Association of 24-hour movement guideline adherence, mental health and quality of life in young adults: the role of e-Health literacy
Bibliographic record
Abstract
Background: The spread of Covid-19 and resultant infection prevention strategies have disturbed the life routine of Chinese young adults, led to reduced physical activity (PA), prolonged screen time (ST) and inadequate sleep duration (SP), and made immense influence on their mental health (MH) and quality of life (QoL). E-Health literacy (EHL) can enable citizens to use available online information to respond to the highly complex information environment and make appropriate health decisions. Objective: This study aims to examine associations between adhering to 24-h movement (24HM) guidelines and MH and QoL among young adults, as well as to identify any mediating or moderating role of EHL in these associations. Methods: 1742 young adults (20.03 ± 1.54 years old, 68.6% females) from north and south China completed self-report measures of 24HM (PA, ST and SP), health indicators (MH and QoL), EHL and demographic information through an online survey between 4 Apr and 16 Jun 2022. Generalized linear mixed models were applied for data analysis. Results: Results showed that adhering to PA, ST and SP guidelines were all positively connected with QoL while MH was associated with adhering ST or SP guidelines. Adhering to more of 24HM guidelines was linked to better MH and QoL. EHL significantly mediated the association of guideline adherence and QoL and moderate that of guideline adherence and MH. Conclusion: This is the first study to investigate the role of EHL on the associations between 24HM and MH as well as QoL during the Covid-19. The findings may contribute to further empirical research or intervention that aims to promote MH or QoL among young adults more effectively or provide valuable references for developing relevant strategies or policy of health promotion or public health events in China.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".