Implementation Intention Strategy to Reduce Salt Intake among Heart Failure Patients: A Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: An Implementation Intention strategy might be effective in transforming a positive intention to reduce salt intake into effective action among heart-failure patients. Objective: To assess the potential efficacy of an Implementation Intention intervention to reduce salt intake among heart-failure patients. Methods: Randomized controlled trial. The 60 heart-failure patients recruited were first randomized into 2 groups: an experimental group (EG) and a control group (CG). The study population was further broken down into 4 groups depending on whether the individuals prepared their own meals: 2 individual groups (EG-Individual and CG-Individual); and 2 collaborative groups, involving the patient and a social referent (EG-Collaborative and CG- Collaborative). The experimental groups developed action and coping plans based on the Implementation Intention. Total salt intake was calculated through discretionary salt, sodium-food frequency questionnaires, and 24-hour recall, obtained at the baseline (T0) and at the 2-month follow-up (T3). Results: 56 patients ended the follow-up. A reduction in the total salt intake was observed in the EGs (Individual and Collaborative) compared to baseline (5.04g/day vs. 12.21g/day for the EG-Individual (p≤0.001); 4.79g/day vs. 11.43g/day for the EG-Collaborative; p≤0.001). The multivariate analysis showed that the 2 EGs had lower salt intake at T3 than the 2 CGs (95% CI 4.19-9.29 for individual groups vs. 95% CI 4.84-10.22 for collaborative groups). There were no differences between the 2 EGs (95% CI –2.77 to 2.41). The total variance explained (R2) by these comparisons was 0.70. Discussion and conclusion: This study suggests that Implementation Intention might be effective in reducing salt intake among heart-failure patients, either individually or collaboratively. Further research testing mediator and moderator effects of the psychosocial variables are recommended.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".