The Impact of Employee Wellness Programs
Phase: NA
Conditions: Health Behavior, Disease, Chronic
Sponsor: Harvard School of Public Health (HSPH)

1 
The Impact of Workplace Wellness on Health Care Spending, Health, and Employment 
Outcomes: A Randomized Controlled Trial† 
 
 
Zirui Song‡, Katherine Baicker§ 
 
 
 
 
 
 
 
Analysis Plan—Phase 2 
January 7, 2020 
 
 
 
 
 
 
 
ClinicalTrials.gov Identifier: NCT03167658 
 
American Economic Association RCT Registry ID: AEARCTR-0000586 
 
 
 
 
 
 
 
 
 
 
 
 
___________________________ 
† We thank Ozlem Barin and Erica Paulos for excellent research assistance in the preparation of this analysis 
plan, and Jose Zubizarreta and Sherri Rose for their guidance on statistical methodology. We thank Anna 
Sinaiko for helpful comments on a prior version of the analysis plan. We are also grateful to Kathryn Clark, 
Yuanxiaoyue Yang, Jack Huang, Harlan Pittell, and Josephine Fisher for excellent research assistance and to 
Bethany Maylone for expert project management in prior years. In addition, we thank our study partners, BJ’s 
Wholesale Club and Wellness Workdays, for their collaboration and expertise in designing and fielding the 
intervention. We gratefully acknowledge funding from the National Institute on Aging, the Abdul Latif Jameel 
Poverty Action Lab North America, and the Robert Wood Johnson Foundation.  
‡ Harvard Medical School, Massachusetts General Hospital 
§ University of Chicago Harris School of Public Policy, Harvard T.H. Chan School of Public Health, NBER 

 
2 
Table of Contents 
 
I. Introduction 
3 
 
II. Treatment  
4 
 
III. Randomization 
5 
 
IV. Data 
6 
A. Administrative data 
6 
B. Primary data 
9 
 
V. Study Sample 
13 
A. Main sample and subsamples 
13 
B. Balance between treatment and control 
13 
 
VI. Statistical Analyses 
14 
A. Intent-to-treat analysis 
14 
B. Local average treatment effect 
15 
C. Addressing endogeneity concerns 
16 
D. Pre-specified subgroup analyses 
17 
E. Worksite-level analyses 
17 
F. Sensitivity analyses 
18 
 
Figures and Tables 
20 
 
Appendices 
39 
 

 
3 
This document describes the second phase of our planned analysis of a randomized controlled 
trial of a workplace wellness program, which pertains to outcomes over three years.  We have 
previously planned, archived, implemented, and published analysis covering the first 18 months 
of this intervention.1,2  
 
I. Introduction 
 
Workplace wellness programs have become increasingly popular across the U.S. Centered on 
awareness, education, and the promotion of healthy behaviors for disease prevention, workplace 
wellness programs comprised a $7.8 billion industry in 2016. In the face of rising health care 
costs for their employees, about 50 percent of small firms and 84 percent of large firms in the 
U.S. now offer a wellness program, often focused on smoking cessation, weight management, or 
behavioral or lifestyle coaching; firms also frequently offer employees a health risk assessment 
and biometric screening.3 In addition to this substantial private sector investment, the growth of 
workplace wellness has been aided by public investments such as the Affordable Care Act, 
which provided incentives for the establishment of such programs. Despite the importance of 
workplace wellness programs for U.S. workers, employees, and the government, little rigorous 
evidence exists on the effect of such programs on health and economic outcomes. 
 
Prior studies of workplace wellness programs, largely observational in nature, have been plagued 
by selection bias, lack of control groups, and small samples. Participants in wellness programs 
and firms offering them are likely different from their non-participants in important observed and 
unobserved ways that affect health outcomes. Thus, it has been difficult to identify the effect of 
such programs using observational studies comparing participants to non-participants. Moreover, 
meta-analyses have produced widely varying estimates of program benefits relative to costs.  
 
Through a partnership with a large multi-state U.S. employer (BJ’s Wholesale Club) and an 
experienced and award-winning wellness vendor (Wellness Workdays), we implemented a 
randomized controlled trial of a workplace wellness program beginning in 2015.  We have 
already analyzed results from the first 18 months of the intervention (phase 1), in which we 
found no statistically significant effects on health care spending or objective measures of health, 
but did find significant effects on self-reported health behaviors, including regular exercise and 
active weight management.2 Our study, which randomized worksites into treatment and control 
arms, complemented another randomized controlled evaluation of a workplace wellness program 
at the University of Illinois, which randomized employees at the individual level, and also found 
no effects on spending or health outcomes after the first year.4  
 
1  The Impact of Employee Wellness Programs. NIH ClinicalTrials.gov. U.S. National Library of Medicine. 2017 
May 30. (https://clinicaltrials.gov/ct2/show/NCT03167658); The Impact of Employee Wellness Programs: A 
Randomized Controlled Trial. American Economic Association Randomized Controlled Trial Registry. 2015 Feb 3. 
(https://www.socialscienceregistry.org/trials/586/history/26720) 
2  Song Z, Baicker K. Effect of a Workplace Wellness Program on Employee Health and Economic Outcomes: A 
Randomized Clinical Trial. JAMA. 2019 Apr 16;321(15):1491-1501. 
3 The Kaiser Family Foundation. Employer Health Benefits: 2019 Annual Survey. 2019. 
(https://www.kff.org/health-costs/report/2019-employer-health-benefits-survey/) 
4 Jones D, Molitor D, Reif J. What do Workplace Wellness Programs do? Evidence from the Illinois Workplace 
Wellness Study. Q J Econ. 2019 Nov;134(4):1747-1791. 

 
4 
 
This second analysis plan details the evaluation of this intervention and specific methodology 
through the three-year period spanning January 2015 through December 2017. This analysis 
evaluates the impact of the workplace wellness program on employee health care spending and 
utilization, health outcomes, employment, and productivity. 
 
This analysis plan seeks to pre-specify the analysis before comparing outcomes for treatment and 
control groups, in order to minimize issues of data mining and specification searching. To create 
this document, we examined data on outcomes for the control group and performed limited 
comparisons of non-outcome variables between the treatment and control groups (such as pre-
randomization demographics).  However, we have not conducted any analysis of differences in 
outcomes between the treatment and control groups post-treatment over this three-year window. 
Institutional review board approval was granted and maintained through Harvard University. 
 
 
II. Treatment 
 
The treatment was a longitudinal multi-component workplace wellness program designed to 
improve the health and wellbeing of workers. It took place at BJ’s Wholesale Club, the largest 
warehouse retail corporation in the Eastern U.S. and third largest warehouse retail company in 
the country, with approximately 25,000 employees serving 9 million members. BJ’s operates 
about 200 worksites or “clubs” from Maine to Florida and has a demographically and 
socioeconomically diverse workforce across a variety of work settings.  
 
The wellness program took place in 2 phases. Phase 1 of the treatment period spanned 18 
months, from January 2015 through June 2016. In phase 2 of the study, the treatment period was 
extended for another 18 months, from January 2015 through December 2017 and is the focus of 
this analysis plan (Table 1). In phase 2, 5 additional treatment worksites and 5 additional 
primary control worksites were added to the experiment. This wellness program was designed 
and implemented by a third-party vendor, Wellness Workdays. Wellness Workdays is a wellness 
vendor that delivers and manages wellness programs across many industries, including finance, 
manufacturing, banking, higher education, and legal across a number of states.  
 
The wellness program consisted of a personal health assessment (PHA), in-person screenings, 
and multiple program modules. Each module took place over 4-10 consecutive weeks. The 
modules centered on themes such as team-based and individual wellness challenges, nutrition, 
stress reduction, and physical activity, as well as workplace culture.  For each module, 
participants could earn modest financial rewards ($25-50) for completion. 
 
The 3-year intervention period comprised 12 modules. Eight modules were offered during the 
first 18 months (phase 1): “Take Charge of Your Health,” which taught proactive strategies for 
participating in health and health care; “Nutrition for a Lifetime,” which aimed to help 
employees achieve and maintain a healthy weight through nutrition; “Club Cardio Challenge” 
(consisting of 2 modules), which focused on cardiovascular activity; “Maintain Don’t Gain,” 
which combined principles of healthy nutrition with physical activity; “Power Down the 
Pressure,” which taught methods for stress management; “Weight Loss Boot Camp,” which 

 
5 
focused on nutrition and exercise methods for weight loss; and “Movin’ in May,” which once 
again focused on physical activity with active tracking of progress.  
 
Four modules were offered during the next 18 months (phase 2): “Healthy and Fit,” which 
combined healthy eating patterns and regular exercise to avoid weight gain during the holiday 
season; “Step Challenge,” which was a team-based module, focusing on increasing physical 
activity by using a fitness device to track steps; and two consecutive “Nutrition for a Lifetime” 
modules, which were designed to help employees achieve and maintain a healthy lifestyle by 
combining healthy nutrition, exercise, stress management and sleep patterns. In addition, 
participants had the opportunity to log their step counts using a wearable device and this 
information was gathered between January 2017 and July 2017. Please refer to Appendix 1 for 
detailed information on the components of the wellness program by module, including 
requirements and incentives. Employees had opportunities to receive incentive payments through 
completion of the PHA and the biometric screenings, in addition to participation in the individual 
modules of the program.  
 
In each treatment worksite, a Registered Dietitian employed by Wellness Workdays coordinated 
and led the wellness programming. The Registered Dietitians engaged employees in the wellness 
program modules, educated them about the content of the program, and led employees in various 
creative activities such as group fitness and cooking demonstrations. Each Registered Dietitian 
had the flexibility to tailor the day-to-day programming around the themes of the modules. The 
Registered Dietitians spent approximately 8 hours per week at each worksite. 
 
 
III. Randomization 
 
The wellness program was implemented in a randomly selected subset of BJ’s Wholesale Clubs. 
Each club is a standalone worksite, with an average of 118 employees at any given time. At the 
beginning of the study, there were 201 BJ’s worksites in the U.S. along the East coast, extending 
from Maine to Florida. We eliminated 41 worksites because they were geographically remote or 
had employee pools with substantially different insurance coverage from the others, leaving 160 
worksites in our sample.   
 
Among these worksites, we randomly selected 25 “treatment” worksites that would receive the 
wellness program and 25 “primary control” worksites for phase 2.5 These primary control 
worksites participated in data collection through PHAs and in-person biometric screenings, but 
did not receive the wellness program treatment. Figure 1 shows the locations of the 25 treatment 
and 25 control worksites across the Eastern U.S. The remaining 110 worksites served as 
“secondary controls.” which were included in analyses of administrative data. Thus, the primary 
data described below was collected from the 50 treatment and primary control worksites; 
administrative data were compiled for all 160 worksites. Figure 2 shows a Consolidated 
Standards of Reporting Trials (CONSORT) flow diagram for our phase 2 evaluation, including 
the sample sizes of treatment and control groups. 
 
 
5 For the first 18 months of the study (phase 1), there were 20 treatment and 20 primary control sites. 

 
6 
Randomization at the worksite level allows us to assess the effects of interventions that aim to 
affect workplace culture or operate on a group level, such as using common break rooms to stage 
nutrition demonstrations, team-based activities, or worksite level prizes.  
 
