Characteristics of Smart Health Ecosystems That Support Self-care Among People With Heart Failure: Scoping Review
Notice bibliographique
Résumé
BACKGROUND People with heart failure are supported by healthcare providers who follow clinical guidelines, and they are also encouraged to participate in self-care behaviors. However, the management of heart failure is complex. Innovative solutions are required to support healthcare providers with decision-making and to support people with heart failure to sustain appropriate self-care behaviors. In recent years, more sophisticated technologies have emerged within healthcare practice. These technologies use data collection, intelligent data processing, and communication to enable new models of healthcare, such as smart health ecosystems, to assist diagnosis and treatment of conditions, support patients in managing a condition, and monitor patients to support disease prevention. Currently, there is little information about the behavioral and technical characteristics of smart health ecosystems for people with heart failure. OBJECTIVE We aimed to identify and describe the characteristics of smart health ecosystems that support self-care for people with heart failure. METHODS We conducted a scoping review using the Joanna Briggs Institute (JBI) methodology. Searches of MEDLINE, Embase, CINAHL, PsycINFO, IEEE Xplore, and ACM Digital Library databases were searched from January 2008 to September 2021. The search strategy focused on studies that described smart health ecosystems to support self-care among people with heart failure. Two reviewers screened studies at the title and abstract level, and full text then extracted relevant data from the included full texts. RESULTS After removing duplicates, 1543 articles were screened, and 34 articles were identified, representing 13 interventions. Articles represented study designs from different stages of the e-health development cycle; conceptual and planning (n=6), development and usability (n=14), pilot/feasibility (n=2), effectiveness testing (n=9), implementation (n=1), all phases (n=2). They collected data from the end user with sensors and questionnaires and used tailoring to provide personalized support. Interventions supported heart failure self-care behaviors using 34 different behavior change techniques (BCTs), which were facilitated by a combination of 8 intervention features; automated feedback, monitoring (integrated and manual input), presentation of data, education, reminders, communication with a healthcare provider and psychological support. Furthermore, features to support healthcare providers included the presentation of data, alarms, and alerts, communication with the end user, remote care plan modification, health record integration, and communication with other members of the care team. CONCLUSIONS This scoping review identified that there are few reports of smart health ecosystems to support self-care among people with heart failure, and those that have been reported do not provide comprehensive support across all domains of self-care. Further research on implementation and effectiveness is required. This review outlines the behavioral and technical components of the identified interventions; this information can be used as a starting point for designing and testing future smart health ecosystem interventions.
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 | 0,010 | 0,051 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,020 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
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 ».