CME AVAILABLE FOR THIS ARTICLE AT ACOEM.ORG
The Association Between Optimal Lifestyle-Related Health Behaviors and Employee Productivity Abigail S. Katz, PhD, Nicolaas P. Pronk, PhD, and Marcia Lowry, MS
Objective: To investigate the association between lifestyle-related health behaviors including sleep and the cluster of physical activity, no tobacco use, fruits and vegetables intake, and alcohol consumption termed the “Optimal Lifestyle Metric” (OLM), and employee productivity. Methods: Data were obtained from employee health assessments (N = 18,079). Regression techniques were used to study the association between OLM and employee productivity, sleep and employee productivity, and the interaction of both OLM and sleep on employee productivity. Results: Employees who slept less or more than 7 or 8 hours per night experienced significantly more productivity loss. Employees who adhered to all four OLM behaviors simultaneously experienced less productivity loss compared with those who did not. Conclusions: Adequate sleep and adherence to the OLM cluster of behaviors are associated with significantly less productivity loss.
Learning Objectives
r Summarize the new findings on the association between ber r
haviors included in an “optimal lifestyle metric” (OLM) and work productivity. Discuss the association between sleep and employee productivity, including the interaction, if any, between sleep and optimal lifestyle behaviors. Discuss the study implications for valuing the effects of workplace health promotion efforts.
METHODS
T
he clustering of specific lifestyle-related health behaviors, such as physical inactivity, poor nutrition, heavy alcohol consumption, and tobacco use, has shown causal relationships with type 2 diabetes and a variety of other chronic conditions.1–7 Simultaneous adherence to four specific positive health behaviors (ie, being physically active, consuming five servings of fruits and vegetables per day, not using tobacco, and the consumption of no more than two (males) or one (females) alcoholic beverages per day), which has previously been termed the “Optimal Lifestyle Metric (OLM),” is associated with lower short-term incidence of chronic disease as well as improved emotional health in employed populations.8,9 Inadequate sleep, another lifestyle-related health behavior, similarly impacts lifestyle-related health factors such as obesity and cardiovascular disease,10–15 and has been cited as a factor impacting employee productivity in the workplace.16–19 Despite scholarship linking sleep and employee productivity, no existing literature has explored the dynamics of sleep and OLM adherence on productivity, specifically how they interact and together influence workplace productivity among employed adults. Improved understanding of this impact may hold significant potential to inform population health improvement strategies implemented via the worksite. The purpose of this study was to investigate the association between OLM and employee productivity, sleep and employee productivity, and the interaction of both OLM and sleep on employee productivity.
From HealthPartners (Dr Katz, Dr Pronk, and Ms Lowry); HealthPartners Institute for Education and Research (Drs Katz and Pronk), Minneapolis, Minn; and Harvard School of Public Health (Dr Pronk), Boston, Mass. This study was partially funded by HealthPartners. Authors Katz, Pronk, and Lowry have no relationships/conditions/circumstances that present potential conflict of interest. The JOEM editorial board and planners have no financial interest related to this research. Address correspondence to: Nicolaas P. Pronk, PhD, Vice President and Chief Science Officer, HealthPartners Mail Stop 21111H, PO Box 1309, 8170 33rd Ave South, Minneapolis, MN 55440 ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000191
708
Study Population All members of the HealthPartners health plan in Minnesota who completed a health assessment (HA) in 2010 as part of a worksite health promotion program were considered potential subjects for the study. This potential subject pool (N = 76,075) was reduced by including only employees (no dependents) and only those whose employers offered an optional HA module designed to measure health-related employee productivity. The subject sample was further limited by including only those employees who had pharmacy coverage, were continuously enrolled for at least 9 months, and worked for an employer group with more than 50 subscribers remaining after all exclusions were applied. A total of 18,079 subjects representing 13 different companies remained in the final study population. The study protocol was reviewed and approved by the HealthPartners Institutional Review Board.
