A 360° Approach to Personalize Lifestyle Treatment in Primary Care for People With Type 2 Diabetes: Feasibility Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".