Evaluation of the Implementation of a Mobile Health App to Support Dutch Primary Care for Diabetes: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Over 1 million Dutch people have diabetes, of whom 90% have type 2 diabetes. Studies show that lifestyle plays an important role in the course of type 2 diabetes. MiGuide (MiGuide Ltd) is an online platform that helps people adopt and sustain lifestyle changes. The platform is integrated into existing diabetes care within primary care. Previous research has shown that implementing new (eHealth) interventions is challenging and may reduce effectiveness. Mapping out the barriers and success factors in the implementation process is essential so that eHealth interventions such as MiGuide can be used effectively in regular health care. OBJECTIVE: This study aimed to evaluate the implementation of MiGuide within Dutch primary care. METHODS: A qualitative study design was used, supplemented by quantitative data from patients. Five general practices participated. Three focus groups (FGs; at baseline, after 6 months, and after 12 months) were conducted with 3 general practitioners, 3 FGs with 8 specialized practice nurses (divided into 2 separate groups with 4 participants per group), 2 FGs (at 6 months and after 12 months) with 5 patients, and 2 FGs (at baseline and after 12 months) with 4 stakeholders from the management of the care group. The implementation process was discussed with health care professionals and management, and usage and user-friendliness were discussed with patients. The framework method was used to analyze the data. The following quantitative data were collected: patient characteristics, user data, and questionnaires at baseline and 6 months, assessing quality of life, usability, and diabetes self-care. The quantitative data were examined using exploratory analyses. RESULTS: Four themes were found in the qualitative data: "innovation," "capability, motivation, and opportunity," "processes," and "setting." Different factors within these themes played an essential role throughout the implementation process, such as facilities, technical difficulties, motivation, COVID-19, and the work processes. Areas for improvement were also identified. The supplemented quantitative data showed that usability scored below average at 6 months (mean 53.8; SD 9.3; n=8). Participants had a mean score of 0.84 (SD 0.13) on the EuroQoL-5 dimension and 81.9 (SD 13.4) on the EuroQoL visual analogue scale at baseline. Moreover, the average number of days someone exercised was 4.2 (SD 1.7), and the number of days someone ate a generally healthy diet was 5.1 (SD 1.3). Insufficient data on quality of life and diabetes self-care were collected at 6 months and therefore not presented in this study. CONCLUSIONS: Implementation is a complex process with multiple barriers and facilitators. It is essential to explore the use of context-specific strategies that are aligned with the implementation process phase. Further research is needed to evaluate the next version of the MiGuide platform, which is being implemented in another setting with lifestyle coaches.
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 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,001 | 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 ».