Effects of a Workplace Wellness Program on Employee Health, Health Beliefs, and Medical Use: A Randomized Clinical Trial | Hypertension | JAMA Internal Medicine | JAMA Network
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Figure.  Flow of Participants in the Illinois Workplace Wellness Study
Flow of Participants in the Illinois Workplace Wellness Study

HRA indicates health risk assessment.

aAll eligible employees were invited to enter the study by taking a baseline survey. Those who did not complete the survey were not included in the study.

bParticipants who received the intervention were invited to participate in wellness program components during the 2-year study. Participation varied across the various components.

cClaims data were collected for participants in the treatment and control groups who were enrolled in the Health Alliance insurance plan.

Table 1.  Baseline Characteristics of the Study Populationa
Baseline Characteristics of the Study Populationa
Table 2.  Mean Values and Effect of Wellness Program on Health Beliefs and Self-reported Health Behaviorsa
Mean Values and Effect of Wellness Program on Health Beliefs and Self-reported Health Behaviorsa
Table 3.  Mean Values and Effect of Wellness Program on Biometricsa
Mean Values and Effect of Wellness Program on Biometricsa
Table 4.  Mean Values and Effect of Wellness Program on Medical Diagnoses and Usea
Mean Values and Effect of Wellness Program on Medical Diagnoses and Usea
Supplement 2.

eAppendix 1. Sample Selection and Study Overview

eAppendix 2. Datasets

eFigure 1. Experimental Design of the Illinois Workplace Wellness Study in Year 1

eFigure 2. Experimental Design of the Illinois Workplace Wellness Study in Year 2

eFigure 3. Overlap Among Datasets

eFigure 4. Front and back sides of invitation postcard sent on July 6, 2016

eFigure 5. Email sent from the UIUC Provost to university employees on July 11, 2016

eFigure 6. Invitation email sent to university employees on July 11, 2016

eFigure 7. Text of the confirmation email sent to study participants who successfully completed the online baseline survey

eFigure 8. Electronic Amazon.com gift card sent to participants who completed the baseline survey

eFigure 9. Text of invitation email sent to participants in treatment group C75 ($350 incentive) on August 9, 2016

eFigure 10. Front and back sides of the postcard mailed to participants selected to participate in iThrive, week of September 8, 2016

eFigure 11. Login page for the iThrive website

eFigure 12. Main home page for the iThrive website

eFigure 13. 2016 screening locations

eFigure 14. First and second pages of the online appointment application used to sign up for a health screening

eFigure 15. Example of a reminder email sent out by the online appointment scheduler

eFigure 16. Example of a reminder email sent by the research team to participants one day prior to their health screening

eFigure 17. Health questionnaire filled out by participants at the health screening

eFigure 18. Health screening form used by clinicians to record health measures

eFigure 19. Health coaching guidelines

eFigure 20. Postcard given to participants on site after they completing their health screening

eFigure 21. Email invitation for the 2016 online health risk assessment

eFigure 22. Email invitation for Fall 2016 wellness activities

eFigure 23. Email invitation for Spring 2017 wellness activities

eFigure 24. Front and back sides of invitation postcard sent on July 6, 2017

eFigure 25. One-year follow-up survey invitation sent to study participants on July 10, 2017

eFigure 26. One-year follow-up survey reminder sent on August 2, 2017

eFigure 27. Text of screening invitation email sent to study participants on August 14, 2017

eFigure 28. Text of reminder email sent to study participants on September 21, 2017

eFigure 29. Main page for the 2017-2018 iThrive website for a control group member in the $125 screening reward group

eFigure 30. Main page for the 2017-2018 iThrive website for a treatment group member in the $125 screening reward group

eFigure 31. Text of the confirmation email sent to one-year follow-up screening participants in the $125 reward group

