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Employee Cardiometabolic Risk Following a Cluster-Randomized Workplace Intervention From the Work, Family and Health
Lisa F Berkman1, Erin L Kelly1, Leslie B Hammer1
1Lisa F. Berkman is with the Harvard Center for Population and Development Studies, Cambridge, MA. Erin L. Kelly is with the Massachusetts Institute of Technology Sloan School of Management, Cambridge. Leslie B. Hammer is with the Center for Occupational Health Sciences, Oregon Health Sciences University, Portland. Frank Mierzwa is with RTI International, Research Triangle Park, NC. Todd Bodner is with the Department of Psychology, Portland State University, Portland, OR. Tay McNamara is with the Women's Studies Research Center, Brandeis University, Waltham, MA. Hayami K. Koga is with the Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA. Soomi Lee is with the Department of Human Development and Family Studies, Pennsylvania State University, University Park. Miguel Marino is with the Department of Family Medicine, Oregon Health & Science University, Portland. Laura C. Klein and Orfeu M. Buxton are with the Department of Biobehavioral Health, Pennsylvania State University, University Park. Thomas W. McDade is with the Department of Anthropology, Northwestern University, Evanston, IL. Ginger Hanson is with the Johns Hopkins School of Nursing, Baltimore, MD. Phyllis Moen is with the Department of Sociology, University of Minnesota, Minneapolis.
Abstract:
Objectives. To examine whether workplace interventions to increase workplace flexibility and supervisor support and decrease work-family conflict can reduce cardiometabolic risk. Methods. We randomly assigned employees from information technology (n = 555) and long-term care (n = 973) industries in the United States to the Work, Family and Health Network intervention or usual practice (we collected the data 2009-2013). We calculated a validated cardiometabolic risk score (CRS) based on resting blood pressure, HbA1c (glycated hemoglobin), HDL (high-density lipoprotein) and total cholesterol, height and weight (body mass index), and tobacco consumption. We compared changes in baseline CRS to 12-month follow-up. Results. There was no significant main effect on CRS associated with the intervention in either industry. However, significant interaction effects revealed that the intervention improved CRS at the 12-month follow-up among intervention participants in both industries with a higher baseline CRS. Age also moderated intervention effects: older employees had significantly larger reductions in CRS at 12 months than did younger employees. Conclusions. The intervention benefited employee health by reducing CRS equivalent to 5 to 10 years of age-related changes for those with a higher baseline CRS and for older employees. Trial Registration. ClinicalTrials.gov Identifier: NCT02050204. (Am J Public Health. 2023;113(12):1322-1331. https://doi.org/10.2105/AJPH.2023.307413).
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