 
IV. Data  
 
Our analysis focuses on outcomes from five data categories. The Table below displays the source 
of each data set and the study population from which it is derived.  
 
Summary of the Components of Data Collected 
Data 
Source 
Treatment 
Worksites (25) 
Primary Control 
Worksites (25) 
Secondary Control 
Worksites (110) 
Administrative Data 
Employment 
records  
BJ’s 
All employees 
All employees 
All employees 
Claims data 
(medical and 
pharmaceutical) 
Cigna (via 
BJ’s) 
Employees 
insured by Cigna 
Employees 
insured by Cigna 
Employees insured by 
Cigna 
Primary Data 
Biometric 
screening data 
Wellness 
Workdays 
Employees 
completing 
screening 
Employees 
completing 
screening 
None (by design) 
Personal Health 
Assessment 
Wellness 
Workdays 
Employees 
completing 
survey 
Employees 
completing 
survey 
None (by design) 
Participation in 
the treatment 
Wellness 
Workdays 
All employees 
None (by design) 
None (by design) 
 
A. Administrative Data 
 
Administrative data consist of employment records and medical and pharmaceutical claims data. 
Employment records are available for all employees across treatment and control worksites (both 
primary and secondary controls). Medical and pharmaceutical claims data are available through 
Cigna for BJ’s employees who are insured through a Cigna plan. In cross-section, approximately   
37 percent of all BJ’s employees were insured through Cigna during June 2016 (the midpoint of 
phase 2). Of note, BJ’s as an organization is self-insured (i.e. it bears risk of health care spending 
of its enrolled members), with Cigna as the administrator of its health plans. We used all 
administrative data available for the entire study period. 
 
 
 

 
7 
A1. Employment records 
 
BJ’s provided us with data from their earnings database, which includes data elements that we 
use both to define our sample (based on hire and termination dates) and to measure key 
outcomes, such as absenteeism and performance reviews. Information included: 
 
• Dates and location of employment actions: Dates and sites of hire, rehire, termination, 
transfer, and performance review. These data capture employment history across clubs. A 
small proportion of employees (4.5%) appeared in more than 1 worksite during the study 
period. We defined these workers’ treatment or control assignment using the randomized 
assignment of their initial worksite, as the decision to leave (or stay) at a worksite could be 
endogenous. Most employees have one performance review per calendar year. 
 
• Demographic characteristics: Characteristics of the working population include age, sex, 
and race. These are discussed in greater in the section on Analysis below. Worksite level 
analyses, as described below, were augmented using 2015 estimates of county-level 
demographic characteristics from the U.S. Census Bureau matched to the locations of each 
worksite. 
 
• Employment characteristics: Characteristics of an individual’s job include type of hired 
arrangement (full time salaried, full time hourly, and part time hourly) and type of job. The 
types of jobs were grouped into three categories: sales, non-sales (including laborers and 
helpers, operatives, and service workers), and other (including administrative support, craft 
workers, and first/mid-level office professionals). 
 
• Hours and earnings:  Each employee’s hours (including working hours, vacation, sick time, 
personal time) and earnings in each pay period. Each observation is at the employee, pay-
period, earnings-type level. These data include a handful of negative payments/hours that 
indicate corrections. 
 
We used these data to study several outcomes: 
 
• Absenteeism: We calculated absenteeism as an employee’s number of sick plus personal 
hours, divided by the sum of an employee’s number of sick, personal, and worked hours. 
This gives the ratio of absence relative to how much an employee usually works. Vacation 
and holiday time are excluded from both the numerator and denominator.  
 
• Performance review: Employee performance reviews were coded on a 5-point scale, with 1 
representing the best performance rating and 5 the worst. We averaged performance review 
scores (weighted by the duration of time over which a score held) and created a binary 
indicator where a score of less than 3 was coded as good performance and 3 and greater as 
poor performance.  
 
• Employment tenure: We calculated each worker’s length of tenure at BJ’s and how many 
hours he or she worked (including the nature of those hours, such as regular hours and 
overtime). We defined tenure as the difference between the hire date and the latest 

 
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termination date.  If there was no hire date recorded in the data, we used the first appearance 
in our database. If there was no termination or retirement date in the data, we used the end of 
the study period.  
 
Table 2 provides a summary of demographic and employment characteristics of the population. 
The data are presented at the employee level (overall and for analytical subsets) and at the 
worksite level.  Table 3 provides summary statistics for tenure, performance review, and 
absenteeism gathered among the control worksites during the treatment period.  
 
A2. Medical and pharmaceutical claims data 
 
Health care claims data were provided at the individual employee level by Cigna and were used 
to calculate spending and utilization variables. These administrative data are available for all 
worksites (treatment, primary control, and secondary controls), but only for the roughly 37% of 
employees who were enrolled in a BJ’s employer-sponsored Cigna health insurance plan at any 
given time. Full time workers were more likely to have Cigna coverage than part-time workers. 
We analyzed medical claims for BJ’s employees (but not their dependents, who were not directly 
exposed to the treatment). 
 
We aggregated medical spending and utilization at the employee level across our 36-month 
treatment period.  To standardize utilization outcome variables across employees who may have 
been employed and in our sample for different lengths of time, we normalized outcomes for 
partial-year enrollees to annual values. For each employee, we exclude any claims with service 
dates prior to either the employee’s hire date or the start of Cigna eligibility status as well as any 
claims with service dates more than 30 days after the employee’s termination or end of Cigna 
enrollment.  
 
We examined the following outcomes at the individual level as well as the worksite level. In 
worksite-level analyses, results are shown per 2000 work hours for all the continuous outcomes 
to normalize across sites of different sizes. An exception was the number of distinct medications, 
which we constructed at the worksite level using a weighted average of individual values based 
on hours worked at the worksite.  
 
• Total medical spending: We defined total medical spending per year as the sum of all 
payments made for health care – deductibles, copayments, coinsurance, insurance payments, 
and “amount paid by other carrier” – that appear on an employee’s claims.  
 
• Medical utilization: We defined three medical utilization variables (i.e. counts of services 
provided):  
 
▪ Office Visits: We defined an office visit as a claim line with site of care as “office” or 
“on-campus outpatient hospitals” and service type as “physician visit.” We counted as 
one office visit such an occurrence at the level of a unique combination of patient, 
service date, and provider specialty. In other words, if an office code appears on multiple 
claim lines on the same date and billed by the same provider specialty, we counted these 
as a single visit. We do not include office visits that occurred with the site of care as 

 
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“inpatient hospital.” Similarly, we defined an urgent care visit as a claim line with a site 
of care as “urgent care facility” and identified preventive care visits by using preventive 
visit CPT codes in claims. 
 
▪ Hospitalizations: We defined a unique hospitalization as a set of contiguous days on 
which a patient has a claim line with site of care “inpatient hospital.” 
 
▪ Emergency Room visits: ER visits are identified from claim lines with site of care 
“Emergency Room – Hospital” and service type “emergency facility” or “emergency 
medical care.” We include the service type restriction so that we avoid double counting 
an ER visit when a claim for imaging, labs, or prescription drug is received a day or two 
later than the actual ER visit. Similar to hospitalizations above, we treat claims for the 
same or continuous days as a single emergency room visit.  
 
• Total prescription drug spending: We defined total prescription drug spending as the sum 
of all payments, inclusive of cost-sharing, that appear on an employee’s claims at the annual 
level, scaled using the method described above.  
 
• Prescription drug utilization: We defined two pharmaceutical utilization variables: 
 
▪ Number of Distinct Medications: We defined number of distinct medications as the total 
count of each unique prescription drug molecule. Unlike the other medical and 
pharmaceutical outcomes, this outcome is not annualized.  
 
▪ Number of Medication Months: We used the total quantity and duration of medication 
supplied to obtain the number of medication months. Because a person can be taking 
multiple medications at the same time, the number of medication months can be greater 
than the number of months a person is enrolled.  
 
Table 4 provides summary statistics for medical and pharmaceutical spending and utilization 
among the control worksites during the treatment period. While substantial additional granularity 
is available in the claims data, our sample sizes do not in general support condition-specific 
analyses. 
 
B. Primary Data 
 
Primary data consist of biometric data collected during in-person screenings conducted by 
registered nurses and self-reported data gathered from PHA surveys. The biometric data includes 
blood pressure, height and weight (inputs into the calculation of BMI), and blood measurements 
of cholesterol and blood sugar. PHAs contain self-reported information on health behaviors. 
Unlike the administrative data, these primary data are available for individuals in the 25 
treatment worksites and the 25 primary control worksites who completed the PHA and biometric 
data collection between 08/01/2017 and 10/31/2017. 
 
 

 
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B1. Biometric screening data 
 
In the treatment and primary control worksites, we conducted biometric screenings after the 
conclusion of the last wellness module. The screenings were conducted by registered nurses in 
the worksites, who met with employees individually. We defined all of the following biometric 
outcomes as continuous variables. 
 
• Total cholesterol 
• High-density lipoprotein (HDL) cholesterol  
• 
Blood glucose (mg/dl)   
• Systolic blood pressure 
• Body mass index (BMI): weight in kilograms divided by the square of height in meters 
 
Table 5 provides summary statistics for biometric screening data from employees who were 
screened in the control worksites. 
 
B2. Personal Health Assessment Data 
 
At the conclusion of the wellness modules, we also fielded PHA surveys in each of the treatment 
and primary control worksites. Employees were asked to complete a paper survey with questions 
relating to their health behaviors, mental health, and general wellbeing. We use this dataset to 
assess the impact of the wellness program on employees’ self-reported health behaviors and 
perceived health status.  
Based on an examination of the distribution of PHA responses collected from the control group, 
we examine the following outcome variables. 
 
• Health behaviors: 
 
Screenings and Exams 
 
▪ Percent of recommended tests received: We pooled commonly recommended tests 
(based on age and sex) covered in the PHA and determined the share of those tests 
that respondents reported obtaining. These tests are cholesterol level, fasting blood 
glucose level, blood pressure, dental exam, colon cancer screening, mammogram, and 
pap smear. 
 
Physical activity 
 
▪ Regular exercise: We defined this as responding yes to the question “Do you engage 
in regular exercise according to any of the definitions listed?” The provided 
definitions of regular exercise read “Regular exercise means doing: moderate physical 
activity that increases your breathing rate and causes you to break a light sweat (such 
as brisk walking, golf, or raking leaves) for at least 150 minutes (2 hours and 30 
minutes) each week OR vigorous physical activity that causes big increases in your 
breathing and heart rate and makes conversation difficult (such as jogging or running) 
for at least 75 minutes (1 hour and 15 minutes) each week OR a mix of moderate and 

 
11 
vigorous physical activity that is equal to at least 150 minutes of moderate activity, 
such as 90 minutes of moderate activity and 30 minutes of vigorous activity each 
week.”6 
 
▪ Number of hours sitting per day: This continuous variable was defined as an 
individual’s answer to the categorical question “How many hours per day do you sit? 
Please consider time at work and at home and include activities such as sitting in 
front of a computer or television.” We use the midpoint value of the chosen category. 
 