eFigure 32. Email invitation for the 2017 online health risk assessment

eFigure 33. Email invitation for Fall 2017 wellness activities

eFigure 34. Email invitation for Spring 2018 wellness activities

eFigure 35. Two-year follow-up survey invitation sent to study participants on July 9, 2018

eFigure 36. Text of screening invitation email sent to study participants on August 13, 2018

eFigure 37. Copy of 2018 health screening form used by clinicians to record health measures

eTable 1. Dates, locations, times, and number of health screenings performed in 2016

eTable 2. Description of and statistics for the Fall 2016 wellness activities

eTable 3. Description of and statistics for the Spring 2017 wellness activities

eTable 4. Dates, locations, times, and number of health screenings performed in 2017

eTable 5. Description of and statistics for the Fall 2017 wellness activities

eTable 6. Description of and statistics for the Spring 2018 wellness activities

eTable 7. Dates, locations, times, and number of health screenings performed in 2018

Supplement 3.

eAppendix 1. Power Calculations

eAppendix 2. Missing Data Bias

eAppendix 3. Local Mean Treatment Effects

eAppendix 4. Subgroup Analysis

eAppendix 5. Primary Care Physician Analysis

eTable 1. Ex Post Minimum Detectable Effects (MDE)

eTable 2. Baseline Characteristics, for Participants Who Completed the 2017 Biometric Screening

eTable 3. Baseline Characteristics, for Participants Who Completed the 2018 Biometric Screening

eTable 4. Local Average Treatment Effect of Wellness Program on Health Beliefs and Self- Reported Health Behaviors

eTable 5. Local Average Treatment Effect of Wellness Program on Biometrics

eTable 6. Local Average Treatment Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 7. Heterogeneity: Male: Interaction Effect of Wellness Program on Health Beliefs and Self- Reported Health Behaviors

eTable 8. Heterogeneity: Male: Interaction Effect of Wellness Program on Biometrics

eTable 9. Heterogeneity: Male: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 10. Heterogeneity: Age 50 and Over: Interaction Effect of Wellness Program on Health Beliefs and Self-Reported Health Behaviors

eTable 11. Heterogeneity: Age 50 and Over: Interaction Effect of Wellness Program on Biometrics

eTable 12. Heterogeneity: Age 50 and Over: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 13. Heterogeneity: White: Interaction Effect of Wellness Program on Health Beliefs and Self-Reported Health Behaviors

eTable 14. Heterogeneity: White: Interaction Effect of Wellness Program on Biometrics

eTable 15. Heterogeneity: White: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 16. Heterogeneity: Academic Professional Employees: Interaction Effect of Wellness Program on Health Beliefs and Self-Reported Health Behaviors

eTable 17. Heterogeneity: Academic Professional Employees: Interaction Effect of Wellness Program on Biometrics

eTable 18. Heterogeneity: Academic Professional Employees: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 19. Heterogeneity: Civil Service Employees: Interaction Effect of Wellness Program on Health Beliefs and Self-Reported Health Behaviors

eTable 20. Heterogeneity: Civil Service Employees: Interaction Effect of Wellness Program on Biometrics

eTable 21. Heterogeneity: Civil Service Employees: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 22. Heterogeneity: Above Median Salary: Interaction Effect of Wellness Program on Health Beliefs and Self-Reported Health Behaviors

eTable 23. Heterogeneity: Above Median Salary: Interaction Effect of Wellness Program on Biometrics

eTable 24. Heterogeneity: Above Median Salary: Interaction Effect of Wellness Program on Medical Diagnoses and Utilization

eTable 25. Mean Values and Effect of Wellness Program on Primary Care Physician (PCP) Utilization

eReferences

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    2 Comments for this article
    EXPAND ALL
    This is the 12th consecutive study to find no impact
    Al Lewis, JD | Quizzify
    For years I've been observing (not opining, observing) that wellness loses money and has no noticeable impact on employee health. Except, that is, when it has a negative impact. https://www.statnews.com/2016/09/27/workplace-wellness-award/

    Prior to this, 11 consecutive studies have shown that wellness doesn't work. https://www.benefitnews.com/opinion/time-to-believe-why-wellness-isnt-lowering-healthcare-costs

    Further, there are serious concerns about the ethics of wellness companies and consultants. https://scholarlycommons.law.case.edu/healthmatrix/vol27/iss1/3/

    It is time to regulate this industry and require, at a minimum, that wellness vendors adhere to US Preventive Services Task Force guidelines, instead of forcing employees to choose between fines or inappropriate testing.
    CONFLICT OF INTEREST: None Reported
    Abstract lacks data
    Denice Ferko-Adams, MPH, RDN, FAND | Wellness Press, LLC
    In this abstract, there are no data listed on the number of participants who actually participated in and completed the interventions. The abstract lists disease management but nothing on disease prevention.
    Who led the interventions?
    Was pre and post qualitative data collected and used to alter the subsequent interventions?
    Were the employees involved in the design of the interventions and the appropriateness of the incentives?
    Change is also necessary in the worksite environment such as easy access to educational and preventive services, options for delicious healthful cafeteria choices, fitness opportunities (as simple as walking routes) and management training
    on why they need to support these efforts.
    Having developed, customized, delivered, collected pre/post qualitative and quantitative data - and used standards above the US Preventive Services Task Force guidelines .
    I agree that there needs to be much higher standards and companies create an environment that promotes health for the employee, family members and communities they serve.
    CONFLICT OF INTEREST: None Reported
    READ MORE
    Original Investigation
    May 26, 2020

    Effects of a Workplace Wellness Program on Employee Health, Health Beliefs, and Medical Use: A Randomized Clinical Trial

    Author Affiliations
    • 1Department of Finance, University of Illinois at Urbana-Champaign
    • 2National Bureau of Economic Research, Cambridge, Massachusetts
    • 3Center for Health Policy, Stanford University, Stanford, California
    • 4Center for Innovation to Implementation, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
    • 5Harris School of Public Policy, University of Chicago, Chicago, Illinois
    • 6Department of Recreation, Sport and Tourism, University of Illinois at Urbana-Champaign
    JAMA Intern Med. 2020;180(7):952-960. doi:10.1001/jamainternmed.2020.1321
    Key Points

    Question  How does a comprehensive workplace wellness program affect health, health beliefs, and medical use among university employees after 24 months?

    Findings  In a 2-year randomized clinical trial of 4834 employees at a large US university, employees invited to join a wellness program showed no significant differences in biometrics, medical diagnoses, or medical use relative to the control group. The intervention increased self-reports of having a primary care physician and improved a set of employee health beliefs among the treatment group.

    Meaning  The workplace wellness changed health beliefs and increased self-reports of having a primary care physician but did not significantly affect clinical outcomes.

    Abstract

    Importance  Many employers use workplace wellness programs to improve employee health and reduce medical costs, but randomized evaluations of their efficacy are rare.

    Objective  To evaluate the effect of a comprehensive workplace wellness program on employee health, health beliefs, and medical use after 12 and 24 months.

    Design, Setting, and Participants  This randomized clinical trial of 4834 employees of the University of Illinois at Urbana-Champaign was conducted from August 9, 2016, to April 26, 2018. Members of the treatment group (n = 3300) received incentives to participate in the workplace wellness program. Members of the control group (n = 1534) did not participate in the wellness program. Statistical analysis was performed on April 9, 2020.

    Interventions  The 2-year workplace wellness program included financial incentives and paid time off for annual on-site biometric screenings, annual health risk assessments, and ongoing wellness activities (eg, physical activity, smoking cessation, and disease management).

    Main Outcomes and Measures  Measures taken at 12 and 24 months included clinician-collected biometrics (16 outcomes), administrative claims related to medical diagnoses (diabetes, hypertension, and hyperlipidemia) and medical use (office visits, inpatient visits, and emergency department visits), and self-reported health behaviors and health beliefs (14 outcomes).

    Results  Among the 4834 participants (2770 women; mean [SD] age, 43.9 [11.3] years), no significant effects of the program on biometrics, medical diagnoses, or medical use were seen after 12 or 24 months. A significantly higher proportion of employees in the treatment group than in the control group reported having a primary care physician after 24 months (1106 of 1200 [92.2%] vs 477 of 554 [86.1%]; adjusted P = .002). The intervention significantly improved a set of employee health beliefs on average: participant beliefs about their chance of having a body mass index greater than 30, high cholesterol, high blood pressure, and impaired glucose level jointly decreased by 0.07 SDs (95% CI, −0.12 to −0.01 SDs; P = .02); however, effects on individual belief measures were not significant.

    Conclusions and Relevance  This randomized clinical trial showed that a comprehensive workplace wellness program had no significant effects on measured physical health outcomes, rates of medical diagnoses, or the use of health care services after 24 months, but it increased the proportion of employees reporting that they have a primary care physician and improved employee beliefs about their own health.

    Trial Registration  American Economic Association Randomized Controlled Trial Registry number: AEARCTR-0001368

    Introduction

    Employers increasingly offer workplace wellness programs to reduce health care costs and improve employee health. Among large US firms offering health benefits in 2019, 84% also offered a wellness program.1Quiz Ref ID The wellness industry has grown rapidly since the passage of the 2010 Patient Protection and Affordable Care Act, which encouraged firms to adopt wellness programs by raising the maximum limit on financial incentives offered to program participants.

    However, evidence of the causal effects of workplace wellness programs is limited. Observational studies that compare participants with nonparticipants are susceptible to selection bias.2 Randomized trials frequently evaluate narrow wellness interventions with only 1 or 2 program components and examine only a few outcomes.3-8 Reviews of the literature have yielded mixed results and raised concerns about publication bias.9,10 A recently published randomized clinical trial (RCT) with 160 randomized worksites reported outcomes at 18 months after the intervention.11 Another recently published RCT with 4834 randomized participants reported effects on medical spending and employee productivity, but not clinical outcomes.2 Neither study investigated the effect of workplace wellness programs on employees’ beliefs about their own health. Measuring these beliefs sheds light on employees’ perceptions about the effectiveness of participating in wellness programs. These beliefs may also shape how much value and effort individuals place on health behaviors, a channel emphasized by social cognitive theory.12,13

    In this study, individual employees were randomly assigned to a treatment group, which was eligible to participate in a 2-year comprehensive workplace wellness program, or a control group, which was not eligible. We evaluated the effects of this program on health beliefs, self-reported health behaviors, clinician-collected biometrics, and claims-based medical diagnoses and medical use during the 24 months after initial randomization into the program.

    Methods
    Study Design

    We conducted an RCT of a workplace wellness program among employees of the University of Illinois at Urbana-Champaign (UIUC). Our preanalysis protocol was publicly archived and is available in Supplement 1. Among the study population, 3300 employees were randomly assigned to be eligible for program participation (treatment group). The other 1534 study participants were ineligible for the program (control group). Randomization was stratified by employee class, sex, age, salary, and race/ethnicity (eAppendix 1 in Supplement 2). We specified the research design, subgroup analysis, and the health belief, biometric, and medical use outcomes prior to analysis. Self-reported health behavior and medical diagnosis outcomes were specified post hoc. The UIUC, University of Chicago, and National Bureau of Economic Research institutional review boards approved the study. All study participants provided written informed consent. We followed the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline.

    Study Participants

    A total of 12 459 benefits-eligible UIUC employees were invited in July 2016 to enroll in the study and complete a survey (Figure). Employees were informed that they might be selected for further participation in the study, but no other details about the intervention were disclosed prior to enrollment. Invitations were sent by mail and email (eFigures 4-6 in Supplement 2). Our study population consisted of 4834 employees who enrolled in the study during a 3-week enrollment period. Random assignment of study participants to treatment and control groups occurred in August 2016, after study enrollment had closed.

