The Effects of Branding on Physical Activity: A Systematic Review
Bibliographic record
Abstract
It is important to increase the number of people regularly physically active to enhance health. Physical activity (PA) promotion organizations with strong brands may be more effective at motivating PA. However, these organizations must know which brand equity variables (e.g., brand awareness) to prioritize in their marketing. No previous review has examined whether brand equity variables are associated with PA-related variables. The primary objective of this study was to learn whether brand equity variables are associated with PA behaviors (e.g., moderate or vigorous PA). A secondary objective was to evaluate whether brand equity variables are associated with potential correlates of PA (e.g., self-efficacy). In addition to other search methods, four databases were searched for articles (PsycINFO, MEDLINE, SPORTDiscus, Business Source Complete). Thirty articles met the eligibility criteria. Regarding behavior, brand awareness and associations were associated with moderate or vigorous PA but not less intense activities such as walking. For correlates, brand awareness was associated with self-efficacy, outcome expectations, attitude, and parental approval of child PA. Brand associations were only associated with attitude. Age and brand awareness measure emerged as moderators of the awareness to moderate or vigorous PA relationship. Future research should examine the antecedents of brand awareness and use experimental designs.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".