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ACSM'S Health & Fitness Journal:
doi: 10.1249/01.FIT.0000414748.25945.58
COLUMNS: Worksite Health Promotion

An Optimal Lifestyle Metric: Four Simple Behaviors That Affect Health, Cost, and Productivity

Pronk, Nico Ph.D., FACSM, FAWHP

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Nico Pronk, Ph.D., FACSM, FAWHP, is vice president for Health Management and Health Science Officer at HealthPartners in Minneapolis, MN, where he also is a senior research investigator at the HealthPartners Research Foundation. Dr. Pronk is an adjunct professor of Society, Human Development, and Health at the Harvard University School of Public Health, where he teaches and conducts research in worker health protection and promotion. He is the current president for the International Association for Worksite Health Promotion, an ACSM Affiliate Society, the editor of ACSM’s Worksite Health Handbook, 2nd Edition, and an associate editor for the ACSM’s Health & Fitness Journal®.

Disclosure: The author declares no conflict of interest and does not have any financial disclosures.

There is widespread agreement among the scientific community, public health practitioners, clinical care delivery providers, and health promotion practitioners that behaviors, such as tobacco use, unhealthy dietary practices, physical inactivity, and excess consumption of alcoholic beverages, contribute greatly to preventable chronic disease morbidity and mortality. More specifically, these four risk behaviors cause type 2 diabetes, heart disease, certain cancers, back pain, stroke, and other chronic conditions. In 2000, the U.S. Centers for Disease Control and Prevention (CDC) estimated that these four lifestyle-related behavioral risk factors accounted for approximately 40% of all deaths in the United States (6). As these behaviors occur in clusters, they also are related to approximately 80% of chronic diseases and, as such, are associated with almost 75% of all medical care expenditures in the United States.

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Rather than maintaining a focus on the negative influence of risk on health, it may be much more engaging for people to focus on the positive influence of achieving health potential when adhering to healthy lifestyle behaviors. To do so, an “optimal lifestyle metric” (OLM) can be created that represents simultaneous adherence to multiple positive health behaviors. Of course, this is not a new idea. However, it seems to be an idea that has received increased attention in recent years.

Studies have shown that adherence to multiple healthy lifestyle behaviors or factors have a significant impact on a variety of health outcomes. For example, Ford and colleagues found that, after an average of 7.8 years of follow-up, 35- to 65-year-old Germans who were active physically, had a body mass index below 30 kg/m2, ate a healthy diet, and never smoked had a 78% lower likelihood of developing a chronic disease than individuals who did not meet these criteria (2). Other studies from Europe show an increased longevity of 14 years (4), reduced death rates due to cardiovascular disease, cancer, and all-causes (4,14), as well as a 7- to 13-year delay in functional status decline (7). These studies make a compelling case that a focus on adherence to a short list of health factors can have a dramatic impact on overall health outcomes. It is of interest to consider this in the context of employed populations and outcomes that are of vital interest to employers.

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To consider this approach to OLM from the perspective of worksite health initiatives, the definition is focused on behaviors consistent with the list of most important causes of death in the United States as described by the CDC (6). Furthermore, to operationalize the OLM at the worksite, the behaviors are defined according to existing guidelines but focused on behavioral aspects with which individual employees can readily identify. The definition is presented in Table 1 and recognizes that adherence to OLM is measured on a five-point scale — OLM-0 represents complete nonadherence, and OLM-4 represents complete adherence to OLM.

TABLE 1: Definition ...
TABLE 1: Definition ...
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Worksite health promotion programs present an excellent opportunity to increase adherence to OLM and thereby reduce the burden of chronic disease, reduce health care costs, and increase productivity. However, if employers are to invest in these programs, the intervention effect must be present among individuals who are healthy enough to participate in the labor force, and it must occur within a relatively short period. Therefore, let’s consider what we know about adherence to multiple health behaviors among employees; the impact of OLM adherence on outcomes related to health, cost, and productivity among employees; and whether it’s possible to change OLM through worksite health promotion programs.

