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

Overview of attention for article published in Implementation Science, August 2016
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Title
Systematic medical assessment, referral and treatment for diabetes care in China using lay family health promoters: protocol for the SMARTDiabetes cluster randomised controlled trial
Published in
Implementation Science, August 2016
DOI 10.1186/s13012-016-0481-8
Pubmed ID
Authors

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

Abstract

Type 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. We 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. 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. Clinicaltrials.gov NCT02726100.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 315 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ghana 1 <1%
Unknown 314 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 60 19%
Student > Ph. D. Student 27 9%
Student > Bachelor 27 9%
Researcher 26 8%
Other 18 6%
Other 46 15%
Unknown 111 35%
Readers by discipline Count As %
Medicine and Dentistry 66 21%
Nursing and Health Professions 42 13%
Psychology 16 5%
Social Sciences 12 4%
Business, Management and Accounting 8 3%
Other 43 14%
Unknown 128 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 August 2016.
All research outputs
#17,285,036
of 25,371,288 outputs
Outputs from Implementation Science
#1,637
of 1,809 outputs
Outputs of similar age
#231,567
of 354,244 outputs
Outputs of similar age from Implementation Science
#40
of 43 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,809 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 354,244 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.