    Intervention

    A comprehensive workplace wellness program named iThrive was introduced at UIUC and ran for 2 years, from August 9, 2016, to April 26, 2018. Quiz Ref IDThe program, designed to be representative of typical comprehensive wellness programs offered by employers, included 3 annual components: an onsite biometric screening and survey, an online health risk assessment (HRA), and a choice of wellness activities (eFigure 1 and eFigure 2 in Supplement 2).14 Employees in the treatment group were eligible to participate in all 3 intervention components using paid time off and received randomly assigned cash awards that ranged from $0 to $200 per year for completing the annual screening and HRA. Treatment group participants who completed the biometric screening and HRA were then eligible to register for 1 wellness activity class per semester, for a total of 2 activities per year. Classes ranged in length from 6 to 12 weeks and addressed numerous topics (eg, physical activity, nutrition, and stress management) (eTables 2, 3, 5, and 6 in Supplement 2). On completion of a wellness activity, participants earned $0 to $75 as a cash reward or Amazon.com gift card. The onsite biometric screenings and surveys were administered by local clinicians. The HRA was designed by Wellsource, an established wellness vendor. The wellness activities were selected and implemented by UIUC’s director of campus well-being services. Details on all study components are provided in eFigures 4 to 37 and eTables 1 to 7 of Supplement 2.

    Employees in the control group were invited to complete the onsite biometric screening and survey in August 2017 (12 months after randomization) and in August 2018 (24 months after randomization) to serve as a comparison group. Control group employees were not eligible to participate in the first onsite biometric screening and survey in August 2016 and were never eligible to participate in any of the HRAs or wellness activities offered throughout the 2-year iThrive program. Although the research team never informed the control group about the intervention, some may have learned about it from coworkers. To assess how often control group members learned about the intervention from coworkers, a 2017 online survey asked study participants whether they ever communicated about iThrive with coworkers. Only 3.4% (39 of 1157) of the control group responded affirmatively, compared with 43.6% (1050 of 2410) of the treatment group.2

    Outcome Measures

    Health beliefs, self-reported health behaviors, and biometrics were collected onsite by clinicians. Study participants were asked to report their height and weight. They also reported, on a scale from 0 to 100, their expected chances (subjective probabilities) of having high cholesterol, high blood pressure, an impaired fasting glucose level, and a body mass index above 30 (calculated as weight in kilograms divided by height in meters squared) (eFigure 17 in Supplement 2). We interpret self-reported height and weight and these expected probabilities as measures of participants’ health beliefs.12,15,16 Study participants were then directed to a station where a clinician measured their height, weight, waist circumference, and blood pressure. The clinician also measured their cholesterol (total, high-density lipoprotein, and low-density lipoprotein), triglycerides, and glucose levels using a CardioChek Plus Analyzer (PTS Diagnostics), and recorded their answers to questions about tobacco use, physical activity, mood, and having a primary care physician (eFigure 18 in Supplement 2).

    Administrative health claims data were obtained for employees enrolled in UIUC’s Health Alliance insurance plan, which covers 69.3% (3350 of 4834) of employees in our sample. These data include all inpatient, outpatient, and prescription drug claims with a date of service between October 1, 2015, and July 31, 2018. Additional details on these and other data sets collected for the study are described in eAppendix 2 and eFigure 3 of Supplement 2.