Nutrition 
 
▪ Number of non-zero calorie drinks per day: This continuous variable was defined as 
the difference between an individual’s answers to two questions:  “How many 
naturally or artificially sweetened beverages do you consume per day?” and “How 
many of these beverages are diet or zero calories?” 
 
▪ Read the Nutrition Facts panel: We defined this as responding yes to the question 
“Do you read the Nutrition Facts panel on food labels?” 
 
▪ Consume at least 2 cups of fruit and 2.5 cups of vegetables per day: We defined this 
as responding yes to the question “Do you eat at least 2 cups of fruit and 21/2 cups of 
vegetables per day?” 
 
Weight Management 
 
▪ Actively managing weight: We defined this as responding yes to either the question 
“In the past month, have you been actively trying to lose weight?” or “In the past 
month, have you been actively trying to keep from gaining weight?” or both.  
 
Tobacco use 
 
▪ Smoking: We defined this as responding yes to the question “Do you currently 
smoke?” or if the answer to “How many cigarettes do you smoke during a typical 
day?” was greater than zero. The indicator is coded as 0 if the answer to the first 
question was “non-smoker.”  
 
6 Interpretation of these self-reports can be informed by supplemental data collected from a subset of the treatment 
group who used an ActivBand wearable step-recording device. This provides an opportunity for us to explore the 
correlation between actual physical activity and self-reported levels of physical activity on the PHA, although such 
correlations are merely suggestive, given that the subset of employees with an ActivBand is endogenous based on 
completion of certain program modules and that the measurement was not comprehensive. Overall, 985 employees 
in the treatment sites used the ActivBand between January and July of 2017; the average number of steps per person 
per month was 96,121. On average, individuals who reported engaging in regular exercise logged 32,144 steps more 
per month than those who reported not engaging in regular exercise (S.E. 8,869; p<0.001). Individuals who reported 
actively managing weight logged 5,021 steps more per month than those who reported otherwise (S.E. 10,063; 
p=0.62). Individuals who reported 3.5 hours or less of sitting per day logged 6,730 steps more per month than those 
who reported more than 3.5 hours of sitting per day (S.E. 9,732; p=0.49). 

 
12 
 
• Health and Well-Being:  
 
▪ SF-8 scores: We used the 8-question Short Form (SF-8) health survey to measure 
self-reported functional health and well-being. Each question of the SF-8 uses a 5- or 
6-point Likert scale. Its standardized scoring system combines responses into a score 
that can be interpreted as a continuous variable, with higher scores denoting better 
self-reported health. Using these scoring methods, we create the two standard scores 
from this survey: the physical summary score and the mental summary score.7 
 
▪ Unmanaged stress: We defined this as responding no to the question “Do you 
effectively practice stress management in your daily life?” The question defines stress 
management to include regular relaxation, physical activity, talking with others, or 
making time for social activities.  
 
▪ Unmanaged depression: We defined this as responding “no” or “I have never been 
depressed” to the question “Depression prevention means using effective methods to 
keep depression from occurring, or if it does occur, to keep it as mild and brief as 
possible. Effective methods include controlling negative thinking every day, engaging 
in healthy, pleasant activities on most days, exercising for 30 minutes or more on 
most days, practicing stress management on most days, and getting professional help 
when needed. Do you effectively practice depression prevention in your daily life?”  
 
▪ Stress at work: We defined this as answering the question “During the last 30 days, 
how often have you found yourself stressed or worried about problems as work?” 
with “sometimes,” “fairly often,” or “very often.”  (rather than “almost never” or 
“never”). 
 
Table 6 provides summary statistics for employees who completed the PHA in the control 
worksites. 
 
B3. Program participation data 
 
At the individual employee level, we examine three different definitions of participation based 
on the number of modules completed. Each module had its own set of requirements that defined 
completion and incentives attached to participation or completion. Details of the modules and 
their requirements are provided in Appendix 1. 
 
• Modules completed: We defined a continuous variable denoting the number of modules 
completed, which ranges from 0 to 12 over the course of the study period. This serves as the 
primary definition of participation for this analysis.  
 
 
7 Ware JE, Kosinski M, Dewey JE, Gandek B. How to Score and Interpret Single-Item Health Status Measures: A 
Manual for Users of the SF-8 Health Survey. Lincoln RI: QualityMetric Incorporated, 2001. 

 
13 
• Participation indicator: We defined a binary indicator of participation based on completing 
at least one module. This is a secondary definition for this analysis.8  
 
• High participation: We defined another secondary binary indicator based on completing 3 
or more modules. We chose 3 modules as the threshold for high participation based on the 
distribution of the number of modules completed. 
 
Table 7 shows summary statistics on participation overall and by module across the treatment 
worksites for phase 1 and for phase 2 of the wellness program. 
 
 
V. Study Sample 
 
A. Main sample and subsamples 
 
We designed our analyses based on two study samples. Our main study sample comprised all 
employees who worked for any duration of time at BJ’s Wholesale Club during the study period. 
In other words, we included any worker who worked at BJ’s for any length of time during the 
study period. This is the most inclusive sample.  
 
A potential drawback of these sample inclusion criteria is the possibility of endogenous entry or 
exit based on the treatment itself. For example, a worker’s decision to join or leave employment 
at BJ’s Wholesale Club could be a function of the availability of, or exposure to, the wellness 
program. Therefore, we also defined a relatively stable subsample of employees who were 
continuously employed at BJ’s in the months before the treatment. This “stably employed” 
subsample comprised all employees working at BJ’s during, at minimum, the 13 consecutive 
weeks immediately prior to the onset of the treatment—and thus cannot have entered because of 
the treatment. Exit from this sample might still be endogenous in theory, however. We will 
therefore gauge the potential bias introduced by any observed differences in exit between 
treatment and control sites using a bounding exercise. Additional details on the construction of 
this subsample are provided below and in the appendix. 
 
B. Balance between treatment and control 
 
We tested balance between treatment and control groups on observable baseline characteristics. 
We examined balance on demographic variables (age, sex, and race) for all of the analytical 
samples. We also examined balance on job and employment characteristics in the pre-
intervention period for the stably employed subsample, as they by construction were in jobs with 
characteristics defined prior to the intervention and thus exogenous to the treatment status.  
 
As Table 8 demonstrates at the employee level, our randomly assigned worksites were balanced 
on many employee characteristics, but not all. Notably, while treatment and control site workers 
 
8 This indicator was our primary definition of participation in our prior phase 1 analysis.  Given that we expanded 
the sample size for phase 2 (with a different total number of modules available), we believe the continuous measure 
is preferable here. 

 
14 
were similar on age and gender, a greater proportion of treatment site workers were white (and a 
lower proportion black and Hispanic) than individuals in control worksites.  To address 
imbalance on some observable characteristics, we followed two common strategies. First, we 
weighted the treatment and control groups to be more balanced on observed characteristics (such 
that each group mirrors the overall employee population).  Second, we include observable 
characteristics as covariates in the regression analyses below. Neither of these strategies, 
however, guarantees that there are no idiosyncratic differences in unobserved confounders.  
 
The sample weights that we generate to address the imbalance on some demographics were 
constructed to balance treatment and control samples on age, sex, and race while minimizing 
variance of the weights for a given level of balance. A given level of balance is defined as the 
tolerance of a certain magnitude of difference in the standardized means between treatment and 
control. The tradeoff for more precise balance is a larger variance in the weights. In our base 
analysis, we will use weights that tolerate a difference in the standardized means in age, sex, and 
race up to 0.001 standard deviations between treatment and control. In sensitivity analyses, we 
will loosen the tolerance in constructing these weights to 0.01 standard deviations between 
treatment and control. The balance weights are calibrated to match the characteristics of the 
overall study population. This method has been shown to perform better than a model-based 
approach that fits a propensity score.9  Once we deploy the sample balance weights, all 
demographic characteristics are balanced. Job characteristics, including prevalence of Cigna 
insurance, are balanced in all analytical subsamples.  
 
Table 9 examines individual-level characteristics collapsed to the worksite level, as well as 
county-level characteristics, using 2015 data from the U.S. Census Bureau. This suggests that the 
employee pools at the worksites roughly reflect the characteristics of the local population. The 
fact that treatment and control groups were located in idiosyncratically different parts of the 
country can be appreciated from Figure 1, where, for example, worksites in Ohio and Virginia 
were disproportionately randomly assigned to treatment and worksites in Florida were more 
likely to be randomly assigned to control.  
 
 
VI. Statistical Analyses 
 
Analyses are conducted at the individual employee level and at the worksite level. The treatment 
period in our phase 2 analysis is defined as the 36-month period spanning January 2015 through 
December 2017. (In comparison, the phase 1 analysis spanned January 2015 through mid-2016). 
 
A. Intention-to-treat analysis 
 
In the individual-level intention-to-treat (ITT) analysis, our goal is to estimate the average effect 
of a worker being randomized into a treatment worksite on outcomes of interest. Given that the 
 
9 Zubizarreta JR. Stable weights that balance covariates for estimation with incomplete outcome data. Journal of the 
American Statistical Association. 2015 Sep;110(511):910-922; Wang X, Zubizarreta JR. Minimal approximately 
balancing weights: Asymptotic properties and practical considerations. Biometrika. 2017;103(1):1-22; Hirshberg 
DA, Zubizarreta JR. On two approaches to weighting in causal inference. Epidemiology. 2017;28(6):812-816. 

 
15 
treatment is implemented at the worksite level and that there are likely fixed costs of the 
program, the ITT estimates (and the worksite level analysis) are informative.  
 
We use a base specification that includes a dose-response measure of the effect of exposure to 
the intervention (as measured by time worked). This specification captures the overall effect of 
randomization into treatment as well as how the effect of randomization to a treatment site varies 
with additional exposure to the treatment. 
 
Yijt = β0 + β1TREATMENTjt+ β2TIME_WORKEDijt  
+ β3TREATMENTjt*TIME_WORKEDijt + β4Xijt + εijt 
(1) 
 
In this representative estimating equation, Yijt denotes an outcome of interest, such as medical 
spending, for individual i who is employed in worksite j at time t. TREATMENTjt is a binary 
indicator of whether the individual’s worksite was randomized into treatment or control. We 
assigned each individual to the worksite at which he or she was employed when the treatment 
began in January 2015. TIME_WORKEDijt is the share of total available work hours in the study 
period that the individual actually worked; it is expressed in percentage terms. The effect of 
randomization to a treatment worksite is allowed to vary based on exposure to the treatment 
(measured via time worked during the treatment period). The treatment effect is captured by β1 + 
β3 * Exposure. The average effect of randomization into the treatment group can be summarized 
by assessing this at the average level of exposure. While we will produce estimates of the 
average effect of randomization into treatment, as captured in the layout of Tables 3-6, we will 
additionally show results for both β1 and β3 for each of our results in a set of parallel tables. 
 
Xijt represents a vector of covariates that may help improve precision as well as account for 
chance differences in characteristics between treatment and control groups, including:  
 
• Age indicators: <20 years (omitted), 20-29, 30-39, 40-49, 50-59, and 60 and greater. 
• Sex indicator: male (omitted), female  
• Age-sex interactions 
• Race: white (omitted), black, Hispanic, and other 
• Employment characteristics (measured at baseline for the stably employed subsample):  
• Part-time (omitted), full-time 
• Salaried (omitted), hourly 
• Sales worker (omitted), non-sales worker, and other worker.  
 
εijt is the idiosyncratic error term. The models are weighted using the sample weights described 
above. Standard errors were clustered at the worksite level. 
 
B. Local average treatment effect (LATE) 
 
While our ITT analysis explores the effect of being randomized into a treatment worksite, a 
related but distinct question is: what is the effect of participating in the wellness program on the 
outcomes of interest? The answer to this second question will be different if not all employees in 
treatment worksites elect to participate.  Because the choice to participate in the program is 
endogenous, simply including participation as a right-hand side variable may produce biased 

 
16 
estimates of the effect of the treatment. Therefore, we model the impact of participation on 
outcomes using a two-stage least squares (2SLS) specification: 
 
Yijt = γ0 + γ1PARTICIPATIONijt + γ2Xijt + μijt 
(3) 
 
Where the endogenous PARTICIPATION variable is estimated via the first stage regression:   
 
PARTICIPATIONijt = π0 + π1TREATMENTjt + π2TIME_WORKEDijt 
+ π3TREATMENTjt*TIME_WORKEDijt + π4Xijt + νijt 
(4) 
 
We include the exposure interaction in the first stage, parallel to our ITT estimates.  The 
coefficient γ1 in equation (3) identifies the effect of participation on outcomes. 
 
On the left-hand side of the first stage, this base specification uses the continuous definition of 
participation, with values from 0-12, which indicates the number of modules an individual 
participated in during the 36-month study period. Table 10 shows the results of first stage 
estimates for this and for our alternative definitions of participation (including indicators for 
completion of 3 or more modules of the program or completion of at least 1 module of the 
program). In robustness specifications, we use these alternative definitions of participation.  
 
If no one in the control group receives the treatment, we can interpret the 2SLS estimates of the 
LATE as a treatment on the treated (TOT). Empirically, we expected this to be essentially true 
by construction because the control worksites did not receive the wellness modules. However, 
because we assigned employees to worksites based on their initial locations of employment at the 
beginning of the study period, a small number of employees who moved from control worksites 
to treatment worksites during the intervention period did receive an opportunity to participate in 
the program. This explains the fact that the control group means in Table 10 are nearly, but not 
exactly, zero.  
 
C. Addressing endogeneity concerns 
 
The key identifying assumption is that the only mechanism through which being employed in a 
treatment worksite affects outcomes is through the availability of the wellness program. There 
are two threats to this assumption. First, to the extent that covariates were not balanced at 
baseline and those covariates affect the outcomes of interest, that imbalance could bias our 
estimates. We control for observed covariates and apply weights to balance covariates between 
treatment and control. Despite these adjustments, unobserved covariates might still be 
imbalanced.  
 
Second, to the extent that the wellness program affected hiring or tenure of employees, their 
presence in the data set may be endogenously determined. We address the issue of endogenous 
entry by pre-specifying a subsample of those who were already employed at the time the 
intervention began.  We defined this more restrictive “stable employment” subsample as 
comprising employees who had worked continuously for 13 weeks prior to the wellness 
program. This comes at a cost of reducing the overall sample size. Our goal in choosing the 
definition for this subsample was to balance the costs and benefits, as detailed in Appendix 2.  

 
17 
 
In this stably employed subsample, however, exit from the firm’s workforce is still potentially 
endogenous. We deal with this concern in two ways. First, we will test balance on exit between 
treatment worksites vs. from control worksites overall and for subgroups. Indeed, tenure and 
time worked are among our primary outcomes. To the extent that we observe any unbalanced 
exit, we will perform a bounding exercise (assuming that those who exited have outcomes 
working against any observed effects of the intervention).   
 
D. Subgroup analyses 
 
We pre-specified three subgroup analyses. We selected these subgroups based on differences in 
key outcomes within the control group for those with different baseline characteristics, such as 
differences in medical spending by age or in reported exercise by sex (Table 11).  
 
We will analyze how the effect of the wellness program varies along 3 pre-specified dimensions: 
age (less than 40 years or 40 and above), sex (male or female), and employment status (full-time 
vs. part-time). In our model, these subgroup analyses are implemented using a set of two-way 
interaction terms and a triple interaction term. Equation 5 below shows these interaction terms in 
our ITT framework, using the subgroup analysis on age as the representative equation, where 
age40it is defined as 1 if the individual is age 40 years or older, and 0 otherwise. 
 
Yijt = β0 + β1TREATMENTjt + β2TIME_WORKEDijt + β3Age40it 
+ β4TREATMENTjt*TIME_WORKEDijt + β5TREATMENTjt*Age40it 
+ β6TIME_WORKEDijt*Age40it + β7TREATMENTjt*TIME_WORKEDijt*Age40it 
+ ρXijt + εijt 
(5) 
 
In this model, the coefficient on the triple interaction term, β7, represents the marginal effect of 
an additional unit of exposure (as measured by time worked) among employees 40 years or older 
who were randomized into treatment. For those under age 40, the effect of the wellness program 
is represented by β1 + β4*Exposure.  For those over age 40, the effect of the wellness program is 
represented by β1 + β4*Exposure + β5 + β7*Exposure, or (β1 + β5) + (β4 + β7)*Exposure. The 
average effects for each group can be derived by evaluating this at the average level of exposure. 
We will evaluate whether any differences are statistically significant. 
 
E. Worksite-level analyses 
 
We complement our analyses at the individual level with analyses at the worksite level. 
Worksite-level data were generated by aggregating values for individuals up to their initial 
worksite. This aggregation was weighted by the number of hours each employee worked during 
the treatment period. The estimating equation below captures the framework of the worksite-
level analyses.  
 
Ykt = β0 + β1TREATMENTkt + β2Xkt + εkt 
(6) 
 
In equation (6), the subscript k denotes a worksite. Analogous to the individual-level analyses, 
Ykt represents an outcome for worksite k at time t. TREATMENTkt is a binary indicator of 

 
18 
randomization into treatment or control, with β1 indicating the average worksite-level effect of 
being randomized into treatment. The vector of covariates Xkt comprises the same set of 
observable characteristics for individuals, aggregated to the worksite level using weighted 
averages (generating percentages for binary variables). The worksite-level regressions are 
weighted by worksite size, as measured by the total hours worked by all employees at a worksite, 
with standard errors adjusted for heteroscedasticity. For estimates of program effect that are 
significant in the individual-level analysis, we will produce parallel LATE estimates at the 
workplace level, analogously using treatment status as an instrument for participation. 
 
Although the employees at each worksite could change over time, the worksites themselves were 
all stably represented in our data. However, at the 18-month mark, 5 worksites previously in the 
control group entered the treatment group. In our base specification, we will assess effects for the 
20 worksites that were continuously treated throughout the 3-year period (omitting the additional 
5 from the analysis altogether). As an alternative specification, we will also evaluate effects at 
the worksite level when considering the 5 additional worksites in the treatment group.  
 
F. Sensitivity analyses 
 
We conduct several types of sensitivity analyses. First, in the statistical analyses above, our base 
regression models use linear regression even for binary outcomes. The OLS estimator has been 
shown to perform better than other non-linear approaches in estimating the average population 
effect, particularly with large samples.10 However, OLS also has limitations, such as its 
sensitivity to outliers. To test the robustness of our main results to changes in functional form, 
we will fit logit models for binary outcome variables.  
 
Second, we will present LATE results with alternative definitions of participation. As defined 
above, these include a binary indicator of participation based on completing at least one module, 
and a high participation indicator defined as completing at least 3 modules.  
 
Additionally, our phase 1 analysis of the first 18 months used a different baseline specification 
that did not allow for treatment effects to vary with individuals’ exposure (rather, weighting 
individuals by exposure). It also included a different sample, as 5 additional worksites from the 
previous secondary control group were added to the treatment group and another 5 to the primary 
control group at the 18-month mark. Therefore, to facilitate comparisons of results between this 
phase 2 analysis to those from phase 1, we will present results using the phase 1 statistical model 
and set of weights on the phase 2 sample, as well as results using the phase 2 model and weights 
on the phase 1 sample. 
 
Specifically, we conduct the ITT and LATE analyses using the phase 1 specifications that 
include just a treatment dummy.  The ITT specification is: 
 
Yijt = β0 + β1TREATMENTjt+ β2Xijt + εijt 
(7) 
 
 
10 See, for example, Buntin and Zaslavsky, 2004; Manning, Basu, and Mullahy, 2005; Ellis and McGuire 2006; 
Jiang, Ellis, and Kuo, 2008. 

 
19 
The LATE specification is: 
 
Yijt = γ0 + γ1PARTICIPATIONijt + γ2Xijt + μijt 
(8) 
 
With first stage regression: 
 
PARTICIPATIONijt = π0 + π1TREATMENTjt + π2Xijt + νijt 
(9) 
 
We conduct this analysis first for our full sample, and then for the subsample of treatment and 
control sites that match the phase 1 sample. 
 
 
 

 
20 
Figure 1: Locations of Treatment and Primary Control Worksites, Phase 2 
 
Notes: This map shows the 25 treatment and 25 control worksites in Phase 2 of the treatment. 
Red markers designate treatment worksites; red triangles are the 5 additional treatment worksites 
that were added in Phase 2. Blue markers designate primary control worksites; blue triangles are 
the 5 additional primary control worksites that were added in Phase 2.

 
21 
Figure 2. CONSORT Flow Diagram 
 
Notes: This CONSORT diagram shows the flow of the trial and sample sizes through phase 2. 

 
22 
Table 1: Timeline of the Workplace Wellness Program 
 
Phase 1 
Phase 2 
Program 
announced 
Registered Dietitians begin working in the 
treatment worksites 
↓ 
↓ 
Year 
2015 
2016 
2017 
Month 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Program Announcement 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 1. Take Charge of 
Your Health Round 1 & 2 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 2. Nutrition for a 
Lifetime  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 3. Club Cardio 
Challenge Round 1  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 4. Club Cardio 
Challenge Round 2  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 5. Maintain Don't Gain    
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 6. Power Down the 
Pressure  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 7. Weight Loss Boot 
Camp  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 8. Movin' in May  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Surveys and Biometrics 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 9. Healthy and Fit 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 10. Nutrition for a 
Lifetime Round 1 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 11. Nutrition for a 
Lifetime Round 2 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Module 12.  Step Challenge 
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
Surveys and Biometrics 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Notes: This table presents a graphical illustration of Phase 1 and Phase 2 of the wellness programs. The treatment began in 2015 with announcements of the 
wellness program worksite assignments (treatment worksites) in January followed by administration of the personal health assessments (PHAs) and in-person 
screenings in February. Phase 1 comprised 8 modules and concluded at the end of June 2016. After Phase 1, PHAs and in-person screenings were conducted 
during the summer of 2016. Afterwards, Phase 2 of the wellness program began in the fall of 2016, with PHAs and in-person screenings in fall of 2017. 

 
23 
Table 2. Summary of Demographic Characteristics for Employees in Control Worksites 
 
 
Notes: Table lists demographic characteristics for the sample covered by Cigna weighted by months of Cigna coverage. About a third of the total sample 
has Cigna coverage. Age is defined as age at the mid-point of the treatment period (June 2016). This is different from the balance table where age is 
defined as of December, 2014 (pre-treatment). Thus the means of age in this table are larger than those in the balance table across all samples. 
 
