Design and Usability of a Heart Failure mHealth System: A Pilot Study
Notice bibliographique
Résumé
BACKGROUND: Despite the advances in mobile health (mHealth) systems, little is known about patients' and providers' experiences using a new mHealth system design. OBJECTIVE: This study aimed to understand challenges and provide design considerations for a personalized mHealth system that could effectively support heart failure (HF) patients after they transition into the home environment. METHODS: Following exploratory interviews with nurses and preventive care physicians, an mHealth system was developed. Patients were asked to measure their weight, blood pressure, and blood glucose (if they had diabetes). They were also instructed to enter symptoms, view notifications, and read messages on a mobile app that we developed. A Bluetooth-enabled weight scale, blood pressure monitor, glucometer, and mobile phone was provided after an introductory orientation and training session. HF nurses used a dashboard to view daily measurements for each patient and received text and email alerts when risk was indicated. Observations of usage, cases of deterioration, readmissions, and metrics related to system usability and quality of life outcomes were used to determine overall effectiveness of the system, whereas focus group sessions with patients were conducted to elicit participants' feedback on the system's design. RESULTS: A total of 8 patients with HF participated over a 6-month period. Overall, the mean users' satisfaction with the system ranked 73%, which was above average. Quality of life improvement was 3.6. Patients and nurses used the system on a regular basis and were able to successfully identify and manage 8 health deteriorations, of which 5 were completely managed remotely. Focus groups revealed that, on one hand, the system was beneficial and helped patients with: recording and tracking readings; receiving encouragement and reassurance from nurses; spotting and solving problems; learning from past experiences; and communication. On the other hand, findings also highlighted design issues and recommendations for future systems such as the need to communicate via other media, personalize symptom questions and messages, integrate other health tracking technologies, and provide additional methods to analyze and visualize their data. CONCLUSIONS: Understanding users' experiences provides important design considerations that could complement existing design recommendations from the literature, and, when combined with physician and nurse requirements, have the potential to yield a feasible telehealth system that is effective in supporting HF self-care. Future studies will include these guidelines and use a larger sample size to validate the outcomes.
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 | 0,011 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».