Table · dataset · 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
Listed in ZivaHub and Deakin Research Online and DMU Figshare and HKU DataHub and Swinburne Figshare and DaYta Ya Rona and SUNScholarData and figshare and Loughborough Research Repository and GRANTS Data and UP Research Data Repository — shown once because both records carry DOI 10.3389/fpsyg.2026.1929181.s001
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.
Description
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.
Read the rest (4 more)
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 R<sup>2</sup> 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>
Links
Where it is published
- DOI doi.org/10.3389/fpsyg.2026.1929181.s001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Applied and developmental psychology · Astronomy & Astrophysics · Chemistry · Chemistry · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Economics & Finance · Economics & Finance · Engineering · Engineering · Engineering · Engineering · Humanities · Humanities · Humanities · Humanities · Life Sciences · Life Sciences · Life Sciences · Life Sciences · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Machine learning · Materials Science · Mathematics & Statistics · Medicine & Health · Medicine & Health · Ocean & Atmospheric Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Psychology & Behavioral Science · Social Science · Social Science · Social Science · Social Science
- Inferred from text
- Longitudinal study 65% · Tabular 65%
Provenance · 11 source records, 88 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34039182 | 11 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/34039182 | 11 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/34039182 | 11 d ago | JSON v1 |
| HKU DataHub | oai:figshare.com:article/34039182 | 11 d ago | JSON v1 |
| Swinburne Figshare | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| DaYta Ya Rona | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| SUNScholarData | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| figshare | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| Loughborough Research Repository | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| GRANTS Data | oai:figshare.com:article/34039182 | 10 d ago | JSON v1 |
| UP Research Data Repository | oai:figshare.com:article/34039182 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].anzsrc:group:4611 | mapping · figshare swinburne edu au | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · scholardata sun ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · grantsdata jst go jp | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:5201 | mapping · dayta nwu ac za | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · scholardata sun ac za | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · grantsdata jst go jp | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · repository lboro ac uk | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · researchdata up ac za | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · datahub hku hk | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].anzsrc:group:5201 | mapping · figshare swinburne edu au | vocabulary-mapper@1.0.0 | keywords['Applied Psychology'] |
| concepts[field].local:field:astronomy | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare swinburne edu au | connector:figshare_swinburne_edu_au@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · scholardata sun ac za | connector:scholardata_sun_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · grantsdata jst go jp | connector:grantsdata_jst_go_jp@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare swinburne edu au | connector:figshare_swinburne_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · grantsdata jst go jp | connector:grantsdata_jst_go_jp@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scholardata sun ac za | connector:scholardata_sun_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:engineering | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:engineering | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:engineering | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:engineering | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:humanities | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:materials-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:mathematics-statistics | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · datahub hku hk | connector:datahub_hku_hk@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · figshare swinburne edu au | connector:figshare_swinburne_edu_au@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · scholardata sun ac za | connector:scholardata_sun_ac_za@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · grantsdata jst go jp | connector:grantsdata_jst_go_jp@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · repository lboro ac uk | connector:repository_lboro_ac_uk@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:social-science | mapping · dayta nwu ac za | connector:dayta_nwu_ac_za@1.0.0 | |
| concepts[field].local:field:social-science | mapping · researchdata up ac za | connector:researchdata_up_ac_za@1.0.0 | |
| concepts[method].local:method:longitudinal-study | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[modality].local:modality:tabular | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |