- Email: [email protected]
Factors Associated With Participation in a University Worksite Wellness Program
Factors Associated With Participation in a University Worksite Wellness Program Angela J. Beck, PhD, MPH,1 Richard A. Hirth, PhD,1 Kristi Rahrig Jenkins, PhD, MPH,2 Kathryn K. Sleeman, MA,3 Wei Zhang, MHSA3 Introduction: Healthcare reform legislation encourages employers to implement worksite wellness activities as a way to reduce rising employer healthcare costs. Strategies for increasing program participation is of interest to employers, though few studies characterizing participation exist in the literature. The University of Michigan conducted a 5-year evaluation of its worksite wellness program, MHealthy, in 2014. MHealthy elements include Health Risk Assessment, biometric screening, a physical activity tracking program (ActiveU), wellness activities, and participation incentives. Methods: Individual-level data were obtained for a cohort of 20,237 employees who were continuously employed by the university all 5 years. Multivariate logistic regression was used to assess the independent predictive power of characteristics associated with participation in the Health Risk Assessment, ActiveU, and incentive receipt, including employee and job characteristics, as well as baseline (2008) healthcare spending and health diagnoses obtained from claims data. Data were collected from 2008 to 2013; analyses were conducted in 2014. Results: Approximately half of eligible employees were MHealthy participants. A consistent profile emerged for Health Risk Assessment and ActiveU participation and incentive receipt with female, white, non-union staff and employees who seek preventive care among the most likely to participate in MHealthy.
Conclusions: This study helps characterize employees who choose to engage in worksite wellness programs. Such information could be used to better target outreach and program content and reduce structural barriers to participation. Future studies could consider additional job characteristics, such as job type and employee attitudinal variables regarding health status and wellness program effectiveness. (Am J Prev Med 2016;](]):]]]–]]]) & 2016 American Journal of Preventive Medicine
Introduction
T
he health status of the U.S. workforce is a growing concern.1,2 The workforce is aging owing to retirement delay3,4 and the increase of “lifestyle diseases” characterized by poor nutrition and inactivity have contributed to the rising prevalence of chronic From the 1Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, Michigan; 2Health and Wellbeing Services, University of Michigan, Ann Arbor, Michigan; and 3 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan Address correspondence to: Angela J. Beck, PhD, MPH, Department of Health Management and Policy, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor MI 48109-2029. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2016.01.028
& 2016 American Journal of Preventive Medicine
diseases among workers of all ages,2,5 both of which result in mounting healthcare costs for employers.5,6 U.S. healthcare spending has risen substantially faster than the general rate of inflation for several decades7-9 and around 60% of Americans obtain health insurance through an employer.10 As a result, many employers are struggling to contain the rise in health insurance premiums.11,12 Healthcare reform legislation has encouraged the implementation of employee wellness programs as a tool to reduce healthcare costs.5,11 Worksite wellness programs consist of employment-based activities or employer-sponsored benefits focused on health promotion and disease management.2,5 In addition to targeting rising costs of healthcare coverage, these programs are posited to reduce economic burden of disease such as
Published by Elsevier Inc.
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health-related loss of productivity (absenteeism and presenteeism).5 Emerging research has also suggested that worksite wellness programs may have other beneficial effects.13,14 They may, for example, contribute to the profitability of an organization by increasing morale and performance8,15 and facilitating employee recruitment and retention,16,17 and healthy employees not only command lower health insurance premiums but are also more productive and satisfied with their jobs.18,19 A RAND study estimates that about half of employees complete Health Risk Assessments (HRAs) or participate in clinical screenings as part of worksite wellness programs.5 Participation rates vary significantly for individual employers.20 Concerns related to selection bias in program participation have surfaced,21 as questions remain as to whether healthier employees participate and those with the most health risk do not.22 Other descriptive studies among wellness programs generally show higher rates of participation among women,22 younger23 and more-educated23 employees; those who perceive benefits to the program24; and those exhibiting high levels of self efficacy.23 Barriers to participation often include factors related to time, interest, convenience, and health beliefs.25 Descriptive studies that focus on specific aspects of wellness programs, such as exercise programs,26 health coaching,27,28 and HRA completion,29–31 show some different and additional participation patterns. Varying participation patterns also exist based on the use of incentives within the wellness program and its components.32,33 Better reporting of factors associated with worksite wellness program participation has been emphasized as key to understanding generalizability of program outcomes and can inform effective implementation and management of interventions.21 The University of Michigan (UM) established an employee wellness program, MHealthy, in 2009 and conducted a 5-year analytic evaluation of the program in 2014. MHealthy has an integrated organizational structure, including wellness and risk reduction services, employee assistance programs, and occupational health services, and has worked to create a model community of health for UM’s approximately 40,000 employees. Since its inception in 2009, MHealthy has implemented a wide variety of programs and services intended to serve everyone on a continuum of health, as well as leadership engagement strategies, communications, incentives/ rewards, and workplace culture and environment improvements. MHealthy uses an annual HRA and periodic biometric screenings to collect employee data, supports a physical activity tracking program (ActiveU), and offers wellness activities and participation incentives.34,35 This paper analyzes factors associated with MHealthy participation by using multivariate analyses to
assess the independent predictive power of employee characteristics associated with various participation metrics.
Methods De-identified individual-level employee data were obtained from MHealthy’s data warehouse vendor and cleaned by the study team. The final study population was limited to a cohort of 20,237 employees who were employed by the university for the entire 5-year period (20092013) and therefore eligible to participate in MHealthy each year (“continuously enrolled”). Data were collected from 2009 to 2013 for all measures except healthcare spending and health factors, which were 2008 data. Analyses were conducted in 2014.
Measures Participation in HRA was defined as Z50% of survey questions completed. ActiveU participation was defined as program enrollment; program completion was defined as logging Z30 minutes of daily activity at least three times per week during the 12 ActiveU weeks. ActiveU data were not available for 2009. MHealthy completion criteria varied by year and included: 1. completion of the 47-question HRA; and 2. biometric screening capturing height, weight, blood pressure, and cholesterol; completion of an MHealthy activity, or both (Appendix Table 1, available online). Employees who met all criteria for MHealthy completion received a $100 pretax incentive. Employee characteristics included: 1. age in years (o30, 3039, 4049, 5059, and Z60 years); 2. sex; 3. race/ethnicity, (white, black/African American, Asian/Pacific Islander, American Indian/Alaskan Native/Native Hawaiian/ other Pacific Islander, Hispanic/Latino, and unknown); 4. faculty or staff designation; 5. annual wage (r$35,000, $35,001$49,999, $50,000$59,999, and Z$60,000); 6. job location, (UM-Ann Arbor main campus, UM Health System, UM-Flint campus, and UM-Dearborn campus); 7. union status, based on union-supported job classes; 8. baseline total healthcare spending (health plan spending plus employee obligation); and 9. baseline health factors (diagnoses from employee claims data): back pain, risky lifestyle (alcohol, tobacco, drugs, or obesity diagnosis), respiratory disease, stress/depression, bone/joint disease, diabetes, hyperlipidemia, hypertension, other cardiovascular disease (congestive heart failure and coronary artery disease), and use of preventive care (routine medical exams and claims coded as preventive care).
Statistical Analysis Descriptive statistics were calculated for HRA participants, ActiveU participants, and those who earned completion incentives. Separate www.ajpmonline.org
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multivariate logistic regression models were estimated for 20092013 to assess predictive characteristics of program participants and completers using SAS, version 9.4. All variables of interest were forced into all models; groups with the largest proportion of employees served as reference categories. No interactions were included in the models. Three models were estimated for the continuously enrolled sample: Model 1 used demographic and job characteristics as predictor variables; Model 2 added the natural log of total healthcare spending in 2008; and Model 3 added 2008 health factor data. Additionally, ordered logistic regression models were run for: 1. HRA participation using categories of never, occasionally (1 or 2 years of participation), usually (3 or 4 years), and always (5 years); 2. at least 1 year of ActiveU participation; and 3. at least 1 year of incentive receipt. Significance level was set at po0.05. This study received exemption from the University of Michigan IRB.
Results Descriptive statistics showed that the majority of the 20,237 employees in the continuously enrolled sample were female (65%), white (77%), non-union (89%), staff members (86%) located at UM-Ann Arbor ( 55%). The percentage of employees aged o30 years decreased from 9% to 3%, and the percentage aged Z60 years increased from 8% to 18% over the 5-year period. Wages shifted higher over time, with a 7% decrease in in the lowest wage category and 12% increase in the highest wage category, likely reflecting both inflation and promotions. Approximately 13% of employees had a stress/depression diagnosis in 2008; 11% had back pain, 10% hyperlipidemia, 9% hypertension, 5% bone/joint disease, 4% respiratory disease, 4% diabetes, 3% risky life style, and 1% other cardiovascular disease. Approximately 28% had no preventive care claims. Total baseline healthcare spending for this cohort averaged $3,525 per person (Appendix Table 2, available online). MHealthy saw a small increase in HRA participation among employees: 51% (n¼14,819) in 2009 to 53% (n¼17,724) in 2013. After dropping in 2010, participation rates were higher and stable from 2011 to 2013. ActiveU participation increased from 29% (n¼8,586) in 2010 to 36% (n¼11,760) in 2013. Nearly half of employees received the MHealthy incentive each year, with the exception of 2010 when the incentive program was substantially modified (Figure 1). For HRA completion, 24% (n¼5,299) never participated, 26% (n¼5,963) occasionally, 28% (n¼6,518) usually, and 23% (n¼2,457) always participated. The full model of the employee cohort showed that employees aged o30 years were significantly less likely to ] 2016
Figure 1. Percentage of continuously enrolled employees who participated in the HRA, received an MHealthy incentive, or participated in ActiveU, 20092013. Note: ActiveU participation data were not available for 2009. HRA, Health Risk Assessment.
participate in the HRA in 2012 and 2013 (AOR¼0.8, po0.05 for both years). Men, faculty, employees earning r$35,000 annually, and union employees were significantly less likely to participate all years in all models (po0.0001 for nearly all years). More variation was observed among racial/ethnic subgroups with black/ African-American employees in 2010 (p¼0.0004), 2011 (po0.0001), and 2013 (po0.0001), and Hispanic/Latino employees in 2011 (p¼0.0013) significantly less likely to participate compared with white employees. Asian/ Pacific Islander employees were significantly less likely to participate in 2011 (p¼0.0015) and significantly more likely to participate in 2012 (po0.0001). Health System employees were significantly less likely to participate than UM-Ann Arbor employees all 5 years (po0.0001), Employees at the UM-Flint were significantly more likely to participate in all years except 2012 compared with UM-Ann Arbor employees (Table 1). Higher total healthcare spending in 2008 was not a significant predictor of participation after controlling for health factors (Table 1). Employees with preventive care claims in 2008 were significantly and substantially (AOR>1.5, po0.0001) more likely to participate than those without across all 5 years; employees with back pain were significantly more likely to participate than those without in 2009 (p¼0.03) and 2012 (p¼0.04); and employees with hyperlipidemia were significantly more likely to participate in 2009 (po0.04). Employees with diabetes or hypertension were significantly less likely to participate all years except 2011. Results of reduced models (i.e., Models 1 and 2) are included as Appendix Tables 3 and 4 (available online). Table 2 reports the results of an ordered logistic regression for the number of years of HRA participation. Frequency of participation was highest among employees who were aged 3039 years, female, white, staff, earning
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Table 1. HRA Participation Controlling for Demographic and Job Characteristics, Baseline Healthcare Cost, and Baseline Health Factors, 20092013 Variables
2009
2010
2011
2012
2013
20,237
20,237
20,237
20,237
20,237
o30 years
0.952 (0.3993)
0.912 (0.1549)
0.883 (0.0767)
0.832 (0.0174)
0.835 (0.0463)
4049 years
0.922 (0.0500)
1.011 (0.7984)
0.909 (0.0287)
0.854 (0.0003)
0.842 (0.0001)
5059 years
0.840 (0.0001)
0.961 (0.3610)
0.783 (o0.0001)
0.822 (o0.0001)
0.725 (o0.0001)
0.483 (o0.0001)
0.767 (o0.0001)
0.507 (o0.0001)
0.597 (o0.0001)
0.540 (o0.0001)
ref
ref
ref
ref
ref
0.624 (o0.0001)
0.506 (o0.0001)
0.570 (o0.0001)
0.570 (o0.0001)
0.591 (o0.0001)
Black/African- American
1.007 (0.8900)
0.825 (0.0004)
0.726 (o0.0001)
1.014 (0.8006)
0.701 (o0.0001)
Asian/Pacific Islander
0.957 (0.4559)
0.946 (0.3451)
0.829 (0.0015)
1.278 (o0.0001)
0.950 (0.3860)
American Indian/Alaskan Native/Native Hawaiian/other Pacific Islander
0.909 (0.6600)
0.818 (0.3666)
0.885 (0.6174)
0.939 (0.7964)
0.725 (0.1894)
Hispanic/Latino
0.000 (0.9256)
0.938 (0.5365)
0.733 (0.0013)
1.075 (0.4547)
0.879 (0.1834)
0.480 (o0.0001)
0.196 (o0.0001)
0.356 (o0.0001)
0.360 (o0.0001)
0.318 (o0.0001)
ref
ref
ref
ref
ref
0.347 (o0.0001)
0.543 (o0.0001)
0.498 (o0.0001)
0.510 (o0.0001)
0.509 (o0.0001)
0.815 (o0.0001)
0.688 (o0.0001)
0.744 (o0.0001)
0.865 (0.0024)
0.732 (o0.0001)
$35,001$49,999
0.987 (0.7420)
0.899 (0.0095)
1.141 (0.0013)
1.245 (o0.0001)
1.161 (0.0003)
$50,000$59,999
1.051 (0.3220)
1.043 (0.3907)
1.223 (0.0001)
1.184 (0.0004)
1.106 (0.0344)
ref
ref
ref
ref
ref
Total no. of employees Age
Z60 years
Sex Male (vs female) Race/ethnicity
Unknown White, non-Hispanic Faculty/staff designation Faculty (vs staff) Annual wage r$35,000
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Z$60,000
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3039 years
] 2016
Table 1. HRA Participation Controlling for Demographic and Job Characteristics, Baseline Healthcare Cost, and Baseline Health Factors, 20092013 (continued) Variables
2009
2010
2011
2012
2013
0.709 (o0.0001)
0.657 (o0.0001)
0.754 (o0.0001)
0.766 (o0.0001)
0.689 (o0.0001)
UM-Flint
1.357 (0.0194)
1.949 (o0.0001)
1.531 (0.0015)
1.251 (0.0842)
2.566 (o0.0001)
UM-Dearborn
0.805 (0.0555)
1.105 (0.3684)
0.985 (0.8967)
1.143 (0.2300)
1.145 (0.2235)
UM-Ann Arbor
ref
ref
ref
ref
ref
0.684 (o0.0001)
0.516 (o0.0001)
0.494 (o0.0001)
0.639 (o0.0001)
0.583 (o0.0001)
0.989 (0.1517)
0.998 (0.8342)
0.988 (0.1052)
0.996 (0.5682)
0.992 (0.3257)
1.114 (0.0292)
0.991 (0.8543)
1.009 (0.8636)
1.106 (0.0427)
1.067 (0.1909)
0.897 (0.2357)
1.013 (0.8854)
1.195 (0.0611)
1.018 (0.8451)
1.022 (0.8154)
Respiratory disease
1.002 (0.9832)
0.899 (0.2177)
0.894 (0.1993)
0.943 (0.4998)
0.892 (0.1897)
Stress/depression
1.057 (0.2745)
1.071 (0.1715)
0.966 (0.5078)
1.100 (0.0602)
1.051 (0.3330)
1.846 (o0.0001)
1.595 (o0.0001)
1.710 (o0.0001)
1.680 (o0.0001)
1.632 (o0.0001)
1.042 (0.5550)
1.010 (0.8823)
1.083 (0.2571)
1.040 (0.5743)
0.968 (0.6437)
0.919 (0.5525)
0.939 (0.6602)
0.984 (0.9084)
1.126 (0.3980)
0.948 (0.7023)
0.733 (o0.0001)
0.836 (0.0159)
0.905 (0.1749)
0.797 (0.0018)
0.832 (0.0117)
1.110 (0.0436)
1.028 (0.5886)
1.084 (0.1223)
1.012 (0.8140)
1.029 (0.5815)
0.745 (o0.0001)
0.870 (0.0121)
0.932 (0.2033)
0.829 (0.0006)
0.882 (0.0224)
Job location Health System
Union (vs non-union) Total healthcare cost, 2008
a
Health factors, 2008 Back pain Risky lifestyle
b
Preventive care Bone/joint disease c
Other cardiovascular disease Diabetes Hyperlipidemia Hypertension
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Union status
Note: Values are AORs (p-values), unless otherwise noted, for logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Healthcare cost is in natural log. b Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. c Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
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Table 2. HRA Participation, ActiveU Participation, and Incentive Receipt Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 2009-2013 HRA participationa
ActiveU participation (Z1 year)
Incentive receipt (Z1 year)
20,237
20,237
20,237
o30 years
0.974 (0.5986)
1.000 (0.9956)
0.960 (0.4242)
4049 years
0.884 (0.0006)
0.881 (0.0026)
0.904 (0.0052)
5059 years
0.788 (o0.0001)
0.732 (o0.0001)
0.816 (o0.0001)
Z60 years
0.356 (o0.0001)
0.348 (o0.0001)
0.378 (o0.0001)
ref
ref
ref
0.529 (o0.0001)
0.462 (o0.0001)
0.532 (o0.0001)
0.803 (o0.0001)
0.936 (0.2276)
0.799 (o0.0001)
Asian/Pacific Islander
0.975 (0.6182)
0.854 (0.0075)
1.003 (0.9582)
American Indian/Alaskan Native/Native Hawaiian/ other Pacific Islander
