Exploring the Feasibility and Initial Impact of an mHealth-Based Disease Management Program for Chronic Ischemic Heart Disease: Formative Study
Notice bibliographique
Résumé
BACKGROUND: Ischemic heart disease (IHD) is a leading cause of morbidity and mortality worldwide, requiring innovative management strategies. Traditional disease management programs often struggle to maintain patient engagement and ensure long-term adherence to lifestyle modifications and treatment plans. Mobile health (mHealth) technologies have emerged as a promising approach to address these challenges by providing continuous, personalized support and monitoring. However, the reported use and effectiveness of mHealth in the management of chronic diseases, such as IHD, have not been fully explored. OBJECTIVE: The primary aim of this study was to evaluate the feasibility and initial impact of an mHealth-based disease management program on coronary risk factors, specifically focusing on low-density lipoprotein cholesterol (LDL-C) levels, in individuals with chronic IHD. This formative study assessed changes in LDL-C and other metabolic health indicators over a 6-month period to determine the initial impact of the program on promoting cardiovascular health and lifestyle modification. METHODS: This study was conducted using data from 266 individuals enrolled in an mHealth-based disease management program between December 2018 and October 2022. Eligibility was based on a documented history of IHD, with participants undergoing a comprehensive cardiac risk assessment before enrollment. The program included biweekly telephone sessions, health tracking via a smartphone app, and regular progress reports to physicians. The study measured change in LDL-C levels as the primary outcome, with secondary outcomes including body weight, triglyceride levels, and other metabolic health indicators. Statistical analysis used paired 2-tailed t tests and stratified analyses to assess the impact of the program. RESULTS: Participants experienced a significant reduction in LDL-C, with LDL-C levels decreasing from a mean of 98.82 (SD 40.92) mg/dL to 86.62 (SD 39.86) mg/dL (P<.001). The intervention was particularly effective in individuals with high baseline LDL-C levels. Additional improvements were seen in body weight and triglyceride levels, suggesting a broader impact on metabolic health. Program adherence and engagement metrics suggested high participant satisfaction and compliance. CONCLUSIONS: The results of this study suggest that the mHealth-based disease management program is feasible and has an initial positive impact on reducing LDL-C levels and improving metabolic health in individuals with chronic IHD. However, the study design does not allow for a definitive conclusion regarding whether mHealth-based disease management programs are more effective than traditional face-to-face care. Future studies are needed to further validate these findings and to examine the comparative effectiveness of these interventions in more detail.
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 | 0,042 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».