Employee-level 
Stably Employed 
Subsample 
Worksite-level 
 
All 
PHA 
Cigna 
All 
PHA 
Cigna 
All 
PHA 
Cigna 
Age (yrs) 
34.5 
39.8 
45.3 
39.6 
44.3 
45.3 
39.4 
42.4 
45.2 
Female (%) 
47.2 
56.9 
45.6 
47.0 
62.5 
45.9 
46.2 
57.6 
45.8 
Race (%) 
  White 
52.0 
57.4 
69.1 
60.2 
60.7 
68.0 
58.7 
59.0 
68.5 
  Black 
25.9 
19.1 
14.4 
19.0 
14.6 
15.0 
20.7 
18.2 
14.5 
  Hispanic 
16.7 
17.8 
13.4 
17.3 
19.6 
14.0 
16.4 
17.7 
14.1 
  Other race 
5.4 
5.6 
3.1 
3.5 
5.1 
3.1 
4.1 
5.1 
2.9 
Employment (%) 
  Full-time salary 
4.7 
7.0 
21.3 
11.6 
12.1 
21.7 
11.5 
11.4 
21.8 
  Full-time hourly 
37.4 
44.3 
66.6 
38.6 
52.4 
66.8 
49.0 
53.6 
66.4 
  Part-time hourly 
57.9 
48.7 
12.1 
49.8 
35.5 
11.4 
39.5 
35.0 
11.8 
Worker Type (%) 
  Sales worker 
43.2 
45.3 
23.2 
38.4 
39.0 
22.3 
36.2 
39.3 
23.3 
  Non-sales worker 
45.9 
41.8 
52.4 
46.4 
42.8 
52.8 
47.4 
43.7 
51.6 
  Other worker 
10.9 
13.0 
24.4 
15.3 
18.2 
25.0 
16.5 
16.9 
25.0 

 
24 
Table 3: Impact on Employment 
 
 
Employee-level 
Stably Employed  
Subsample 
Worksite-level 
 
Mean Value 
in Control 
Group 
Reduced 
Form 
(Linear) 
2SLS 
(Linear) 
Mean Value 
in Control 
Group 
Reduced 
Form 
(Linear) 
2SLS 
(Linear) 
Mean Value 
in Control 
Group 
Reduced 
Form 
(Linear) 
2SLS 
(Linear) 
 
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
 
 
 
 
 
 
 
 
 
 
Absenteeism (%) 
2.23 
3.39 
2.90 
 
 
 
(2.30) 
(1.97) 
(0.35) 
 
 
 
41,376 
 
 
13,000 
 
 
135 
 
 
 
 
 
 
 
 
 
 
 
 
Performance Review 
(% ≤ 3) 
46.59 
63.64 
65.94 
 
 
(49.88) 
 
 
(48.11) 
 
 
(12.41) 
 
 
 
27,150 
 
 
12,916 
 
 
135 
 
 
 
 
 
 
 
 
 
 
 
 
Tenure§ 
416.69 
 
 
786.57 
 
 
76.08 
 
 
(399.87) 
(376.96) 
(4.35) 
 
 
41,376 
 
 
13,000 
 
 
135 
 
 
 
Notes: Table reports the coefficient on TREATMENT from estimating equation (1) by OLS (column 2), and the coefficient on PARTICIPATION from 
estimating equation (2) by IV (column 3). Standard errors are listed in parentheses. Column 1 reports the mean of each employment outcome in the control group 
for each sample (with standard deviation in parentheses, followed by the number of observations for each outcome). All regressions will include demographic 
and employment controls (age, sex, age-sex interactions, race/ethnicity, Cigna coverage status, full-time status, paid hourly status, and job category) and cluster 
standard errors at the worksite (for employee-level regressions). Employee-level and stably employed subsample control means are weighted by a weight that 
balances treatment and control on demographic characteristics. Worksite-level control means are not traditionally weighted: individual-level records are 
collapsed at the worksite-level and weighted by the total number of hours worked at each worksite. For worksite level results on tenure, the outcome was defined 
as the percent of total days at the worksite during the study period that was worked by employees at the worksite. 
 
§ Tenure was defined as the number of days worked during the treatment period for the employee-level mean and stably employed subsample mean; it was 
defined as the percent of the entire treatment period worked by employees in the worksite-level mean. 

 
25 
Table 4: Impact on Medical & Pharmaceutical Spending and Utilization 
 
Employee-level 
Stably Employed  
Subsample 
Worksite-level 
(per 2000 work hours) 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS (Linear) 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS (Linear) 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS (Linear) 
  
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
Medical Spending 
  
  
  
  
  
Total Spending 
4800.57 
4721.85 
5044.74 
  
(18559.82) 
(19056.92) 
(2994.54) 
  
 
 
 
 
 
 
 
 
 
 
Medical Utilization 
 
 
 
 
 
 
 
 
 
Number of Office visits 
3.90 
3.95 
4.21 
  
(4.47) 
(4.33) 
(0.93) 
  
 
 
 
 
 
 
 
 
 
 
Number of Hospitalizations 
0.09 
0.08 
0.08 
  
(0.40) 
(0.36) 
(0.05) 
  
 
 
 
 
 
 
 
 
 
 
Number of ER Visits 
0.28 
0.26 
0.27 
  
(0.75) 
(0.61) 
(0.11) 
  
 
 
 
 
 
 
 
 
 
 
Pharmaceutical Spending 
 
 
 
 
 
 
 
 
 
Total Spending 
1237.60 
1227.32 
1317.72 
  
(6921.32) 
(7115.67) 
(1145.17) 
  
 
 
 
 
 
 
 
 
 
 
Pharmaceutical Utilization 
 
 
 
 
 
 
 
 
 
Number of Distinct Medications 
5.49 
5.91 
6.00 
  
(6.44) 
(6.54) 
(1.30) 
  
 
 
 
 
 
 
 
 
 
 
Total Medication Months 
11.20 
11.41 
12.39 
  
(19.31) 
(19.37) 
(4.38) 
  
N 
7,174 
 
 
5,779 
 
 
135 
 
 
Notes: Table reports the coefficient on TREATMENT from estimating equation (1) by OLS (column 2), and the coefficient on PARTICIPATION from 
estimating equation (2) by IV (column 3). Standard errors are listed in parentheses. Column 1 reports the mean of each outcome in the control group for each 
sample (with standard deviation in parentheses). All regressions include demographic and employment controls (age, sex, age-sex interactions, race/ethnicity, 
full-time status, paid hourly status, and job category) and cluster standard errors at the worksite (for employee-level regressions). Employee-level and stably 
employed subsample control means are weighted by a weight that balances treatment and control on demographic characteristics. Worksite-level control means 
are not traditionally weighted: individual-level records are collapsed at the worksite-level and weighted by the total number of hours worked at each worksite.  

 
26 
Table 5: Impact on Biometrics 
 
 
Employee-level 
Stably Employed  
Subsample 
 
 
 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS  
(Linear) 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS  
(Linear) 
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
  
  
  
Cholesterol (mg/dl) 
179.87 
 
 
184.88 
 
 
(38.71) 
(37.85) 
 
1,392 
 
 
739 
 
 
 
 
 
 
 
 
 
HDL (mg/dl) 
49.47 
50.51 
 
(14.58) 
 
 
(14.87) 
 
 
 
1,366 
 
 
720 
 
 
 
 
 
 
 
 
 
Glucose (mg/dl) 
104.76 
105.79 
(35.71) 
(38.17) 
 
1390 
 
 
736 
 
 
 
 
 
 
 
 
 
Systolic BP (mmHg) 
124.84 
127.22 
(18.55) 
(18.73) 
 
1,400 
 
 
738 
 
 
 
 
 
 
 
 
 
BMI 
29.79 
30.04 
 
(7.27) 
 
 
(6.71) 
 
 
1,403 
742 
 
Notes: Table reports the coefficient on TREATMENT from estimating equation (1) by OLS (column 2), and the coefficient on 
PARTICIPATION from estimating equation (2) by IV (column 3). Standard errors are listed in parentheses. Column 1 reports 
the mean of each biometric outcome in the control group for each sample (with standard deviation in parentheses, followed by 
the number of observations for each outcome). All regressions include demographic and employment controls (age, sex, age-
sex interactions, race/ethnicity, Cigna coverage status, full-time status, paid hourly status, and job category) and cluster 
standard errors at the worksite (for employee-level regressions). Control means are weighted by a weight that balances 
treatment and control on demographic characteristics.  

 
27 
Table 6: Impact on Self-Reported PHA Responses 
 
 
Employee-level 
Stably Employed  
Subsample 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS 
(Linear) 
Mean in 
Control 
Group 
Reduced 
Form 
(Linear) 
2SLS 
(Linear) 
(1) 
(2) 
(3) 
(1) 
(2) 
(3) 
 
Domain 1: Health Behaviors 
 
 
 
 
 
 
 
Screenings and Exams 
  
  
  
Percent of recommended tests 
received 
58.00 
64.03 
(32.51) 
(30.80) 
  
 
1,454 
 
 
747 
 
 
Physical Activity 
  
  
  
  
Regular exercise (%) 
53.79 
 
 
54.56 
 
 
 
(49.87) 
 
 
(49.83) 
 
 
 
1,440 
 
 
737 
 
 
 
  
 
 
  
 
 
Number of hours sitting per day 
3.73 
 
 
3.71 
 
 
 
(1.78) 
 
 
(1.77) 
 
 
 
1,444 
 
 
741 
 
 
 
  
 
 
  
 
 
Nutrition 
  
 
 
  
 
 
Number of non-zero calorie drinks 
per day 
1.36 
 
 
1.21 
 
 
 
(1.71) 
 
 
(1.60) 
 
 
 
1,443 
 
 
742 
 
 
 
 
 
 
 
 
 
Read the Nutrition Facts panel (%) 
58.91 
 
 
61.66 
 
 
 
(49.22) 
 
 
(48.65) 
 
 
 
1,443 
 
 
739 
 
 
 
 
 
 
 
 
 
Consume at least 2 cups of fruit 
and 2.5 cups of vegetables per day  
54.13 
 
 
57.60 
 
 
(49.85) 
 
 
(49.45) 
 
 
(%) 
1,441 
 
 
738 
 
 
 
  
 
 
  
 
 
Weight Management 
  
 
 
  
 
 
Actively managing weight (%) 
61.52 
 
 
61.40 
 
 
 
(48.67) 
 
 
(48.72) 
 
 
 
1,417 
 
 
726 
 
 
 
  
 
 
  
 
 
 
 
 
 
 
 
 
 
  
 
 
  
 
 

 
28 
Tobacco Use 
Smoker (%) 
14.67 
 
 
13.00 
 
 
 
(35.40) 
 
 
(33.65) 
 
 
 
1,444 
 
 
741 
 
 
 
  
 
 
  
 
 
Domain 2: Health and Well-Being 
  
  
  
SF-8 score – physical summary 
score 
51.56 
51.65 
  
(6.78) 
(6.76) 
  
 
1,427 
 
 
735 
 
 
 
 
 
 
 
 
 
SF-8 score – mental summary 
score 
51.87 
52.26 
  
(8.47) 
(8.07) 
  
 
1,427 
 
 
735 
 
 
 
 
 
 
 
 
 
Unmanaged stress (%) 
30.68 
31.77 
  
(46.13) 
(46.59) 
  
 
1,441 
 
 
738 
 
 
 
 
 
 
 
 
 
Unmanaged depression (%) 
30.58 
 
 
29.42 
 
 
 
(46.09) 
 
 
(45.60) 
 
 
 
1,444 
 
 
740 
 
 
 
 
 
 
 
 
 
Stress at work (%) 
39.69 
 
 
44.35 
  
(48.94) 
 
 
(49.71) 
  
1,447 
743 
  
 
Notes: Table reports the coefficient on TREATMENT from estimating equation (1) by OLS (column 2), and the 
coefficient on PARTICIPATION from estimating equation (2) by IV (column 3). Standard errors are listed in 
parentheses. Column 1 reports the mean of each self-reported health outcome in the control group for each sample 
(with standard deviation in parentheses, followed by the number of observations for each outcome). All regressions 
include demographic and employment controls (age, sex, age-sex interactions, race/ethnicity, Cigna coverage status, 
full-time status, paid hourly status, and job category) and cluster standard errors at the worksite (for employee-level 
regressions).  Control means are weighted by a weight that balances treatment and control on demographic 
characteristics. 
 