0.721 (0.0837)
1.043 (0.8498)
0.766 (0.1631)
Hispanic/Latino
0.000 (0.8935)
0.000 (0.9234)
0.000 (0.8976)
0.403 (o0.0001)
0.554 (o0.0001)
0.387 (o0.0001)
ref
ref
ref
0.433 (o0.0001)
0.488 (o0.0001)
0.411 (o0.0001)
0.769 (o0.0001)
0.773 (o0.0001)
0.828 (o0.0001)
$35,001$49,999
1.024 (0.5172)
0.950 (0.2239)
1.072 (0.0563)
$50,000$59,999
1.139 (0.0027)
1.114 (0.0341)
1.191 (0.0001)
Z$60,000 or more
ref
ref
ref
Health System
0.694 (o0.0001)
0.679 (o0.0001)
0.662 (o0.0001)
UM-Flint
1.746 (o0.0001)
1.311 (0.0429)
1.713 (o0.0001)
UM-Dearborn
1.055 (0.5776)
1.206 (0.1044)
1.027 (0.7801)
UM-Ann Arbor
ref
ref
ref
0.565 (o0.0001)
0.331 (o0.0001)
0.568 (o0.0001)
0.984 (0.0185)
0.982 (0.0238)
0.986 (0.0347)
1.073 (0.1019)
0.966 (0.4874)
1.081 (0.0737)
0.993 (0.9319)
1.149 (0.1449)
1.015 (0.8561)
Variables Total number of employees Age
3039 years Sex Male (vs female) Race/ethnicity Black/African-American
Unknown White, non-Hispanic Faculty/staff designation Faculty (vs staff) Wage r$35,000
Job location
Union status Union (vs non-union) Total healthcare cost, 2008b Health factors, 2008 Back pain c
Risky lifestyle
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Table 2. HRA Participation, ActiveU Participation, and Incentive Receipt Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 2009-2013 (continued) HRA participationa
ActiveU participation (Z1 year)
Incentive receipt (Z1 year)
Respiratory disease
0.935 (0.3696)
0.922 (0.3541)
0.974 (0.7267)
Stress/depression
1.076 (0.0982)
1.047 (0.3801)
1.016 (0.7281)
1.949 (o0.0001)
1.767 (o0.0001)
1.944 (o0.0001)
Bone/joint disease
1.040 (0.5171)
0.993 (0.9226)
1.044 (0.4751)
Other cardiovascular diseased
1.056 (0.6555)
1.010 (0.9432)
0.967 (0.7871)
Diabetes
0.803 (0.0006)
0.812 (0.0054)
0.788 (0.0002)
Hyperlipidemia
1.083 (0.0764)
1.056 (0.3043)
1.089 (0.0586)
Hypertension
0.846 (0.0005)
0.923 (0.1553)
0.859 (0.0017)
Variables
Preventive care
Note: Values are AORs (p-values), unless otherwise noted, for ordered logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Participation levels included never (participated 0 years); occasionally (participated 12 years); usually (participated 34 years); always (participated all 5 years). b Healthcare cost is in natural log. c Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. d Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
$50,000$59,999, UM-Flint employees, and non-union. Baseline healthcare spending was a significant predictor of participation in the reduced model, with those with higher spending participating more frequently. However, when including 2008 health factors in the model, the association between baseline costs and participation showed that those with higher spending participated less frequently (p¼0.0185). Baseline health factor data showed that employees with preventive care claims had significantly higher frequencies of HRA completion than those without (po0.0001), and employees with diabetes or hypertension participated less frequently than those without (p¼0.0006 and p¼0.0005, respectively). Participation in the ActiveU was greatest among those aged 3039 years and declined at higher ages. Men were significantly less likely to participate in ActiveU (po0.0001), as were faculty (po0.0001), union employees (po0.0001), and employees whose race/ethnicity was Asian/Pacific Islander (p¼0.0075). Compared with continuously enrolled employees earning Z$60,000, those earning r$35,000 were significantly less likely to participate in ActiveU (po0.0001), whereas employees earning $50,000$59,999 were significantly more likely to participate (p¼0.0001). Relative to UM-Ann Arbor employees, those working at the Health System were significantly less likely to participate (po0.0001) and those at UM-Flint were significantly more likely to participate (p¼0.04). Health factor data showed that employees with preventive care claims in 2008 were significantly more likely to participate in ActiveU than those without (po0.0001), ] 2016
and employees with diabetes were significantly less likely to participate than those without (p¼0.005) (Table 2). Many of the same associations held true for program completion (Table 3). Wage presented a more mixed picture. Compared with employee participants earning Z$60,000, participants earning r$35,000 were significantly more likely to complete the program in 2011 in the continuously enrolled models (p¼0.007). Participants earning $35,001$49,999 were significantly more likely to finish the program in 2011 (p¼0.0007) and 2013 (p¼0.002). Finally, participants earning $50,000–$59,999 were significantly more likely to finish the program, compared with employees making Z$60,000 in 2013 (p¼0.03). Union employee participants were significantly less likely to complete ActiveU in 2013 (p¼0.028) and faculty were significantly less likely to complete the program every year except 2012. Total baseline healthcare spending did not significantly predict completion of ActiveU. Baseline health factors showed higher likelihood of program completion in 2010 among participants with respiratory disease and users of preventive care compared with those without. Participants with baseline claims diagnoses of stress/depression in 2010, 2012, and 2013; bone/joint disease in 2013; diabetes in 2010; and hypertension in 2010 were significantly less likely to complete the ActiveU compared to participants without those diagnoses (Table 3). About three quarters of employees received an incentive payment at least once during the 5 years, and 12% received an incentive every year of the program. Table 2 shows
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Table 3. Completion of ActiveU Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 20102013 Variables
2010
2011
2012
2013
ActiveU participants, n
5,880
5,501
5,662
7,197
3,172 (54)
4,230 (77)
2,975 (53)
4,175 (58)
1.054 (0.6402)
1.021 (0.8970)
0.852 (0.2905)
0.825 (0.2050)
4049 years
1.351 (o0.0001)
1.332 (0.0013)
1.175 (0.0365)
1.155 (0.0415)
5059 years
1.730 (o0.0001)
1.545 (o0.0001)
1.468 (o0.0001)
1.522 (o0.0001)
Z60 years
1.992 (o0.0001)
1.941 (o0.0001)
1.711 (o0.0001)
1.639 (o0.0001)
ref
ref
ref
ref
0.871 (0.0491)
0.931 (0.4000)
0.931 (0.3237)
0.823 (0.0016)
0.485 (o0.0001)
0.535 (o0.0001)
0.755 (0.0056)
0.707 (0.0001)
Asian/Pacific Islander
0.999 (0.9935)
1.133 (0.3846)
1.021 (0.8590)
1.094 (0.3549)
American Indian/Alaskan Native/ Native Hawaiian/other Pacific Islander
0.629 (0.2235)
1.311 (0.7305)
1.683 (0.3550)
0.896 (0.8156)
Hispanic/Latino
0.509 (0.0008)
0.617 (0.0305)
0.635 (0.0252)
0.640 (0.0060)
Unknown
0.604 (0.1043)
0.775 (0.2551)
0.963 (0.8503)
1.019 (0.9132)
ref
ref
ref
ref
0.696 (0.0006)
0.765 (0.0359)
0.871 (0.1941)
0.785 (0.0060)
r$35,000
1.081 (0.3637)
1.339 (0.0072)
1.035 (0.7007)
1.110 (0.2136)
$35,001$49,999
1.123 (0.1030)
1.337 (0.0007)
1.075 (0.3037)
1.220 (0.0019)
$50,000$59,999
1.140 (0.1213)
1.192 (0.0712)
0.879 (0.1140)
1.168 (0.0343)
ref
ref
ref
ref
0.707 (o0.0001)
0.778 (0.0004)
0.781 (o0.0001)
0.763 (o0.0001)
UM-Flint
0.786 (0.2343)
0.711 (0.1494)
0.641 (0.0348)
0.974 (0.8966)
UM-Dearborn
0.700 (0.0673)
0.464 (0.0003)
0.810 (0.2614)
0.854 (0.3755)
UM-Ann Arbor
ref
ref
ref
ref
0.883 (0.3853)
0.834 (0.3232)
0.739 (0.0503)
0.763 (0.0280)
0.983 (0.2612)
0.999 (0.9695)
0.993 (0.6473)
1.015 (0.2684)
0.947 (0.5264)
0.832 (0.0690)
0.988 (0.8863)
Completing ActiveU, n (%) Age o30 years
3039 years Sex Male (vs female) Race/ethnicity Black/African-American
White, non-Hispanic Faculty/staff designation Faculty (vs staff) Annual wage
Z$60,000 Job location Health System
Union status Union (vs non-union) Total healthcare cost, 2008
a
Health factors, 2008 Back pain
0.903 (0.1828) (continued on next page)
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Table 3. Completion of ActiveU Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 20102013 (continued) Variables
2010
2011
2012
2013
Risky lifestyleb
0.988 (0.9406)
0.948 (0.7737)
1.165 (0.3357)
0.903 (0.4799)
Respiratory disease
1.424 (0.0169)
0.913 (0.6166)
1.177 (0.2820)
1.194 (0.1975)
Stress/depression
0.657 (o0.0001)
0.833 (0.0820)
0.689 (o0.0001)
0.746 (0.0002)
1.336 (0.0014)
0.992 (0.9413)
1.152 (0.1304)
1.021 (0.7990)
0.823 (0.1182)
0.906 (0.5161)
0.912 (0.4539)
0.798 (0.0415)
1.101 (0.7436)
0.956 (0.8936)
1.004 (0.9871)
0.901 (0.6631)
Diabetes
0.699 (0.0109)
1.045 (0.7996)
0.942 (0.6794)
0.901 (0.4182)
Hyperlipidemia
1.055 (0.5615)
0.913 (0.4085)
0.935 (0.4700)
0.940 (0.4661)
Hypertension
0.786 (0.0196)
0.839 (0.1544)
0.919 (0.4255)
0.882 (0.1750)
Preventive care Bone/joint disease Other cardiovascular disease
c
Note: Values are AORs (p-values), unless otherwise noted, for logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Healthcare cost is in natural log. b Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. c Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
predictors of receiving an incentive at least once during the 5-year program period. Those most likely to receive incentives were white, female, non-union staff members. Compared with those aged 3039 years, those of all other age categories except o30 years were significantly less likely to receive any incentive. Compared with employees earning Z$60,000, those making r$35,000 were significantly less likely to receive any incentive (po0.0001), whereas those with wages of $50,000$59,999 were significantly more likely to receive an incentive (p¼0.0001). UM-Flint employees were significantly more likely to receive an incentive during the 5-year period than UM-Ann Arbor employees (po0.0001), whereas Health System employees were significantly less likely (po0.0001). Higher total baseline healthcare spending was significantly associated with being less likely to receive an incentive (p¼0.03). Those with preventive care claims were significantly more likely (po0.0001) and those with diabetes or hypertension were significantly less likely to receive an incentive (p¼0.0002 and p¼0.0017, respectively) than those without those claims.
Discussion This study has several notable features that help address literature gaps on employee wellness program participation. First, having 5 years of data allows the authors to assess participation trends and predictors of participation frequency. Most predictors of participation were quite stable across years, but the effects of income and race on HRA participation evolved somewhat over time. Second, the program’s participation rate of close to 50% implies ] 2016
near maximal variation in participation, allowing differentiation between two equally sized groups. Third, three distinct measures of participation and engagement were used: HRA completion, ActiveU participation, and incentive receipt. Fourth, this study used a large set of employee characteristics, including medical spending, preventive care use, and health factors present at baseline to assess selection into the program by health status. Finally, the use of multivariate analysis to assess the independent predictive power of employee characteristics adds a unique perspective to the literature. MHealthy’s success in attracting participation from approximately half of employees is consistent with RAND study findings for HRA participation.5 The overall percentage of employees receiving incentives increased since 2010, which is a positive finding. MHealthy appears to have strong participation from employees who are female, white, and in non-union staff positions, as well as those who seek preventive care. Variation in participation among employees of different ages and wage categories is also apparent. MHealthy has used a uniform communications strategy to recruit participants; these findings show that tailored communications may help enhance participation among employees who are less likely to participate: men, faculty members, union members, employees of minority racial or ethnic backgrounds, and those with lower wages, particularly those with an annual income r$35,000. The low participation by lower-wage workers, despite the provision of financial incentives, is notable because it implies some regressivity in the incentive program. It may also indicate that these
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workers could be having difficulty meeting the incentive requirements (i.e., limited access to computers to complete the HRA) or have jobs that are more physically demanding, making programming related to physical activity less appealing. In addition, increased efforts to recruit Health System employees may also enhance overall participation levels. The incentive program may have had some influence on employees’ participation choices. For example, ActiveU completion was higher in years when it counted toward incentive receipt. Selection into the program by baseline health factors also has important implications for program design and evaluation. There was some evidence that employees with higher medical spending were more likely to participate, but this relationship to total spending tended not to persist once measures of preventive care use and other health factors were added to the model. This suggests that the selection process is too complicated to capture in a unidimensional “index” of health status, such as total spending. Rather, a multidimensional pattern emerged. Those employees with a “revealed preference” for prevention (i.e., those utilizing preventive services before the program) were more likely to participate, suggesting some favorable selection by those most engaged in preventive activities. Not accounting for such selection when evaluating the outcomes of a wellness program can overestimate its impact by attributing favorable outcomes to the program, which may have occurred in any case owing to the pre-existing engagement of workers in preventive activities. Other health characteristics had different effects on participation. For example, workers with diabetes or hypertension were consistently less likely to participate, indicating favorable selection into the program based on important and prevalent chronic conditions. It also suggests that targeted outreach and an examination of how the program could be made more attractive to workers with diabetes could be valuable. Several other health factors were associated with greater participation in some years and for some participation measures, indicating that selection patterns were too heterogeneous to allow a simple statement that the program received a favorable selection based on risks. Participation in a worksite wellness program appears to be influenced by both person-level and program-level factors. Examples of person-level factors that can impact likelihood of engaging in health-promoting behavior include perceived benefits and barriers to participation, perceived threat of disease, and self-efficacy, which may be further studied for MHealthy.36 Modifiable programlevel factors likely to drive participation include program features, outreach/marketing, and incentive amount, which is currently less than other programs with substantially higher participation.37
Limitations This study is subject to limitations. First, multicollinearity between spending and baseline health factors may be a concern, but reduced models that eliminate health factors show that the impact of this on other variables is insignificant. Also, the authors did not adjust for multiple comparisons because the same factors are consistently significant across years and models. The definition of participation varied from engagement to completion depending on the activity, so this term may represent different participation outcomes compared to similar studies. Additionally, claims data were used to identify the baseline health factors used in the predictive models, likely leading to under-identification, as only those who sought care will have a claims-based diagnosis. This employee population may differ from others, so caution should be taken when generalizing results to other employee groups. Finally, the authors do not know how many non-participants were engaged in wellness or disease management activities outside of MHealthy.
Conclusions MHealthy has attracted participation from roughly half of the employee population. A consistent demographic and job characteristic profile emerged among program participants, which better characterizes employees who choose to engage in employee wellness activities. The results of this study can guide efforts to identify the causes of participation trends, both in terms of employee attitudes and workplace barriers. Such information could be used to better target outreach and program content and reduce structural barriers to participation. Future studies could consider additional job characteristics, such as job type and employee attitudinal variables regarding health status and wellness program effectiveness. This study was supported by the University of Michigan. No financial disclosures were reported by the authors of this paper.
