Promoting Collaborative Goal Setting for Cancer Prevention Among Primary Care Patients Through mHealth: Mixed Methods Evaluation of a New App
Notice bibliographique
Résumé
BACKGROUND: Many newly diagnosed cancers are associated with modifiable lifestyle behaviors, such as diet, exercise, smoking cessation, and maintaining a healthy weight. However, primary care providers rarely discuss cancer prevention behaviors with their patients. OBJECTIVE: This study aims to assess the usability, acceptability, and user engagement of the Healthier Together mobile app, which is designed to promote cancer prevention behaviors among non-Hispanic Black primary care patients, by using social networks and goal-setting theories of behavior change. METHODS: In an 8-week pilot study, we enrolled primary care patients (N=41) and provided them with a cancer prevention mobile app that allowed them to select, track, and share progress on cancer prevention goals with other users. App usability was assessed using the System Usability Scale. We assessed the app's acceptability by qualitatively analyzing open-ended responses regarding participants' overall experience with the app. We assessed participants' engagement by analyzing the built-in data capture device, which included the number of times participants checked in (out of a maximum of 8) during the study. RESULTS: The mean age of the 41 participants was 51 years (SD 12), and 76% (31/41) were women. App use data were captured from all participants, and 83% (34/41) completed the exit survey and interview. The mean System Usability Scale score was 87 (SD 12; median 90; IQR 78-95). The analysis of open-ended responses revealed several key themes, and participants complemented the app's ease of use and health behavior-promoting features while also commenting on the need for more feedback and social interactions through the app. On average, participants checked in 5.7 times (SD 2.7) out of 8 possible opportunities. Of the 41 participants, 76% (31/41) checked in during at least 4 of the 8 weeks. Secondary analyses revealed that participants often accomplished their set goals (mean 5.1, SD 2.7) for each week. The qualitative analysis of comments given by participants within the app after each weekly check-in revealed several themes on how the app assisted participants in behavioral change, highlighting that some participants created exercise programs, ate healthier foods, lost a significant amount of weight, and stopped smoking during this study. CONCLUSIONS: The implementation of a mobile cancer prevention goal-setting app in a primary care setting was feasible, and the app achieved high usability, acceptability, and engagement among participants. User feedback revealed an influence on health behaviors. These findings suggest the promise of the Healthier Together app in facilitating behavioral change to reduce cancer risk among non-Hispanic Black primary care patients.
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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 | 0,023 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».