Web-Based Targeted Nutrition Counselling and Social Support for Patients at Increased Cardiovascular Risk in General Practice: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Using the Internet may prove useful in providing nutrition counselling and social support for patients with chronic diseases. OBJECTIVE: We evaluated the impact of Web-based nutrition counselling and social support on social support measures, anthropometry, blood pressure, and serum cholesterol in patients at increased cardiovascular risk. METHODS: We conducted a randomized controlled trial among patients with increased cardiovascular risk in Canadian family practices. During 8 months, patients in the intervention group and control groups received usual care. Patients in the intervention group also had access to a Web-based nutrition counselling and social support tool (Heartweb). Site use during the study was monitored. We measured social support, body mass index, waist/hip ratio, blood pressure, and cholesterol levels at baseline and at 4 and 8 months to assess the effectiveness of the intervention. RESULTS: We randomized 146 patients into the Web-based intervention (n=73) or the control group (n=73). Within the Web-based intervention group, Heartweb was used by only 33% (24/73) of patients, with users being significantly younger than nonusers (P=.03). There were no statistically significant differences between the intervention group and the control group in changes in social support, anthropometry, blood pressure, and serum cholesterol levels. CONCLUSIONS: Uptake of the Web-based intervention was low. This study showed no favourable effects of a Web-based nutrition counselling and social support intervention on social support, anthropometry, blood pressure, and serum cholesterol. Improvements in reach and frequency of site use are needed to increase the effectiveness of Web-based interventions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".