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22. Lewis RJ, Huebner WW, Yarborough CM. Characteristics of participants and nonparticipants in worksite health promotion. Am J Health Promot. 1996;11(2):99–106. http://dx.doi.org/10.4278/0890-1171-11.2.99. 23. Alexy BB. Factors associated with participation or nonparticipation in a workplace wellness center. Res Nurs Health. 1991;14(1):33–40. http: //dx.doi.org/10.1002/nur.4770140106. 24. Middlestat SE, Sheats JL, Geshnizjani A, Sullivan MR, Arvin CS. Factors associated with participation in work-site wellness programs: implications for increasing willingness among rural service employees. Health Educ Behav. 2011;38(5):502–509. http://dx.doi.org/10.1177/ 1090198110384469. 25. Person AL, Colby SE, Bulova JA, Eubanks JW. Barriers to participation in a worksite wellness program. Nutr Res Pract. 2010;4(2):149–154. http://dx.doi.org/10.4162/nrp.2010.4.2.149. 26. Abraham JM, Feldman R, Nyman JA, Barleen N. What factors influence participation in an exercise-focused, employer-based wellness program? Inquiry. 2011;48:221–241. http://dx.doi.org/10.5034/ inquiryjrnl_48.03.01. 27. Grossmeier J. The influence of worksite and employee variables on employee engagement in telephonic health coaching programs: a retrospective multivariate analysis. Am J Health Promot. 2013;27(3): e69–e81. http://dx.doi.org/10.4278/ajhp.100615-QUAN-190. 28. Norman GJ, Heltemes KJ, Drew J. Examining incentive design strategies for worksite wellness program engagement. Popul Health Manag. 2014;17(6):324–331. http://dx.doi.org/10.1089/pop.2014.0008. 29. Huskamp HH, Rosenthal MB. Health risk appraisals: how much do they influence employees’ health behavior? Health Affairs. 2009;28 (3):1532–1540. http://dx.doi.org/10.1377/hlthaff.28.5.1532. 30. Taitel MS, Haufle V, Heck D, Loeppke R, Fetterolf D. Incentives and other factors associated with employee participation in health risk assessments. J Occup Environ Med. 2008;50(8):863–872. http://dx.doi. org/10.1097/JOM.0b013e3181845fe2. 31. Toker SA, Heaney CA, Ein-Gar D. Why won’t they participate? Barriers to participation in worksite health promotion programmes. Eur J Work Organ Psychol. 2015;24(6):866–881. http://dx.doi.org/ 10.1080/1359432X.2014.968131. 32. Fronstin P, Roebuck MC. Financial incentives and workplace wellnessprogram participation. EBRI Issue Brief. 2015;412:1–20. 33. Terry PE, Grossmeier J, Mangen DJ, Gingerich SB. Analyzing best practices in employee health management: how age, sex, and program components relate to employee engagement and health outcomes. J Occup Environ Med. 2013;55(4):378–392. http://dx.doi.org/10.1097/ JOM.0b013e31828dca09. 34. Health Enhancement Research Organization and Population Health Alliance. Program measurement and evaluation guide: core metrics for employee health management. www.populationhealthalliance.org/pub lications/program-measurement-evaluation-guide-core-metrics-foremployee-health-management.html. Accessed May 20, 2015. 35. University of Michigan. MHealthy annual report: CY 2013. 2014. https://hr. umich.edu/sites/default/files/cy13-annual-report.pdf. Accessed October 16, 2015. 36. Janz NK, Becker MH. The Health Belief Model: a decade later. Health Educ Behav. 1984;11(1):1–47. http://dx.doi.org/10.1177/109019818401100101. 37. Anderson DR, Grossmeier J, Seaverson ELD, Snyder D. The role of financial incentives in driving employee engagement in health management. ACSMs Health Fit J. 2008;12(4):18–22.
Appendix Supplementary data Supplementary data associated with this article can be found at http://dx.doi.org/10.1016/j.amepre.2016.01.028.
Conclusions: This study helps characterize employees who choose to engage in worksite wellness programs. Such information could be used to better target outreach and program content and reduce structural barriers to participation. Future studies could consider additional job characteristics, such as job type and employee attitudinal variables regarding health status and wellness program effectiveness. (Am J Prev Med 2016;](]):]]]–]]]) & 2016 American Journal of Preventive Medicine
Introduction
T
he health status of the U.S. workforce is a growing concern.1,2 The workforce is aging owing to retirement delay3,4 and the increase of “lifestyle diseases” characterized by poor nutrition and inactivity have contributed to the rising prevalence of chronic From the 1Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, Michigan; 2Health and Wellbeing Services, University of Michigan, Ann Arbor, Michigan; and 3 Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, Michigan Address correspondence to: Angela J. Beck, PhD, MPH, Department of Health Management and Policy, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor MI 48109-2029. E-mail: [email protected]. 0749-3797/$36.00 http://dx.doi.org/10.1016/j.amepre.2016.01.028
& 2016 American Journal of Preventive Medicine
diseases among workers of all ages,2,5 both of which result in mounting healthcare costs for employers.5,6 U.S. healthcare spending has risen substantially faster than the general rate of inflation for several decades7-9 and around 60% of Americans obtain health insurance through an employer.10 As a result, many employers are struggling to contain the rise in health insurance premiums.11,12 Healthcare reform legislation has encouraged the implementation of employee wellness programs as a tool to reduce healthcare costs.5,11 Worksite wellness programs consist of employment-based activities or employer-sponsored benefits focused on health promotion and disease management.2,5 In addition to targeting rising costs of healthcare coverage, these programs are posited to reduce economic burden of disease such as
Published by Elsevier Inc.
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health-related loss of productivity (absenteeism and presenteeism).5 Emerging research has also suggested that worksite wellness programs may have other beneficial effects.13,14 They may, for example, contribute to the profitability of an organization by increasing morale and performance8,15 and facilitating employee recruitment and retention,16,17 and healthy employees not only command lower health insurance premiums but are also more productive and satisfied with their jobs.18,19 A RAND study estimates that about half of employees complete Health Risk Assessments (HRAs) or participate in clinical screenings as part of worksite wellness programs.5 Participation rates vary significantly for individual employers.20 Concerns related to selection bias in program participation have surfaced,21 as questions remain as to whether healthier employees participate and those with the most health risk do not.22 Other descriptive studies among wellness programs generally show higher rates of participation among women,22 younger23 and more-educated23 employees; those who perceive benefits to the program24; and those exhibiting high levels of self efficacy.23 Barriers to participation often include factors related to time, interest, convenience, and health beliefs.25 Descriptive studies that focus on specific aspects of wellness programs, such as exercise programs,26 health coaching,27,28 and HRA completion,29–31 show some different and additional participation patterns. Varying participation patterns also exist based on the use of incentives within the wellness program and its components.32,33 Better reporting of factors associated with worksite wellness program participation has been emphasized as key to understanding generalizability of program outcomes and can inform effective implementation and management of interventions.21 The University of Michigan (UM) established an employee wellness program, MHealthy, in 2009 and conducted a 5-year analytic evaluation of the program in 2014. MHealthy has an integrated organizational structure, including wellness and risk reduction services, employee assistance programs, and occupational health services, and has worked to create a model community of health for UM’s approximately 40,000 employees. Since its inception in 2009, MHealthy has implemented a wide variety of programs and services intended to serve everyone on a continuum of health, as well as leadership engagement strategies, communications, incentives/ rewards, and workplace culture and environment improvements. MHealthy uses an annual HRA and periodic biometric screenings to collect employee data, supports a physical activity tracking program (ActiveU), and offers wellness activities and participation incentives.34,35 This paper analyzes factors associated with MHealthy participation by using multivariate analyses to
assess the independent predictive power of employee characteristics associated with various participation metrics.
Methods De-identified individual-level employee data were obtained from MHealthy’s data warehouse vendor and cleaned by the study team. The final study population was limited to a cohort of 20,237 employees who were employed by the university for the entire 5-year period (20092013) and therefore eligible to participate in MHealthy each year (“continuously enrolled”). Data were collected from 2009 to 2013 for all measures except healthcare spending and health factors, which were 2008 data. Analyses were conducted in 2014.
Measures Participation in HRA was defined as Z50% of survey questions completed. ActiveU participation was defined as program enrollment; program completion was defined as logging Z30 minutes of daily activity at least three times per week during the 12 ActiveU weeks. ActiveU data were not available for 2009. MHealthy completion criteria varied by year and included: 1. completion of the 47-question HRA; and 2. biometric screening capturing height, weight, blood pressure, and cholesterol; completion of an MHealthy activity, or both (Appendix Table 1, available online). Employees who met all criteria for MHealthy completion received a $100 pretax incentive. Employee characteristics included: 1. age in years (o30, 3039, 4049, 5059, and Z60 years); 2. sex; 3. race/ethnicity, (white, black/African American, Asian/Pacific Islander, American Indian/Alaskan Native/Native Hawaiian/ other Pacific Islander, Hispanic/Latino, and unknown); 4. faculty or staff designation; 5. annual wage (r$35,000, $35,001$49,999, $50,000$59,999, and Z$60,000); 6. job location, (UM-Ann Arbor main campus, UM Health System, UM-Flint campus, and UM-Dearborn campus); 7. union status, based on union-supported job classes; 8. baseline total healthcare spending (health plan spending plus employee obligation); and 9. baseline health factors (diagnoses from employee claims data): back pain, risky lifestyle (alcohol, tobacco, drugs, or obesity diagnosis), respiratory disease, stress/depression, bone/joint disease, diabetes, hyperlipidemia, hypertension, other cardiovascular disease (congestive heart failure and coronary artery disease), and use of preventive care (routine medical exams and claims coded as preventive care).
Statistical Analysis Descriptive statistics were calculated for HRA participants, ActiveU participants, and those who earned completion incentives. Separate www.ajpmonline.org
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3
multivariate logistic regression models were estimated for 20092013 to assess predictive characteristics of program participants and completers using SAS, version 9.4. All variables of interest were forced into all models; groups with the largest proportion of employees served as reference categories. No interactions were included in the models. Three models were estimated for the continuously enrolled sample: Model 1 used demographic and job characteristics as predictor variables; Model 2 added the natural log of total healthcare spending in 2008; and Model 3 added 2008 health factor data. Additionally, ordered logistic regression models were run for: 1. HRA participation using categories of never, occasionally (1 or 2 years of participation), usually (3 or 4 years), and always (5 years); 2. at least 1 year of ActiveU participation; and 3. at least 1 year of incentive receipt. Significance level was set at po0.05. This study received exemption from the University of Michigan IRB.
