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
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».