“We are in this together” – Social Presence and Groupness predict satisfaction and intention to return in Zwift cycling groups
Bibliographic record
Abstract
Exercising in groups has been linked to various adaptive outcomes. Yet, most research on the perceptions of physical activity groups has been conducted in face-to-face groups. Virtual exercise groups have been underexplored. Zwift, a mixed reality cycling application, offers a highly immersive experience and the opportunity to interact (via chat or voice) with other riders. The present study examined the relationship between social presence, groupness, satisfaction, and intention to return in mixed reality cycling (Zwift) group rides. A sample of 182 regular Zwift users completed a battery of online surveys, measuring social presence (i.e., the perception of being with others in a virtual space), groupness (i.e., being part of a group in the virtual space), as well as satisfaction with the cycling experience and intention to return. The data were analyzed using structural equation modelling. Social presence and groupness predicted over 40% of the variance of satisfaction, which in turn predicted 7.5% of the variance of intention to return to Zwift group rides. The goodness-of-fit indices suggested a good model fit (chi2/df = 2.01, TLI = .97, CFI = .99, RMSEA = .078, CI 95% = .000 - .149). Participants perceived a high degree of groupness despite exercising virtually. Similar to existing research, the perceptions of groupness were linked to adaptive outcomes. These findings suggest the potential of the group rides offered in Zwift for the promotion of physical activity and adherence. Furthermore, it may serve individuals who are seeking convenience, flexibility, and motivation when working out from home.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".