Efficacy of the Self-management Support System DialBetesPlus for Diabetic Kidney Disease: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Diabetic kidney disease (DKD) is one of the main complications of type 2 diabetes mellitus (T2DM). DKD is a known risk factor for end-stage renal disease, cardiovascular disease, and all-cause death. Effective intervention for early-stage DKD is vital to slowing down the progression of kidney disease and improve prognoses. Mobile health (mHealth) is reportedly effective in supporting patients' self-care and improving glycemic control, but the impact of mHealth on DKD has yet to be shown. OBJECTIVE: The purpose of this study is to evaluate the efficacy of standard therapy with the addition of a self-management support system, DialBetesPlus, in patients with DKD and microalbuminuria. METHODS: This study is a prospective, randomized, open-label, multicenter clinical trial. The target population consists of 160 patients diagnosed with T2DM accompanied by microalbuminuria. We randomly assigned the patients to 2 groups-the intervention group using DialBetesPlus in addition to conventional therapy and the control group using conventional therapy alone. DialBetesPlus is a smartphone application that supports patients' self-management of T2DM. The study period was 12 months, with a follow-up survey at 18 months. The primary outcome was a change in albuminuria levels at 12 months. Secondary outcomes included changes in physical parameters, blood test results (glycemic control, renal function, and lipid metabolism), lifestyle habits, self-management scores, medication therapy, and quality of life. RESULTS: The study was approved in April 2018. We began recruiting patients in July 2018 and completed recruiting in August 2019. The final 18-month follow-up was conducted in March 2021. We recruited 159 patients and randomly allocated 70 into the intervention group and 61 into the control group, with 28 exclusions due to withdrawal of consent, refusal to continue, or ineligibility. The first results are expected to be available in 2021. CONCLUSIONS: This is the first randomized controlled trial assessing the efficacy of mHealth on early-stage DKD. We expect that albuminuria levels will decrease significantly in the intervention group due to improved glycemic control with ameliorated self-care behaviors. TRIAL REGISTRATION: UMIN-CTR UMIN000033261; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000037924. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/31061.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 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) | 0.055 | 0.007 |
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".