The Impact of mHealth-Based Continuous Care on Disease Knowledge, Treatment Compliance, and Serum Uric Acid Levels in Chinese Patients With Gout: Randomized Controlled Trial
Bibliographic record
Abstract
Background: In patients with gout, suboptimal management refers to a lack of disease knowledge, low treatment compliance, and inadequate control of serum uric acid (SUA) levels. Several studies have shown that continuous care is recommended for disease management in patients with gout. However, in China, the continuous care model commonly used for patients with gout requires significant labor and time costs, and its efficiency and coverage remain low. Mobile health (mHealth) may be able to address these issues. Objective: This study aimed to explore the impact of mHealth-based continuous care on improving gout knowledge and treatment compliance and reducing SUA levels. Methods: This study was a single-center, single-blind, and parallel-group randomized controlled trial. Participants were recruited at the West China Hospital of Sichuan University in Chengdu, China, between February 2021 and July 2021 and were randomly assigned to the intervention and control groups. The intervention group received continuous care via an mHealth app, which includes modules for health records, 24 weeks of gout-related health education materials, and interactive support. The control group received routine continuous care, including face-to-face health education, paper-based health education materials consistent with the content for the intervention group, and telephone consultations initiated by the patient. Follow-up was conducted at 6 months. Participants' gout knowledge levels and treatment compliance were measured at baseline and the 12th and 24th weeks, and participants' SUA levels were measured at baseline and the 24th week. The intention-to-treat principle and a generalized estimating equation model were used to test the effect of the intervention. Results: Overall, 258 potential participants underwent eligibility assessments, and 120 were recruited and randomized into the intervention (n=60, 50%) and control (n=60, 50%) groups. Of the 120 participants, 93 (77.5%) completed the 24-week study. The 2 groups had no significant differences in sociodemographic or clinical characteristics, and the baseline measurements were comparable (all P>.05). Compared with the control group, the intervention group exhibited a significant improvement in gout knowledge levels over time (β=0.617, 95% CI 0.104-1.129; P=.02 and β=1.300, 95% CI 0.669-1.931; P<.001 at the 12th and 24th weeks, respectively). There was no significant difference in treatment adherence between the 2 groups at the 12th week (β=1.667, 95% CI -3.283 to 6.617; P=.51), while a statistical difference was observed at the 24th week (β=6.287, 95% CI 1.357-11.216; P=.01). At the 24th week, SUA levels in both the intervention and control groups were below baseline, but there was no significant difference in SUA changes between the 2 groups (P=.43). Conclusions: Continuous care based on the mHealth app improved knowledge levels and treatment compliance among patients with gout. We suggest incorporating this intervention modality into standard continuous care for patients with gout.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".