ArticlePloS one2025
Understanding smartphone use patterns in higher education: A latent class approach to behavioral and health risk typologies.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Associations Between Smartphone Addiction and Physical Education Learning Engagement Among Chinese University Students: A Variable-Centered and Person-Centered Analysis.Behavioral sciences (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe widespread use of smartphones among university students has raised concern because of their potential effects and the need to detect profiles of problematic use. This study aimed to identify, characterize and differentiate different profiles of smartphone users in a sample of university students on the basis of variables such as use, nomophobia, risk and sociodemographic characteristics.
methodsA total of 681 university students participated. A total of 681 university students participated in this study. The sample was recruited using a non-probabilistic, convenience sampling method. Latent class analysis -LCA- was performed to identify profiles from variables that included smartphone use patterns such as daily hours, messaging, social networks, browsing, history of technology adoption, situational use, NMPQ nomophobia questionnaire -a scale designed to assess the fear of being without a smartphone-, and reported consequences such as accidents, visual or musculoskeletal problems. The resulting classes were compared in subsequent analyses using chi-square tests for categorical variables and Mann‒Whitney U tests for ordinal variables.
resultsLCA revealed two clearly differentiated user profiles. Class 1 (n = 348) grouped users with moderate use and less exposure to risks and was characterized by shorter daily use of smartphones (mean = 5.46 hours), significantly lower scores on the total scale of nomophobia (mean NMPQ = 65.4 out of 140 possible points, moderate level), a lower frequency of accidents reported due to mobile use and lower reports of visual and musculoskeletal health problems. Class 2 (n = 333) grouped users with high digital involvement and multiple vulnerabilities and showed a significantly more intensive use pattern (mean = 11.01 hours per day), higher levels of nomophobia (mean NMPQ = 74.3 out of 140 possible points, moderate level), and a higher frequency of accidents and major visual and musculoskeletal health problems.
conclusionWhile both groups of undergraduate students could benefit from awareness and training programs, interventions could be differentiated and designed to mitigate the risks associated with problematic smartphone use. These findings provide evidence for higher education institutions and health professionals in the development of programs aimed at promoting digital well-being among university students.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.