    Statistical Analysis

    Statistical analysis was performed on April 9, 2020. We performed power calculations for all outcomes by calculating ex post minimum detectable effects.17 The results are provided in eAppendix 1 and eTable 1 in Supplement 3. We estimated the effect of being invited to participate in the iThrive wellness program in the available population. Some employees in our sample ceased employment with the university during the 24-month study. For administrative health claims outcomes, we restricted comparisons to employees enrolled in Health Alliance. For all other outcomes, we compared participants in the treatment group who completed the follow-up (2017 or 2018) onsite screening and survey with all employees in the control group who completed the follow-up (2017 or 2018) onsite screening and survey (Figure). Baseline characteristics of the treatment and control groups were compared to evaluate the potential for bias due to missing data (eAppendix 2 and eTables 2 and 3 in Supplement 3).18

    For each outcome, we estimated an individual-level linear model with a binary indicator for treatment assignment as the key independent variable. For biometric and self-reported outcomes, we included all study participants who completed the onsite follow-up screening and survey in 2017 (n = 2004) or 2018 (n = 1761). For medical diagnoses and medical use outcomes, we included all study participants (n = 4834) and weighted each individual by the number of months with Health Alliance insurance coverage. We included baseline values of the outcome (when available) and stratification variables as controls in our linear model to improve precision. Analyses were performed using Stata, version 15 (StataCorp).19 We calculated SEs that are robust to arbitrary heteroscedasticity and used 2-tailed tests with a significance level of P = .05.

    To account for less-than-universal participation among the treatment group, we used an instrumental-variable approach to estimate the local mean treatment effect of participating in the program, instrumenting participation with assignment into the treatment group.11,20,21 Participation was defined as completing the first (2016) screening component, which was offered only to members of the treatment group (Figure). The results are provided in eAppendix 3 and eTables 4 to 6 in Supplement 3.

    Because we estimated our model for many outcomes, the probability that we incorrectly reject at least 1 null hypothesis is greater than the significance level used for each individual hypothesis test. We accounted for this multiple testing concern in 2 ways. First, we calculated a standardized treatment effect for a “family” of outcomes by dividing the estimate for each individual outcome by its SD and then averaging across all the outcomes within the family.11,22 This method gives equal weight to each outcome in the family, which may be undesirable. Therefore, we also used resampling to calculate an adjusted P value for each outcome that corrects for the number of hypothesis tests within a family of outcomes.2,23 We considered effects to be statistically significant at an adjusted P < .05 or a standardized treatment effect P < .05.

    Results
    Baseline Characteristics and Program Participation

    Table 1 reports baseline characteristics for the treatment (n = 3300) and control (n = 1534) groups. Among all 4834 study participants, the mean (SD) age was 43.9 (11.3) years, 2770 (57.3%) were female, 786 (16.3%) were nonwhite, 963 (19.9%) were faculty, and 1172 (24.2%) earned less than $40 000 per year. A total of 3217 participants (66.5%) were enrolled in Health Alliance insurance coverage during the 10-month preintervention period from October 2015 to July 2016. Among this subsample and during this time, study participants had 2.5 outpatient visits on average and had medical claims with diagnoses codes related to 3 common chronic conditions in the following proportions: type 1 and 2 diabetes (172 [5.3%]), hypertension (440 [13.7%]), and hyperlipidemia (508 [15.8%]). Inpatient and emergency department visits were uncommon in this sample. Overall, baseline participant characteristics were well balanced across both study groups.

    Of the 3300 participants in the treatment group, 1848 (56.0%) completed both the biometric screening and online HRA in the first year and 1036 (31.4%) completed the biometric screening, online HRA, and at least 1 wellness activity in the first year. During the 2-year program, 2123 participants (64.3%) in the treatment group completed at least 1 component of the iThrive wellness program. These completion rates are similar to those reported for other comprehensive wellness programs.11,14

    Effects of the Intervention

    Table 2 reports effects of the intervention on health beliefs and self-reported health behaviors. When combined into a standardized treatment effect, participant beliefs about their chance of having a body mass index greater than 30, high cholesterol, high blood pressure, and impaired glucose level jointly decreased by 0.07 SDs (95% CI, −0.12 to −0.01 SDs; P = .02). Although these health beliefs changed significantly as a group, changes in specific measures of health beliefs were less precise and thus not individually significant.

