Illinois Workplace Wellness Study
Phase: NA
Conditions: Employee Health and Well-being
Sponsor: National Bureau of Economic Research, Inc.

Study Protocol and Statistical Analysis Plan 
Illinois Workplace Wellness Study  
NCT03164330 
 
April 22, 2020 
 
Principal Investigators: 
Damon Jones 
University of Chicago 
 
David Molitor 
University of Illinois at Urbana-Champaign 
 
Julian Reif 
University of Illinois at Urbana-Champaign 
 

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Illinois Workplace Wellness Study 
 
Pre-Analysis Plan 
Damon Jones, University of Chicago and NBER 
David Molitor, University of Illinois at Urbana-Champaign and NBER 
Laura Payne, University of Illinois at Urbana-Champaign  
Julian Reif, University of Illinois at Urbana-Champaign 
 
July 11, 2016 
Updated August 7, 2016 
Updated January 11, 2016 
Updated July 10, 2017 
 
1. Introduction 
 
This plan outlines the hypotheses to be tested in the analysis of the impact and 
behavioral response to the introduction of a comprehensive workplace wellness 
program at the University of Illinois at Urbana-Champaign (UIUC). This pre-analysis plan 
has been created prior to the collection of data.  
 
We updated this plan on August 7, 2016. We made three changes. First, we removed a 
treatment group that used a cluster-based assignment. The purpose of that treatment 
group was to identify peer effects, but we decided that an alternative approach for 
estimating peer effects, which is outlined in this updated plan, was preferable. Second, 
we now allow for two different sets of control variables in our regressions. One consists 
of only the strata variables. The second consists of variables identified by LASSO. Third, 
we added additional details about a contingency plan that will be executed in the event 
of low turnout during biometric screening. This updated was submitted prior to the 
beginning of the study’s intervention. 
 
We updated this plan on January 11, 2016. We made following changes: 
 
a. In Section 5.a we added a specification to our selection analysis, where average 
selection is first estimated before we estimate selection separately within each 
treatment group. This simpler specification is nested within our original, more 
flexible specification. At the time of the update, analysis of the data had not yet 
begun, except for one comparison of mean amount paid for health care claims in 
the 18 months prior to the study, among those who completed a screening and 
those who did not. This preliminary statistic was calculated in response to a last-
minute funding opportunity. Importantly, our ultimate test of selection will 
include a richer set of participation stages: screening, HRA, and wellness 
activities. This update to the analysis plan took place prior to any analysis of HRA 
or wellness activity participation.  

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b. In Section 5.a, we added lagged health claims data to the set of potential control 
variables to be chosen within a LASSO framework. This update was made prior to 
any analysis of participation conditional on treatment group. 
c. In Section 5.a, we added a treatment effect regression where treatment groups 
are pooled and compared to the control group. We also added a treatment 
effects regression that compares members of each treatment group to the 
control group, allowing for separate effects. Our original specification only 
compared members of treatment group C to the control group. This update was 
made before any analysis of post-treatment outcomes. 
d. In Section 5.a, we added two new 2SLS regressions. The first features an 
indicator for participating in at least one wellness activity. The second features a 
separate indicator for a fall wellness activity and a spring wellness activity. These 
are now considered in addition to our original specification, which features a 
model with the count of wellness activities as the regressor. This update was 
made before any analysis of post-treatment outcomes. 
e. In Section 5.b, heterogeneous effects models were extended to include the 
above-mentioned additions to our specifications. This update was made before 
any analysis of post-treatment outcomes. 
f. We made a nominal change to section 5.e, where we relabeled data previously 
referred to as “untruthful” to “inconsistent.” This edit did not involve any 
substantive changes to our experimental intervention or methodology. 
 
We updated this plan on July 10, 2017. We made the following changes: 
 
a. In Section 2.b and Section 2.c, we have expanded our follow-up sample to 
include all study participants. Incentives for the follow-up survey were changed 
to a flat $20 gift card for all survey respondents. The incentives for the follow-up 
biometric screening are now randomized between $0 and $125. This update was 
made on the same day as the launch of the follow-up study. 
b. In Section 2.b we have also added an additional year of the study, including Fall 
and Spring wellness courses in 2017-2018 and a second follow-up survey and 
biometric screening in the summer of 2018. This update was done prior to the 
launch of any of these interventions. 
c. In Section 3, our description of the follow-up survey and follow-up biometric 
screening indicate that all study participants will be invited. This update was 
made on the day of the launch of the follow-up study. 
d. In Section 4, Hypothesis C.1 and Hypothesis C.3, follow-up survey variables were 
correctly labeled as “G” instead of “A.” This update has no bearing on the 
experimental design. 
e.  In Section 4, Hypothesis C.2, new survey questions on job satisfaction, 
presenteeism, productivity, and job search were added to the follow-up survey. 
This update was done before any data was collected from the follow-up survey. 