Results Descriptive statistics showed that the majority of the 20,237 employees in the continuously enrolled sample were female (65%), white (77%), non-union (89%), staff members (86%) located at UM-Ann Arbor ( 55%). The percentage of employees aged o30 years decreased from 9% to 3%, and the percentage aged Z60 years increased from 8% to 18% over the 5-year period. Wages shifted higher over time, with a 7% decrease in in the lowest wage category and 12% increase in the highest wage category, likely reflecting both inflation and promotions. Approximately 13% of employees had a stress/depression diagnosis in 2008; 11% had back pain, 10% hyperlipidemia, 9% hypertension, 5% bone/joint disease, 4% respiratory disease, 4% diabetes, 3% risky life style, and 1% other cardiovascular disease. Approximately 28% had no preventive care claims. Total baseline healthcare spending for this cohort averaged $3,525 per person (Appendix Table 2, available online). MHealthy saw a small increase in HRA participation among employees: 51% (n¼14,819) in 2009 to 53% (n¼17,724) in 2013. After dropping in 2010, participation rates were higher and stable from 2011 to 2013. ActiveU participation increased from 29% (n¼8,586) in 2010 to 36% (n¼11,760) in 2013. Nearly half of employees received the MHealthy incentive each year, with the exception of 2010 when the incentive program was substantially modified (Figure 1). For HRA completion, 24% (n¼5,299) never participated, 26% (n¼5,963) occasionally, 28% (n¼6,518) usually, and 23% (n¼2,457) always participated. The full model of the employee cohort showed that employees aged o30 years were significantly less likely to ] 2016
Figure 1. Percentage of continuously enrolled employees who participated in the HRA, received an MHealthy incentive, or participated in ActiveU, 20092013. Note: ActiveU participation data were not available for 2009. HRA, Health Risk Assessment.
participate in the HRA in 2012 and 2013 (AOR¼0.8, po0.05 for both years). Men, faculty, employees earning r$35,000 annually, and union employees were significantly less likely to participate all years in all models (po0.0001 for nearly all years). More variation was observed among racial/ethnic subgroups with black/ African-American employees in 2010 (p¼0.0004), 2011 (po0.0001), and 2013 (po0.0001), and Hispanic/Latino employees in 2011 (p¼0.0013) significantly less likely to participate compared with white employees. Asian/ Pacific Islander employees were significantly less likely to participate in 2011 (p¼0.0015) and significantly more likely to participate in 2012 (po0.0001). Health System employees were significantly less likely to participate than UM-Ann Arbor employees all 5 years (po0.0001), Employees at the UM-Flint were significantly more likely to participate in all years except 2012 compared with UM-Ann Arbor employees (Table 1). Higher total healthcare spending in 2008 was not a significant predictor of participation after controlling for health factors (Table 1). Employees with preventive care claims in 2008 were significantly and substantially (AOR>1.5, po0.0001) more likely to participate than those without across all 5 years; employees with back pain were significantly more likely to participate than those without in 2009 (p¼0.03) and 2012 (p¼0.04); and employees with hyperlipidemia were significantly more likely to participate in 2009 (po0.04). Employees with diabetes or hypertension were significantly less likely to participate all years except 2011. Results of reduced models (i.e., Models 1 and 2) are included as Appendix Tables 3 and 4 (available online). Table 2 reports the results of an ordered logistic regression for the number of years of HRA participation. Frequency of participation was highest among employees who were aged 3039 years, female, white, staff, earning
4
Table 1. HRA Participation Controlling for Demographic and Job Characteristics, Baseline Healthcare Cost, and Baseline Health Factors, 20092013 Variables
2009
2010
2011
2012
2013
20,237
20,237
20,237
20,237
20,237
o30 years
0.952 (0.3993)
0.912 (0.1549)
0.883 (0.0767)
0.832 (0.0174)
0.835 (0.0463)
4049 years
0.922 (0.0500)
1.011 (0.7984)
0.909 (0.0287)
0.854 (0.0003)
0.842 (0.0001)
5059 years
0.840 (0.0001)
0.961 (0.3610)
0.783 (o0.0001)
0.822 (o0.0001)
0.725 (o0.0001)
0.483 (o0.0001)
0.767 (o0.0001)
0.507 (o0.0001)
0.597 (o0.0001)
0.540 (o0.0001)
ref
ref
ref
ref
ref
0.624 (o0.0001)
0.506 (o0.0001)
0.570 (o0.0001)
0.570 (o0.0001)
0.591 (o0.0001)
Black/African- American
1.007 (0.8900)
0.825 (0.0004)
0.726 (o0.0001)
1.014 (0.8006)
0.701 (o0.0001)
Asian/Pacific Islander
0.957 (0.4559)
0.946 (0.3451)
0.829 (0.0015)
1.278 (o0.0001)
0.950 (0.3860)
American Indian/Alaskan Native/Native Hawaiian/other Pacific Islander
0.909 (0.6600)
0.818 (0.3666)
0.885 (0.6174)
0.939 (0.7964)
0.725 (0.1894)
Hispanic/Latino
0.000 (0.9256)
0.938 (0.5365)
0.733 (0.0013)
1.075 (0.4547)
0.879 (0.1834)
0.480 (o0.0001)
0.196 (o0.0001)
0.356 (o0.0001)
0.360 (o0.0001)
0.318 (o0.0001)
ref
ref
ref
ref
ref
0.347 (o0.0001)
0.543 (o0.0001)
0.498 (o0.0001)
0.510 (o0.0001)
0.509 (o0.0001)
0.815 (o0.0001)
0.688 (o0.0001)
0.744 (o0.0001)
0.865 (0.0024)
0.732 (o0.0001)
$35,001$49,999
0.987 (0.7420)
0.899 (0.0095)
1.141 (0.0013)
1.245 (o0.0001)
1.161 (0.0003)
$50,000$59,999
1.051 (0.3220)
1.043 (0.3907)
1.223 (0.0001)
1.184 (0.0004)
1.106 (0.0344)
ref
ref
ref
ref
ref
Total no. of employees Age
Z60 years
Sex Male (vs female) Race/ethnicity
Unknown White, non-Hispanic Faculty/staff designation Faculty (vs staff) Annual wage r$35,000
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Z$60,000
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3039 years
] 2016
Table 1. HRA Participation Controlling for Demographic and Job Characteristics, Baseline Healthcare Cost, and Baseline Health Factors, 20092013 (continued) Variables
2009
2010
2011
2012
2013
0.709 (o0.0001)
0.657 (o0.0001)
0.754 (o0.0001)
0.766 (o0.0001)
0.689 (o0.0001)
UM-Flint
1.357 (0.0194)
1.949 (o0.0001)
1.531 (0.0015)
1.251 (0.0842)
2.566 (o0.0001)
UM-Dearborn
0.805 (0.0555)
1.105 (0.3684)
0.985 (0.8967)
1.143 (0.2300)
1.145 (0.2235)
UM-Ann Arbor
ref
ref
ref
ref
ref
0.684 (o0.0001)
0.516 (o0.0001)
0.494 (o0.0001)
0.639 (o0.0001)
0.583 (o0.0001)
0.989 (0.1517)
0.998 (0.8342)
0.988 (0.1052)
0.996 (0.5682)
0.992 (0.3257)
1.114 (0.0292)
0.991 (0.8543)
1.009 (0.8636)
1.106 (0.0427)
1.067 (0.1909)
0.897 (0.2357)
1.013 (0.8854)
1.195 (0.0611)
1.018 (0.8451)
1.022 (0.8154)
Respiratory disease
1.002 (0.9832)
0.899 (0.2177)
0.894 (0.1993)
0.943 (0.4998)
0.892 (0.1897)
Stress/depression
1.057 (0.2745)
1.071 (0.1715)
0.966 (0.5078)
1.100 (0.0602)
1.051 (0.3330)
1.846 (o0.0001)
1.595 (o0.0001)
1.710 (o0.0001)
1.680 (o0.0001)
1.632 (o0.0001)
1.042 (0.5550)
1.010 (0.8823)
1.083 (0.2571)
1.040 (0.5743)
0.968 (0.6437)
0.919 (0.5525)
0.939 (0.6602)
0.984 (0.9084)
1.126 (0.3980)
0.948 (0.7023)
0.733 (o0.0001)
0.836 (0.0159)
0.905 (0.1749)
0.797 (0.0018)
0.832 (0.0117)
1.110 (0.0436)
1.028 (0.5886)
1.084 (0.1223)
1.012 (0.8140)
1.029 (0.5815)
0.745 (o0.0001)
0.870 (0.0121)
0.932 (0.2033)
0.829 (0.0006)
0.882 (0.0224)
Job location Health System
Union (vs non-union) Total healthcare cost, 2008
a
Health factors, 2008 Back pain Risky lifestyle
b
Preventive care Bone/joint disease c
Other cardiovascular disease Diabetes Hyperlipidemia Hypertension
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Union status
Note: Values are AORs (p-values), unless otherwise noted, for logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Healthcare cost is in natural log. b Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. c Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
5
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Table 2. HRA Participation, ActiveU Participation, and Incentive Receipt Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 2009-2013 HRA participationa
ActiveU participation (Z1 year)
Incentive receipt (Z1 year)
20,237
20,237
20,237
o30 years
0.974 (0.5986)
1.000 (0.9956)
0.960 (0.4242)
4049 years
0.884 (0.0006)
0.881 (0.0026)
0.904 (0.0052)
5059 years
0.788 (o0.0001)
0.732 (o0.0001)
0.816 (o0.0001)
Z60 years
0.356 (o0.0001)
0.348 (o0.0001)
0.378 (o0.0001)
ref
ref
ref
0.529 (o0.0001)
0.462 (o0.0001)
0.532 (o0.0001)
0.803 (o0.0001)
0.936 (0.2276)
0.799 (o0.0001)
Asian/Pacific Islander
0.975 (0.6182)
0.854 (0.0075)
1.003 (0.9582)
American Indian/Alaskan Native/Native Hawaiian/ other Pacific Islander
0.721 (0.0837)
1.043 (0.8498)
0.766 (0.1631)
Hispanic/Latino
0.000 (0.8935)
0.000 (0.9234)
0.000 (0.8976)
0.403 (o0.0001)
0.554 (o0.0001)
0.387 (o0.0001)
ref
ref
ref
0.433 (o0.0001)
0.488 (o0.0001)
