Efficacy of Automated Digital Counseling for Chronic Heart Failure (ODYSSEE-vCHAT-CHF): A Randomized Controlled Pilot Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Chronic Heart Failure (CHF) is an increasing health burden associated with psychological distress, decreased quality of life, and high one year hospitalization rates (40%). CHF patients benefit from appropriate self-care and there is an increasing need for automated scalable programs to motivate and educate these patients. OBJECTIVE A Virtual Community Promoting Health Literacy, Self-care and Peer Support for Heart Failure (vCHAT-CHF Trial) used an automated scalable digital program: prOmoting health with DigitallY based counSeling of Self-care bEhavior and quality of life (ODYSSEE) to improve adherence and self-care in CHF patients, thereby improving health and health-related quality of life vs. enhanced usual care (eUC). The primary outcome was a composite index of all-cause hospitalization and emergency room (ER) visits. Secondary outcomes included changes in self-reported physical and psychosocial well-being. METHODS The vCHAT-CHF Trial was a 2-armed single-blind randomized control pilot trial. This was an evidence-based online multimedia digital health platform, based on cognitive behavioral and motivational interviewing techniques. ODYSSEE-vCHAT employed weekly topics on an interactive digital health platform, as well as weekly webcast presentations on various self-care themes and moderated online chat rooms, in addition to usual care. RESULTS 61 CHF patients recruited from three tertiary hospitals in Canada were enrolled in the program for a median of 7 months. 44 (72%) of participants completed the endpoint assessment questionnaires (n= 19 (63%) in ODYSSEE-vChat vs. 25 (81%) in enhanced Usual Care (eUC)). Primary and secondary outcomes were investigated through linear and/or logistic multivariate models as appropriate, comparing program groups (e.g. ODYSSEE-vCHAT vs. eUC) by both intention-to-treat and treatment-received methods. ODYSSEE-vCHAT was not associated with a composite of unplanned hospitalizations and/or ER visits in the current trial (23% usual care vs. 23% ODYSSEE, P = .78). However, multivariate logistic regression demonstrated that individuals in ODYSSEE-vCHAT (vs. eUC) were more likely (P = .003, OR = 6.0) to show clinically relevant change in the Kansas City Cardiomyopathy Questionnaire (KCCQ) as defined by a change score ≥ 5 from baseline to end of trial. Multivariate linear regression showed that ODYSSEE-vCHATwas also associated with positive change in Self-Efficacy for Managing Chronic Disease (SEMCD6; P = .03, B = 4.38). CONCLUSIONS In summary, the vCHAT-CHF pilot trial showed that ODYSSEE-vCHATwas associated with clinically relevant change on the KCCQ and an increase in self-efficacy in management of chronic disease vs eUC, but failed to show a difference in number of hospitalizations and unplanned ER visits. This trial illustrated the potential of evidence-based automated digital health programs in self-management of CHF. Future research with a larger sample size may illuminate further relationships. CLINICALTRIAL ClinicalTrials.gov NCT04966104.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".