Evidence map›Paper›PMID 34377298›Full record

ArticleAmerican journal of translational research2021

Application research of chronic disease health management in an urban community based on the PRECEDE-PROCEED model in the long-term management of diabetes mellitus.

Wei Kan, Rong Yang, Meifang Tang

Abstract read
In one paragraph

Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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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

9 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

3 authors.

Wei KanHangzhou Road Community Health Service Center, The Fifth Affiliated Hospital of Xinjiang Medical University Urumqi, Xinjiang Uygur Autonomous Region, China.
Rong YangDepartment of General Practice, The Fifth Affiliated Hospital of Xinjiang Medical University Urumqi, Xinjiang Uygur Autonomous Region, China.
Meifang TangDepartment of Nursing, The Fifth Affiliated Hospital of Xinjiang Medical University Urumqi, Xinjiang Uygur Autonomous Region, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the application of chronic disease health management in an urban community in the long-term management of diabetes mellitus (DM) based on the PRECEDE-PROCEED model.

methodsThe PRECEDE-PROCEED model combines PRECEDE (predisposing, enabling and reinforcing constructs in educational diagnosis and evaluation) with PROCEED (policy, management and organization constructs in educational and environmental intervention). A total of 96 diabetic patients treated in our hospital were selected and divided into two groups by random number table, with 48 cases in each group. The routine group was given routine health management, while the PP group was given the urban community chronic disease health management based on the PRECEDE-PROCEED model in addition to the routine health management. After six months of management, the patients' effect was evaluated by comparing the blood glucose, diabetes knowledge, self-efficacy, self-management level and quality of life between the two groups.

resultsThe FPG, 2hPG and HbAlc levels of the PP group were lower than those of the routine group after six months of management (all P<0.05). The 6-month awareness rate, self-efficacy, self-management level and quality of life scores of the PP group were higher than those of the routine group (all P<0.05).

conclusionThe chronic disease health management in urban communities based on the PRECEDE-PROCEED model in long-term diabetes management can effectively improve patients' diabetes knowledge, lower blood glucose levels, improve self-efficacy and self-management, and improve the quality of life.

Indexed as

chronic disease managementLong-term management of diabetesquality of lifeself-efficacythe PRECEDE-PROCEED model

Identifiers

PMID34377298
PMCPMC8340156

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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.