Health Assessment Description The HealthPartners Achieve Your Health Potential HA20 was used as the data collection tool for all self-reported measures. These measures included personal demographics, an assessment of productivity loss, and behavioral variables.
Demographics A set of demographic covariates was selected from the dataset on the basis of their use in other studies involving lifestyle behavior.21–25 The final set of covariates used in the statistical models included age, sex, body mass index (BMI), race/ethnicity, and job type. Table 1 presents a full listing of variables.
Productivity The Work Productivity and Activity Impairment (WPAI) Questionnaire was integrated into the HA. The WPAI is a self-report productivity impairment assessment tool that contains six productivity related questions designed to measure (1) the amount of work time missed because of health-related and non–health-related issues; (2) the number of hours actually worked, and (3) the degree to which a person’s health affects both work productivity and regularly scheduled activities. The recall period for all questions is the “past 7 days.” The 7-day recall period is typically used to avoid recall bias associated with longer recall periods. The 7-day recall period is extrapolated to a 12-month period similar to other measures on the HA. This JOEM r Volume 56, Number 7, July 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
Optimal Lifestyle and Employee Productivity
TABLE 1. Descriptive Characteristics of the Study Population (N = 18,079) Variable
Category
Percent (n)
Age, yr
18–29 30–39 40–49 50–59 ≥60 Men Women American Indian or Alaska Native Asian or Pacific Islander African American or black White Some other race Choose not to answer or unknown Hispanic Administrative Labor or production Professional or management Sales Service Skilled craft Student Technical Other Average employer-reported salary Average national salary (2010) Average BMI Current nonsmoker Meeting physical activity guidelines Meeting guidelines for consuming five servings of fruits and vegetables daily Meeting guidelines for alcohol consumption Adherence to 0 optimal lifestyle components Adherence to one optimal lifestyle component Adherence to two optimal lifestyle components Adherence to three optimal lifestyle components Adherence to four optimal lifestyle components ≥9 7–8 6
The Association Between Optimal Lifestyle-Related Health Behaviors and Employee Productivity Abigail S. Katz, PhD, Nicolaas P. Pronk, PhD, and Marcia Lowry, MS
Objective: To investigate the association between lifestyle-related health behaviors including sleep and the cluster of physical activity, no tobacco use, fruits and vegetables intake, and alcohol consumption termed the “Optimal Lifestyle Metric” (OLM), and employee productivity. Methods: Data were obtained from employee health assessments (N = 18,079). Regression techniques were used to study the association between OLM and employee productivity, sleep and employee productivity, and the interaction of both OLM and sleep on employee productivity. Results: Employees who slept less or more than 7 or 8 hours per night experienced significantly more productivity loss. Employees who adhered to all four OLM behaviors simultaneously experienced less productivity loss compared with those who did not. Conclusions: Adequate sleep and adherence to the OLM cluster of behaviors are associated with significantly less productivity loss.
Learning Objectives
r Summarize the new findings on the association between ber r
haviors included in an “optimal lifestyle metric” (OLM) and work productivity. Discuss the association between sleep and employee productivity, including the interaction, if any, between sleep and optimal lifestyle behaviors. Discuss the study implications for valuing the effects of workplace health promotion efforts.
METHODS
T
he clustering of specific lifestyle-related health behaviors, such as physical inactivity, poor nutrition, heavy alcohol consumption, and tobacco use, has shown causal relationships with type 2 diabetes and a variety of other chronic conditions.1–7 Simultaneous adherence to four specific positive health behaviors (ie, being physically active, consuming five servings of fruits and vegetables per day, not using tobacco, and the consumption of no more than two (males) or one (females) alcoholic beverages per day), which has previously been termed the “Optimal Lifestyle Metric (OLM),” is associated with lower short-term incidence of chronic disease as well as improved emotional health in employed populations.8,9 Inadequate sleep, another lifestyle-related health behavior, similarly impacts lifestyle-related health factors such as obesity and cardiovascular disease,10–15 and has been cited as a factor impacting employee productivity in the workplace.16–19 Despite scholarship linking sleep and employee productivity, no existing literature has explored the dynamics of sleep and OLM adherence on productivity, specifically how they interact and together influence workplace productivity among employed adults. Improved understanding of this impact may hold significant potential to inform population health improvement strategies implemented via the worksite. The purpose of this study was to investigate the association between OLM and employee productivity, sleep and employee productivity, and the interaction of both OLM and sleep on employee productivity.
