Automated digital counselling with social network support for chronic heart failure fails to improve health status but promotes health-related quality of life: ODYSSEE-vCHAT pilot trial
Bibliographic record
Abstract
Abstract Background Automated digital counselling programs have the potential to provide a scalable, complementary behavioural intervention to improve clinical outcomes for chronic heart failure (CHF). Purpose The primary hypothesis for this pilot trial was that automated digital counseling with social network support (ODYSSEE-vCHAT) vs. Usual Care would decrease the incidence of all-cause re-hospitalization or ED visits. Secondary outcomes included: minimal clinically important difference for improvement on the Kansas City Cardiomyopathy Questionnaire-Overall Summary (KCCQ-OS), and improvement on ENRICHD Social Support Index (ESSI), Patient Health Questionnaire for Depression (PHQ-9) Godin-Shephard Leisure Time Physical Activity Scale (GSLTPAQ), and Self-Efficacy in Managing Chronic Disease (SEMCD6). Methods This 2-arm, parallel group, single-blind pilot trial randomized patients to ODYSSEE-vCHAT vs. Usual Care. Patients were ≥18 years of age with CHF (reduced ejection fraction [HFrEF] < 40%, mid-range [HFmrEF] 40-49%, or preserved [HFpEF] ≥ 50%). Exclusion was based on inability to participate due to co-morbidities. All-cause hospitalization or ED visit were assessed digitally by hospital administrative data. Questionnaires were administered at baseline and end-of-study (approximately 8 months). The digital counselling protocol aimed to improve CHF self-care, HRQL, and adherence to medications, exercise, diet, and smoke-free living (Figure 1). The primary outcome was assessed using multivariable binary logistic regression. Secondary outcomes were assessed using multivariable linear regression analyses with ethno-racial group as a covariate. Results 61 patients were randomized: ODYSSEE-vCHAT, n = 30 (49%), vs. Usual Care, n = 31 (51%). There were 2 deaths, 3 withdrawals, and 12 patients failed to complete secondary outcome assessments. Sample characteristics included mean age = 59 years (95% Confidence Interval, CI, 30, 76), gender identity as women, n = 23 (38%), ethno-racial group other than White, n = 17, (28%) and CHF with HFrEF, n = 47 (77%), HFmrEF, n = 8 (13%), and HFpEF, n = 4 (7%). Duration of enrollment to trial endpoint: mean = 247 days, (95% CI, 75, 382). Figure 2 indicates that ODYSSEE-vCHAT was not associated with lower incidence of all-cause hospitalization/ED visit: composite index events for Usual Care, n = 8 (28%), ODYSSEE-vCHAT, n = 7 (23%). However, ODYSSEE-vCHAT was associated with greater prevalence of KCCQ-OS change (≥ 5 points), and greater improvement in self-efficacy (SEMCD6) for managing CHF. There was a statistical trend to decreased prevalence of depression. Social support was inversely associated with ODYSSEE-vCHAT. No benefit was observed for physical activity (Figure 2). Conclusions Automated digital counselling with social network support promotes improvement in HRQL indices, but not health status. Findings for this pilot trial support a follow-up phase 2 randomized controlled trial.Digital Counselling ProtocolODYSSEE-vCHAT Outcomes
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".