Choosing healthier foods in recreational sports settings: a mixed methods investigation of the impact of nudging and an economic incentive
Bibliographic record
Abstract
BACKGROUND: Nudging is an approach to environmental change that alters social and physical environments to shift behaviors in positive, self-interested directions. Evidence indicates that eating is largely an automatic behavior governed by environmental cues, suggesting that it might be possible to nudge healthier dietary behaviors. This study assessed the comparative and additive efficacy of two nudges and an economic incentive in supporting healthy food purchases by patrons at a recreational swimming pool. METHODS: An initial pre-intervention period was followed by three successive and additive interventions that promoted sales of healthy items through: signage, taste testing, and 30% price reductions; concluding with a return to baseline conditions. Each period was 8 days in length. The primary outcome was the change in the proportion of healthy items sold in the intervention periods relative to pre- and post-intervention in the full sample, and in a subsample of patrons whose purchases were directly observed. Secondary outcomes included change in the caloric value of purchases, change in revenues and gross profits, and qualitative process observations. Data were analyzed using analysis of covariance, chi-square tests and thematic content analysis. RESULTS: Healthy items represented 41% of sales and were significantly lower than sales of unhealthy items (p < 0.0001). In the full sample, sales of healthy items did not differ across periods, whereas in the subsample, sales of healthy items increased by 30% when a signage + taste testing intervention was implemented (p < 0.01). This increase was maintained when prices of healthy items were reduced by 30%, and when all interventions were removed. When adults were alone they purchased more healthy items compared to when children were present during food purchases (p < 0.001), however parental choices were not substantially better than choices made by children alone. CONCLUSIONS: This study found mixed evidence for the efficacy of nudging in cueing healthier dietary behaviors. Moreover, price reductions appeared ineffectual in this setting. Our findings point to complex, context-specific patterns of effectiveness and suggest that nudging should not supplant the use of other strategies that have proven to promote healthier dietary behaviors.
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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 | 0.037 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".