Evidence map›Paper›PMID 27535128›Full record

Trial reportImplementation science : IS2016

Systematic medical assessment, referral and treatment for diabetes care in China using lay family health promoters: protocol for the SMARTDiabetes cluster randomised controlled trial.

David Peiris, Lei Sun, Anushka Patel, Maoyi Tian, Beverley Essue, Stephen Jan, Puhong Zhang

Registry-linked trialOpen access · goldAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Implementation science : IS, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02726100 (Systematic Medical Assessment, Referral and Treatment for Diabetes Care in China Using Lay Family Health Promoters), which is not on this map. Cited by 16 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 3 pooled it
0.9field-weighted citation impact, top 22% 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.

NCT02726100 nacompletednot on this mapstarted 2017, after this paper: background citation

Systematic Medical Assessment, Referral and Treatment for Diabetes Care in China Using Lay Family Health Promoters

TypeinterventionalSponsorThe George Institute for Global Health, ChinaRan2017 to 2021Enrolled2,073ConditionsDiabetes Mellitus, Type 2, Hypertension, DyslipidemiasArmsSMARTHealth Diabetes
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 3 syntheses or guidelines pooled it, 23 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

7 authors at 3 institutions in 2 countries.

David PeirisThe George Institute for Global Health, Sydney, Australia.
Lei SunThe George Institute for Global Health, Beijing, China.
Anushka PatelThe George Institute for Global Health, Sydney, Australia.
Maoyi TianThe George Institute for Global Health, Beijing, China.
Beverley EssueThe University of Sydney, Sydney, Australia.
Stephen JanThe George Institute for Global Health, Sydney, Australia.
Puhong ZhangThe George Institute for Global Health, Beijing, China. zpuhong@georgeinstitute.org.cn.ORCID 0000-0003-4610-9848
George Institute for Global Health · CNThe George Institute for Global Health · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundType 2 diabetes (T2DM) affects 113.9 million people in China, the largest number of any country in the world (JAMA 310:948-59, 2013). T2DM prevalence has risen dramatically from around 1 % in the 1980s to now over 10 % and is expected to continue rising. Despite the growing disease burden, few people with T2DM are achieving adequate management targets to prevent complications. Health system infrastructure in China is struggling to meet these gaps in care, and innovative, cost-effective and affordable solutions are needed. One promising strategy that may be particularly relevant to the Chinese context is improving support for lay family members to care for their relatives with T2DM.

methodsWe hypothesise that an interactive mobile health management system can support lay family health promoters (FHP) and healthcare staff to improve clinical outcomes for family members with T2DM through medical assessment, regular monitoring, lifestyle advice and the prescribing of guidelines recommended medications. This intervention will be implemented as a cluster randomised controlled trial involving 80 communities (40 communities in Beijing and 40 rural villages in Hebei province) and 2000 people with T2DM. Outcome analyses will be conducted blinded to intervention allocation. The primary outcome is the proportion of patients achieving ≥2 "ABC" goals (HbA1c <7.0 %, blood pressure (BP) <140/80 mmHg and LDL cholesterol <100 mg/dl or 2.6 mmol/L) at the end of follow-up (Diabetes Care 36(Supplement 1):S11-S66, 2013). Secondary outcomes include the proportion of patients achieving individual ABC targets; mean changes in HbA1c, BP, LDL, renal function (serum creatinine and urinary albumin), body mass index, quality of life (QOL, EQ-5D), and healthcare utilisation from baseline; and cost-effectiveness/utility of intervention. Trial outcomes will be accompanied by detailed process and economic evaluations. DISCUSSION: The Chinese government has prioritised prevention and treatment of diabetes as 1 of 11 National Basic Public Health Services. Despite great promise for mHealth interventions to improve access to effective health care, there remains uncertainty about how this can be successfully achieved. The findings are likely to inform policy on a scalable strategy to overcome sub-optimal access to effective health care in China.

trial registrationClinicaltrials.gov NCT02726100.

Indexed as

CaregiversAdultAgedAmbulatory CareChinaCluster AnalysisDiabetes Mellitus, Type 2Family HealthFeasibility StudiesHealth PromotionHumansMiddle AgedReferral and ConsultationSelf-ManagementSocial SupportCapacity strengtheningChinaImplementationmHealthType 2 diabetes

Identifiers

PMID27535128
PMCPMC4989287
OpenAlexW2517706150

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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.