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One way to gain a better understanding of both the problems and the opportunities associated with adherence to OLM is to create a profile of all the various possibilities that exist related to the adherence to the individual behaviors and OLM clusters. In essence, the idea is to create an overview of all possibilities, so we can appreciate the entire “universe” around OLM and adherence to its component parts. To accomplish this, we gathered data from 500,344 employees who had completed a health assessment as part of their worksite health promotion program and analyzed the data related to the OLM criteria (8). Table 2 presents the OLM “universe” and indicates that less than 0.5% adhere to none of the OLM behaviors, 5% adheres to one behavior, 27.5% adheres to two behaviors, 54% adheres to three behaviors, and 13% engages in all four behaviors simultaneously. Other observations include the fact that 83.6% does not adhere to the diet components of OLM. Clearly, consumption of five fruits and vegetables daily seems to be a challenging proposition for employees.

TABLE 2: The Optimal...
TABLE 2: The Optimal...
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New Cases of Chronic Disease among Employees

The first outcome to consider is the occurrence of new cases of chronic disease within a two-year time frame. Employers interested in health promotion programs recognize the need for such programs and approaches to take hold among employees and appreciate that time is needed for behaviors to change and that positive outcomes only emerge after behaviors have been adopted and maintained by a sufficient number of employees. Hence, considering the impact of adherence to OLM on incidence of new disease within 2 years seems a very reasonable consideration.

The result of an analysis conducted among 6,848 employees who completed a health assessment and were followed for 2 years after allows for this question to be answered (11). In this project, the associations between OLM and the development of six chronic conditions were investigated. The chronic conditions included diabetes, heart disease, cancer, hypertension, high cholesterol, and chronic back pain. Newly reported chronic conditions were identified by the health assessment responses obtained at the end of year two. Using questions embedded in the health assessment, presence of chronic conditions was assessed by asking if a health care provider had ever told the employee that they had diabetes (other than pregnancy), heart disease, colon cancer, prostate cancer, high blood pressure (other than pregnancy), or high cholesterol and asking how often they experienced back pain.

Results indicated that adherence to OLM-3 (i.e., adherence to any three components of OLM) was associated with 44% less risk of new diabetes and 29% less risk of new back pain. Adherence to all OLM-4 was significantly associated with 66% less risk of new back pain cases. This project showed that adherence to OLM, in particular adequate physical activity, is associated with less risk of developing several chronic conditions within a 2-year timeframe.

Finally, the results also showed a dose-response relationship between the number of OLM behaviors adhered to and the number of new cases of disease observed. This relationship is shown in Figure 1 and argues for continued adherence of behaviors already adopted and ongoing promotion for the adoption of additional healthy behaviors.

Figure 1
Figure 1
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Employees’ Emotional Health Concerns

The second outcome considered is emotional health concerns, and this outcome was studied among 34,603 employees who completed a health assessment and indicated their adherence to OLM as well as a set of emotional health indicators (12). The emotional health indicators studied were feelings of depression, stress risk, and the impact of emotional health on daily life. Analyses were considered regardless of age, sex, socioeconomic status (using the area deprivation index), sleep, self-perceived health status, self-efficacy, and chronic conditions including depression. Adherence to any three or four components of the OLM was associated with all three emotional health issues studied: a lower likelihood of feeling depressed, reporting stress risk, and emotional health affecting daily life. Adherence to any two components of the optimal lifestyle metric was associated with a lower likelihood of feeling depressed and emotional health affecting daily life. All four individual lifestyle behaviors had significant associations with at least two of the three emotional health outcomes studied.

Adherence to optimal lifestyle is associated with significantly more positive emotional health status as measured by feeling depressed, risk for high stress, and impact of emotional health on daily life. It is of course recognized that adherence to optimal lifestyle is easier when a person has less emotional distress. That is, this relationship works both ways. Regardless, the beneficial relationship is clear and should be explored further.

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An early study on the relationship between modifiable health risk factors and health care costs showed that physical activity, history of tobacco use, current smoking status, and BMI were related to health care charges over the following 18 months and subsequently annualized, even after adjustment for age, race, sex, and chronic disease status (10). In fact, health care costs were 4.7% lower for every additional physically active day per week, 1.9% higher for every unit of BMI increase, 18% higher when currently smoking, and 25.8% higher when a history of tobacco use was reported.

