A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study
Notice bibliographique
Résumé
BACKGROUND: Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. OBJECTIVE: The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. METHODS: Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. RESULTS: Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. CONCLUSIONS: Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».