0.411 (o0.0001)
0.769 (o0.0001)
0.773 (o0.0001)
0.828 (o0.0001)
$35,001$49,999
1.024 (0.5172)
0.950 (0.2239)
1.072 (0.0563)
$50,000$59,999
1.139 (0.0027)
1.114 (0.0341)
1.191 (0.0001)
Z$60,000 or more
ref
ref
ref
Health System
0.694 (o0.0001)
0.679 (o0.0001)
0.662 (o0.0001)
UM-Flint
1.746 (o0.0001)
1.311 (0.0429)
1.713 (o0.0001)
UM-Dearborn
1.055 (0.5776)
1.206 (0.1044)
1.027 (0.7801)
UM-Ann Arbor
ref
ref
ref
0.565 (o0.0001)
0.331 (o0.0001)
0.568 (o0.0001)
0.984 (0.0185)
0.982 (0.0238)
0.986 (0.0347)
1.073 (0.1019)
0.966 (0.4874)
1.081 (0.0737)
0.993 (0.9319)
1.149 (0.1449)
1.015 (0.8561)
Variables Total number of employees Age
3039 years Sex Male (vs female) Race/ethnicity Black/African-American
Unknown White, non-Hispanic Faculty/staff designation Faculty (vs staff) Wage r$35,000
Job location
Union status Union (vs non-union) Total healthcare cost, 2008b Health factors, 2008 Back pain c
Risky lifestyle
(continued on next page)
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Table 2. HRA Participation, ActiveU Participation, and Incentive Receipt Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 2009-2013 (continued) HRA participationa
ActiveU participation (Z1 year)
Incentive receipt (Z1 year)
Respiratory disease
0.935 (0.3696)
0.922 (0.3541)
0.974 (0.7267)
Stress/depression
1.076 (0.0982)
1.047 (0.3801)
1.016 (0.7281)
1.949 (o0.0001)
1.767 (o0.0001)
1.944 (o0.0001)
Bone/joint disease
1.040 (0.5171)
0.993 (0.9226)
1.044 (0.4751)
Other cardiovascular diseased
1.056 (0.6555)
1.010 (0.9432)
0.967 (0.7871)
Diabetes
0.803 (0.0006)
0.812 (0.0054)
0.788 (0.0002)
Hyperlipidemia
1.083 (0.0764)
1.056 (0.3043)
1.089 (0.0586)
Hypertension
0.846 (0.0005)
0.923 (0.1553)
0.859 (0.0017)
Variables
Preventive care
Note: Values are AORs (p-values), unless otherwise noted, for ordered logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Participation levels included never (participated 0 years); occasionally (participated 12 years); usually (participated 34 years); always (participated all 5 years). b Healthcare cost is in natural log. c Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. d Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
$50,000$59,999, UM-Flint employees, and non-union. Baseline healthcare spending was a significant predictor of participation in the reduced model, with those with higher spending participating more frequently. However, when including 2008 health factors in the model, the association between baseline costs and participation showed that those with higher spending participated less frequently (p¼0.0185). Baseline health factor data showed that employees with preventive care claims had significantly higher frequencies of HRA completion than those without (po0.0001), and employees with diabetes or hypertension participated less frequently than those without (p¼0.0006 and p¼0.0005, respectively). Participation in the ActiveU was greatest among those aged 3039 years and declined at higher ages. Men were significantly less likely to participate in ActiveU (po0.0001), as were faculty (po0.0001), union employees (po0.0001), and employees whose race/ethnicity was Asian/Pacific Islander (p¼0.0075). Compared with continuously enrolled employees earning Z$60,000, those earning r$35,000 were significantly less likely to participate in ActiveU (po0.0001), whereas employees earning $50,000$59,999 were significantly more likely to participate (p¼0.0001). Relative to UM-Ann Arbor employees, those working at the Health System were significantly less likely to participate (po0.0001) and those at UM-Flint were significantly more likely to participate (p¼0.04). Health factor data showed that employees with preventive care claims in 2008 were significantly more likely to participate in ActiveU than those without (po0.0001), ] 2016
and employees with diabetes were significantly less likely to participate than those without (p¼0.005) (Table 2). Many of the same associations held true for program completion (Table 3). Wage presented a more mixed picture. Compared with employee participants earning Z$60,000, participants earning r$35,000 were significantly more likely to complete the program in 2011 in the continuously enrolled models (p¼0.007). Participants earning $35,001$49,999 were significantly more likely to finish the program in 2011 (p¼0.0007) and 2013 (p¼0.002). Finally, participants earning $50,000–$59,999 were significantly more likely to finish the program, compared with employees making Z$60,000 in 2013 (p¼0.03). Union employee participants were significantly less likely to complete ActiveU in 2013 (p¼0.028) and faculty were significantly less likely to complete the program every year except 2012. Total baseline healthcare spending did not significantly predict completion of ActiveU. Baseline health factors showed higher likelihood of program completion in 2010 among participants with respiratory disease and users of preventive care compared with those without. Participants with baseline claims diagnoses of stress/depression in 2010, 2012, and 2013; bone/joint disease in 2013; diabetes in 2010; and hypertension in 2010 were significantly less likely to complete the ActiveU compared to participants without those diagnoses (Table 3). About three quarters of employees received an incentive payment at least once during the 5 years, and 12% received an incentive every year of the program. Table 2 shows
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Table 3. Completion of ActiveU Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 20102013 Variables
2010
2011
2012
2013
ActiveU participants, n
5,880
5,501
5,662
7,197
3,172 (54)
4,230 (77)
2,975 (53)
4,175 (58)
1.054 (0.6402)
1.021 (0.8970)
0.852 (0.2905)
0.825 (0.2050)
4049 years
1.351 (o0.0001)
1.332 (0.0013)
1.175 (0.0365)
1.155 (0.0415)
5059 years
1.730 (o0.0001)
1.545 (o0.0001)
1.468 (o0.0001)
1.522 (o0.0001)
Z60 years
1.992 (o0.0001)
1.941 (o0.0001)
1.711 (o0.0001)
1.639 (o0.0001)
ref
ref
ref
ref
0.871 (0.0491)
0.931 (0.4000)
0.931 (0.3237)
0.823 (0.0016)
0.485 (o0.0001)
0.535 (o0.0001)
0.755 (0.0056)
0.707 (0.0001)
Asian/Pacific Islander
0.999 (0.9935)
1.133 (0.3846)
1.021 (0.8590)
1.094 (0.3549)
American Indian/Alaskan Native/ Native Hawaiian/other Pacific Islander
0.629 (0.2235)
1.311 (0.7305)
1.683 (0.3550)
0.896 (0.8156)
Hispanic/Latino
0.509 (0.0008)
0.617 (0.0305)
0.635 (0.0252)
0.640 (0.0060)
Unknown
0.604 (0.1043)
0.775 (0.2551)
0.963 (0.8503)
1.019 (0.9132)
ref
ref
ref
ref
0.696 (0.0006)
0.765 (0.0359)
0.871 (0.1941)
0.785 (0.0060)
r$35,000
1.081 (0.3637)
1.339 (0.0072)
1.035 (0.7007)
1.110 (0.2136)
$35,001$49,999
1.123 (0.1030)
1.337 (0.0007)
1.075 (0.3037)
1.220 (0.0019)
$50,000$59,999
1.140 (0.1213)
1.192 (0.0712)
0.879 (0.1140)
1.168 (0.0343)
ref
ref
ref
ref
0.707 (o0.0001)
0.778 (0.0004)
0.781 (o0.0001)
0.763 (o0.0001)
UM-Flint
0.786 (0.2343)
0.711 (0.1494)
0.641 (0.0348)
0.974 (0.8966)
UM-Dearborn
0.700 (0.0673)
0.464 (0.0003)
0.810 (0.2614)
0.854 (0.3755)
UM-Ann Arbor
ref
ref
ref
ref
0.883 (0.3853)
0.834 (0.3232)
0.739 (0.0503)
0.763 (0.0280)
0.983 (0.2612)
0.999 (0.9695)
0.993 (0.6473)
1.015 (0.2684)
0.947 (0.5264)
0.832 (0.0690)
0.988 (0.8863)
Completing ActiveU, n (%) Age o30 years
3039 years Sex Male (vs female) Race/ethnicity Black/African-American
White, non-Hispanic Faculty/staff designation Faculty (vs staff) Annual wage
Z$60,000 Job location Health System
Union status Union (vs non-union) Total healthcare cost, 2008
a
Health factors, 2008 Back pain
0.903 (0.1828) (continued on next page)
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Table 3. Completion of ActiveU Controlling for Demographic and Job Characteristics, Baseline Healthcare Costs, and Baseline Health Factors, 20102013 (continued) Variables
2010
2011
2012
2013
Risky lifestyleb
0.988 (0.9406)
0.948 (0.7737)
1.165 (0.3357)
0.903 (0.4799)
Respiratory disease
1.424 (0.0169)
0.913 (0.6166)
1.177 (0.2820)
1.194 (0.1975)
Stress/depression
0.657 (o0.0001)
0.833 (0.0820)
0.689 (o0.0001)
0.746 (0.0002)
1.336 (0.0014)
0.992 (0.9413)
1.152 (0.1304)
1.021 (0.7990)
0.823 (0.1182)
0.906 (0.5161)
0.912 (0.4539)
0.798 (0.0415)
1.101 (0.7436)
0.956 (0.8936)
1.004 (0.9871)
0.901 (0.6631)
Diabetes
0.699 (0.0109)
1.045 (0.7996)
0.942 (0.6794)
0.901 (0.4182)
Hyperlipidemia
1.055 (0.5615)
0.913 (0.4085)
0.935 (0.4700)
0.940 (0.4661)
Hypertension
0.786 (0.0196)
0.839 (0.1544)
0.919 (0.4255)
0.882 (0.1750)
Preventive care Bone/joint disease Other cardiovascular disease
c
