Evidence map›Paper›PMID 24691410›Full record

Trial reportJournal of medical Internet research2014

A smart web aid for preventing diabetes in rural China: preliminary findings and lessons.

Penglai Chen, Jing Chai, Jing Cheng, Kaichun Li, Shaoyu Xie, Han Liang, Xingrong Shen, Rui Feng, Debin Wang

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 pooled it
1.7field-weighted citation impact, top 16% 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

12 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

9 authors at 2 institutions in 1 country.

Penglai ChenSchool of Health Services Management, Anhui Medical University, Hefei, China.
Jing Chai
Jing Cheng
Kaichun Li
Shaoyu Xie
Han Liang
Xingrong Shen
Rui Feng
Debin Wang
Anhui Medical University · CNLu'an First People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIncreasing cases of diabetes, a general lack of routinely operational prevention, and a long history of separating disease prevention and treatment call for immediate engagement of frontier clinicians. This applies especially to village doctors who work in rural China where the majority of the nation's vast population lives.

objectiveThis study aims to develop and test an online Smart Web Aid for Preventing Type 2 Diabetes (SWAP-DM2) capable of addressing major barriers to applying proven interventions and integrating diabetes prevention into routine medical care.

methodsDevelopment of SWAP-DM2 used evolutionary prototyping. The design of the initial system was followed by refinement cycles featuring dynamic interaction between development of practical and effective standardized operation procedures (SOPs) for diabetes prevention and Web-based assistance for implementing the SOPs. The resulting SOPs incorporated proven diabetes prevention practices in a synergetic way. SWAP-DM2 provided support to village doctors ranging from simple educational webpages and record maintenance to relatively sophisticated risk scoring and personalized counseling. Evaluation of SWAP-DM2 used data collected at baseline and 6-month follow-up assessment: (1) audio recordings of service encounters; (2) structured exit surveys of patients' knowledge, self-efficacy, and satisfaction; (3) measurement of fasting glucose, body mass index, and blood pressure; and (4) qualitative interviews with doctors and patients. Data analysis included (1) descriptive statistics of patients who received SWAP-DM2-assisted prevention and those newly diagnosed with prediabetes and diabetes; (2) comparison of the variables assessed between baseline and follow-up assessment; and (3) narratives of qualitative data.

resultsThe 17 participating village doctors identified 2219 patients with elevated diabetes risk. Of these, 84.85% (1885/2219) consented to a fasting glucose test with 1022 new prediabetes and 113 new diabetes diagnoses made within 6 months. The prediabetic patients showed substantial improvement from baseline to 6-month follow-up in vegetable intake (17.0%, 43/253 vs 88.7%, 205/231), calorie intake (1.6%, 4/253 vs 71.4%, 165/231), leisure-time exercises (6.3%, 16/253 vs 21.2%, 49/231), body weight (mean 62.12 kg, SD 9.85 vs mean 58.33 kg, SD 9.18), and body mass index (mean 24.80 kg/m(2), SD 3.21 vs mean 23.36 kg/m(2), SD 2.95). The prediabetic patients showed improvement in self-efficacy for modifying diet (mean 5.31, SD 2.81 vs mean 8.53, SD 2.25), increasing physical activities (mean 4.52, SD 3.35 vs mean 8.06, SD 2.38), engaging relatives (mean 3.93, SD 3.54 vs mean 6.93, SD 2.67), and knowledge about diabetes and risks of an imbalanced diet and inadequate physical activity. Most participating doctors and patients viewed SWAP-DM2 as useful and effective.

conclusionsSWAP-DM2 is helpful to village doctors, acceptable to patients, and effective in modifying immediate determinants of diabetes at least in the short term, and may provide a useful solution to the general lack of participation in diabetes prevention by frontier clinicians in rural China.

trial registrationInternational Standard Randomized Controlled Trial Number (ISRCTN): 66772711; http://www.controlled-trials.com/ISRCTN66772711.

Indexed as

InternetRural Health ServicesAgedBlood GlucoseBody Mass IndexChinaDiabetes Mellitus, Type 2FemaleHealth PromotionHumansMaleMiddle AgedPrediabetic StateSelf EfficacyBlood Glucosediabetes mellituseHealthevaluationInternetprediabetic stateprevention

Identifiers

PMID24691410
PMCPMC4004141
OpenAlexW1996776688

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.