Fitness training as a body-centered hobby: The serious leisure perspective for explaining exercise practice
Bibliographic record
Abstract
Physical exercise is an activity whose health-related benefits have been promoted by health professionals and social institutions. However, given that the levels of practice are not ideal, the subjective variables - that give meaning, provide continuity and may increase exercise adherence - need to be studied in depth. In this sense, fitness training is analyzed as a form of serious leisure, a body-centered hobby - a way to practice and relate to the activity that leads its practitioners to adhere more to it, orienting them towards a career in acquiring and expressing skills, knowledge and experience. In total, 1,134 people (588 men, 546 women) doing fitness training, aged between 18 and 70 years old (M = 34.7, SD = 13.06), answered a questionnaire about time dedicated to exercise as serious leisure and its derived and complementary benefits. Student's t Coefficient and ANOVA were used to show the significance of the differences among the scores obtained for the rewards related to exercise and the other variables of the study. The results highlight that exercise as serious leisure is an activity whose weekly time investment makes it to acquire a central role when rewarded by sense of accomplishment, contact with others, improved health and being outdoors with the family. To conclude, this study enhances that characterizing fitness training as a body-centered hobby - which shares the principles of serious leisure - implies a new approach to the analysis of exercise while also suggesting new ways of promoting it.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".