Evaluating the Carrot Rewards app, a population-level incentive-based intervention promoting step counts across two Canadian provinces: a quasi-experimental study (Preprint)
Notice bibliographique
Résumé
BACKGROUND The Carrot Rewards application (‘app’) was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards and groceries, for engaging in healthy behaviors such as walking. OBJECTIVE The purpose of this study was to examine whether a multi-component intervention including goal setting, graded tasks, biofeedback and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL). METHODS A 12-week quasi-experimental (single group pre/post) study was conducted. Among eligible participants (n=78,882), 44.39% (n=35,014) enrolled in the Carrot Rewards “Steps” walking program during the recruitment period (June 13th – July 10th 2016). During the two-week baseline (or ‘run-in’) period, mean steps/day were calculated for participants. Thereafter, participants earned incentives in the form of loyalty points (worth $0.04 CAD) every day they reached their personalized daily step goal (i.e. baseline mean + 1,000 steps = level of first daily step goal). Participants earned additional points (worth $0.40 CAD) for meeting their step goal 10+ non-consecutive times in a 14-day period (called a “Step Up Challenge”). Participants could earn up to $5.00 CAD during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, with the objective of gradually increasing the number of steps participants take each day by 3,000. Only participants with five or more valid days (days with step counts between 1,000 and 40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps/day (by week), and was analyzed using linear mixed-effects models. RESULTS Of the 32,229 participants with valid baseline data, the mean age was 33.7 11.6 years and 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6,511.22. Just over half of users (50.69%, 16,336/32,229) were categorized as “physically inactive”, accumulating less than 5,000 daily steps at baseline. Results from the mixed-effects models revealed statistically significant increases in mean daily step counts when comparing baseline with each Study Week (P<.0001). Compared to baseline, participants walked 115.70 more steps (95% CI: 74.59,156.81; P<.0001) at Study Week 12. Users classified as “high engagers” (app engagement above the sample median; 48.13%, 15,511/32,229) in BC and NL walked 738.70 (95% CI: 673.81, 803.54; P<.0001) and 346.00 (95% CI: 239.26, 452.74; P<.0001) more steps, respectively. Among physically inactive, high engagers (21.08%; 7,022/32,229) an average increase of 1,224.66 steps per day (95% CI: 1160.69, 1288.63; P<.0001) was observed. Effect sizes were modest CONCLUSIONS Providing very small but immediate rewards for personalized daily step goal achievement as part of a multi-component intervention increased daily step counts on a population-scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC and NL provide evidence of replicability.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».