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f. In Section 5.f, we include references for the method of bounding treatment 
effects in the presence of differential attrition. This update was made before any 
bounding calculations were conducted. 
g. In Section 4.c, we added one additional variable, “Retention (C3)”. This update 
was made after receiving HR data from the university that covered the period up 
through May 31, 2017. 
h. We have clarified that, when examining multiple outcomes within a domain, that 
we will be adjusting standard errors using the step-down procedure of Westfall 
and Young (1993). We have also removed statements that we will be employing 
standardized treatment effects when analyzing online survey or HR variables. 
This update was made after receiving HR data from the university that covered 
the period up through May 31, 2017, but before any data was collected from the 
follow-up survey. 
 
We updated this plan on August 5, 2017. We made the following changes: 
 
a. In Section 4, Hypotheses C.1, C.2, and C.3 we added additional outcomes to each 
subgroup, comprised of indices of all subgroup variables. This includes either a 
standardized index (following e.g. Finkelstein et al. (2012) or Liebman and 
Luttmer (2015)) or a subset of the principal components. These changes were 
made before any follow-up survey data was analyzed, and before follow-up 
biometric data had been collected. 
b. In Section 4, Hypothesis C.2, we added an additional survey response regarding 
worker’s perception of the workplace (G62) and measuring hours worked (G45). 
These changes were made before any follow-up survey data was analyzed. 
 
2. Overview of the Study 
 
a. Motivation 
 
Workplace wellness programs have become a $6 billion industry and are widely 
touted as a way to improve employee well-being, reduce health care costs by 
promoting prevention, and increase workplace productivity. Yet, there is little 
rigorous evidence available to support these claims, partly because the voluntary 
nature of these programs means that participants may differ from 
nonparticipants for reasons unrelated to the causal effects of the wellness 
program. We will implement a randomized control trial to identify the effects of 
incentives on wellness program participation, produce causal estimates of the 
effect of wellness programs on health outcomes, determine what kinds of 
employees benefit from wellness programs the most, and test for the presence 
of peer effects in wellness participation. 
 
b. Experimental Design 
 

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Our experiment consists of a baseline survey, followed by random assignment to 
a control or one of three treatment groups, A, B, or C. Individuals assigned to a 
treatment group are offered incentives to complete a biometric screening and 
health risk assessment. Thereafter, members of the treatment group are further 
given incentives to participate in up to two wellness programs, one in each 
semester of the school year. One year later, we will follow up with a subset of 
study participants, again offering a follow-up survey and biometric screening. 
Survey, biometric screening, and health risk assessment data will be combined 
with administrative data from health insurers and the university human 
resources department. 
 
At baseline, employees will be invited via postcard and email to participate in the 
study by completing an online survey. In return for completing the survey, 
participants will be given a $30 Amazon.com gift card. Consent will be obtained 
before the survey is taken. 
 
Following the baseline survey, employees will be assigned to either the control 
group or a treatment group. Members of the control group have no further 
intervention in the first year of the program. Members of the treatment group 
will be invited to participate in the iThrive wellness program consisting of the 
following: 
  
i. First, they are given the opportunity to participate in a biometric 
screening and health risk assessment. The biometric screening is 
scheduled on campus and takes approximately 15 minutes. The biometric 
test will measure: (1) anthropometrics such as height, weight, and waist 
circumference (to assess obesity and overweight status); (2) resting blood 
pressure (to assess hypertension); (3) blood glucose (to assess diabetes 
risk); and (4) total, LDL, and HDL cholesterol levels, total cholesterol ratio, 
and triglycerides (to assess risk of cardiovascular disease). Biometric 
screening is carried out by a third-party vendor, Presence Health. 
 
ii. After completing the biometric screening, the participants are invited to 
complete an online health risk assessment (HRA). The HRA is 
questionnaire designed to identify areas of health improvement, by 
asking a series of questions related to wellness, health status, nutrition, 
healthy activities, desire to improve health, preventative health 
measures. The HRA is also prepopulated with biometric information from 
the above screening. Upon completion, participants are given customized 
feedback on areas of improvement. The HRA is administered by a third-
party vendor, Wellsource. 
 
iii. Upon completion of the HRA, participants are given the option to enroll 
in up to two wellness courses, one in the fall semester and one in the 

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spring semester. Courses are designed by the UI Wellness Center and 
include an Active Living class; self-paced online health challenges in 
physical activity, weight management, and healthy eating; a weight 
management class; a tobacco cessation hotline; a stress management 
class; a Tai Chi class; and a chronic disease management class. 
 
Members of the treatment group will be offered financial incentives for 
completion of the different stages of the iThrive program. Treatment groups A, B 
and C will receive $0, $100, and $200, respectively, upon successfully completing 
both the biometric screening and HRA. Within each treatment group, half of the 
participants will be offered $25 for each wellness course completed, for up to 
two courses. The other half will be offered $75 for completion of each wellness 
course. All incentives will be made known to treatment group members at the 
onset of treatment group assignment. Throughout the first year of the program, 
members of the treatment group will have access to an online portal that 
provides information on treatment group assignment, current progress, accrued 
incentives, scheduling for biometric screening, HRA access, and wellness 
program enrollment. 
 
One year after the launch of the study, we will administer a follow-up survey and 
biometric screening among all study participants. Members of the follow-up 
study will be offered $20 to complete a follow-up survey. We will also randomly 
offer $0 or $125 for completion of a biometric screening at follow-up, with equal 
probability. Follow-up study participants will be made aware of these incentives 
after the first year of the study is complete. Survey incentives will be shared 
prior to the launch of the follow-up survey and screening incentives will be 
shared after the follow-up survey, but before the screening. 
 
The timeline of the study is as follows: 
 
i. 
July 11 – Aug 1, 2016: Baseline Survey is administered online 
ii. 
August 1 – August 7, 2016: Treatment assignment 
iii. 
August 8 – September 9, 2016: Biometric screenings 
iv. 
August 22 – September 23, 2016: HRA is administered online 
v. 
September 26 – December 2, 2016: Fall wellness courses 
vi. 
January – May, 2017: Spring wellness courses 
vii. 
July – August, 2017: Follow-up survey 
viii. 
August – September, 2017: Follow-up biometric screening 
ix. 
September – December, 2017: Fall wellness courses 
x. 
January – May 2018: Spring wellness courses 
xi. 
July – August, 2018: Second follow-up survey 
xii. 
August – September, 2018: Second follow-up biometric screening 
 

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c. Sample Selection and Treatment Assignment 
 
The initial pool for our study includes 12,459  benefits eligible employees at the 
UIUC. Specifically, the set of eligible employees include all University of Illinois 
employees satisfying three criteria as of June 10, 2016: a) physically located on 
the UIUC campus, b) not terminated, and c) eligible for benefits through the 
Illinois Department of Central Management Services. From this set of employees, 
15 were excluded due to their direct involvement with the approval, 
implementation or design of the study—members of the research team, 
members of the IRB review panel involved in the study design, staff directly 
involved in collecting program data, and family members of the research team. 
 
Of the 12,459 employees in the initial pool, the “Core Sample” will consist of all 
employees who respond to a baseline survey. We estimate a response rate of 
approximately 50%, resulting in a Core Sample of 6,000 employees. All 
subsample sizes below will be based on this estimate of a 50% baseline survey 
response rate. We will assign 1,100 employees to treatment group A, 1,100 
employees to treatment group B, and 1,100 employees to treatment group C. 
The remaining 2,700 employees will be assigned to the control group.  
 
In the second year of the study, all study participants will be invited to 
participate in a follow-up survey and biometric screening. 
 
At the time of taking the baseline survey, participants will be informed that they 
may be contacted for follow up treatments, but will not know control or 
treatment group status. After the baseline survey, employees will be assigned to 
a treatment or control group using pseudo-random numbers generated by a 
computer program according to the following steps: 
 
i. 
Employees will be randomly assigned to the control group or treatment 
group A, B or C. Randomization will be stratified by age, gender, annual 
salary, race, and employment class (Faculty, Academic Professional, and 
Civil Service). We will require there to be at least 20 people per strata 
(two for each control and/or treatment group) in each treatment cell. If 
there are fewer than 20 people, then we will aggregate strata as 
necessary. 
ii. 
One year following the original study, all study participants will be 
invited to participate in the follow-up survey and biometric screening. 
Incentives for the follow-up screening will likewise be randomized using 
age, gender, annual salary, race, and employment class strata. 
 
