Evidence map›Paper›PMID 38678094›Full record

ArticleNPJ digital medicine2024

Effectiveness of DialBetesPlus, a self-management support system for diabetic kidney disease: Randomized controlled trial.

Kayo Waki, Mitsuhiko Nara, Syunpei Enomoto, Makiko Mieno, Eiichiro Kanda, Akiko Sankoda, Yuki Kawai, Kana Miyake, Hiromichi Wakui, Yuya Tsurutani and 14 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
9.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 3 syntheses or guidelines pooled it, 11 citations in OpenAlex.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

24 authors at 11 institutions in 1 country.

Kayo Waki *Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. kwaki-tky@m.u-tokyo.ac.jp.ORCID http://orcid.org/0000-0003-0046-2523
Mitsuhiko Nara *Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.ORCID http://orcid.org/0000-0002-7844-8564
Syunpei EnomotoDepartment of Planning, Information and Management, University of Tokyo Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0002-8770-9068
Makiko MienoDepartment of Medical Informatics, Center for Information, Jichi Medical University, Shimotsuke, Japan.
Eiichiro KandaMedical Science, Kawasaki Medical School, Kurashiki, Japan.
Akiko SankodaDepartment of Planning, Information and Management, University of Tokyo Hospital, Tokyo, Japan.
Yuki KawaiDepartment of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Kana MiyakeDepartment of Planning, Information and Management, University of Tokyo Hospital, Tokyo, Japan.
Hiromichi WakuiDepartment of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Yuya TsurutaniEndocrinology and Diabetes Center, Yokohama Rosai Hospital, Yokohama, Japan.
Nobuhito HirawaDepartment of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
Tadashi YamakawaDepartment of Endocrinology and Diabetes, Yokohama City University Medical Center, Yokohama, Japan.
Shiro KomiyaDepartment of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan.
Akihiro IsogawaDivision of Diabetes, Mitsui Memorial Hospital, Tokyo, Japan.
Shinobu SatohDepartment of Endocrinology and Metabolism, Chigasaki Municipal Hospital, Chigasaki, Japan.
Taichi MinamiDepartment of Diabetes and Endocrinology, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan.
Tamio IwamotoDepartment of Nephrology and Hypertension, Saiseikai Yokohamashi Nanbu Hospital, Yokohama, Japan.
Tatsuro TakanoDepartment of Diabetes and Endocrinology, Fujisawa City Hospital, Fujisawa, Japan.
Yasuo TerauchiDepartment of Endocrinology and Metabolism, Yokohama City University Graduate School of Medicine, Yokohama, Japan.
Kouichi TamuraDepartment of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan.ORCID http://orcid.org/0000-0002-0660-5372
Toshimasa YamauchiDepartment of Diabetes and Metabolic Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Masaomi NangakuDivision of Nephrology and Endocrinology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Naoki KashiharaDepartment of Nephrology and Hypertension, Kawasaki Medical School, Kurashiki, Japan.
Kazuhiko OheDepartment of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
University of Tokyo Hospital · JPThe University of Tokyo · JPYokohama City University · JPYokohama City University Medical Center · JPKawasaki Medical School · JPSaiseikai Ibaraki Hospital · JPAkita University · JPFujisawa City Hospital · JPJichi Medical University · JPMitsui Memorial Hospital · JPYokohama Rosai Hospital · JP

Funding

Japan Agency for Medical Research and Development (AMED) JP19ek0210095
6 · The paper itself

Abstract

We evaluated the effectiveness of a mobile health (mHealth) intervention for diabetic kidney disease patients by conducting a 12-month randomized controlled trial among 126 type 2 diabetes mellitus patients with moderately increased albuminuria (urinary albumin-to-creatinine ratio (UACR): 30-299 mg/g creatinine) recruited from eight clinical sites in Japan. Using a Theory of Planned Behavior (TPB) behavior change theory framework, the intervention provides patients detailed information in order to improve patient control over exercise and dietary behaviors. In addition to standard care, the intervention group received DialBetesPlus, a self-management support system allowing patients to monitor exercise, blood glucose, diet, blood pressure, and body weight via a smartphone application. The primary outcome, change in UACR after 12 months (used as a surrogate measure of renal function), was 28.8% better than the control group's change (P = 0.029). Secondary outcomes also improved in the intervention group, including a 0.32-point better change in HbA1c percentage (P = 0.041). These improvements persisted when models were adjusted to account for the impacts of coadministration of drugs targeting albuminuria (GLP-1 receptor agonists, SGLT-2 inhibitors, ACE inhibitors, and ARBs) (UACR: -32.3% [95% CI: -49.2%, -9.8%] between-group difference in change, P = 0.008). Exploratory multivariate regression analysis suggests that the improvements were primarily due to levels of exercise. This is the first trial to show that a lifestyle intervention via mHealth achieved a clinically-significant improvement in moderately increased albuminuria.

Identifiers

PMID38678094
PMCPMC11055918
OpenAlexW4395696522

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.