Participants’ and Health Care Providers’ Insights Regarding a Web-Based and Mobile-Delivered Healthy Eating Program for Disadvantaged People With Type 2 Diabetes: Descriptive Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Healthy eating is a key element of type 2 diabetes (T2D) self-management. Digital interventions offer new avenues to reach broad audiences to promote healthy eating behaviors. However, acceptance of these interventions by socioeconomically disadvantaged people (eg, those with lower levels of education and income or from ethnic minority groups) has not yet been fully evaluated. OBJECTIVE: This study aimed to investigate the acceptability and usability of EatSmart, a 12-week web-based and mobile-delivered healthy eating behavior change support program, from the perspective of intervention participants living with T2D and health care providers (HCPs) involved in diabetes care. METHODS: This study used a qualitative descriptive design. Overall, 60 disadvantaged adults with T2D, as determined by receipt of either a HealthCare Card or a pension or benefit as the main source of income, were recruited. Data from participants regarding their experiences with and perceptions of the program and longer-term maintenance of any behavior or attitudinal changes were collected through a web-based self-report survey with open-ended questions administered 12 weeks after baseline (54/60, 90%) and semistructured telephone interviews administered 36 weeks after baseline (16/60, 27%). Supplementary semistructured interviews with 6 HCPs involved in diabetes care (endocrinologists, accredited practicing dietitians, and diabetes nurse educators) were also conducted 36 weeks after baseline. These interviews aimed to understand HCPs' views on successful and unsuccessful elements of EatSmart as a technology-delivered intervention; any concerns or barriers regarding the use of these types of interventions; and feedback from their interactions with patients on the intervention's content, impact, or observed benefits. All data from the surveys and interviews were pooled and thematically analyzed. RESULTS: In total, 5 key themes emerged from the data: program impact on food-related behaviors and routines, satisfaction with the program, reasons for low engagement and suggestions for future programs, benefits and challenges of digital interventions, and cultural considerations. Results showed that EatSmart was acceptable to participants and contributed positively to improving food-related behaviors. Most participants (27/43, 63%) mentioned that they enjoyed their experience with EatSmart and expressed high satisfaction with its content and delivery. The educational and motivational content was considered the most useful part of the program. Benefits discussed by intervention participants included gaining health knowledge and skills, positive changes in their food purchasing and cooking, and eating greater quantities and varieties of fruits and vegetables. HCPs also described the intervention as beneficial and persuasive for the target audience and had specific suggestions for future tailoring of such programs. CONCLUSIONS: The findings suggested that this digitally delivered intervention with supportive educational modules and SMS text messages was generally appealing for both participants and HCPs. This intervention medium shows promise and could feasibly be rolled out on a broader scale to augment usual diabetes care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/19488.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,010 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».