Associations between mobile phone involvement, BMI levels, and sleep quality among Chinese university students

Evidence from a multi-regional large-scale survey

Bibliographic Data

ID22076239
AuthorsYukun Lu, Yu‐Kun Lu (0000-0003-4840-0487, Southwest University), Haodong Tian (0000-0001-7467-1876, Southwest University), Wentao Shi (0009-0007-7970-8712, Southwest University), Haowei Liu (0000-0002-3026-7262, Southwest University), Jinlong Wu (0000-0002-0694-3144, Southwest University), Yunfei Tao (0000-0002-5230-5928, Southwest University), Li Peng (0000-0003-0016-2977, Southwest University, corresponding author)
Year2025
Volume13
Pages1533613-1533613
Publication date2025-02-17
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1533613
PMID40034171
OpenAlexW4407613371
LanguageEN
References cited72

Objective This study aims to explore the association between mobile phone involvement, body mass index (BMI) levels, and the sleep quality of Chinese university students. Methods Using a cluster sampling method, we selected 17,085 university students from three universities in eastern, central, and western China as the study subjects. Demographic information such as age and sex were collected. The Pittsburgh Sleep Quality Index (PSQI) and the Mobile Phone Involvement Questionnaire (MPIQ) were utilized to measure their sleep quality scores and mobile phone involvement scores, respectively. Pearson correlation analysis, two-way ANOVA, and multiple linear regression were employed to examine the relationship between BMI levels, mobile phone involvement, and sleep quality. Results The results show that 15.87% (2,712 participants) are classified as overweight, and 18.45% (3,151 participants) are classified as obese. Additionally, 35.87% (6,125 participants) exhibit mobile phone involvement, while 57.94% (9,899 participants) reported poor sleep quality. Pearson correlation analysis indicates a significant negative correlation ( p < 0.01) between sleep quality and both BMI levels and mobile phone involvement. Two-way ANOVA shows the significant effect of BMI levels ( p < 0.001) and mobile phone involvement ( p < 0.001) on sleep quality, and there is no interaction effect between the two. Additionally, the sleep quality of overweight and obese individuals is significantly poorer than that of those with normal weight ( p < 0.05), while the sleep quality of overweight individuals is significantly lower than that of obese individuals ( p < 0.05). Multiple linear regression analysis indicates that, after controlling for age and gender, both BMI ( β = −2.69) levels and mobile phone involvement ( β = −1.34) are significantly negatively associated with sleep quality ( p < 0.001), accounting for 19% of the variance in poor sleep quality. Conclusion This study found that BMI levels and mobile phone involvement are both independently associated with sleep quality among Chinese university students. However, among individuals with excess BMI, although their sleep quality is worse than individuals with normal weight, overweight individuals may have poorer sleep quality than obese individuals. This study also revealed high rates of overweight and obesity, with over half of participants reporting poor sleep quality, highlighting the need for targeted interventions to address weight management and mobile phone usage to improve sleep health in this population

Analysis of variance · Body mass index · Insomnia · Mobile phone · Obesity · Overweight · Pittsburgh Sleep Quality Index · Psychiatry · Sleep quality · Demography · Impact of Technology on Adolescents · Medicine · Mobile Health and mHealth Applications · Psychology · Sleep and related disorders · Gerontology · Internal Medicine

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