 
3. Data Sources 
 

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a. Baseline Survey (A): Self-reported health, workplace, and demographic 
information will be collected via an online survey with up to 66 questions. Only a 
subset of questions are answered by participants, based on skip logic. 
 
b. Health Insurance Claims Data (B): As a part of consenting to our study, 
participants will grant us access to health insurance claims data. These include 
total costs of services, bill amounts, and diagnosis codes. Health insurance data 
will be collected from 2015 – 2020. Claims data are currently only available for 
employees in two insurance companies, which together comprise 70% of 
employees in the initial pool with health benefits through the University.  
 
c. Human Resources Data (C): Participants will also grant us access to UIUC human 
resources data, including absenteeism, turnover, employment unit, department, 
tenure, salary, age, race, sex, and benefit elections. HR data will be collected 
from 2015 – 2020. 
 
d. Biometric Screening (D): A third-party vendor will measure height, weight, waist 
circumference, resting blood pressure, total cholesterol, total cholesterol ratio, 
HDL cholesterol, LDL cholesterol, triglycerides, glucose levels. In addition, a set of 
questions will be asked prior to the revelation of the results of the biometric 
screening, to measure expectations of weight, height, cholesterol level, blood 
pressure, glucose level, and body mass index. A second biometric screening will 
be administered among a subset of participants during the second year of the 
study. 
 
e. Health Risk Assessment (E): The health risk assessment survey will measure self-
reported wellness, health status, nutrition, healthy activities, desire to improve 
health, preventative health measures. Answers to the adaptive survey are 
combined to create customized indices of health risk. 
 
f. Wellness Course Data (F): We will collect data on enrollment, participation and 
completion of wellness courses. Enrollment is recorded using our online 
registration system. Participation and completion are monitored by wellness 
instructors. Completion is defined as participation in at least 80% of the weeks 
that a course is provided. 
 
g. Follow-Up Survey (G): Similar to the baseline survey, offered to all study 
participants.  
 
h. Follow-Up Biometric Screening (H): Identical to the first biometric screening, 
offered to all study participants. 
 
4. Hypotheses 
 

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Using our combined data, we will identify the effects of incentives on wellness program 
participation, produce causal estimates of the effect of wellness programs on health 
outcomes, determine what kinds of employees are most likely to select into wellness 
programs, and test for the presence of peer effects in wellness participation. See 
Appendix 1 for a glossary of data source references (e.g. A1 is question 1 on our baseline 
survey). Our hypotheses can be summarized in the following 5 groups: 
 
a. Participation Outcomes: As we increase the incentives for completion of the 
biometric screening, HRA, and wellness programs, we expect the level of 
participation to increase. 
 
b. Selection Outcomes: Participants may differ from the average employee, in 
terms of health, health care utilization, and productivity. The average level of 
health among wellness participants compared to all baseline survey participants 
is theoretically ambiguous. Under average advantageous selection, participants 
are healthier, lower cost, and more productive, while under average adverse 
selection, the opposite is true. In addition, the marginal participant may differ 
from the average participant, causing the composition of participants to differ as 
incentives for participation are varied. Under marginal advantageous selection, 
higher incentives draw in healthier, lower cost, and more productive 
participants, while under marginal adverse selection, the opposite is true. 
 
c. Short-Run and Medium-Run Health and Productivity Outcomes: Take-up of 
biometric screening, HRA, and wellness programs may potentially increase 
health, wellbeing, satisfaction, and productivity measures measured one year 
following the intervention. Even though health measures may improve, health 
care utilization may also increase, causing an ambiguous effect on health care 
costs in the short-run. 
 
d. Peer Effects: Having a close workplace friend or colleague who also participates 
in a biometric screening, HRA, or wellness activity may increase one’s own 
participation in wellness programs. 
 
e. Heterogeneous Treatment Effects: The baseline characteristics of participants 
may cause differential impacts of the incentives on participation and health 
outcomes. In addition, the extent to which biometric screening information 
deviates from elicited expectations may cause differential impacts on wellness 
participation and health outcomes. 
 
Hypothesis Group A: Incentives for participation in the biometric screening, HRA, and 
wellness programs will have a positive effect on participation, increasing in the size of 
incentive. 
 

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The following indicators will comprise the family of outcomes in this domain: 
  
Participation Outcomes: 
 
i. Scheduled a biometric screening (D10) 
ii. Completed a biometric screening (D11) 
iii. Completed HRA (E1) 
iv. Enrolled in a fall wellness course (F1) 
v. Completed fall wellness course (F2) 
vi. Enrolled in a spring wellness course (F3) 
vii. Complete spring wellness course (F4) 
 
 Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
Hypothesis Group B: In the case of average advantageous (adverse) selection, 
employees who select into wellness programs have higher (lower) baseline health, lower 
(higher) health costs, and higher (lower) productivity. Consequently, differences in 
baseline health and productivity between participants and non-participants will be 
positive (negative), while the analogous difference in baseline health costs will be 
(negative) positive. In addition, under marginal advantageous (adverse) selection, the 
differences in baseline health and productivity between participants and non-
participants will be negatively (positively) correlated with incentive size, while the 
analogous difference in baseline health costs will be positively (negatively) correlated 
with incentive size. 
 
