Smartphone App–Based Eating Behavior Monitoring and Feedback Intervention for Glucocorticoid-Induced Appetite Increase in Patients With Systemic Lupus Erythematosus: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Increased appetite and weight gain are common adverse effects of glucocorticoid (GC) therapy in patients with systemic lupus erythematosus (SLE). Concerns about appearance-related changes due to weight gain can reduce medication adherence. Moreover, the complex interplay among GCs, mood changes, sleep disturbances, and appetite can influence eating behaviors. Daily data collection using an ecological momentary assessment (EMA) and analysis of interrelations may help clarify these dynamics. Furthermore, real-time feedback based on daily eating behavior may help patients regulate appetite and eating patterns. Accordingly, we developed Mogu!☆Log, a smartphone-based application that enables daily self-reporting of eating behaviors, appetite, and mood, and provides graphical feedback on meal frequency and perceived control over eating. OBJECTIVE: This study presents a protocol for a pilot randomized controlled trial (RCT) designed to evaluate the effects of real-time feedback on eating behaviors using the Mogu!☆Log app among patients with newly diagnosed SLE who had started GC therapy. METHODS: This multicenter study recruited Japanese patients with newly diagnosed SLE who had started GC therapy across 15 hospitals with rheumatology services. Participants were randomly assigned (1:1) to two groups: (1) the immediate feedback group, which receives graphical feedback on meal frequency and perceived control over eating starting from day 1, and (2) the delayed feedback group, which uses the same app without feedback for the first 14 days and begins receiving identical feedback from day 15. Participants enter data daily from day 1 to day 21 after randomization. The primary outcome is the mean number of meals on day 14 after GC initiation. Secondary outcomes include the loss-of-control-over-eating score and the five-item visual analog scale-based appetite score, both recorded on day 14. Between-group mean differences will be analyzed using t-tests. The target sample size is 60. In an embedded observational Study Within a Trial (SWAT), linear mixed models will examine whether GC dose influences appetite scores through mood and sleep changes. RESULTS: We hypothesized that participants receiving immediate feedback will have fewer meals on day 14, reduced loss of control over eating, and better appetite scores. The study received funding in April 2019, April 2022, and April 2024. Recruitment began in October 2024, and as of May 2025, 17 participants were enrolled. Data collection is expected to be completed by March 2027, with data analysis yet to begin. Results will be submitted for publication and reported to the UMIN registry in summer 2027. CONCLUSIONS: This pilot trial will provide foundational data on the feasibility and efficacy of smartphone-based real-time feedback in managing GC-induced appetite increase in patients with SLE. These findings may contribute to the growing body of literature on app-based interventions for medication-related adverse effects. CLINICALTRIAL: UMIN Clinical Trials Registry UMIN000052113 https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000059479.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.006 |
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".