Beyond the first session: unraveling immersion’s dual effects on user retention in high-participation online fitness videos
Bibliographic record
Abstract
Purpose This study aims to investigate the impact of immersion on user retention within high-participation online fitness contexts, where users are required to concurrently process visual instructions and execute physical movements. This dual-task requirement introduces additional complexity to user retention. Design/methodology/approach Using a BERT-based classifier and manual content coding, we analyzed user comments and video content from 550 Douyin (internationally known as TikTok) fitness videos. We investigated the direct influence of immersion on users’ intention to continue exercising by following creators’ fitness videos (ICEFV) and examined its moderating effect on the relationship between values of fitness videos (namely, fitness video-influenced fitness outcomes, entertainment, and co-participation experience) and ICEFV. Findings Immersion significantly enhances ICEFV. Furthermore, immersion positively moderates the relationship between fitness video-influenced fitness outcomes and ICEFV, but does not moderate the effects of entertainment and co-participation experience on ICEFV. Originality/value This study contributes to the literature on user retention in high-participation contexts by revealing a technology-driven dual pathway. We demonstrate that immersion not only directly fosters continued participation but also amplifies the impact of fitness outcomes on continued participation. By focusing on how immersion influences user retention beyond traditional content and influencer paradigms, our findings provide actionable insights for enhancing user participation in competitive online fitness markets.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".