Effects of Online Bodyweight High-Intensity Interval Training Intervention and Health Education on the Mental Health and Cognition of Sedentary Young Females
Bibliographic record
Abstract
This study aimed to assess the effectiveness of an online high-intensity interval training (HIIT) intervention and health education on the behaviors, mental health, and cognitive function of sedentary young females. A single-blinded, six-week, randomized controlled pilot trial involving 70 sedentary young Chinese females, aged 18–30 years, was conducted. An intervention group (IG) (n = 33) underwent a HIIT intervention and health education, while a waitlist group (WG) (n = 37) only received health education. In pre-, mid-, and post-tests, both groups filled out questionnaires about physical activity, sedentary behavior, and mental health. Cognitive functions were assessed at the pre- and post-tests by computer-administered cognitive tests. A mixed-effect model with repeated measures was used to analyze outcomes of interest. The retention rate of the IG and WG was 100% and 78.38%, respectively. The IG were found to have significantly increased rates of moderate-to-vigorous physical activity (MVPA) (Mdiff = 940.61, p < 0.001, 95% confidence interval (95% CI): 576.67, 1304.55) from pre-test to post-test, while the WG demonstrated a more marked reduction in sedentary time (Mdiff = −73.02, p = 0.038, 95% CI: −141.90, −4.14) compared with the IG in the post-test. Moreover, anxiety and stress levels were shown to significantly reduce in the IG over the six-week period (Mdiff = −4.73, p = 0.002, 95% CI: −7.30, −2.15 and Mdiff = −5.09, p = 0.001, 95% CI: −8.29, −1.89, respectively). In addition, we observed a significant improvement in verbal ability (p = 0.008, ηp2 = 0.19) following the HIIT intervention and effects of the interaction with time on processing speed (p = 0.050, ηp2 = 0.10) and episodic memory (p = 0.048, ηp2 = 0.11). Moreover, the IG had better global cognitive performance than the WG in the post-test (Mdiff = 8.28, p = 0.003, 95% CI: 3.06, 13.50). In summary, both an online bodyweight HIIT intervention combined with health education, or health education alone, can effectively improve health-related behaviors, but the behavioral consequences may differ based on the emphasis of different intervention modalities. Furthermore, the “bodyweight HIIT plus health education” modality might be a more promising online intervention strategy to mitigate against negative emotions and improve cognitive function.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".