Evidence map›Paper›PMID 38712947›Full record

Trial reportJournal of diabetes investigation2024

Internet of things-based approach for glycemic control in people with type 2 diabetes: A randomized controlled trial.

Ryotaro Bouchi, Kazuo Izumi, Naoki Ishizuka, Yukari Uemura, Hiroshi Ohtsu, Kengo Miyo, Shigeho Tanaka, Noriko Satoh-Asahara, Kazuo Hara, Masato Odawara and 8 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of diabetes investigation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Ryotaro BouchiDiabetes and Metabolism Information Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.ORCID https://orcid.org/0000-0002-7664-4342
Kazuo IzumiCenter for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.
Naoki IshizukaCenter for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.
Yukari UemuraCenter for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan.
Hiroshi OhtsuClinical Research and Trial Center, Juntendo University, Tokyo, Japan.
Kengo MiyoCenter for Medical Informatics Intelligence, National Center for Global Health and Medicine, Tokyo, Japan.
Shigeho TanakaFaculty of Nutrition, Kagawa Nutrition University, Saitama, Japan.
Noriko Satoh-AsaharaDepartment of Endocrinology, Metabolism and Hypertension Research, Clinical Research Institute, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.ORCID https://orcid.org/0000-0002-8645-3144
Kazuo HaraDepartment of Endocrinology and Metabolism, Saitama Medical Center, Jichi Medical University, Saitama, Japan.
Masato OdawaraDepartment of Diabetology, Metabolism and Endocrinology, Tokyo Medical University Hospital, Tokyo, Japan.
Yoshiki KusunokiDepartment of Diabetes, Endocrinology and Clinical Immunology, School of Medicine, Hyogo Medical University, Hyogo, Japan.ORCID https://orcid.org/0000-0002-9361-9317
Hidenori KoyamaDepartment of Diabetes, Endocrinology and Clinical Immunology, School of Medicine, Hyogo Medical University, Hyogo, Japan.
Takeshi OnoueDepartments of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya, Japan.ORCID https://orcid.org/0000-0002-8589-9937
Hiroshi ArimaDepartments of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Kazuyo TsushitaFaculty of Nutrition, Kagawa Nutrition University, Saitama, Japan.
Hirotaka WatadaDepartment of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID https://orcid.org/0000-0001-5961-1816
Takashi KadowakiToranomon Hospital, Tokyo, Japan.
Kohjiro UekiDepartment of Diabetes and Endocrinology and Metabolism, Center Hospital, National Center for Global Health and Medicine, Tokyo, Japan.

Funding

Japan Agency for Medical Research and Development 19le0110001h
6 · The paper itself

Abstract

aimsThe utilization of long-term effect of internet of things (IoT) on glycemic control is controversial. This trial aimed to examine the effect of an IoT-based approach for type 2 diabetes. MATERIALS AND

methodsThis randomized controlled trial enrolled 1,159 adults aged 20-74 years with type 2 diabetes with a HbA1c of 6.0-8.9% (42-74 mmol/mol), who were using a smartphone on a daily basis were randomly assigned to either the IoT-based approach group (ITG) or the control group (CTG). The ITG were supervised to utilize an IoT automated system that demonstrates a summary of lifelogging data (weight, blood pressure, and physical activities) and provides feedback messages that promote behavioral changes in both diet and exercise. The primary end point was a HbA1c change over 52 weeks.

resultsAmong the patients, 581 were assigned to the ITG and 578 were in the CTG. The changes in HbA1c from baseline to the final measurement at 52 weeks [mean (standard deviation)] were -0.000 (0.6225)% in ITG and - 0.006 (0.6449)% in CTG, respectively (P = 0.8766). In the per protocol set, including ITG using the IoT system almost daily and CTG, excluding those using the application almost daily, the difference in HbA1c from baseline to 52 weeks were -0.098 (0.579)% and 0.027 (0.571)%, respectively (P = 0.0201). We observed no significant difference in the adverse event profile between the groups.

conclusionsThe IoT-based approach did not reduce HbA1c in patients with type 2 diabetes. IoT-based intervention using data on the daily glycemic control and HbA1c level may be required to improve glycemic control.

Indexed as

Blood GlucoseDiabetes Mellitus, Type 2Glycated HemoglobinGlycemic ControlInternet of ThingsAdultAgedExerciseFemaleFollow-Up StudiesHumansMaleMiddle AgedYoung AdultBlood GlucoseGlycated Hemoglobinhemoglobin A1c protein, humanBehavioral changeInternet of thingsType 2 diabetes

Identifiers

PMID38712947
PMCPMC11363111

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.