A 360° Approach to Personalize Lifestyle Treatment in Primary Care for People With Type 2 Diabetes: Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Given the multifactorial nature of type 2 diabetes (T2D), health care for this condition would benefit from a holistic approach and multidisciplinary consultation. To address this, we developed the web-based 360-degree (360°) diagnostic tool, which assesses 4 key domains: "body" (physical health parameters), "thinking and feeling" (eg, mental health and stress), "behavior" (lifestyle factors), and "environment" (eg, work and housing conditions). OBJECTIVE: This work examines the acceptability, implementation, and potential effects of the 360° diagnostic tool and subsequent tailored treatment (360° approach) in a 6-month intervention and feasibility study conducted in standard primary health care settings in the Netherlands. METHODS: A single-group design with baseline, 3-month, and 6-month follow-ups was used. A total of 15 people with T2D and their health care providers from 2 practices participated in a 6-month intervention, which included the 360° diagnosis, tailored treatment, and both individual and group consultations. The 360° diagnosis involved clinical measurements for the "body" domain and self-reports for the "thinking and feeling," "behavior," and "environment" domains. After multidisciplinary consultations involving the general practitioner, pharmacist, nurse practitioner (NP), and dietitian, the NP and dietitian provided tailored advice, lifestyle treatment, and ongoing support. At the end of the intervention, face-to-face semistructured interviews were conducted with health care professionals (n=6) and participants (n=13) to assess the acceptability and implementation of the 360° approach in primary health care. Additionally, data from 14 participants on the "thinking and feeling" and "behavior" domains at baseline, 3 months, and 6 months were analyzed to assess changes over time. RESULTS: The semistructured interviews revealed that both participants with T2D and health care professionals were generally positive about various aspects of the 360° approach, including onboarding, data collection with the 360° diagnosis, consultations and advice from the NP and dietitian, the visual representation of parameters in the profile wheel, counseling during the intervention (including professional collaboration), and the group meetings. The interviews also identified factors that promoted or hindered the implementation of the 360° approach. Promoting factors included (1) the care, attention, support, and experience of professionals; (2) the multidisciplinary team; (3) social support; and (4) the experience of positive health effects. Hindering factors included (1) too much information, (2) survey-related issues, and (3) time-consuming counseling. In terms of effects over time, improvements were observed at 3 months in mental health, diabetes-related problems, and fast-food consumption. At 6 months, there was a reduction in perceived stress and fast-food consumption. Additionally, fruit intake decreased at both 3 and 6 months. CONCLUSIONS: Our findings suggest that the 360° approach is acceptable to both people with T2D and health care professionals, implementable, and potentially effective in fostering positive health changes. Overall, it appears feasible to implement the 360° approach in standard primary health care. TRIAL REGISTRATION: Netherlands Trial Register NL-7509/NL-OMON45788; https://onderzoekmetmensen.nl/nl/trial/45788.
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 | 0,009 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».