Testing the Feasibility of Sensor-Based Home Health Monitoring (TEC4Home) to Support the Convalescence of Patients With Heart Failure: Pre–Post Study
Notice bibliographique
Résumé
BACKGROUND: Patients with heart failure (HF) can be affected by disabling symptoms and low quality of life. Furthermore, they may frequently need to visit the emergency department or be hospitalized due to their condition deteriorating. Home telemonitoring can play a role in tracking symptoms, reducing hospital visits, and improving quality of life. OBJECTIVE: Our objective was to conduct a feasibility study of a home health monitoring (HHM) solution for patients with HF in British Columbia, Canada, to prepare for conducting a randomized controlled trial. METHODS: Patients with HF were recruited from 3 urban hospitals and provided with HHM technology for 60 days of monitoring postdischarge. Participants were asked to monitor their weight, blood pressure, and heart rate and to answer symptomology questions via Bluetooth sensors and a tablet computer each day. A monitoring nurse received this data and monitored the patient's condition. In our evaluation, the primary outcome was the combination of unscheduled emergency department revisits of discharged participants or death within 90 days. Secondary outcomes included 90-day hospital readmissions, patient quality of life (as measured by Veterans Rand 12-Item Health Survey and Kansas City Cardiomyopathy Scale), self-efficacy (as measured by European Heart Failure Self-Care Behaviour Scale 9), end-user experience, and health system cost-effectiveness including cost reduction and hospital bed capacity. In this feasibility study, we also tested the recruitment strategy, clinical protocols, evaluation framework, and data collection methods. RESULTS: Seventy participants were enrolled into this trial. Participant engagement to monitoring was measured at 94% (N=70; ie, data submitted 56/60 days on average). Our evaluation framework allowed us to collect sound data, which also showed encouraging trends: a 79% reduction of emergency department revisits post monitoring, an 87% reduction in hospital readmissions, and a 60% reduction in the median hospital length of stay (n=36). Cost of hospitalization for participants decreased by 71%, and emergency department visit costs decreased by 58% (n=30). Overall health system costs for our participants showed a 56% reduction post monitoring (n=30). HF-specific quality of life (Kansas City Cardiomyopathy Scale) scores showed a significant increase of 101% (n=35) post monitoring (P<.001). General quality of life (Veterans Rand 12-Item Health Survey) improved by 19% (n=35) on the mental component score (P<.001) and 19% (n=35) on the physical component score (P=.02). Self-efficacy improved by 6% (n=35). Interviews with participants revealed that they were satisfied overall with the monitoring program and its usability, and participants reported being more engaged, educated, and involved in their self-management. CONCLUSIONS: Results from this small-sample feasibility study suggested that our HHM intervention can be beneficial in supporting patients post discharge. Additionally, key insights from the trial allowed us to refine our methods and procedures, such as shifting our recruitment methods to in-patient wards and increasing our scope of data collection. Although these findings are promising, a more rigorous trial design is required to test the true efficacy of the intervention. The results from this feasibility trial will inform our next step as we proceed with a randomized controlled trial across British Columbia. TRIAL REGISTRATION: ClinicalTrials.gov NCT03439384; https://clinicaltrials.gov/ct2/show/NCT03439384.
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 | 0,020 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».