Measures of Engagement With mHealth Interventions in Patients With Heart Failure: Scoping Review
Notice bibliographique
Résumé
BACKGROUND: Despite the potential of mobile health (mHealth) interventions to facilitate the early detection of signs of heart failure (HF) decompensation and provide personalized management of symptoms, the outcomes of such interventions in patients with HF have been inconsistent. As engagement with mHealth is required for interventions to be effective, poor patient engagement with mHealth interventions may be associated with mixed evidence. It is crucial to understand how engagement with mHealth interventions is measured in patients with HF, and the effects of engagement on HF outcomes. OBJECTIVE: In this review, we aimed to describe measures of patient engagement with mHealth interventions and the effects of engagement on HF outcomes. METHODS: We conducted a systematic literature search in 7 databases for relevant studies published in the English language from 2009 to September 2021 and reported the descriptive characteristics of the studies. We used content analysis to identify themes that described patient engagement with mHealth interventions in the qualitative studies included in the review. RESULTS: We synthesized 32 studies that operationalized engagement with mHealth interventions in 4771 patients with HF (3239/4771, 67.88%, male), ranging from a sample of 7 to 1571 (median 53.3) patients, followed for a median duration of 90 (IQR 45-180) days. Patient engagement with mHealth interventions was measured only quantitatively based on system usage data in 72% (23/32) of the studies, only qualitatively based on data from semistructured interviews and focus groups in 6% (2/32) of studies, and by a combination of both quantitative and qualitative data in 22% (7/32) of studies. System usage data were evaluated using 6 metrics of engagement: number of physiological parameters transmitted (19/30, 63% studies), number of HF questionnaires completed (2/30, 7% studies), number of log-ins (4/30, 13% studies), number of SMS text message responses (1/30, 3% studies), time spent (5/30, 17% studies), and the number of features accessed and screen viewed (4/30, 13% studies). There was a lack of consistency in how the system usage metrics were reported across studies. In total, 80% of the studies reported only descriptive characteristics of system usage data. The emotional, cognitive, and behavioral domains of patient engagement were identified through qualitative studies. Patient engagement levels ranged from 45% to 100% and decreased over time. The effects of engagement on HF knowledge, self-care, exercise adherence, and HF hospitalization were inconclusive. CONCLUSIONS: The measures of patient engagement with mHealth interventions in patients with HF are underreported and lack consistency. The application of inferential analytical methods to engagement data is extremely limited. There is a need for a working group on mHealth that may consolidate the previous operational definitions of patient engagement into an optimal and standardized measure.
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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 | 0,034 | 0,179 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,021 | 0,024 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| 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 ».