Hypothesis B.1: Under average advantageous (adverse) selection, the difference in 
baseline health measures between participants and non-participants will be positive 
(negative). Under marginal advantageous (adverse) selection, the difference in baseline 
health measures between participants and non-participants will be negatively 
(positively) correlated with incentive size. 
 
The following variables will be used to measure baseline health: 
 
Baseline Survey: 
 
i. Had at least one previous health screening (Any of A1-A5, A8-A9=”yes”) 
ii. Physically active (A11=”More active”) 
iii. Trying to be more active (A12=”Yes” or A13=”Yes”) 
iv. Smoking status: 
1. Current smoker: A16=”Yes” and A17=”Every day” or “Some days” 
2. Former smoker: A16=”Yes” and A17”=”Not at all” 
v. Other tobacco use (A22 and A23 != “Not at all”) 
vi. Drinking: 

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1. Drinker: A24!=0 
2. Heavy drinker: A25>=4 if female, A25>=5 if male 
vii. Has at least one chronic health condition (A27) 
viii. Self-reported health (A28) 
1. Health is excellent or very good 
2. Health is not poor 
ix. Problems with physical activities or pain (A29-A31) 
1. A29=”Somewhat”, “Quite a lot”, “Could not do physical activities” 
or A30 = “Some”, “Quite a lot”, “Could not do daily work” or 
A31=”Mild”, ”Moderate”, “Severe”, “Very severe” 
x. Energy (A32=”An extraordinary amount”, “Quite a lot”) 
xi. Emotional health (A33=”Moderately”, ”Quite a lot”, “Extremely”) 
xii. Overweight status (A39=”Overweight” or A40=”Very overweight”) 
xiii. Bad health status (A40=”High”, =”Very high” or A41=”High” or “Very 
high” or A42=”High” or “Very high”) 
xiv. Sedentary job (A53=”None at all” or =”Some, but less than 1 hour”) 
 
These baseline health variables can be divided up into two different domains: primary 
outcomes of interest, and secondary outcomes of interest. The first domain (“primary 
outcomes”) will include the following variables: current smoker, has at least one chronic 
health condition, both self-reported health questions, overweight status, and problems 
with physical activities or pain. Any baseline health variables not in the first domain will 
be in the second domain (“secondary outcomes”).  Whenever we analyze these 
variables together, we will adjust the standard errors using the step-down procedure of 
Westfall and Young (1993). 
 
Hypothesis B.2: Under average advantageous (adverse) selection, the difference in 
baseline health costs and utilization between participants and non-participants will be 
negative (positive). Under marginal advantageous (adverse) selection, the difference in 
baseline health costs and utilization between participants and non-participants will be 
negatively (positively) correlated with incentive size. 
 
The following variables will be used to measure baseline health costs and utilization: 
 
Baseline Survey: 
 
i. Drug utilization (A34>0 or A35>0) 
ii. Physician or ER utilization (A36!=”None”) 
iii. Hospital utilization (A37!=”None”) 
 
Insurance Claims: 
 
iv. Number of claims (B1) 
v. Number of bed days (B2) 

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vi. Allowed amount for claims (B4) 
vii. Amount paid by plan for claim (B5) 
 
 Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
Hypothesis B.3: Under average advantageous (adverse) selection, the difference in 
baseline productivity between participants and non-participants will be positive 
(negative). Under marginal advantageous (adverse) selection, the difference in baseline 
productivity between participants and non-participants will be positively (negatively) 
correlated with incentive size. 
 
The following variables will be used to measure baseline productivity: 
 
Baseline Survey: 
 
i. Days missed (A45) 
 
Human Resources Data: 
 
i. Salary (C1) 
ii. Absenteeism rate (C2) 
 
 Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
Hypothesis B.4: Under marginal advantageous (adverse) selection, average health 
measures, as measured by biometric screening results, will be negatively (positively) 
correlated with incentive size, conditional on participating in the biometric screening. 
 
The following variables will be used to measure health conditional on biometric 
screening: 
 
Biometric Screening Data: 
 
i. BMI (D1) 
ii. Waist circumference (D2) 
iii. Resting blood pressure (D3) 
iv. Total cholesterol (D4) 
v. Total cholesterol ratio (D5) 
vi. HDL cholesterol (D6) 
vii. LDL cholesterol (D7) 
viii. Triglycerides (D8) 
ix. Glucose levels (D9) 

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A standardized health measure impact will be obtained, as described in our 
methodology section below. In addition, we will report each outcome separately. 
Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
Hypothesis B.5: Under marginal advantageous (adverse) selection, average health 
measures, as measured by HRA screening results, will be negatively (positively) 
correlated with incentive size, conditional on participating in the HRA. 
 