A secondary part of the study was to consider the impact of a cluster of risk factors on health care costs. The risk factor cluster reported on included a “low risk” cluster of people who were active physically 3 days per week, never-smokers with a BMI of 25 kg/m2. They were compared with a group of people with “high risk” profiles defined as current smokers who were physically inactive and had a BMI of 27.5 kg/m2. Figure 2 depicts the results of this analysis and indicates that the low-risk group incurred 49% lower health care costs over the following 18 months as compared with the high-risk group. The graph also shows the impact of race and sex.

Figure 2
Figure 2
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Based on an analysis conducted by HealthPartners in 2009 on 33,956 employees who had completed a health assessment as well as the Work Productivity and Activity Impairment Scale (WPAI), a clear association is noted between the degree of adherence to OLM and work productivity (3). As adherence to OLM improves, productivity loss reduces. The WPAI, a self-report measurement tool integrated into the health assessment, measures absenteeism (work time missed), presenteeism (impairments at work or reduced on-the-job effectiveness), work productivity loss (overall work impairment or absenteeism plus presenteeism), and activity impairment (15).

Figure 3 shows the results of this analysis as the productivity data has been monetized using an average worker salary of $50,000 per year, reported as per person excess health-related productivity loss, and expressed in 2009 dollars. The difference between adherence to none of the OLM behaviors (OLM-0) and all four (OLM-4) is a per-person annual cost of more than $3,000 or a 455% higher productivity loss because of nonadherence.

Figure 3
Figure 3
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Based on the data presented, higher levels of adherence to OLM are associated with better health status, fewer new cases of chronic disease, lower health care costs, and less productivity loss. In addition, it seems that there is a dose-response relationship, which indicates that adding any behavior to those already adhered to generates additional benefit to the outcomes of interest. Furthermore, these observations seem to occur in the near-term, that is, within 18 months to 2 years. Unfortunately, the adherence to all four behaviors that are part of the OLM is only 13% among employees.

Others also have reported on adherence to clusters of healthy behaviors, similar to the ones described here, although not identical. Those clusters defined the behavior somewhat differently (e.g., diet quality as opposed to five fruits and vegetables per day) and may include other health factors, such as obesity or overweight, which are not behaviors. However, those reports also estimate a low proportion of the population to be in simultaneous adherence of the behaviors included in those clusters. Estimates range between 6% and 10.8% based on National Health and Nutrition Examination Survey data or health plan samples (1,5,9). As a result, the potential to improve adherence levels for OLM and thereby improve health, cost, and productivity outcomes is large.

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If the OLM is to be useful as a metric for improvement, it has to be responsive to interventions. A well-documented case study provides an opportunity to study the impact of a comprehensive and multi-component worksite health promotion program on OLM adherence (13). In this case study, significant population health improvements were measured over a 3-year period. Three different measurement approaches and analyses were conducted to estimate the return on investment of this program, and the conclusion showed that for every dollar invested, the return was three dollars in medical cost savings. Furthermore, significant increases in productivity were measured. The experience of the employees was measured using satisfaction levels and shown to be extremely high in every program year. In the context of this successful program, baseline and 3-year follow-up levels of OLM and its component parts were measured and are presented in Table 3.

TABLE 3: ChangeOptim...
TABLE 3: ChangeOptim...
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As a result of this analysis, it is clear that OLM is responsive to interventions and can be used to show improvement. However, it is important to note that this movement is in response to a comprehensive program. Therefore, it should be kept in mind that the program’s design principles are important to consider in ensuring that such improvements are obtained.

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So what may be a set of reasonable, actionable, and yet powerful recommendations that worksite health promotion practitioners could put into action? Well, based on the summary observations outlined above, the following recommendations may prove useful.

* Communicate a simple message around the importance of OLM: “Some is better than none; more is better than some.”

* Make taking action on OLM a simple decision for employees: “Pick one, add one.” Whatever behaviors you are not adhering to currently, pick one you think you can do and would enjoy doing, and add it to the mix of behaviors you already are engaged in on a daily basis.

* Communicate population adherence to all employees. Let everyone know what the association is between adherence to OLM and medical costs, health status, and what this may mean for the employees themselves. This type of communication will increase awareness, educate importance, and generate interest in OLM.