 
29 
Table 7: Participation Rates 
 
Panel A. Average Participation Rates by Module 
 
Phase 1 
Phase 2 
Module 
Take 
Charge 
of Your 
Health 
Nutrition 
for a 
Lifetime 
Club 
Cardio 
Challenge 
Round 1 
Club 
Cardio 
Challenge 
Round 2 
Maintain 
Don't 
Gain 
Power 
Down 
the 
Pressure 
Weight 
Loss 
Boot 
Camp 
Movin’ 
in May 
Healthy 
and Fit 
Nutrition 
for a 
Lifetime 
Round 1 
Nutrition 
for a 
Lifetime 
Round 2 
Step 
Challenge 
Participation 
rate (%) 
12.4 
25.7 
37.8 
28.6 
31.6 
33.4 
28.7 
28.5 
26.3 
32.6 
27.2 
29.7 
Notes: The average participation rate for each module was calculated as the percentage of individuals who completed a module out of all who were 
eligible to complete a module during the time that the module was running. Participation is equivalent to completion of a module, with an incentive of a 
gift card for completion of the module. Descriptions of each module and their criteria for completion are provided in Appendix 1. Employees could only 
participate in the Take Charge of Your Health module once, though it was run twice. Club Cardio Challenge had two rounds and completion of either 
round 1 or round 2 earned a gift card; completion of both rounds did not earn an additional gift card, but rather an entry into a raffle for a Fitbit, unless the 
employee had Cigna health insurance, in which case they could complete both rounds of Club Cardio Challenge for an additional fitness reimbursement. 
Nutrition for a Lifetime occurred in two rounds; individuals who completed round 1 earned an ActivBAND or gift card. Completion of round 1 and round 
2 earned an additional gift card or weight management reimbursement for Cigna members. Values were weighted by the number of days an individual 
was working during a given module’s timeframe. 
 
Panel B. Intensity of Participation by Definition 
Definition of Participation 
Employees at Treatment Worksites 
(N = 7288) 
Participants at Treatment Worksites  
(N = 2071) 
Completed any module (%) 
28.4 
100.0 
Modules completed (#) 
1.17 
4.11 
3 or more modules (%) 
16.7 
58.8 
Notes: These summary statistics show the intensity of participation in the treatment group according to the 3 definitions of participation. In contrast to 
Panel A above, the sample (denominator) for these calculations was all employees at treatment worksites (first column) and all employees at treatment 
worksites who completed any module (second column) throughout the study period. Because of this difference in the denominators, participation rates for 
a given module in Panel A could exceed the average participation study-wide in Panel B.  
 

 
30 
Table 8: Balance Between Treatment and Control—Employee Level 
 
Panel A.1: All Employees – Unweighted 
  
(1) 
(2) 
(3) 
(4) 
(5) 
Treatment 
Primary 
Control 
Primary + 
Secondary 
Control 
(1) vs (2) 
(1) vs (3) 
  
(n=7288) 
(n=7377) 
(n=41376) 
P value 
P value 
Demographics 
Age (yrs) 
33.5 
32.8 
33.0 
0.333 
0.358 
Female (%) 
46.9 
46.8 
45.6 
0.942 
0.444 
Race (%) 
<0.001 
<0.001 
Black 
21.1 
25.2 
26.7 
White 
57.7 
51.6 
48.8 
Hispanic 
14.2 
17.4 
17.7 
Other 
7.0 
5.8 
6.9 
 
 
Panel A.2: All Employees – Weighted 
  
(1) 
(2) 
(3) 
(4) 
(5) 
  
Treatment 
Primary 
Control 
Primary + 
Secondary 
Control 
(1) vs (2) 
(1) vs (3) 
  
(n=7288) 
(n=7377) 
(n=41376) 
P value 
P value 
Demographics 
Age (yrs) 
33.0 
32.9 
33.1 
0.867 
0.934 
Female (%) 
45.9 
46.9 
45.8 
0.614 
0.964 
Race (%) 
0.999 
>0.999 
Black 
25.8 
24.4 
25.9 
  
White 
50.0 
52.9 
50.1 
  
Hispanic 
17.3 
16.9 
17.2 
  
Other 
7.0 
5.8 
6.8 
  
  
Notes: Demographic characteristics are plausibly unaffected by the treatment. Data are from the Team 
Member database supplied by BJ's and based on the first entry for an individual during the treatment period. 
Age is defined as of December, 2014 (pre-treatment). Column 1 reports the means for employees in the 
treatment group while columns 2 and 3 report the means for the primary control employees and all control 
employees (primary and secondary), respectively. Treatment status is defined by the first worksite an 
employee appears in during the treatment period. Column 4 reports the p-value for the comparison between the 
employees at treatment worksites and the employees at primary control worksites and column 5 reports the p-
value for the comparison between the employees at treatment worksites and all employees at control worksites. 
In Table A.1, regressions are unweighted and standard errors are clustered by worksite. In Table A.2, all 
regressions are weighted by a weight that balances treatment and control on demographic characteristics and 
standard errors are clustered by worksite.  
 
 

 
31 
Panel B: Personal Health Assessment (PHA) Subsample 
  
(1) 
(2) 
(3) 
  
Treatment 
Primary Control 
(1) vs (2) 
  
(n=1339) 
(n=1336) 
P value 
Demographics 
Age (yrs) 
36.8 
37.9 
0.251 
Female (%) 
56.0 
57.3 
0.621 
Race (%) 
0.999 
Black 
21.4 
18.6 
White 
55.4 
57.5 
Hispanic 
16.7 
18.2 
Other 
6.5 
5.7 
Notes: Employees are included if they answered at least 1 question on the PHA. Demographic characteristics 
are plausibly unaffected by the treatment. Demographics are taken from the Team Member database supplied 
by BJ's and based on the first entry for an individual during the treatment period. Age is defined as of 
December, 2014 (pre-treatment). Column 1 reports the means for employees in the treatment group while 
column 2 reports the means for the primary control employees. Treatment status is defined by the first worksite 
an employee appears in during the treatment period. Column 3 reports the p-value for the comparison between 
the employees at treatment worksites and the employees at primary control worksites. All regressions are 
weighted by a weight that balances treatment and control on demographic characteristics and cluster standard 
errors by worksite.  
 
Panel C: Cigna Insured Subsample 
  
(1) 
(2) 
(3) 
(4) 
(5) 
  
Treatment 
Primary 
Control 
Primary + 
Secondary 
Control 
(1) vs (2) 
(1) vs (3) 
  
(n=1385) 
(n=1354) 
(n=7174) 
P value 
P value 
Demographics 
Age (yrs) 
42.7 
42.1 
42.6 
0.321 
0.836 
Female (%) 
44.4 
43.8 
44.6 
0.768 
0.949 
Race (%) 
0.996 
>0.999 
Black 
16.9 
14.9 
17.7 
  
White 
62.7 
66.6 
61.2 
  
Hispanic 
14.7 
14.9 
15.9 
  
Other 
5.6 
3.7 
5.2 
  
  
Notes: Employees are included if they had at least 1 month of Cigna health insurance coverage. Demographic 
characteristics are plausibly unaffected by the treatment. Demographics are taken from the Team Member 
database supplied by BJ's and based on the first entry for an individual during the treatment period. Age is 
defined as of December, 2014 (pre-treatment). Column 1 reports the means for employees in the treatment 
group while columns 2 and 3 report the means for the primary control employees and all control employees 
(primary and secondary), respectively. Treatment status is defined by the first worksite an employee appears in 
during the treatment period. Column 4 reports the p-value for the comparison between the employees at 
treatment worksites and the employees at primary control worksites and column 5 reports the p-value for the 
comparison between the employees at treatment worksites and all employees at control worksites. All 
regressions are weighted by a weight that balances treatment and control on demographic characteristics and 
cluster standard errors by worksite. 
 

 
32 
Panel D: Stably Employed Subsample 
  
(1) 
(2) 
(3) 
(4) 
(5) 
  
Treatment 
Primary 
Control 
Primary + 
Secondary 
Control 
(1) vs (2) 
(1) vs (3) 
  
(n=2344) 
(n=2378) 
(n=13000) 
P value 
P value 
Demographics 
Age (yrs) 
39.0 
38.1 
38.7 
0.193 
0.623 
Female (%) 
47.4 
47.7 
46.9 
0.905 
0.747 
Race (%) 
0.998 
>0.999 
Black 
20.5 
18.1 
20.3 
 
  
White 
57.9 
60.5 
56.2 
 
  
Hispanic 
16.2 
17.4 
17.7 
 
  
Other 
5.4 
4.0 
5.8 
 
  
Employment 
Worker type (%) 
>0.999 
>0.999 
FT salary 
11.7 
11.8 
12.0 
 
  
FT hourly 
36.2 
37.3 
38.2 
 
  
PT hourly 
52.1 
50.9 
49.9 
 
  
Annual rate ($) 
  
FT salary 
49,304 
47,910 
48,301 
0.178 
0.218 
FT hourly 
24,933 
24,237 
24,773 
0.285 
0.764 
PT hourly 
10,142 
9,944 
10,027 
0.220 
0.430 
Standard Hours Per Week 
>0.999 
>0.999 
FT salary 
40 
40 
40 
 
  
FT hourly 
35.6 
35.6 
35.8 
 
  
PT hourly 
20 
20 
20 
 
  
Job Category 
>0.999 
>0.999 
Sales workers 
38.1 
38.3 
37.8 
 
  
Laborers/Helpers 
18.9 
17.8 
18.4 
 
  
Operatives 
15.7 
15.1 
15.4 
 
  
Service workers 
12.3 
13.1 
12.5 
 
  
First/Mid level officials 
9.2 
9.2 
9.4 
 
  
Admin Support 
3.8 
3.9 
4.2 
 
  
Other 
2.1 
2.6 
2.2 
 
  
Health Insurance 
  
Ever Enrolled in Cigna (2014) 
0.4 
0.4 
0.4 
0.964 
0.244 
Months in Cigna 
11.4 
11.3 
11.4 
0.834 
0.607 
Total medical spending 
5,476 
3,634 
4,659 
0.135 
0.493 
Notes: Employees are included if they were part of the stably employed subsample. Health insurance variables 
are pre-randomization characteristics. Demographics and employment characteristics are taken from the Team 
Member database supplied by BJ's and based on the first entry for an individual during the treatment period. 
Age is defined as of December, 2014 (pre-treatment) and employees who were under 18 are excluded. Column 
1 reports the means for employees in the treatment group while columns 2 and 3 report the means for the 

 
33 
primary control employees and all control employees (primary and secondary), respectively. Treatment status 
is defined by the first worksite an employee appears in during the treatment period. Column 4 reports the p-
value for the comparison between the employees at treatment worksites and the employees at primary control 
worksites and column 5 reports the p-value for the comparison between the employees at treatment worksites 
and all employees at control worksites.  All regressions are weighted by a weight that balances treatment and 
control on demographic characteristics and cluster standard errors by worksite. 
 
