Multidisciplinary Smartphone-Based Interventions to Empower Patients With Acute Coronary Syndromes: Qualitative Study on Health Care Providers’ Perspectives
Notice bibliographique
Résumé
BACKGROUND: Postdischarge interventions are limited in patients with acute coronary syndrome (ACS) due to few scheduled visits to outpatient clinics and travel from remote areas. Smartphones have become a viable lifestyle technology to deliver educational and health interventions following discharge from hospital. OBJECTIVE: The purpose of this study was to identify the requirements for the delivery of a mobile health intervention for the postdischarge management of patients with ACS via a multidisciplinary focus group. METHODS: We conducted a focus group among health care professionals (n=10) from a large metropolitan hospital in May 2017. These participants from a multidisciplinary team contributed to a 1-hour discussion by responding to 8 questions relating to the applicability of smartphone-based educational and health interventions. Descriptive statistics of the focus group data were analyzed using SPSS. The qualitative data were analyzed according to relevant themes extracted from the focus group transcription, using a qualitative description software program (NVivo 11) and an ontology-based concept mapping approach. RESULTS: The mean age of the participants was 47 (SD 8) years: 3 cardiologists; 2 nurse practitioners; 2 clinical nurses; 2 research scientists; and 1 physiotherapist. Of these participants, 70% (7/10) had experience using electronic health intervention during their professional practice. A total of 7 major themes and their subthemes emerged from the qualitative analysis. Health care providers indicated that comprehensive education on diet, particularly providing daily meal plans, is critical for patients with ACS. In terms of ACS symptoms, a strong recommendation was to focus on educating patients instead of daily monitoring of chest pain and shortness of breathing due to subjectivity and insufficient information for clinicians. Participants pointed that monitoring health measures such as blood pressure and body weight may result in increased awareness of patient physical health, yet may not be sufficient to support patients with ACS via the smartphone-based intervention. Therefore, monitoring pain and emotional status along with other health measures was recommended. Real-time support via FaceTime or video conferencing was indicated as motivational and supportive for patient engagement and self-monitoring. The general demographics of patients with ACS being older, having a low educational level, and a lack of computer skills were identified as potential barriers for engagement with the smartphone-based intervention. CONCLUSIONS: A smartphone-based program that incorporates the identified educational materials and health interventions would motivate patients with ACS to engage in the multidisciplinary intervention and improve their health outcomes following discharge from hospital.
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 | 0,017 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,005 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».