Effects of Basketball and Baduanjin Exercise Interventions on Problematic Smartphone Use and Mental Health among College Students: A Randomized Controlled Trial
Bibliographic record
Abstract
Problematic smartphone use (PSU) has become a prevalent issue worldwide. Previous studies suggest that physical exercising may effectively reduce smartphone users’ addiction levels. Comparisons and further evaluations on the long-term effects of different types of exercise-based interventions on treating PSU remain to be investigated. Objective. We investigated if group-based basketball and Baduanjin exercise (a type of Qigong) would reduce PSU and improve the mental health of college students and whether such effects would be sustained. A twelve-week experiment was conducted, where 96 eligible Chinese college students with PSU were randomly assigned to two intervention arms (i.e., basketball and Baduanjin exercises) and a control arm. Outcome measures, including PSU (measured by the Mobile Phone Addiction Index in Chinese (MPAI)) and mental health indices for anxiety (measured by Self-Rating Anxiety Scale (SRAS)), loneliness (measured by the short-form of the UCLA Loneliness Scale (UCLA-LS)), inadequacy (measured by the revised Janis and Field’s Feelings of Inadequacy Scale (FIS)), and stress (measured by the Chinese version of Perceived Stress Scale (CPSS)) were collected at the baseline, the end of week 12, and the two-month follow-up. A Generalized Estimating Equations (GEE) model for longitudinal data was utilized in analyses. Results. Both exercise interventions demonstrated significant effects on decreasing PSU (basketball: p < 0.01 ; Baduanjin: p < 0.01 ), feelings of anxiety (basketball: p < 0.01 ; Baduanjin: p = 0.04 ), loneliness (basketball: p < 0.01 ; Baduanjin: p < 0.01 ), inadequacy (basketball: p < 0.01 ; Baduanjin: p < 0.01 ), and perceived stress (basketball: p < 0.01 ; Baduanjin: p = 0.04 ), at the end of interventions. At two months after interventions, both exercise interventions demonstrated significant effects on decreasing PSU (basketball: p < 0.05 ; Baduanjin: p < 0.05 ), feelings of anxiety (basketball: p < 0.01 ; Baduanjin: p = 0.03 ), loneliness (basketball: p < 0.01 ; Baduanjin: p < 0.01 ), and inadequacy (basketball: p < 0.01 ; Baduanjin: p = 0.01 ), but not for feeling of stress. Furthermore, group-based basketball demonstrated larger improvements for all these significant results on reducing PSU and meanwhile improving their related mental health parameters among college students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".