2026-10-12T12:33:50Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34039182 2026-10-01T05:29:48Z category_173 portal_316 item_type_3 month_year_10_2026

Table 1_Does learning burnout link physical exercise and problematic short-video use? Evidence from a three-wave random-intercept cross-lagged panel model and supplementary machine-learning analysis among Chinese high school students.docx Tian Yin (836162) Yanfeng Peng (9238528) Longjiang Chen (24313088) Zetian Wang (16486590) Xu Liu (276996) Changcheng Xie (24478123) Zeng Gao (3386267) Applied Psychology high school students learning burnout machine learning physical exercise problematic short-video use random-intercept cross-lagged panel model Background<p>Problematic short-video use has become a growing behavioral health concern among adolescents, yet its longitudinal links with physical exercise and learning burnout remain insufficiently understood. This study examined the within-person bidirectional associations among physical exercise, learning burnout, and problematic short-video use, tested whether learning burnout served as a longitudinal pathway linking physical exercise to later problematic short-video use, and explored the predictive value of these variables for subsequent problematic short-video use.</p>Methods<p>A three-wave longitudinal survey with 6-month intervals was conducted among 1,327 students aged 16–18 years from five high schools in Yunnan Province, China. Physical exercise, learning burnout, and problematic short-video use were assessed using the Physical Activity Rating Scale-3 (PARS-3), the Adolescent Student Burnout Inventory, and the Short-Video Addiction Scale. Random-intercept cross-lagged panel modeling was used to separate stable between-person differences from within-person fluctuations while controlling for demographic and educational covariates. The longitudinal indirect association was estimated using 5,000 bias-corrected bootstrap samples. Supplementary ridge regression and extreme gradient boosting models, using nested cross-validation and a holdout test set, examined the incremental predictive value of physical exercise and learning burnout for later problematic short-video use.</p>Results<p>At the between-person level, higher long-term physical exercise was associated with lower learning burnout and problematic short-video use, whereas higher learning burnout was associated with higher problematic short-video use. At the within-person level, higher-than-usual physical exercise predicted lower subsequent learning burnout and problematic short-video use. Learning burnout and problematic short-video use showed positive reciprocal associations over time, and both predicted lower subsequent physical exercise. The longitudinal indirect association from physical exercise to later problematic short-video use through learning burnout was statistically significant but small. In supplementary machine-learning analyses, the best holdout R² was 0.311, but adding physical exercise and learning burnout did not improve prediction beyond prior problematic short-video use. SHAP results from the extended XGBoost model were therefore interpreted as exploratory feature attributions.</p>Conclusion<p>Physical exercise, learning burnout, and problematic short-video use showed interconnected longitudinal associations among Chinese high school students. Learning burnout may represent one small longitudinal indirect pathway linking physical exercise to later problematic short-video use, while recent problematic short-video use provides the most informative signal for future risk. These findings may inform the development and evaluation of school-based health-promotion strategies that support physical exercise, address learning burnout, and respond to recent problematic-use symptoms.</p> 2026-10-01T05:29:48Z Dataset Dataset 10.3389/fpsyg.2026.1929181.s001 https://figshare.com/articles/dataset/Table_1_Does_learning_burnout_link_physical_exercise_and_problematic_short-video_use_Evidence_from_a_three-wave_random-intercept_cross-lagged_panel_model_and_supplementary_machine-learning_analysis_among_Chinese_high_school_students_docx/34039182 CC BY 4.0