Evidence map›Paper›PMID 29233806›Full record

ArticleJMIR research protocols2017

Mobile Health Technology (mDiab) for the Prevention of Type 2 Diabetes: Protocol for a Randomized Controlled Trial.

Shruti Muralidharan, Viswanathan Mohan, Ranjit Mohan Anjana, Sidhant Jena, Nikhil Tandon, Steven Allender, Harish Ranjani

Open access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
8.4field-weighted citation impact, top 3% 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

14 citing papers in PubMed, 26 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Article
  4. Article
  5. Digital health and diabetes: experience from India.Therapeutic advances in endocrinology and metabolism · 2021
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Nutrition and diabetes in South Asia.European journal of clinical nutrition · 2018
    Review
  14. Article
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.

Shruti MuralidharanGlobal Obesity Centre, World Health Organization Collaborating Centre for Obesity Prevention, Deakin University, Geelong, Australia.ORCID http://orcid.org/0000-0003-2254-0262
Viswanathan MohanTranslational Research Department, Madras Diabetes Research Foundation, Dr. Mohan's Diabetes Specialities Centre, Chennai, India.ORCID http://orcid.org/0000-0001-5038-6210
Ranjit Mohan AnjanaTranslational Research Department, Madras Diabetes Research Foundation, Dr. Mohan's Diabetes Specialities Centre, Chennai, India.ORCID http://orcid.org/0000-0002-4843-1374
Sidhant JenaJanacare Solutions Private Limited, Bengaluru, India.ORCID http://orcid.org/0000-0002-1132-6925
Nikhil TandonDepartment of Endocrinology, All India Institute of Medical Sciences, New Delhi, India.ORCID http://orcid.org/0000-0003-4604-1986
Steven AllenderGlobal Obesity Centre, World Health Organization Collaborating Centre for Obesity Prevention, Deakin University, Geelong, Australia.ORCID http://orcid.org/0000-0002-4842-3294
Harish RanjaniTranslational Research Department, Madras Diabetes Research Foundation, Dr. Mohan's Diabetes Specialities Centre, Chennai, India.ORCID http://orcid.org/0000-0002-1521-7398
Madras Diabetes Research Foundation · INDeakin University · AUAll India Institute of Medical Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe prevalence of type 2 diabetes is increasing in epidemic proportions in low- and middle-income countries. There is an urgent need for novel methods to tackle the increasing incidence of diabetes. The ubiquity of mobile phone use and access to Internet makes mobile health (mHealth) technology a viable tool to prevent and manage diabetes.

objectiveThe objective of this randomized controlled trial is to implement and evaluate the feasibility, cost-effectiveness, and sustainability of a reality television-based lifestyle intervention program. This intervention program is delivered via a mobile phone app (mDiab) to approximately 1500 Android smartphone users who are adults at a high risk for type 2 diabetes from three cities in India, namely, Chennai, Bengaluru, and New Delhi.

methodsThe mDiab intervention would be delivered via a mobile phone app along with weekly coach calls for 12 weeks. Each participant will go through a maintenance phase of 6 to 8 months post intervention. Overall, there would be 3 testing time points in the study: baseline, post intervention, and the end of follow-up. The app will enable individuals to track their weight, physical activity, and diet alongside weekly video lessons on type 2 diabetes prevention.

resultsThe study outcomes are weight loss (primary measure of effectiveness); improvement in cardiometabolic risk factors (ie, waist circumference, blood pressure, glucose, insulin, and lipids); and improvement in physical activity, quality of life, and dietary habits. Sustainability will be assessed through focus group discussions.

conclusionsIf successful, mDiab can be used as a model for translational and implementation research in the use of mHealth technology for diabetes prevention and may be further expanded for the prevention of other noncommunicable diseases such as hypertension and cardiovascular diseases.

trial registrationClinical Trials Registry of India CTRI/2015/07/006011 http://ctri.nic.in/Clinicaltrials/pdf_generate.php? trialid=11841 (Archived by WebCite at http://www.webcitation.org/6urCS5kMB).

Indexed as

diabetes mellitus, type 2mHealthprevention

Identifiers

PMID29233806
PMCPMC5743924
OpenAlexW2774391870

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.