Patient-Reported Experiences With Long-Term Lifestyle Self-Monitoring in Heart Disease: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Lifestyle behaviors strongly predict cardiovascular morbidity and mortality, emphasizing the need for strategies that support sustained lifestyle changes in patients with cardiac disease. Digital health solutions, including wearables, mobile apps, and chatbots, enable self-monitoring of lifestyle behaviors but often face challenges with engagement and usability. While self-monitoring systems can increase awareness and accountability, maintaining user engagement remains crucial for their effectiveness in promoting behavior change and long-term improvements. OBJECTIVE: This study evaluated patient experiences with a lifestyle monitoring system combining a web application, health watch, and chatbot. We explored facilitators of and barriers to long-term adherence and assessed the impact of self-monitoring on lifestyle awareness and behavior change in patients with cardiac disease. METHODS: We conducted a mixed methods study with patients who used an eHealth platform for self-monitoring lifestyle behaviors during 1 year following an invasive cardiac procedure. This study included 100 patients (mean age 61.6, SD 10.4 y; n=88, 88% male) comprising both completers (n=57, 57%) and dropouts (n=43, 43%). Patients engaged in quarterly phone interviews and questionnaires and completed an end-of-study questionnaire. Completers participated in a structured evaluation interview; dropouts provided a reason for discontinuation. Quantitative and qualitative data analyses focused on usability, long-term adherence facilitators and barriers, lifestyle awareness, and behavior change. RESULTS: Patients completed 157 quarterly questionnaires (n=145, 92.4% by completers and n=12, 7.6% by dropouts) and 217 phone interviews (n=171, 78.8% with completers and n=46, 21.2% with dropouts). In total, 77 patients (of whom n=54, 70% were completers and n=23, 30% were dropouts) completed end-of-study questionnaires, and 98% (56/57) of completers participated in the evaluation interviews. Completers reported higher perceptions of the platform's usefulness, ease of use, and satisfaction (P<.001 in all cases) than dropouts. Dropout reasons linked to self-monitoring (34/43, 79%) included high self-report burden and dissatisfaction with the chatbot, poor overall usability experience, health watch technical challenges causing frustration, limited perceived usefulness, mental stress from self-monitoring, and low motivation. Key facilitators of long-term engagement included routine formation, structured reminders, and minimal effort associated with the wearable. Barriers included repetitive chatbot questions (causing cognitive burden) and technical issues with the health watch. Self-monitoring increased lifestyle awareness among completers, particularly regarding physical activity (25/56, 45%) and nutrition (29/56, 52%), with smaller effects for sleep quality (7/56, 13%) and mental stress (1/56, 2%). It facilitated behavior change in physical activity and nutrition (16/56, 29% each) and sleep quality (4/56, 7%) but not in mental stress. Adaptive personalization, mobile accessibility, and real-time feedback could improve adherence. CONCLUSIONS: Fostering routine formation while minimizing patient burden through personalized, flexible, and adaptive features is important for sustained patient engagement in eHealth monitoring systems. Enhancing relevance and usability while reducing complexity and technical barriers can optimize digital health tools and promote lasting behavior change. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1186/s12872-023-03222-x.
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 | 0,008 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».