Assessing The Impact Of A Healthy Lifestyle Initiative On Sport Fans’ Attitudes And Behaviour
Bibliographic record
Abstract
Innovative strategies have emerged that utilize sport as a catalyst or "hook" to encourage healthy lifestyles. Corporate social responsibility (CSR) and sport-for-development initiatives have significant potential in harnessing the power of sport to involve men with heightened chronic disease risk to improve their health (e.g., weight loss) through health behaviour change (e.g., increased physical activity, diet). PURPOSE: To assess the impact that participation in a health CSR initiative has on fans’ attitudes and behaviour. METHODS: Hockey fans [N = 997; men; mean age: 48.6 years (± 8.3 (SD)) participated in a 12-week healthy lifestyle program (Hockey Fans In Training: Hockey FIT), which was offered in partnership with 40 teams across North America. Sites were randomly assigned to either the intervention group (immediate participation) or a control group (participation in initiative after a 12-month delay). The attitudes and behaviours of both groups of fans were assessed at the program's outset (baseline), immediately after its conclusion (12 weeks), and again at 12 months. At each assessment point, fans completed surveys aimed at gauging cognitive awareness of the team's CSR, affective evaluation, behavioural intentions, attendance (and intentions), team identification, as well as sustained loyalty, merchandise consumption (and intentions), and media consumption. RESULTS: After controlling for age and attendance, there were no significant differences between groups at 3 and 12 months; however, significant differences were noted within the intervention group for loyalty, attendance intentions, word of mouth, and fan identification (see Table 1). There was no statistically significant interaction between the intervention and time for the remaining outcomes. CONCLUSION: While sport-based CSR initiatives may be successful in improving the health of fans and their communities, they may be less impactful on improving fan attitudes and behaviour. Table 1: - Difference within groups Characteristics Mean change at 12 weeks compared to baseline (95% CI) Mean change at 12 months compared to baseline (95% CI) LoyaltyInterventionControl -.241***(-.374 to -.109)-.031(-.144, .082) -.322***(-.473 to -.170)-.163**(-.292, .033) Attendance intentionsInterventionControl -.374***(-.598, -.150).006(-.186, .197) -.400***(-.631, -.169)-.172(-.369, .026) Word of mouthInterventionControl -.272***(-.440, -.103)-.114(-.260, .032) -.376***(-.556, -.196)-.028(-.184, .128) Fan identificationInterventionControl -.270***(-.432, -.107)-.080(-.215, .056) -.307***(-.476, -.139)-.082(-.223, .058) Canadian Institutes of Health Research (grant number PJT – 156088) and the Public Health Agency of Canada (Multi-Sectoral Partnerships to Promote Healthy Living and Prevent Chronic Disease Program)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".