 
 

 
34 
Table 9: Balance Between Treatment and Control—Worksite Level 
 
  
(1) 
(2) 
(3) 
 
 
  
Treatment 
Primary Control 
Primary + 
Secondary Control 
(1) vs (2) 
(1) vs (3) 
  
(n=25) 
(n=25) 
(n=135) 
P value 
P value 
Employee Demographics 
 
 
 
 
Age (yrs) 
38.5 
37.6 
38.1 
0.165 
0.458 
Female (%) 
48.3 
46.6 
45.7 
0.348 
0.065 
Race (%) 
 
 
 
 
 
Black 
17.6 
19.4 
20.4 
0.723 
0.463 
White 
65.6 
60.0 
57.2 
0.427 
0.098 
Hispanic 
11.4 
16.1 
16.6 
0.321 
0.150 
Other 
5.5 
4.4 
5.8 
0.295 
0.665 
County-Level Demographics 
 
 
 
 
Age (yrs) 
40.0 
39.8 
39.7 
0.798 
0.725 
Female (%) 
51.1 
51.4 
51.3 
0.183 
0.228 
Race (%) 
 
 
 
 
 
Black 
12.4 
12.8 
13.3 
0.909 
0.725 
White 
75.9 
76.9 
74.7 
0.773 
0.687 
Hispanic 
11.0 
14.3 
13.7 
0.341 
0.170 
Other 
11.7 
10.2 
12.0 
0.396 
0.833 
Notes: Demographic characteristics are plausibly unaffected by the treatment. Employee demographics are 
taken from the Team Member database supplied by BJ's and based on the first entry for an individual during 
the treatment period. Age is defined as of December, 2014 (pre-treatment). ACS demographics are taken from 
the 2015 American Community Survey (ACS) Population Estimates for the county each worksite is located in. 
Worksite-level analyses are obtained by first calculating a weighted average for each worksite (weighted by an 
employee's hours worked during the treatment period). Column 1 reports the means for employees in the 
treatment group while columns 2 and 3 report the means for the primary control employees and all control 
employees (primary and secondary), respectively. Treatment status is defined by the first worksite an 
employee appears in during the treatment period. Column 4 reports the p-value for the comparison between the 
employees at treatment worksites and the employees at primary control worksites and column 5 reports the p-
value for the comparison between the employees at treatment worksites and all employees at control worksites.  
 
 
 

 
35 
Table 10: First Stage Estimates 
 
Panel A: Employee Level—All 
All 
Completed PHA 
Cigna Enrolled 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
Control 
Mean 
Estimated First 
Stage 
Control 
Mean 
Estimated First 
Stage 
Control 
Means 
Estimated First 
Stage 
Completed any 
module (%) 
0.21 
27.53 
2.29 
63.06 
0.91 
57.31 
(1.25) 
(1.98) 
(2.81) 
[0.00] 
[0.00] 
[0.00] 
 
 
 
 
 
 
 
Modules completed 
(#) 
0.01 
1.12 
0.08 
3.45 
0.03 
3.13 
(0.09) 
(0.24) 
(0.26) 
[0.00] 
[0.00] 
[0.00] 
 
 
 
 
 
 
 
3 or more modules 
(%) 
0.12 
15.95 
1.60 
46.74 
0.53 
43.29 
(1.08) 
(2.31) 
(2.74) 
[0.00] 
[0.00] 
[0.00] 
 
 
 
 
 
 
 
Average total 
incentive payment ($) 
0.26 
37.97 
3.20 
126.54 
1.37 
148.83 
 
(3.20) 
 
(9.40) 
 
(11.93) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
 
 
 
 
 
 
 
N 
41,376 
48,664 
1,454 
2,793 
7,174 
8,559 
Note: Control means and first stage estimates of the impact of TREATMENT on alternate definitions of 
PARTICIPATION. All regressions include demographic and employment controls (age, sex, age-sex 
interactions, race/ethnicity, full-time status, paid hourly status, and job category) and cluster standard errors at 
the worksite level. All samples other than the sample with Cigna coverage also include a control for whether or 
not the employee ever had Cigna coverage during the treatment period. Employee-level regressions are 
weighted by a weight that balances treatment and control on demographic characteristics. Standard errors 
shown in parenthesis and p-values in brackets.  
 
 
 

 
36 
Panel B: Employee-level—Stably Employed Sub-sample 
All 
Surveyed 
Cigna 
(7) 
(8) 
(9) 
(10) 
(11) 
(12) 
Control 
Mean 
Estimated First 
Stage 
Control 
Mean 
Estimated First 
Stage 
Control 
Means 
Estimated First 
Stage 
Completed any 
module (%) 
0.57 
49.75 
3.90 
79.22 
0.97 
59.54 
(2.82) 
(2.74) 
(3.24) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
Modules completed 
(#) 
0.02 
2.56 
0.15 
5.41 
0.04 
3.41 
(0.21) 
(0.39) 
(0.29) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
3 or more modules 
(%) 
0.35 
35.04 
2.96 
67.78 
0.61 
45.79 
(2.47) 
(3.39) 
(3.15) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
 
 
 
 
 
 
 
Average total 
incentive payment ($) 
0.75 
97.62 
5.92 
217.91 
1.54 
163.36 
 
(8.45) 
 
(15.74) 
 
(13.80) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
 
 
 
 
 
 
 
N 
13,000 
15,344 
747 
1,396 
5,779 
6,907 
Note: Control means and first stage estimates of the impact of TREATMENT on alternate definitions of 
PARTICIPATION. All regressions include demographic and employment controls (age, sex, age-sex 
interactions, race/ethnicity, full-time status, paid hourly status, and job category) and cluster standard errors at 
the worksite level. All samples other than the sample with Cigna coverage also include a control for whether or 
not the employee ever had Cigna coverage during the treatment period. Employee-level regressions are 
weighted by a weight that balances treatment and control on demographic characteristics. Standard errors 
shown in parentheses and p-values in brackets. 
 
 
 

 
37 
Panel C: Worksite level 
All 
Surveyed 
Cigna 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
Control 
Mean 
Estimated First 
Stage 
Control 
Mean 
Estimated First 
Stage 
Control 
Means 
Estimated First 
Stage 
Completed any 
module (%) 
0.22 
28.19 
3.07 
62.25 
0.91 
57.29 
(1.27) 
(1.89) 
(2.83) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
Modules completed 
(#) 
0.01 
1.17 
0.11 
3.49 
0.03 
3.18 
(0.09) 
(0.22) 
(0.27) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
3 or more modules 
(%) 
0.12 
16.69 
2.27 
46.65 
0.54 
43.68 
(1.12) 
(2.16) 
(2.76) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
Average total 
incentive payment ($) 
0.27 
40.31 
4.40 
129.92 
1.37 
150.85 
 
(3.47) 
 
(8.84) 
 
(12.47) 
 
 
[0.00] 
 
[0.00] 
 
[0.00] 
 
 
 
 
 
 
 
N 
135 
160 
85 
110 
135 
160 
Note: Control means and first stage estimates of the impact of TREATMENT on alternate definitions of 
PARTICIPATION. All regressions include demographic and employment controls (age, sex, age-sex 
interactions, race/ethnicity, full-time status, paid hourly status, and job category) and adjust standard errors for 
heteroscedasticity. All samples other than the sample with Cigna coverage also include a control for the 
percent of workers per worksites every having Cigna coverage during the treatment period. Worksite level 
regressions weighted by worksite size (total hours worked in the worksite). Standard errors shown in 
parentheses and p-values in brackets.  
 
 
 

 
38 
Table 11: Heterogeneity 
 
Absenteeism (%) 
Total medical 
spending ($) 
Regular 
exercise (%) 
Considering losing 
weight (%) 
N 
Control 
Mean 
N 
Control 
Mean 
N 
Control 
Mean 
N 
Control 
Mean 
Gender 
Female 
18886 
2.40 
3182 
5772.21 
813 
49.75 
753 
71.21 
Male 
22490 
2.08 
3992 
4019.28 
627 
59.04 
583 
49.30 
Age 
Below 40 
27495 
2.07 
2964 
2627.73 
744 
54.38 
685 
60.48 
40 and above 
13881 
2.54 
4210 
6323.57 
696 
53.16 
651 
62.91 
Employment type 
 
 
 
 
 
 
 
 
Full-time 
16901 
2.47 
5976 
5162.78 
765 
53.59 
712 
62.41 
Part-time 
24475 
2.06 
1198 
2993.43 
675 
54.01 
624 
60.82 
 
SF-8 physical summary 
score 
SF-8 mental summary 
score 
Non-zero calorie 
Drinks (No.) 
BMI 
Systolic BP 
(mmHg) 
N 
Control 
Mean 
N 
Control 
Mean 
N 
Control 
Mean 
N 
Control 
Mean 
N 
Control 
Mean 
Gender 
Female 
810 
50.87 
810 
51.11 
815 
1.24 
789 
30.36 
785 
121.62 
Male 
617 
52.47 
617 
52.87 
628 
1.53 
614 
29.04 
615 
128.97 
Age 
Below 40 
733 
52.58 
733 
50.73 
743 
1.69 
718 
29.23 
717 
118.43 
40 and above 
694 
50.49 
694 
53.06 
700 
1.02 
685 
30.37 
683 
131.52 
Employment type 
 
 
 
 
 
 
 
 
 
 
Full-time 
761 
51.25 
761 
52.36 
769 
1.20 
767 
30.13 
764 
126.42 
Part-time 
666 
51.91 
666 
51.31 
674 
1.55 
636 
29.37 
636 
122.93 
Note: Table reports the control group sample size in column 1 and control mean in column 2 for each outcome. Control means are weighted by a weight 
that balances treatment and control on demographic characteristics. 
 
