Using a brief web-based 5A intervention to improve weight management in primary care: results of a cluster-randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The primary health care setting is considered a major starting point in successful obesity management. However, research indicates insufficient quality of weight counseling in primary care. Aim of the present study was to implement and evaluate a 5A online tutorial aimed at improving weight management and provider-patient-interaction in primary health care. The online tutorial is a stand-alone low-threshold minimal e-health intervention for general practitioners based on the 5As guidance for obesity management by the Canadian Obesity Network. METHODS: In a cluster-randomized controlled trial, 50 primary care practices included 160 patients aged 18 to 60 years with obesity (BMI ≥ 30). The intervention practices had continuous access to the 5A online tutorial for the general practitioner. Patients of control practices were treated as usual. Primary outcome was the patients' perspective of the doctor-patient-interaction regarding obesity management, assessed with the Patient Assessment of Chronic Illness Care before and after (6/12 months) the training. Treatment effects over time (intention-to-treat) were evaluated using mixed-effects linear regression models. RESULTS: More than half of the physicians (57%) wished for more training offers on obesity counseling. The 5A online tutorial was completed by 76% of the physicians in the intervention practices. Results of the mixed-effects regression analysis showed no treatment effect at 6 months and 12 months' follow-up for the PACIC 5A sum score. Patients with obesity in the intervention group scored lower on self-stigma and readiness for weight management compared to participants in the control group at 6 months' follow-up. However, there were no significant group differences for weight, quality of life, readiness to engage in weight management, self-stigma and depression at 12 months' follow-up. CONCLUSION: To our knowledge, the present study provides the first long-term results for a 5A-based intervention in the context of the German primary care setting. The results suggest that a stand-alone low-threshold minimal e-health intervention for general practitioners does not improve weight management in the long term. To improve weight management in primary care, more comprehensive strategies are needed. However, due to recruitment difficulties the final sample was smaller than intended. This may have contributed to the null results. TRIAL REGISTRATION: The study has been registered at the German Clinical Trials Register (Identifier: DRKS00009241 , Registered 3 February 2016).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".