The following variables will be used to measure health conditional on HRA participation: 
 
HRA Data: 
 
i. Health risk score (E1) 
ii. Nutrition risk score (E2) 
iii. Healthy activities risk score (E4) 
iv. Readiness to change score (E5) 
v. Biometrics score (E6) 
 
A standardized health measure impact will be obtained, as described in our 
methodology section below. In addition, we will report each outcome separately. 
Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
Hypothesis Group C: Participation in biometric screening, HRA, and wellness activities 
will result in increases in health status and productivity one year later. Participation will 
have an ambiguous effect on health care costs and utilization. 
 
Hypothesis C.1: Follow-up health status will be positively affected by wellness 
treatment, as measured by the reduced form relationship between follow-up health and 
treatment incentive sizes for biometric screening/HRA and/or wellness activity 
participation. The same will hold for the IV estimate of the effect of participation in 
biometric screening/HRA and/or wellness activity participation on follow-up health, 
instrumented for by incentive size. 
 
The following variables will be used to measure follow-up health: 
 
Follow-Up Survey: 
 
i. Had at least one previous health screening (Any of G1-G5, G8-G9=”yes”) 
ii. Physically active (G11=”More active”) 
iii. Trying to be more active (G12=”Yes” or A13=”Yes”) 
iv. Smoking status: 

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1. Current smoker: G16=”Yes” and G17=”Every day” or “Some days” 
2. Former smoker: G16=”Yes” and G17”=”Not at all” 
v. Other tobacco use (G22 and G23 != “Not at all”) 
vi. Drinking: 
1. Drinker: G24!=0 
2. Heavy drinker: G25>=4 if female, G25>=5 if male 
vii. Has at least one chronic health condition (G27) 
viii. Self-reported health (G28) 
1. Health is excellent or very good 
2. Health is not poor 
ix. Problems with physical activities or pain (G29-G31) 
1. G29=”Somewhat”, “Quite a lot”, “Could not do physical activities” 
or G30 = “Some”, “Quite a lot”, “Could not do daily work” or 
G31=”Mild”, ”Moderate”, “Severe”, “Very severe” 
x. Energy (G32=”An extraordinary amount”, “Quite a lot”) 
xi. Emotional health (G33=”Moderately”, ”Quite a lot”, “Extremely”) 
xii. Overweight status (G39=”Overweight” or G40=”Very overweight”) 
xiii. Bad health status (G40=”High”, ”Very high” or G41=”High” or “Very high” 
or G42=”High” or “Very high”) 
xiv. Sedentary job (G53=”None at all”, ”Some, but less than 1 hour”) 
xv. Indices 
1. Standardized index of above variables 
2. Principal component(s) of above variables 
 
Follow-Up Biometric Screening: 
 
xvi. BMI (H1) 
xvii. Waist circumference (H2) 
xviii. Resting blood pressure (H3) 
xix. Total cholesterol (H4) 
xx. Total cholesterol ratio (H5) 
xxi. HDL cholesterol (H6) 
xxii. LDL cholesterol (H7) 
xxiii. Triglycerides (H8) 
xxiv. Glucose levels (H9) 
xxv. Deviation in expected biometrics from actual biometrics (D10) 
xxvi. Indices 
1. Standardized index of above variables 
2. Principal component(s) of above variables 
 
 Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 

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Hypothesis C.2: Follow-up productivity will be positively affected by wellness treatment, 
as measured by the reduced form relationship between follow-up productivity and 
treatment incentive sizes for biometric screening/HRA and/or wellness activity 
participation. The same will hold for the IV estimate of the effect of participation in 
biometric screening/HRA and/or wellness activity participation on follow-up 
productivity, instrumented for by incentive size. 
 
The following variables will be used to measure follow-up productivity and job 
satisfaction: 
 
Follow-Up Survey: 
 
i. Days missed (G46) 
ii. Hours Worked (G45) 
iii. Job satisfaction  
1. G53=”Very satisfied” 
2. G53=”Very satisfied”, ”Somewhat satisfied” 
3. G54=”Yes” 
4.  
iv. Presenteeism 
1. Stanford Presenteeim Scale (SPS-6), using G47-G52 
v. Productivity 
1. G56=”Very productive”,”Somewhat productive” 
2. G56=”Yes” 
vi. Job Search 
1. G64=”Very likely” 
2. G64=”Very likely”,”Somewhat likely” 
vii. Workplace Perceptions 
1. G62 = “Very high priority”,”Some priority” 
viii. Indices 
1. Standardized index of above variables 
2. Principal component(s) of above variables 
 
Human Resources Data: 
 
ix. Salary (C1) 
x. Absenteeism rate (C2) 
xi. Retention (C3) 
 
Finally, the HR data allow us to additionally analyze the dynamics of these outcomes 
using an event-study framework. 
 
