Development of a Theory-Based mHealth App for Fatigue Management in Lupus: Human-Centered Design Approach
Notice bibliographique
Résumé
Background: Fatigue is a highly prevalent and debilitating symptom of systemic lupus erythematosus (SLE), significantly affecting the quality of life and employment of those living with the disease. Nonpharmacologic approaches, such as physical activity interventions, have shown promise in reducing fatigue but are often resource-intensive and lack grounding in behavior change theory. Mobile health (mHealth) technology offers a scalable approach to delivering behavioral interventions. Objective: This study describes the development of an mHealth app, grounded in behavior change theory, to support fatigue self-management in individuals with SLE by promoting physical activity. Methods: We used a human-centered design (HCD) approach to develop an mHealth app grounded in the self-determination theory of motivation and the social cognitive theory of behavior change. The process included two phases: (1) inspiration and (2) ideation. In the inspiration phase, key user needs were identified from focus groups of adults with SLE. During the ideation phase, a prototype was developed and iteratively refined based on feedback from additional individuals with SLE who participated in multiple rounds of semistructured interviews and online feedback surveys. Results: In the inspiration phase, 12 individuals participated in 2 focus groups and identified key priorities for the mHealth intervention, including symptom tracking (fatigue, pain, sleep, and physical activity), reliable educational content, social connection, and reminders. In the ideation phase, a prototype was developed based on these findings and refined through 2 rounds of user feedback interviews with 12 additional adults with SLE. Participants rated the features and format of the prototype favorably, with average scores ranging from 1.4 to 2.1 on a 5-point Likert scale (1=highest rating), and 78% (7/9) of interviewees reported they were likely or highly likely to use the app. Several themes around preferences for the app emerged from the interviews, including the importance of: (1) community and social connection, (2) accessibility and inclusion, (3) options for customization, (4) integration of the app with existing digital health tools, and (4) notifications for reminders and motivational messages. Based on this feedback, the prototype was refined, and a digital messaging feature was created. A library of 154 reminders and motivational messages was developed with input from 10 individuals with SLE who took part in a third round of interviews. Conclusions: Using an HCD approach, we developed an mHealth app tailored to the needs of individuals with SLE, integrating behavior change theory to support fatigue self-management. Through engagement with end users, we iteratively refined the app to address key priorities and enhance usability. This study demonstrates the feasibility of using HCD to develop an mHealth app grounded in behavior change theory and provides a model for creating rigorous digital health interventions for individuals with SLE and other chronic conditions.
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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 | 0,008 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| 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 | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».