Evidence map›Paper›PMID 32547700›Full record

ArticleWorld journal of diabetes2020

Effects of lifestyle interventions on rural patients with type 2 diabetes mellitus.

Bo Wang, Xiao-Li Mu, Juan Zhao, Hai-Ping Jiang, Shan-Shan Li, Ge Yan, Ying-Ying Hua, Xue-Yi Ren, Li-Xia Xing, Yan Liang and 2 more

Open access · diamondAbstract read
In one paragraph

Article in World journal of diabetes, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

  1. Trial
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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

12 authors at 1 institution in 1 country.

Bo WangDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Xiao-Li MuDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Juan ZhaoDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Hai-Ping JiangDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Shan-Shan LiDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Ge YanDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Ying-Ying HuaDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Xue-Yi RenDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Li-Xia XingDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Yan LiangDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Shu-Dong ZhangDepartment of Internal Medicine, Yantaishan Hospital, Yantai 264025, Shandong Province, China.
Yu-Chi ZhaoDepartment of Osteoarthropathy, Yantaishan Hospital, Yantai 264025, Shandong Province, China. zhaoyuchizyc@163.com.
Yantaishan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of type 2 diabetes mellitus (T2DM) is rising rapidly in rural areas, and lifestyle interventions can effectively reduce the blood glucose levels of patients with T2DM. However, current dietary and exercise guidelines are still at experimental stages and are difficult for subjects to understand and implement. The Human Metabolism Analyzer provides real life interventions for the prevention and treatment of T2DM, and our pilot research has demonstrated its effectiveness and good compliance.

aimTo investigate the effect of and compliance with lifestyle interventions in rural patients with T2DM.

methodsA total of ten rural villages were randomly selected in Chaoshui Township, Penglai City, Shandong Province, China, to conduct health screening among residents aged 50 years or older. Each rural village represented a group, and 12 patients with T2DM were randomly selected from each group (total: 120) to participate in this study and receive real life lifestyle interventions and medication guidance. Lifestyle interventions included changing the meal order (A), postprandial activities (B), resistance exercise (C), and reverse abdominal breathing (D). Diabetes education was conducted at least once a month with a weekly phone follow-up to monitor exercise and diet. Waist circumference, blood pressure, body mass index (BMI), motor function, body composition, fasting blood glucose, and glycated hemoglobin (HbA1c) were analyzed before and 3 mo after the intervention. Moreover, patient compliance and adjustments of hypoglycemic drugs were evaluated.

resultsA total of 109 subjects completed the study. The compliance rates for lifestyle interventions A, B, C, and D were 57.79%, 60.55%, 64.22%, and 75.23%, respectively. Among the subjects who received hypoglycemic drugs, the dose was reduced 2 to 3 times based on blood glucose in 54 (67.50%) subjects and was tapered and discontinued in 5 (6.25%) subjects within 3 mo, with no significant fluctuations in blood glucose after dose reduction and withdrawal. After lifestyle interventions, waist circumference, BMI, fasting blood glucose, and HbA1c significantly decreased (

conclusionFor patients with T2DM, compliance to real-life lifestyle interventions is good, and the interventions significantly improve metabolic indicators such as waist circumference, BMI, blood pressure, HbA1c, body composition, and motor function. Some patients are able to taper or discontinue hypoglycemic drugs.

Indexed as

Body mass indexLifestyle interventionsType 2 diabetes mellitus

Identifiers

PMID32547700
PMCPMC7284015
OpenAlexW3033167898

What OpenQuestion holds

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LicenceCC BY-NC
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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.