Note: Values are AORs (p-values), unless otherwise noted, for logistic regression for sample of continuously benefits enrolled employees. Boldface indicates statistical significance (po0.05). a Healthcare cost is in natural log. b Risky lifestyle includes alcohol, smoking, and/or drug use, and obesity. c Other cardiovascular disease includes congestive heart failure and coronary artery disease. HRA, Health Risk Assessment; UM, University of Michigan.
predictors of receiving an incentive at least once during the 5-year program period. Those most likely to receive incentives were white, female, non-union staff members. Compared with those aged 3039 years, those of all other age categories except o30 years were significantly less likely to receive any incentive. Compared with employees earning Z$60,000, those making r$35,000 were significantly less likely to receive any incentive (po0.0001), whereas those with wages of $50,000$59,999 were significantly more likely to receive an incentive (p¼0.0001). UM-Flint employees were significantly more likely to receive an incentive during the 5-year period than UM-Ann Arbor employees (po0.0001), whereas Health System employees were significantly less likely (po0.0001). Higher total baseline healthcare spending was significantly associated with being less likely to receive an incentive (p¼0.03). Those with preventive care claims were significantly more likely (po0.0001) and those with diabetes or hypertension were significantly less likely to receive an incentive (p¼0.0002 and p¼0.0017, respectively) than those without those claims.
Discussion This study has several notable features that help address literature gaps on employee wellness program participation. First, having 5 years of data allows the authors to assess participation trends and predictors of participation frequency. Most predictors of participation were quite stable across years, but the effects of income and race on HRA participation evolved somewhat over time. Second, the program’s participation rate of close to 50% implies ] 2016
near maximal variation in participation, allowing differentiation between two equally sized groups. Third, three distinct measures of participation and engagement were used: HRA completion, ActiveU participation, and incentive receipt. Fourth, this study used a large set of employee characteristics, including medical spending, preventive care use, and health factors present at baseline to assess selection into the program by health status. Finally, the use of multivariate analysis to assess the independent predictive power of employee characteristics adds a unique perspective to the literature. MHealthy’s success in attracting participation from approximately half of employees is consistent with RAND study findings for HRA participation.5 The overall percentage of employees receiving incentives increased since 2010, which is a positive finding. MHealthy appears to have strong participation from employees who are female, white, and in non-union staff positions, as well as those who seek preventive care. Variation in participation among employees of different ages and wage categories is also apparent. MHealthy has used a uniform communications strategy to recruit participants; these findings show that tailored communications may help enhance participation among employees who are less likely to participate: men, faculty members, union members, employees of minority racial or ethnic backgrounds, and those with lower wages, particularly those with an annual income r$35,000. The low participation by lower-wage workers, despite the provision of financial incentives, is notable because it implies some regressivity in the incentive program. It may also indicate that these
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workers could be having difficulty meeting the incentive requirements (i.e., limited access to computers to complete the HRA) or have jobs that are more physically demanding, making programming related to physical activity less appealing. In addition, increased efforts to recruit Health System employees may also enhance overall participation levels. The incentive program may have had some influence on employees’ participation choices. For example, ActiveU completion was higher in years when it counted toward incentive receipt. Selection into the program by baseline health factors also has important implications for program design and evaluation. There was some evidence that employees with higher medical spending were more likely to participate, but this relationship to total spending tended not to persist once measures of preventive care use and other health factors were added to the model. This suggests that the selection process is too complicated to capture in a unidimensional “index” of health status, such as total spending. Rather, a multidimensional pattern emerged. Those employees with a “revealed preference” for prevention (i.e., those utilizing preventive services before the program) were more likely to participate, suggesting some favorable selection by those most engaged in preventive activities. Not accounting for such selection when evaluating the outcomes of a wellness program can overestimate its impact by attributing favorable outcomes to the program, which may have occurred in any case owing to the pre-existing engagement of workers in preventive activities. Other health characteristics had different effects on participation. For example, workers with diabetes or hypertension were consistently less likely to participate, indicating favorable selection into the program based on important and prevalent chronic conditions. It also suggests that targeted outreach and an examination of how the program could be made more attractive to workers with diabetes could be valuable. Several other health factors were associated with greater participation in some years and for some participation measures, indicating that selection patterns were too heterogeneous to allow a simple statement that the program received a favorable selection based on risks. Participation in a worksite wellness program appears to be influenced by both person-level and program-level factors. Examples of person-level factors that can impact likelihood of engaging in health-promoting behavior include perceived benefits and barriers to participation, perceived threat of disease, and self-efficacy, which may be further studied for MHealthy.36 Modifiable programlevel factors likely to drive participation include program features, outreach/marketing, and incentive amount, which is currently less than other programs with substantially higher participation.37
Limitations This study is subject to limitations. First, multicollinearity between spending and baseline health factors may be a concern, but reduced models that eliminate health factors show that the impact of this on other variables is insignificant. Also, the authors did not adjust for multiple comparisons because the same factors are consistently significant across years and models. The definition of participation varied from engagement to completion depending on the activity, so this term may represent different participation outcomes compared to similar studies. Additionally, claims data were used to identify the baseline health factors used in the predictive models, likely leading to under-identification, as only those who sought care will have a claims-based diagnosis. This employee population may differ from others, so caution should be taken when generalizing results to other employee groups. Finally, the authors do not know how many non-participants were engaged in wellness or disease management activities outside of MHealthy.
Conclusions MHealthy has attracted participation from roughly half of the employee population. A consistent demographic and job characteristic profile emerged among program participants, which better characterizes employees who choose to engage in employee wellness activities. The results of this study can guide efforts to identify the causes of participation trends, both in terms of employee attitudes and workplace barriers. Such information could be used to better target outreach and program content and reduce structural barriers to participation. Future studies could consider additional job characteristics, such as job type and employee attitudinal variables regarding health status and wellness program effectiveness. This study was supported by the University of Michigan. No financial disclosures were reported by the authors of this paper.
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Appendix Supplementary data Supplementary data associated with this article can be found at http://dx.doi.org/10.1016/j.amepre.2016.01.028.