Observational studyScientific reports2023
Latent profile analysis for health-related quality of life, sleep quality, morning and evening type, and internet addiction among medical students.
Observational study in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Risk factors for Problematic Internet Use (PIU) among Polish students: the role of loneliness, depression, and health behaviours in a predictive analysis.BMC psychiatry · 2026Article
- Sleep in the academic sphere: identifying sleep profiles and their influencing factors using latent profile analysis in German university students.BMC psychology · 2025Article
- Article
- Problematic Internet use and attitudes towards persons with disabilities - cross-sectional research among Polish students.BMC medical education · 2023Article
- Impact of social media addiction on anxiety symptoms as modified by social support and its subscales.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Health-related quality of life, sleep quality, morning and evening types, and internet addiction are of significant importance to the development of medical students, yet they have rarely been studied. Taking this into consideration, the study aimed to confirm latent profiles in health-related quality of life, sleep quality, morning and evening types, and internet addiction in medical students and investigate the characteristics of participants in each profile to provide suggestions for students' health. This was an observational cross-sectional study including 1221 medical student subjects at China Medical University in 2019. Multiple correspondence analysis was the initial step to verify the correspondence, dispersion, and approximation of variable categories. Latent profile analysis was used to identify the multiple correspondences between the levels of variables. Three profiles were found, including: (1) The Low sleep quality profile was characterized by the lowest sleep quality among the three existing profiles. (2) The High health-related quality of life and Low internet addiction profile was characterized by the highest level of health-related quality of life but the lowest level of internet addiction. (3) The Low health-related quality of life and High internet addiction profile was characterized by the highest standardized values of internet addiction but the lowest standardized values of health-related quality of life. This study had important implications for improving student health and supported the medical universities and hospitals in implementing targeted policies based on distinctive student characteristics.
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Registered trials
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