 

 
39 
The Impact of Workplace Wellness on Health Care Spending, Health, and Employment 
Outcomes: A Randomized Controlled Trial 
 
 
 
Appendices 
 
 
 
Appendix 1: Description of Phase 2 Modules and Incentives 
 
Appendix 2: Determination of the Stably Employed Subsample 
 
 
 
 
 
 

 
40 
Appendix 1: Description of Phase 1 Modules and Incentives 
 
Module 1 
Take Charge of Your Health Rounds 1 and 2 (2/23/2015-3/27/2015, 4/13/2015-5/15/2015) 
- 
Summary: These two five-week programs were presented as a series of webinars with 
corresponding PowerPoints designed to help employees who participate take their health 
care into their own hands. Topics covered included: 
o how to choose a health plan and primary care physician, 
o what to expect from a routine visit,  
o routine tests and screenings and recommended frequencies,  
o how to get the most from a doctor’s visit,  
o choosing generic medications over the corresponding brand name,  
o staying healthy by eating well, staying active, sleeping enough, and managing 
stress, and  
o primary care vs urgent care vs the emergency room and when to use each. 
- 
Incentive: Employees who completed the webinars and returned the verification form 
received a $25 BJ’s gift card. 
 
Module 2 
Nutrition for a Lifetime (6/1/2015-7/10/2015) 
- 
Summary: This six-week program was presented as a series of webinars with 
corresponding PowerPoints designed to help employees who participate achieve and 
maintain a healthy weight for life through the four pillars of health: nutrition, exercise, 
stress management, and sleep. Topics covered included: 
o the negatives consequences of chronic stress and poor sleep habits and techniques 
to manage stress and improve sleep, 
o good nutrition, including an overview on the different food groups and the 
amounts of each recommended per day, 
o reasons for making exercise a priority and how to get the most out of a workout, 
o foods to limit and foods to increase in a diet, 
o appropriate portion sizing, especially for weight loss and weight maintenance, and 
o choosing the right fats and the importance of fiber. 
- 
Incentive: Employees with Cigna coverage received a $150 Weight Management 
Reimbursement and all other employees received at $50 gift card if they completed 5 out 
of the 6 webinars and returned the verification form. 
 
Modules 3 and 4 
Club Cardio Challenge Rounds 1 and 2 (8/10/2015-9/25/2015, 9/26/2015-11/16/2015) 
- 
Summary: These two seven-week programs were exercise-based. Employees were 
supposed to complete 20 minutes or more of cardiovascular exercise at least 3 days per 
week and track their activity in an exercise log. 
- 
Incentive: Employees who completed 6 of the 7 weeks in either round 1 or round 2 
earned a $25 BJs gift card. Employees who completed 12 out of 14 weeks over both 
rounds were eligible to enter a raffle at their worksite for a Fitbit. Employees with Cigna 
coverage who completed 12 out of 14 weeks received a $150 fitness reimbursement from 
Cigna on top of the raffle entry and gift card. Worksites were also in competition with the 

 
41 
top worksite based on % participation and the top worksite with the highest average 
weekly minutes of exercise reported each receiving a trophy to display in the worksite, 
winner buttons for employee lanyards, and bragging rights. 
 
Module 5 
Maintain Don’t Gain (11/23/2015-12/20/2015) 
- 
Summary: This four-week challenge helped employees track their weight each week and 
offered tips on how to add physical activity to a daily routine and substitutions for 
options with fewer calories when dining out. 
- 
Incentive: Employees who completed at least 3 out of the 4 weeks of weight tracking and 
returned the verification form received a $25 BJ’s gift card. 
 
Module 6 
Power Down the Pressure (1/18/2016-2/19/2016) 
- 
Summary: This four-week program encouraged employees to learn effective methods for 
managing stress by asking them to complete at least one activity from a list of options for 
the week for at least 3 days of the week. Week 1 was called “Unplug” and included 
activities such as refraining from watching TV for a day or having an electronic-free meal 
with family or friends. Week 2 was titled “Boost Your Mood” and included activities like 
doing a random act of kindness, getting 8 hours of sleep, or spending time with a friend. 
Week 3 was “Exercise” and asked employees to take a new exercise class or do a 30-
minute workout/activity outdoors. The final week was called “Relaxation and 
Meditation” and encouraged employees to keep a stress journal, color, and meditate. 
- 
Incentive: Employees who completed all four weeks of the program by completing at 
least 3 days of stress management activities a week and returned the verification form 
received a $25 BJ’s gift card. 
 
Module 7 
Weight Loss Boot Camp (3/14/2016-4/8/2016) 
- 
Summary: This four-week program aimed to teach employees methods for losing weight. 
For each of the four weeks, employees had to complete four activities (eating five or 
more servings of fruits and vegetables, exercising for at least 30 minutes, avoiding 
sweetened beverages, and weighing themselves weekly) a minimum number of days each 
week, from two days the first week up to five days the final week. 
- 
Incentive: Employees who completed all four weeks and return the verification form 
received a $25 BJ’s gift card. 
 
Module 8 
Movin’ in May (5/1/2016-5/31/2016) 
- 
Summary: This four-week program encouraged employees to exercise for at least 30 
minutes 3 days per week and track their exercise. 
- 
Incentive: Employees who completed all four weeks of the challenge and returned the 
verification form were entered to win one of two $250 visa gift cards at their worksite.  
 

 
42 
Module 9 
Healthy and Fit (11/14/2016-12/11/2016) 
- 
Summary: This four-week program encouraged employees to stay healthy and fit through 
the holiday season. Weekly activities included; weight check-in, 10-minute wellness 
coaching sessions, de-stressing (yoga, meditation, planning for the week ahead…etc.), 
“me” time, and a soda free day. Activities that as practiced  multiple times a week 
included; exercising for at least 30 minutes, 15 minutes walking breaks, packing a 
healthy lunch, and practicing gratitude and kindness.  
- 
Incentive: Employees who earned at least 800 points during this program received a 
Fitrax ActivBand.  
 
Modules 10 and 11 
Nutrition for a Lifetime Rounds 1 and 2 (1/23/2017- 4/02/2017) 
- 
Summary: NFL was a 10-week program which required completing at least 4 weeks of 
the program to receive a prize. Employees who completed 8 weeks of the program (4 + 4 
weeks as two NFL rounds) received additional prizes. Each week a new module was 
available and employees were able to start the program until the 6th week. Weekly topics 
included: 
o Initial counseling session to set the goals 
o Healthy diet and nutrition webinars  
o Cardio Challenge (logging miles of exercise every day for at least week) 
o Fiber, fat facts, and good night’s sleep webinars 
o Stress management 
o Healthy dining out  
o At home exercises 
o Final counseling session to continue the goals 
- 
Incentive: Employees who completed 4 weeks out of 10 weeks earned a $25 BJs gift card 
or a Fitrax ActivBand. Employees who completed 8 weeks (conditional on having both 
counseling sessions) receive an additional $25 BJ’s Gift Card. Employees with Cigna 
coverage who completed 8 weeks of the program qualified for the $150 weight 
management reimbursement benefit.  
 
Module 12 
Step Challenge (5/8/2017-6/30/2017 or 7/30/2017 if Cigna eligible) 
- 
Summary: The step challenge was an 8-week team-based program. Each team consisted 
of 4 team members and each member tracked their steps using an ActivBAND, Fitbit or a 
smart phone app. Each employee was required track at least 150,000 steps during the 
module to qualify as a team member. 
- 
Incentive: Employees received both team-based and individual incentives based on their 
performance. At each worksite, 1st place team received a $100 gift card, 2nd place team 
received a $50 gift card, the 3rd place team received a $25 gift card and top 10 
individuals received a $25 gift card which may be additional to the team based incentive. 
Cigna-Eligible employees needed to complete an additional 4 weeks (12 weeks in total) 
and at least 225,000 steps in total to qualify for a $150 fitness reimbursement from Cigna. 
 
 

 
43 
Appendix 2. Determination of the Stably Employed Subsample 
 
We conducted an analysis of the duration of employment (tenure), which informed our definition 
of the pre-specified stably employed subsample. This analysis of tenure is described below.  
 
Figure 1A looks at a number of scenarios where we take samples of treatment (T) and control 
(C) workers who were employed for varying numbers of consecutive weeks starting on 1/1/2014. 
In each scenario, we follow the samples of workers until they reach 1/1/2015 and look at how 
many of them are still employed. To be precise, for each restriction criterion of the number of 
consecutive weeks worked starting 1/1/2014 (X axis), the height of the dark blue bar (C 14) 
depicts the total number of control workers in the sample and the height of the light blue bar (C 
15) depicts the total number of control workers who were still working on 1/1/2015. 
Analogously, the height of the black bar (T 14) represents treatment workers who started in the 
sample on 1/1/2014 and the height of the gray bar (T 15) represents treatment workers who were 
still working on 1/1/2015. Of note, the bars are overlapping for each X (i.e. they are not stacked; 
rather they all originate at 0). The solid and dotted lines merely reflect the percentages of C and 
T employees, respectively, who were still working at BJ’s on 1/1/2015 (i.e. light blue bar divided 
by dark blue bar, gray bar divided by black bar).  
 
 
 
 

 
44 
Thus, for example, the interpretation of the bar when X=17 is as follows. There were about 
10,000 employees in control worksites who were employed at BJ’s on 1/1/2014 and who worked 
through the first 17 weeks of 2014 (dark blue bar). Among these employees, about 79% (or about 
7,900) were still working on 1/1/2015 (light blue bar). The same retention of 79% was found 
among employees in treatment worksites—calculated using the gray (numerator) and black 
(denominator) bars. The bar originating at X=0 represents the case of no sample restrictions (i.e. 
all employees in the data). 
 
Similarly, Figure 1B shows a similar analysis when we extend the definition of retention to 
12/1/2015. This graph contains only control worksite employees, because tenure itself may be 
affected by the wellness program and is an outcome we will examine formally in the analysis. To 
extend the above example of interpretation, of the 10,000 employees in control worksites who 
were employed through the first 17 weeks of 2014, about 64% were still employed on 12/1/2015. 
This decrease is from 79% at the beginning of 2015, implying that 15% of the sample (79% – 
64% = 15%) were “lost” from the sample (e.g. terminated, left BJ’s) during the first 11 months 
of 2015.  
 
 
 
 
Analyzing samples defined with respect to 3/1/2015 (start of the first module): Figure 2 takes a 
different approach to looking at tenure. It looks at retention for samples of employees defined 
based on the number of continuous weeks worked immediately before the wellness treatment 

 
45 
launched (i.e. defined by counting backwards from 3/1/2015). Retention here is still defined as 
appearing in 12/1/2015. As above the figure contains only employees from control worksites. 
 
As an example of interpretation, there were about 13,500 employees in control worksites who 
worked during the 13 continuous weeks (~3 months) before the start of the wellness program 
(counting back from 3/1/2015 -- i.e. Feb '15, Jan '15, and Dec '14). Among these 13,500 
employees, about 10,000 remained actively working on 12/1/2015. This amounts to about a 75% 
retention rate.  
 
 
 
We examined the rate of decline of this sample of employees among control worksites 
throughout the treatment. Figure 3 shows the rate of decline of the above control sample (those 
who worked for the 13 consecutive weeks leading up to the start of the treatment (3/1/2015), and 
illustrates the decline in the number and percent of this sample through 2017. The X axis shows 
the months elapsed since start of the treatment (0 is the end of February 2015, while 34 is the end 
of December 2017). This graph shows a smooth decline in the sample of employees to reach 
51% by the end of December 2017 
 
 
 
 
 

 
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