Evidence map›Paper›PMID 36927708›Full record

ArticlePreventing chronic disease2023

Using Patient Profiles for Sustained Diabetes Management Among People With Type 2 Diabetes.

Shang-Jyh Chiou, Yen-Jung Chang, Chih-Dao Chen, Kuomeng Liao, Tung-Sung Tseng

Open access · diamondFull text read
In one paragraph

Article in Preventing chronic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 26% 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

8 citing papers in PubMed, 4 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

5 authors at 5 institutions in 2 countries.

Shang-Jyh ChiouDepartment of Health Care Management, National Taipei University of Nursing and Health Sciences, Taipei City, Taiwan, Republic of China.
Yen-Jung ChangDepartment of Health Promotion and Health Education, National Taiwan Normal University, Taipei City, Taiwan, Republic of China.
Chih-Dao ChenDepartment of Family Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan, Republic of China.
Kuomeng LiaoDepartment of Endocrinology and Metabolism, Zhongxiao Branch, Taipei City Hospital, Taipei City, Taiwan, Republic of China.
Tung-Sung TsengBehavioral and Community Health Sciences, School of Public Health, Louisiana State University Health Sciences Center-New Orleans, 2020 Gravier St, Ste 213, New Orleans, LA 70112 (ttseng@lsuhsc.edu).
Far Eastern Memorial Hospital · TWLouisiana State University Health Sciences Center New Orleans · USNational Taipei University of Nursing and Health Science · TWNational Taiwan Normal University · TWTaipei City Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOur objective was to evaluate the association between patient profiles and sustained diabetes management (SDM) among patients with type 2 diabetes.

methodsWe collected HbA

resultsThe good management group demonstrated better SDM than the poor management group (β = 0.183; P = .003). Using the most recent HbA

conclusionBy using patient profiles, we confirmed a positive relationship between optimal patient behavior in self-care management and SDM. Patients with type 2 diabetes exhibited effective self-care management behavior and engaged in more health care activities, which may have led to better SDM. In promoting patient-centered care, using patient profiles and customized health education materials could improve diabetes care.

Indexed as

Diabetes Mellitus, Type 2HumansMedication AdherenceSurveys and QuestionnairesTaiwan

Identifiers

PMID36927708
PMCPMC10038094
OpenAlexW4324026678

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read15
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.