Using handgrip strength to screen for diabetes in developing countries

Molly Eckman, Christopher Gigliotti, Staci Sutermaster, Peter J. Butler, Khanjan Mehta

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Lack of access to healthcare in the developing world has created a need for locally-based primary and pre-primary healthcare systems. Many regions of the world have adopted Community Health Worker (CHW) programmes, but volunteers in these programmes lack the tools and resources to screen for disease. Because of its simplicity of operation, handgrip strength (HGS) measurements have the potential to be an affordable and effective screening tool for conditions that cause muscle weakness in this context. In the study described in this report, translators were used to collect data on age, gender, height, weight, blood pressure, HGS and key demographic data. HGS was significantly lower for diabetics than patients without diabetes. A simple binary logistic model was created that used HGS, age, blood pressure and BMI to predict a patients probability of having diabetes. This study develops a predictive model for diabetes using HGS and other basic health measurements and shows that HGS-based screening is a viable method of early detection of diabetes.

Original languageEnglish (US)
Pages (from-to)8-14
Number of pages7
JournalJournal of Medical Engineering and Technology
Volume40
Issue number1
DOIs
StatePublished - Jan 2 2016

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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