Using Cluster Analysis to Explore Associations between Cardiovascular Risk and Lifestyle Factors in a Workplace Wellness Program

Abstract

Background: Cardiovascular disease (CVD) is the number one cause of death in the United States with risk factors including hypertension, hyperlipidemia, diabetes, obesity, smoking, physical inactivity, age, genetics, and unhealthy diets. A university-based workplace wellness program (WWP) consisting of an annual biometric screening assessment with targeted, individualized health coaching was implemented in an effort to reduce these risk factors while encouraging and nurturing ideal cardiovascular health.

Objective: The purpose of this study was to examine and describe the prevalence of single and combined, or multiple, CVD risk factors within a workplace wellness dataset.

Methods: Cluster analysis was used to determine CVD risk factors within biometric screening data (BMI, waist circumference, LDL, total cholesterol, HDL, triglycerides, blood glucose age, ethnicity, and gender) collected during WWP interventions.

Results: The cluster analysis provided visualizations of the distributions of participants having specific CVD risk factors. Of the 8,802 participants, 1,967 (22.4%) had no CVD risk factor, 1,497 (17%) had a single risk factor, and 5,529 (60.5%) had two or more risk factors. The majority of sample members are described as having more than one CVD risk factor with 78% having multiple.

Conclusion: Cluster analysis demonstrated utility and efficacy in categorizing participant data based on their CVD risk factors. A baseline analysis of data was captured and provided understanding and awareness into employee health and CVD risk. This process and analysis facilitated WWP planning to target and focus on education to promote ideal cardiovascular health.