Evidence map›Paper›PMID 32159518›Full record

ArticleJMIR mHealth and uHealth2020

A Mobile-Based Intervention for Glycemic Control in Patients With Type 2 Diabetes: Retrospective, Propensity Score-Matched Cohort Study.

Jing Li, Li Sun, Yaogang Wang, Lichuan Guo, Daiqing Li, Chang Liu, Ning Sun, Zheng Xu, Shu Li, Yunwen Jiang and 3 more

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

13 authors.

Jing Li *National Health Commission Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-2428-1439
Li Sun *School of Nursing, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-0151-4949
Yaogang Wang *School of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-7325-0663
Lichuan GuoNational Health Commission Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-4718-1780
Daiqing LiNational Health Commission Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.ORCID 0000-0001-5814-0872
Chang LiuYiducloud Technologies Co, Ltd, Beijing, China.ORCID 0000-0002-4372-4311
Ning SunYu-Tang Department, iHealth Labs Inc, Beijing, China.ORCID 0000-0002-4256-6120
Zheng XuYu-Tang Department, iHealth Labs Inc, Beijing, China.ORCID 0000-0002-4849-384X
Shu LiSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0001-6380-4127
Yunwen JiangSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0002-4689-755X
Yuan WangSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0001-8251-4029
Shunming ZhangSchool of Public Health, Tianjin Medical University, Tianjin, China.ORCID 0000-0003-2590-3487
Liming ChenNational Health Commission Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.ORCID 0000-0003-0684-6149

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile-based interventions appear to be promising in ameliorating huge burdens experienced by patients with type 2 diabetes. However, it is unclear how effective mobile-based interventions are in glycemic management of patients with type 2 diabetes based on real-world evidence.

objectiveThis study aimed to evaluate the effectiveness of a mobile-based intervention on glycemic control in patients with type 2 diabetes based on real-world population data.

methodsThis retrospective, propensity score-matched cohort study analyzed longitudinal data from a clinical electronic health database. The study population included 37,913 patients with type 2 diabetes at cohort entry between October 1, 2016, and July 31, 2018. A total of 2400 patients were matched 1:1, using propensity score matching, into the usual care and mobile health (mHealth) groups. The primary outcomes of glycemic control included control rates of glycated hemoglobin (HbA

resultsOf the 2400 patients included in the analysis, 1440 (60.00%) were male and the mean age was 52.24 years (SD 11.56). At baseline, the control rates of HbA

conclusionsThis mobile-based intervention delivered by a multidisciplinary team can better improve glycemic control rates of patients with type 2 diabetes than usual care. These effects were best sustained within the first 6 months. Starting from the sixth month, intensive management needs to be conducted to maintain long-term effectiveness of the mobile-based intervention.

Indexed as

Diabetes Mellitus, Type 2FemaleGlycemic ControlHumansMaleMiddle AgedPropensity ScoreRetrospective Studiesglycemic controlmobile healthpropensity score matchingtype 2 diabetes

Identifiers

PMID32159518
PMCPMC7097724

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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.