Incorporating sedentary behaviour for identifying risk of type 2 diabetes: Implications for primary care

Introduction

Type 2 diabetes mellitus (DM2) is one of the main public health threats of the 21st century. Half of the people with DM2 worldwide are not diagnosed. The high prevalence, underdiagnosis and complications of diabetes highlight the need for identifying people at risk. Sedentary behaviour (SB) or prolonged sitting is a major predisposing risk factor for the increasing prevalence of DM2. Incorporating SB measures into clinical practice systems for identifying individuals more likely to have DM2 should be considered.

Objective

To develop a mathematical model for clinical practice that allows early identification of office employees at risk of DM2 based on objective data on SB.

Methods

A cross-sectional study with a cross-validation procedure was conducted. Anthropometric variables (sex, age and body mass index, BMI), sleep time (hours; measured by ActivPAL3M devices), and SB patterns (sedentary breaks and time spent in sedentary bouts of four different lengths; measured by ActivPAL3M devices) of two groups of office employees (adults with and without diabetes) were compared. Eighty-one participants had DM2 and 132 had normal glucose metabolism (NGM). The risk of having DM2 was modelled using generalized linear models (GLM), particularly a logistic regression model.

Results

Five non-invasive clinical variables that were significantly correlated to DM2 with no collinearity were included in the mathematical model: sex, age, BMI, sleep time (hours) and sedentary breaks < 20 minutes (number/day). The validated model correctly classified 94.58 % of the participants with DM2 and 97.99 % of participants with NGM. The sensitivity was 94.58 % and the specificity 97.99 %. Additionally, the model allowed the design of a preventive tool to recommend changes in the SB pattern based on the participant’s anthropometric profile, aiming to reduce the risk of developing DM2 in office employees.

Conclusion

This study highlights the importance of incorporating SB measures in primary care clinical practice. Our mathematical model suggests that including SB could enhance the early identification of adults at risk of DM2. Further research is needed to validate these findings and assess the practical application of the mathematical model in clinical practice.