Hypothesis C.3: Follow-up health costs will be decreased by wellness treatment, as 
measured by the reduced form relationship follow-up health costs and between 

15 
 
treatment incentive sizes for biometric screening/HRA and wellness activity 
participation. The same will hold for the IV estimate of the effect of participation in 
biometric screening/HRA and wellness activity participation, instrumented by incentive 
size. 
 
The following variables will be used to measure follow-up health costs and utilization: 
 
Follow-Up Survey: 
 
i. Drug utilization (G34>0 or G35>0) 
ii. Physician or ER utilization (G36!=”None”) 
iii. Hospital utilization (G37!=”None”) 
iv. Indices 
1. Standardized index of above variables 
2. Principal component(s) of above variables 
 
Insurance Claims: 
 
v. Number of claims (B1) 
vi. Number of bed days (B2) 
vii. Allowed amount for claims (B4) 
viii. Amount paid by plan for claim (B5) 
ix. Indices 
1. Standardized index of above variables 
2. Principal component(s) of above variables 
 
 
Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). Finally, the insurance claims 
data allow us to additionally analyze the dynamics of these outcomes using an event-
study framework. 
 
Hypothesis Group D: The share of one’s peers who are induced to participate in the 
biometric screening/HRA and/or wellness programs increases one’s own likelihood to 
participate in these activities. 
 
The following indicators will comprise the family of outcomes in this domain: 
  
Participation Outcomes: 
 
i. Scheduled a biometric screening (D10) 
ii. Completed a biometric screening (D11) 
iii. Completed HRA (E1) 
iv. Enrolled in a fall wellness course (F1) 

16 
 
v. Completed fall wellness course (F2) 
vi. Enrolled in a spring wellness course (F3) 
vii. Complete spring wellness course (F4) 
 
A standardized participation outcome impact will be obtained, as described in our 
methodology section below. In addition, we will report each outcome separately. 
Whenever we analyze these variables together, we will adjust the standard errors using 
the step-down procedure of Westfall and Young (1993). 
 
In addition, peer networks will be identified based on self-reported close friends at 
work, via the baseline survey: 
 
Baseline Survey: 
 
i. Close co-workers (A43) 
 
Hypothesis Group E: The effect of treatment incentives on participation and short-run 
outcomes will vary based on baseline characteristics. 
 
We will look for heterogeneity in participation with respect to the following: 
 
i. Age (C3 ≥ 50) 
ii. Sex (C4) 
iii. Race (C5, white v. Non-white) 
iv. Ethnicity (C6, Hispanic v. Non-Hispanic) 
v. Baseline health status  
1. All categories from baseline survey measuring health (these are 
outlined above in Hypothesis B.1) 
vi. Annual Salary (C1, above median) 
vii. Employment Class (C7) 
viii. Deviation in expected biometrics from actual biometrics (D10) 
 
5. Estimation Methodology 
a. Treatment Effects 
 
For our main participation outcomes, we will restrict analysis to members of 
treatment groups A, B, and C. First, we will pool groups B and C, and compare 
them to group A, as follows: 
 
!" = $ + &',)*",',) + +,-*",,- + Γ/" + 0" 
 
where !" is the participation outcome, *",',) is an indicator for membership in 
treatment group B or C, *",,- is an indicator for receiving the $75 incentive for 
wellness program completion, and /" is a vector of baseline control variables. 

17 
 
We will consider two sets of variables to include in /". The first set will include 
only variables that were used for stratification (i.e., employee class, gender, age, 
annual salary, and race). The second set will include only variables that are good 
predictors of the outcome variable. We will identify those control variables by 
estimating a LASSO regression with five-fold cross validation. We will include in 
that LASSO regression all available variables from our baseline survey, HR data, 
and lagged health claims data. We will present one table that shows how results 
differ using these two different sets of control variables. We will then choose 
one set as our preferred specification for the remaining tables.  
 
Next, we will separately estimate the effect of treatments B and C, relative to 
treatment A, as follows: 
 
!" = $ + &'*",' + &)*",) + +,-*",,- + Γ/" + 0" 
 
where now *",' and *",) are indicators for membership in treatment groups B 
and C, respectively. 
 
