Can Automated Digital Counseling Enhance Mental Health in Patients with Chronic Heart Failure or Kidney Disease? The ODYSSEE-vCHAT Study
Bibliographic record
Abstract
Background: Chronic Kidney Disease (CKD) and Chronic Heart Failure (CHF) significantly impact morbidity, mortality, and quality of life. We evaluated the ODYSSEE-vCHAT automated digital counseling program, including social network support, to enhance mental health and quality of life in patients with CHF and CKD. The primary aim was to assess the effect of ODYSSEE-vCHAT on Mental Component Summary (MCS) of the SF-36 health survey. Methods: This 11-month, multicenter, open-label trial evaluated adults with CHF (reduced EF) or CKD (2-year KFRE≥ 10%), focusing on self-care skills (medication adherence, exercise, diet, smoke-free living) via a digital platform. The primary outcome was achieving a minimal clinically important difference in MCS (ΔMCS ≥ 3.8 or MCS ≥ 65). Results: Of 215 enrolled participants, 174 completed the study, with a mean age of 54.4 years; 61% (n=106) had CKD. Significant improvements in MCS scores were noted for both CKD and CHF patients. However, no statistically significant changes were found in KDQOL or MCS scores for CKD patients alone, though small-to-moderate effect sizes were observed: Vitality (0.40, p=0.16), Emotional Well-Being (-0.11, p=0.70), Social Functioning (-0.37, p=0.19), Role Limitations (0.43, p=0.12), aggregate MCS (0.15, p=0.60), and KDQOL Subscale CKD Burden (0.36, p=0.12). These suggest meaningful small-to-moderate improvements (Fig 1). Participants experienced reduced CKD burden and enhanced quality of life. Conclusion: The ODYSSEE-vCHAT program showed potential in enhancing mental health and quality of life among CKD patients, indicating value in digital health interventions in managing chronic diseases. These findings underscore the need for further trials to establish the efficacy of digital counseling in CKD patient care. Funding: Other NIH Support - Canadian Institutes of Health Research-MS2 173076 - Type of funding sources: Public grant(s) – National budget onlyFigure 1. Change in KDQOL-CKD Burden Scale (End of Study – Baseline) by Program Engagement
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".