Usage of a Multipurpose mHealth App Among Adults With Sickle Cell Disease: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: While mobile health (mHealth) apps have been made for various diseases, including sickle cell disease (SCD), most focus on a single purpose. SCD is a chronic disease that requires knowledge of the disease, self-management, and adherence to treatment plans. While mHealth apps have been made with single features for SCD, there is limited understanding of using an mHealth app with a more comprehensive set of features that could engage adults with SCD, depending on what features they prefer and need to engage and empower them in living with their disease. Objective: We evaluated the usage of an mHealth app with various features, including pain tracking, quizzes for patient-facing guidelines, pain and asthma action plans, and goal setting. Methods: Adults with SCD were enrolled at 2 sickle cell centers between 2018 and 2022 as part of a 6-month feasibility randomized controlled trial with participants completing surveys at baseline and 6 months. Participants were randomized into receiving either an mHealth app and booklet with patient-facing guidelines or a booklet with the guidelines alone. The mHealth app comprised web pages with patient-facing guideline material and a Research Electronic Data Capture (REDCap) project. The REDCap project included a personal profile, a pain tracker, goal setting, quizzes about the guidelines, and pain or asthma action plans. The REDCap project also included the ability to send daily text messages at a time they chose, which contained a message they could create and a link to their profile. Outcomes included SCD-specific knowledge and acute health care utilization (emergency room visits and hospitalizations). We evaluated the usage of these different features and relationships with baseline variables, each other, and study outcomes. Results: Approximately 75% (50/67) of the enrolled and randomized participants completed all the study components, and 100% (26/26) of the participants who were randomized to the mHealth app arm and completed the study used the mHealth app. Further, 15/30 (50%) participants used multiple features. Baseline sickle cell knowledge and female gender were associated with more usage of pain diary (P=.04) and mission (P=.046) features, respectively. While not significant, mission completion was associated with lower hospitalizations (P=.06). Conclusions: Adults with SCD engaged differently with an mHealth app with multiple features. As this study was not focused on one part of our app, engagement with features in this app was entirely patient-driven, which may demonstrate the expected real-world use of an mHealth app in this population. A multipurpose app can help engage participants in self-management strategies through different features and potentially improve 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 | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 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 ».