EatSmart, a Web-Based and Mobile Healthy Eating Intervention for Disadvantaged People With Type 2 Diabetes: Protocol for a Pilot Mixed Methods Intervention Study
Notice bibliographique
Résumé
BACKGROUND: People of low socioeconomic position (SEP) are disproportionately affected by type 2 diabetes (T2D), partly due to unhealthy eating patterns that contribute to inadequate disease self-management and prognosis. Digital technologies have the potential to provide a suitable medium to facilitate diabetes education, support self-management, and address some of the barriers to healthy eating, such as lack of nutritional knowledge or shopping or cooking skills, in this target group. OBJECTIVE: This study aims to test the feasibility, appeal, and potential effectiveness of EatSmart, a 12-week, evidence-based, theoretically grounded, fully automated web-based and mobile-delivered healthy eating behavior change program to help disadvantaged people living with T2D to eat healthily on a budget and improve diabetes self-management. METHODS: EatSmart is a mixed methods (quantitative and qualitative) pre-post design pilot study. Sixty socioeconomically disadvantaged people with T2D aged 18 to 75 years will be recruited. Participants will complete self-reported baseline assessments of their basic demographic and clinical data, dietary intake, dietary self-efficacy, and barriers to healthy eating. They will be provided with login access to the EatSmart web program, which includes six progressive skill-based modules covering healthy eating planning; smart food budgeting and shopping; time-saving meal strategies, healthy cooking methods, modifying recipes; and a final reinforcement and summary module. Over the 3-month intervention, participants will also receive 3 text messages weekly, encouraging them to review goals, continue to engage with different components of the EatSmart web program, and eat healthily. Participants will undertake follow-up assessments directly following the intervention 3 months post baseline and again after a 6-month postintervention follow-up period (9 months post baseline). Feasibility will be evaluated using the number of participants recruited and retained and objective indicators of engagement with the website. Program appeal and potential effects on primary and secondary outcomes will be assessed via the same surveys used at baseline, with additional questions asking about experience with and perceptions of the program. In-depth qualitative interviews will also be conducted 6 months post intervention to provide deeper insight into experiences with EatSmart and a more comprehensive description of the program's appeal. RESULTS: The EatSmart website has been developed, and all participants have viewed the modules as of May 2020. Results are expected to be submitted for publication in December 2020. CONCLUSIONS: This study will provide data to address the currently limited evidence regarding whether disadvantaged populations with T2D may benefit from digitally delivered behavior change programs that facilitate eating healthily on a budget. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry, ACTRN12619001111167; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12619001111167. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19488.
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 | 0,019 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,063 | 0,009 |
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 ».