Using structural equation modeling to explore the influences of physical activity, mental health, well-being, and loneliness on Douyin usage at bedtime
Bibliographic record
Abstract
Douyin is the Chinese version of TikTok. Using Douyin at bedtime is a very common behavior among Douyin users. However, the reasons why users like using Douyin before sleep are yet unclear. We conducted a cross-section survey from January 1st to January 16th, 2023 to capture data to examine the associations of depression, anxiety, life satisfaction, well-being, loneliness, and physical activity with Douyin usage at bedtime. The mediation role of insomnia in these associations was examined. A total of 3,392 participants who met the inclusion criteria were included for analysis. Our structural equation modeling analysis showed that depression on ( β = 0.08; p < 0.05), anxiety ( β = 0.06; p < 0.05), and loneliness ( β = 0.14; p < 0.05) were directly associated with increased Douyin usage at bedtime, and were also indirectly associated with Douyin usage through insomnia (depression: β = 0.18; p < 0.05, anxiety: β = 0.16; p < 0.05, and loneliness: β = 0.12; p < 0.05). Life satisfaction ( β = −0.05; p < 0.05) and well-being ( β = −0.20; p < 0.05) were directly associated with decreased Douyin usage at bedtime, and were also indirectly associated with Douyin usage through insomnia (life satisfaction: β = −0.09; p < 0.05, and well-being: β = −0.11; p < 0.05). However, physical activity was unexpectedly associated with increased Douyin usage at bedtime ( β = 0.20; p < 0.05). In conclusion, our findings shed new light on the specific reasons why Douyin users like using Douyin at bedtime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".