Effects and Processes of an mHealth Intervention for the Management of Chronic Diseases: Prospective Observational Study
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth) interventions for self-management are a promising way to meet the needs of patients with chronic diseases in primary care practices. Therefore, an mHealth intervention, TelePraCMan, was developed and evaluated for patients with type 2 diabetes mellitus, chronic obstructive pulmonary disease, high blood pressure, or heart failure in a German primary care setting. TelePraCMan entails a symptom diary, an appointment manager, a manager to document goals, and a warning system. The app should foster the self-management of participating patients. OBJECTIVE: We aimed to examine the effects of TelePraCMan on patient activation and quality of life and explored the underlying contextual factors, impacts, and degree of implementation. METHODS: In a prospective observational study design, we collected data by using interviews and written questionnaires from participating patients (intervention and control groups) and primary care workers (physicians and practice assistants). The primary outcomes of interest were patient-reported quality of life (12-Item Short Form Survey) and patient activation (patient activation measure). The quantitative analysis focused on differences between patients in the intervention and control groups, as well as before (T0) and after (T1) the intervention. Interviews were analyzed by using qualitative content analysis via MAXQDA (VERBI GmbH). RESULTS: At baseline, 25 patients and 24 primary care workers completed the questionnaire, and 18 patients and 21 primary care workers completed the follow-up survey. The patients were predominantly male and, on average, aged 64 (SD 11) years (T0). The primary care workers were mostly female (62%) and, on average, aged 47 (SD 10) years (T0). No differences were observed in the outcomes before and after the intervention or between the intervention and control groups. In the additional interviews, 4 patients and 11 primary care workers were included. The interviewees perceived that the intervention was useful for some patients. However, contextual factors and problems with implementation activities negatively affected the use of the app with patients. The main reasons for the low participation were the COVID-19 pandemic and the target group, which seemed to have less interest in mHealth; the interviewees attributed this to the older age of patients. However, the respondents felt that the app would be better accepted in 5 or 10 years. CONCLUSIONS: Although the TelePraCMan app was rated as very good and important by the participants, few patients used it. The digital intervention was hardly implemented and had limited impact in the current setting of German primary care. TRIAL REGISTRATION: German Clinical Trials Register DRKS00017320; https://tinyurl.com/4uwrzu85.
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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 | 0,005 | 0,008 |
| 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 | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».