From HealthPartners (Dr Katz, Dr Pronk, and Ms Lowry); HealthPartners Institute for Education and Research (Drs Katz and Pronk), Minneapolis, Minn; and Harvard School of Public Health (Dr Pronk), Boston, Mass. This study was partially funded by HealthPartners. Authors Katz, Pronk, and Lowry have no relationships/conditions/circumstances that present potential conflict of interest. The JOEM editorial board and planners have no financial interest related to this research. Address correspondence to: Nicolaas P. Pronk, PhD, Vice President and Chief Science Officer, HealthPartners Mail Stop 21111H, PO Box 1309, 8170 33rd Ave South, Minneapolis, MN 55440 ([email protected]). C 2014 by American College of Occupational and Environmental Copyright Medicine DOI: 10.1097/JOM.0000000000000191
708
Study Population All members of the HealthPartners health plan in Minnesota who completed a health assessment (HA) in 2010 as part of a worksite health promotion program were considered potential subjects for the study. This potential subject pool (N = 76,075) was reduced by including only employees (no dependents) and only those whose employers offered an optional HA module designed to measure health-related employee productivity. The subject sample was further limited by including only those employees who had pharmacy coverage, were continuously enrolled for at least 9 months, and worked for an employer group with more than 50 subscribers remaining after all exclusions were applied. A total of 18,079 subjects representing 13 different companies remained in the final study population. The study protocol was reviewed and approved by the HealthPartners Institutional Review Board.
Health Assessment Description The HealthPartners Achieve Your Health Potential HA20 was used as the data collection tool for all self-reported measures. These measures included personal demographics, an assessment of productivity loss, and behavioral variables.
Demographics A set of demographic covariates was selected from the dataset on the basis of their use in other studies involving lifestyle behavior.21–25 The final set of covariates used in the statistical models included age, sex, body mass index (BMI), race/ethnicity, and job type. Table 1 presents a full listing of variables.
Productivity The Work Productivity and Activity Impairment (WPAI) Questionnaire was integrated into the HA. The WPAI is a self-report productivity impairment assessment tool that contains six productivity related questions designed to measure (1) the amount of work time missed because of health-related and non–health-related issues; (2) the number of hours actually worked, and (3) the degree to which a person’s health affects both work productivity and regularly scheduled activities. The recall period for all questions is the “past 7 days.” The 7-day recall period is typically used to avoid recall bias associated with longer recall periods. The 7-day recall period is extrapolated to a 12-month period similar to other measures on the HA. This JOEM r Volume 56, Number 7, July 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
JOEM r Volume 56, Number 7, July 2014
Optimal Lifestyle and Employee Productivity
TABLE 1. Descriptive Characteristics of the Study Population (N = 18,079) Variable
Category
Percent (n)
Age, yr
18–29 30–39 40–49 50–59 ≥60 Men Women American Indian or Alaska Native Asian or Pacific Islander African American or black White Some other race Choose not to answer or unknown Hispanic Administrative Labor or production Professional or management Sales Service Skilled craft Student Technical Other Average employer-reported salary Average national salary (2010) Average BMI Current nonsmoker Meeting physical activity guidelines Meeting guidelines for consuming five servings of fruits and vegetables daily Meeting guidelines for alcohol consumption Adherence to 0 optimal lifestyle components Adherence to one optimal lifestyle component Adherence to two optimal lifestyle components Adherence to three optimal lifestyle components Adherence to four optimal lifestyle components ≥9 7–8 6
