep quality and attention may correlate with hand grip strength: FARM study. Ann Rehabil Med 42:822–832 - PubMed - PMC
    1. Ma T, Shi G, Zhu Y, Wang Y, Chu X, Jiang X, Liu Z, Cai J, Wang H, Jin L, Wang Z, Wang X (2017) Sleep disturbances and risk of falls in an old Chinese population-Rugao longevity and ageing study. Arch Gerontol Geriatr 73:8–14 - PubMed

LinkOut - more resources