Evidence map›Paper›PMID 29094299›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2017

Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis.

Xuying Wang, Jiageng Chen, Xiaoqian Liu, Fei Gao, Haozuo Zhao, Duolan Han, Xiyue Jing, Yuanyuan Liu, Zhuang Cui, Changping Li and 1 more

Open access · goldAbstract read
In one paragraph

Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.4field-weighted citation impact, top 34% 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

5 citing papers in PubMed, 16 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Xuying WangDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Jiageng ChenDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Xiaoqian LiuDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Fei GaoDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Haozuo ZhaoDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Duolan HanDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Xiyue JingDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Yuanyuan LiuDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Zhuang CuiDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China.
Changping LiDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China. lichangping@tmu.edu.cn.
Jun MaDepartment of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, People's Republic of China. junma@tmu.edu.cn.
Tianjin Medical University · CN

Funding

Ministry of education of Humanities and Social Science project 17YJAZH048National Natural Science Foundation of China 71373175Tianjin Municipal Human Resources and Social Security Bureau TMIR 201502
6 · The paper itself

Abstract

introductionLifestyle behaviors are essential elements of diabetes care. The aims of this study were to identify distinct subgroups of people with type 2 diabetes based on personal levels of lifestyle behaviors and explore the different characteristics across these subgroups.

methodsIn 2015 and 2016, 1504 outpatients with a diagnosis of type 2 diabetes were selected via two-stage simple random sampling from 10 municipal district hospitals in Tianjin. Participants accepted an invitation by experienced physicians to complete a questionnaire containing demographic and lifestyle content. Clinical data were collected by reviewing medical records. Latent class analysis was applied to identify patterns of lifestyle behaviors. Multinomial logistic regression was used to investigate the characteristics of the subgroups.

resultsThe final model yielded a four-class solution: the healthy behavioral group, unhealthy diet and less activity group, smoking and drinking group, and sedentary and extremely inactive group. Further analysis found that variables, including age, sex, general/central obesity, treatment modalities, glycemic control, diabetes duration, and diabetes-related complications and comorbidities, were disproportionately distributed across the four latent classes (P < 0.05). Participants in the unhealthy diet and less activity group were more likely to have a longer duration of diabetes, poor glycemic control and more diabetes-related diseases relative to the other three latent classes.

conclusionsIdentification and characterization of subgroups based on lifestyle behaviors in individuals with type 2 diabetes can help health care providers to shift to targeted intervention strategies.

Indexed as

Latent class analysisLifestyle behaviorsType 2 diabetes mellitus

Identifiers

PMID29094299
PMCPMC5688992
OpenAlexW2766202639

What OpenQuestion holds

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LicenceCC BY-NC
Read underepoch 390

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.