* Highlight specific actions that one could engage in to meet the criteria for the individual OLM behaviors. For example, to meet the five fruits and vegetables criterion, an employee could take two handfuls of frozen berries, place them in a bowl, and put them in the fridge before going to sleep at night. In the morning, the berries will be sufficiently defrosted to add some yogurt for an excellent breakfast dish. Add a banana and a glass of fresh-squeezed orange juice, and this person will have met already the criteria for daily consumption of five fruits and vegetables (two servings of berries, one whole banana may be counted as two servings, and orange juice [two to three oranges] as one serving = five servings total). And it’s not even 8 a.m. yet!

Changing behavior is not an easy thing to do for individuals. Helping people prioritize and focus on behaviors that generate high yield in terms of health will be helpful. However, we should not forget that the idea is to adopt and maintain healthy behaviors, not to only work on things we’re not doing while forgetting to continue those good habits we already have. Keeping it simple, making it real, and adding some fun into the mix will prove a useful and impactful strategy to improve health, lower costs, and optimize work performance.

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1. Berrigan D, Dodd K, Troiano RP, Krebs-Smith SM, Ballard-Barbash R. Patterns of health behavior in US adults. Prev Med. 2003; 36: 615–23.

2. Ford ES, Bergmann MM, Kroger J, Schienkiewitz A, Weikert C, Boeing H. Healthy living is the best revenge. Findings from the European Prospective Investigation into Cancer and Nutrition — Potsdam Study. Arch Intern Med. 2009; 169: 1355–62.

3. HealthPartners Health Assessment Database. OLM and productivity analysis, 2009. Unpublished internal document. Presented at the University of Minnesota Occupational Health and Safety Seminar, 2010.

4. Khaw K–T, Wareham N, Bingham S, Welch A, Luben R, Day N. Combined impact of health behaviors and mortality in men and women: the EPIC-Norfolk prospective population study. PLoS Med. 2008; 5: e12.

5. King DE, Mainous AG III, Caremolla M, Everett CJ. Adherence to healthy lifestyle habits in US adults, 1998–2006. Am J Med. 2009; 122: 528–34.

6. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004; 291: 1238–45.

7. Myint PK, Luben RN, Wareham NJ, Bingham SA, Khaw KT. Combined effect of health behaviors and risk of first ever stroke in 20,040 men and women over 11 years’ follow-up in Norfolk cohort of European Prospective Investigation of Cancer (EPIC) Norfolk): Prospective population study. BMJ. 2009; 338: b349.

8. OLM Universe Analysis. HealthPartners Health Assessment Database, 2011. Unpublished internal document. Presented at the HealthPartners Health Council, 2011.

9. Pronk NP, Anderson L, Crain AL, et al.. Meeting multiple health behavior recommendations: Prevalence and clustering among adolescent, adult, and senior health plan members. Am J Prev Med. 2004; 27 (2S): 25–33.

10. Pronk NP, Goodman MJ, O’Connor PJ, Martinson BC. Short-term cost to health plans of obesity, smoking status, and physical activity. JAMA. 1999; 282 (23): 2235–39.

11. Pronk NP, Lowry M, Kottke TE, Austin E, Gallagher J, Katz A. The association between optimal lifestyle adherence and short-term incidence of chronic conditions among employees. Popul Health Manag. 2010; 13 (6): 289–95.

12. Pronk NP, Katz AS, Gallagher J, et al. Adherence to optimal lifestyle behaviors is related to emotional health indicators among employees. Popul Health Manag. 2011; 14 (2): 59–67.

13. Thygeson NM, Gallagher J, Cross K, Pronk NP. Employee health at BAE Systems: An employer-health plan partnership approach. In: Pronk NP, editor. ACSM’s Worksite Health Handbook, Second Edition. A Guide to Building Healthy and Productive Companies. Champaign (IL): Human Kinetics; 2009; Chapter 36.

14. vanDam RM, Li T, Spiegelman D, Franco OH, Hu FB. Combined impact of lifestyle factors on mortality: prospective cohort study in US women. BMJ. 2008; 337: a1440.

15. Work Productivity and Activity Impairment Scale. Available from: (Accessed December 2, 2011).

© 2012 American College of Sports Medicine


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