Challenges of Telemonitoring Programs for Complex Chronic Conditions: Randomized Controlled Trial With an Embedded Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Despite the growing prevalence of people with complex conditions and evidence of the positive impact of telemonitoring for single conditions, little research exists on telemonitoring for this population. OBJECTIVE: This randomized controlled trial and embedded qualitative study aims to evaluate the impact on and experiences of patients and health care providers (HCPs) using a telemonitoring system with decision support to manage patients with complex conditions, including those with multiple chronic conditions, compared with the standard of care. METHODS: A pragmatic, unblinded, 6-month randomized controlled trial sought to recruit 146 patients with ≥1 diagnosis of heart failure (HF), uncontrolled hypertension (HT), and insulin-requiring diabetes mellitus (DM) from outpatient specialty settings in Toronto, Ontario, Canada. Participants were randomized into the control and telemonitoring groups, with the latter being instructed to take readings relevant to their conditions. The telemonitoring system contained an algorithm that generated decision support in the form of actionable self-care directives to patients and alerts to HCPs. The primary outcome was health status (36-Item Short Form Health Survey questionnaire). Secondary outcomes included anxiety and depression, self-efficacy in chronic disease management, and self-reported health service use. HF-related quality of life and self-care measures were also collected from patients followed for HF. Within- and between-group change scores were analyzed for statistical significance (P<.05). A convenience sample of HCPs and patients in the intervention group was interviewed about their experiences. RESULTS: A total of 96 patients were recruited and randomized. Recruitment was terminated early because of implementation challenges and the onset of the COVID-19 pandemic. No significant within- and between-group differences were found for the main primary and secondary outcomes. However, a within-group analysis of patients with HF found improvements in self-care maintenance (P=.04) and physical quality of life (P=.046). Opinions expressed by the 5 HCPs and 13 patients who were interviewed differed based on the monitored conditions. Although patients with HF reported benefitting from actionable self-care guidance and meaningful interactions with their HCPs, patient and HCP users of the DM and HT modules did not think telemonitoring improved the clinical management of those conditions to the same degree. These differing experiences were largely attributed to the siloed nature of specialty care and the design of the decision support, whereby fluctuations in the status of HT and DM typically required less urgent interventions compared with patients with HF. CONCLUSIONS: We recommend that future research conceive telemonitoring as a program and that self-management and clinical decision support are necessary but not sufficient components of such programs for patients with complex conditions and lower acuity. We conclude that telemonitoring for patients with complex conditions or within multidisciplinary care settings may be best operationalized through nurse-led models of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT03127852; https://clinicaltrials.gov/ct2/show/NCT03127852. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/resprot.8367.
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 | 0,031 | 0,036 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».