Development of a Theory-Based mHealth App for Fatigue Management in Lupus: Human-Centered Design Approach
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".