Patients’ Perspectives About the Treatment They Receive for Cardiovascular Diseases and Mental Disorders: Web-Based Survey Study
Notice bibliographique
Résumé
BACKGROUND: Noncommunicable disease (NCD)-related deaths account for 71% of deaths worldwide. The World Health Organization recently developed a global action plan to address the impact of NCDs, with the goal of reducing the number of premature NCD-related deaths to 25% by the year 2025. Appropriate therapeutic adherence is critical for effective disease management; however, approximately 30%-50% of patients with an NCD do not comply with disease management activities as prescribed. Web-based patient communities can represent platforms from which specific information on patients' perception of treatment adherence can be gathered outside of a clinical trial setting. OBJECTIVE: This study aims to better understand patients' perspectives regarding therapeutic adherence and iatrogenic risk in 2 major groups of NCDs for which poor disease management can have fatal consequences: cardiovascular diseases and mental disorders. Therapeutic adherence, motivational factors, patients' awareness and perception of iatrogenesis, and treatment tools used by patients were assessed. METHODS: A web-based survey was performed among patients with cardiovascular diseases or mental disorders or both conditions who were registered on the French Carenity platform, a web-based community in which patients with an NCD can share experiences and receive support and information. The study inclusion criteria were defined as follows: diagnosis of cardiovascular disease or mental disorder or both conditions (self-declared), age ≥18 years, residence in France, registration on the French Carenity platform, and ongoing pharmaceutical treatment for the condition. Patients who met the inclusion criteria were then invited to complete a self-administered web-based questionnaire that included questions addressing therapeutic adherence and iatrogenic risk. RESULTS: A total of 820 patients were enrolled in the study, including patients with cardiovascular diseases (403/820, 49.2%), patients with mental disorders (292/820, 35.6%), and patients with both cardiovascular diseases and mental disorders (125/820, 15.2%). The mean age of the participants was 55.2 (SD 12.7) years. We found that 82.8% (679/820) of patients experienced adverse effects of medication. Patients tended to perceive themselves to be more adherent than they actually were; a significant number of patients disregarded their prescription and stopped or interrupted medication without consulting with a doctor. Patients with cardiovascular diseases were nearly twice as adherent as patients with a mental disorder (P≤.001). Adherence was significantly associated with gender (P≤.001), age (P≤.001), and treatment complexity (P≤.001). Finally, for each disease type, 3 patient profiles were identified, which provide interesting insight for improving therapeutic adherence and adjustment strategies specifically according to patient behavior. CONCLUSIONS: This study provides insight into the perspectives of patients receiving therapy for cardiovascular diseases or mental disorders or both conditions, which could help improve the management of NCDs and prevent premature death. Our study also shows that web-based patient platforms provide new opportunities to improve disease management by understanding patients' experiences.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».