    Quiz Ref IDSelf-reports of having a primary care physician significantly increased by 6.1 percentage points (95% CI, 3.0-9.2 percentage points; adjusted P = .002) after 24 months. There were no significant effects on self-reported tobacco use, physical activity intensity, or mood after 12 or 24 months.

    Quiz Ref IDThe intervention had no significant effects on height, weight, waist circumference, body mass index, blood pressure, cholesterol, or glucose level (Table 3). There were also no significant changes in diagnoses of hypertension, diabetes, or hyperlipidemia after 12 or 24 months (Table 4). Likewise, no significant effects were found for office visits, inpatient visits, or emergency department visits. The 95% CI for systolic blood pressure (–1.48 to 1.18 mm Hg) after 24 months rules out a decrease of 1.48 mm Hg compared with a control group mean of 122.4 mm Hg (Table 3). The 95% CI for diagnoses of hyperlipidemia (–2.47% to 3.07%) after 24 months rules out a decrease of 2.47% compared with a control group mean of 26.5% (Table 4). Likewise, the 95% CI for office visits (–0.30 to 0.46) after 24 months rules out a decrease of 0.30 compared with a control group mean of 6.67. For emergency department visits after 24 months, the 95% CI rules out a decrease of 0.1 compared with a control group mean of 0.28. Additional analysis also found no significant effects for primary care physician visits (eAppendix 5 and eTable 25 in Supplement 3).

    Subgroup Analysis

    eAppendix 4 and eTables 7 to 24 in Supplement 3 report effects for prespecified subgroups. Compared with women, men had higher effects on claims-based diabetes diagnoses after 12 months (2.4%; 95% CI, 0.6%-4.2%; adjusted P = .04), but not after 24 months (1.5%; 95% CI, −0.6% to 3.7%; adjusted P = .49) (eTable 9 in Supplement 3). Compared with younger employees, employees 50 years or older had lower effects on self-reports of having a primary care physician after 24 months (−9.9%; 95% CI, −15.1% to −4.7%; adjusted P = .006) (eTable 10 in Supplement 3). No significant heterogeneity was found with respect to race/ethnicity, employee classification (faculty, civil service, or academic professional), or salary.

    Discussion

    This individual-level RCT of a 2-year comprehensive workplace wellness program demonstrated that the program significantly improved employee beliefs about their own health and increased the proportion of employees reporting that they have a primary care physician. However, no significant effects were found on biometrics, medical diagnoses, or medical use after 24 months. Our study was powered to detect clinically meaningful effects across these 3 domains.

    These results complement recent RCT evidence that workplace wellness programs affect some self-reported outcomes but have limited effects on clinical or administrative outcomes. Prior findings showed that the iThrive program increased self-reported lifetime health screening rates and improved employee perceptions of management, but did not significantly affect administrative measures of medical spending.2 A cluster RCT of a wellness program at BJ’s Wholesale Club found significant effects on self-reports of engaging in regular exercise and actively managing weight but found no significant effects on medical spending or biometric outcomes after 18 months.11 The similarity in these RCT findings using different randomization designs in different populations increases confidence in their reliability and generalizability.

    Our measures of health beliefs, elicited using self-reported subjective probabilities, are a contribution to the literature on wellness interventions. Employees in the treatment group believed they had lower chances of poor biometric health, suggesting that they expected their participation in the wellness program to improve their health. However, there was no significant effect of the program on biometrics or medical use, and prior findings showed no significant effects on administratively measured health behaviors.2 These results demonstrate a mismatch between employee perceptions and physical and administrative measures of health.

    Findings from the Illinois Workplace Wellness Study2 and the BJ’s Wholesale Club study,11 both RCTs, differ from those of many prior studies that found that wellness programs improve employee health and reduce medical use. Many of these prior studies used observational research designs, which can result in significant selection bias even after controlling for many covariates.2 Findings from RCTs are less susceptible to selection bias.

    Limitations

    This study has several limitations. The results may not be generalizable to other workplace settings with different populations or different wellness programs.24Quiz Ref ID Our 95% CIs do not rule out meaningful effects for some outcomes—such as a decrease in emergency department visits after 24 months of 0.1 compared with a control group mean of 0.28. Also, the outcomes were measured during the first 24 months after randomization. We do not know whether the significant effects on self-reported outcomes persisted beyond 24 months, or whether detectable effects on biometrics, medical diagnoses, or medical use emerged beyond 24 months.

    Finally, data were not available for all study participants. Medical diagnoses and use outcomes were obtained only for participants enrolled in Health Alliance. Biometric and self-reported outcomes were obtained only for participants who completed the onsite screening and survey in 2017 or 2018. However, Health Alliance enrollment was well balanced between the treatment and control groups (Table 1). Baseline characteristics of participants who completed the onsite screenings and surveys were well balanced between the treatment and control groups (eTables 2 and 3 in Supplement 3). The balance between treatment and control groups suggests that bias from missing data is unlikely to be substantial.

    Conclusions

    Among employees of a large employer, a comprehensive workplace wellness program significantly changed a set of beliefs about biometric outcomes and significantly increased self-reports of having a primary care physician, but no significant effects on clinician-measured biometrics, medical diagnoses, or medical use were found after 24 months. These findings shed light on employees’ perceptions of workplace wellness programs, which may influence long-term effects. However, we add to a growing body of evidence from RCTs that workplace wellness programs are unlikely to significantly improve employee health or reduce medical use in the short term.

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    Article Information

    Accepted for Publication: March 23, 2020.

    Corresponding Author: David Molitor, PhD, Gies College of Business, University of Illinois at Urbana-Champaign, 31206 S Sixth St, 40 Wohlers Hall, Champaign, IL 61820 (dmolitor@illinois.edu).

    Published Online: May 26, 2020. doi:10.1001/jamainternmed.2020.1321

    Author Contributions: Drs Reif and Molitor had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Reif, Jones, Payne, Molitor.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: All authors.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Reif, Jones, Molitor.

    Obtained funding: Reif, Jones, Payne, Molitor.

    Administrative, technical, or material support: All authors.

    Supervision: Reif, Jones, Molitor.

    Conflict of Interest Disclosures: Drs Reif, Jones, Payne, and Molitor reported receiving grants from the National Institutes of Health, the Abdul Latif Jameel Poverty Action Lab (J-PAL) North America US Health Care Delivery Initiative, the National Science Foundation, the Robert Wood Johnson Foundation, and the W. E. Upjohn Institute for Employment Research during the conduct of the study. No other disclosures were reported.

    Funding/Support: This research was supported by award R01AG050701 from the National Institute on Aging of the National Institutes of Health; grant 1730546 from the National Science Foundation; the J-PAL North America US Health Care Delivery Initiative; Evidence for Action (E4A), a program of the Robert Wood Johnson Foundation; and the W. E. Upjohn Institute for Employment Research. Illinois Human Resources provided in-kind logistical support for developing the program.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Data Sharing Statement: See Supplement 4.

    Additional Contributions: Lauren Geary, MPH, University of Illinois at Urbana-Champaign, provided project management; she was compensated for her contribution. Michele Guerra, MS, Certificate of Advanced Study, University of Illinois at Urbana-Champaign, provided input into the design of the wellness program and the selection of wellness activities; she was not compensated for her contribution. Illinois Human Resources provided institutional support without financial compensation. Marian Huhman, PhD, University of Illinois at Urbana-Champaign, Mark Stehr, PhD, Drexel University, David Studdert, LLB, ScD, MPH, Stanford University, and seminar participants at the American Society of Health Economists provided comments on this article; they were not compensated for their contributions.

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