Our next set of estimates will test for adverse or advantageous selection, again 
among members of treatment groups A, B, and C. First, we will estimate the 
following regression: 
 
/" = $ + 2!" + 0", 
 
where /" is a baseline variable measured from either the baseline survey, 
administrative data, or health insurance data. To test for average advantageous 
or adverse selection, we will test the sign of 2. We will then estimate the 
following regressions: 
 
/" = $3*",3 + $'*",' + $)*",) + 23*",3×!" + 2'*",'×!" + 2)*",)×!" + 0" 
 
where *",3 is an indicator for membership in treatment group A. To further test 
for average advantageous or adverse selection, we will test the signs of the δ 
coefficients. To test for marginal advantageous or adverse selection, we will 
compare the magnitudes of the δ coefficients across treatment groups. In the 
case that a variable is top-coded, we will use a censored regression model to 
account for the structure of the limited dependent variable. 
 
Our second set of selection estimates are estimated conditional on either 
completing the biometric screening or the HRA. In particular, we will estimate 
the following: 
 
/" = $ + 6'*",' + 6)*",) + 0" 
 

18 
 
In this case, we can only test for marginal advantageous or adverse selection by 
comparing the magnitudes of the π coefficients. In the case that a variable is top-
coded, we will use a censored regression model to account for the structure of 
the limited dependent variable. 
 
In the above specifications, we will extend the analysis to test for selection with 
respect to the wellness program incentives, by adding an interaction term 
*,-×!". 
 
Our next set of treatment effects measure the short-run effect of wellness 
participation on health, health utilization, and productivity. We will compare 
members of the control group to all members of the treatment group in the 
following regression: 
 
8" = $ + 9*" + Γ/" + 0", 
where 8" is one of our above-mentioned outcomes of interest and *" is an 
indicator variable for membership in any of the treatment groups. Next, we will 
estimate a more flexible specification as follows: 
 
8" = $ + 93*",3 + 9'*",' + 9)*",) + 9:-*",:- + 9,-*",,- + Γ/" + 0". 
 
Finally, we will use a specification that allows for relatively unrestricted 
interactions between the screening/HRA and wellness activity incentives: 
 
8" = $ + 93,:-*",3,:- + 93,,-*",3,,- + 9',:-*",',:- + 9',,-*",',,- + 9),:-*",),:-
+ 9),,-*",),,- + Γ/" + 0", 
 
where *",<,= is an indicator for membership in treatment group > ∈@, A, B  with 
a wellness activity incentive of C ∈25,75 . 
 
In addition, we will estimate local average treatment effects of HRA/biometric 
completion and wellness program completion using a two-stage least squares 
regression (2SLS): 
 
8" = $ + +GH3/JKLMMNOP@/QRSTTU" + +VMWWNMXXYTZZUT[[" + Γ/" + 0" 
 
where OP@/QRSTTU" is an indicator for completion of the screening and 
YTZZUT[[" is an indicator for completion of at least one wellness activity. We will 
instrument for these regressors using indicators for the 6 treatment groups. 
Alternatively, we will estimate the following specification:  
 
8" = $ + +GH3/JKLMMNOP@/QRSTTU" + +VMWWNMXX,\]WWYTZZUT[[",\]WW
+ +VMWWNMXX,J^L"N_YTZZUT[[",J^L"N_ + Γ/" + 0", 

19 
 
  
where YTZZUT[[",\]WW and YTZZUT[[",J^L"N_ are indicators for finishing either a 
fall or spring wellness activity. Finally, we will estimate a model with a linear 
effect of wellness activities: 
 
8" = $ + +GH3/JKLMMNOP@/QRSTTU" + +VMWWNMXX#YTZZUT[[" + Γ/" + 0", 
 
where #YTZZUT[[" is a count of the number of wellness activities completed. 
 
In certain cases, i.e. when looking at productivity measures from HR data and 
health cost and utilization measures from insurance claims data, we will have 
longitudinal data, which allows us to look at the dynamics of outcomes over 
time. In this case, we will also conduct event-study analysis using the following 
specification: 
8"a = $ +
b<
=
<cd=
e"a
< + f" + ga + ΓX"a + 0"a 
where e"a
<  is a dummy variable indicating treatment assignment has initiated > 
periods ago, and f" and ga are individual and time fixed effects, respectively. 
 
In order to test for peer effects, we will estimate how participation in an 
individual’s peer group effects the individual’s own participation outcome: 
 
!" = $ + i!d" + Γ/" + 0" 
 
where !d" is the leave-out mean of participation in individual i’s peer group. We 
will address the endogeneity of !d" by instrumenting for it using variables 
indicating which treatment group, if any, the peers were assigned to. In this 
analysis, standard errors will be clustered by peer group. 
 
In addition to the previous specification, we calculate exact p-values for null 
hypotheses regarding treatment spillovers using the method of Athey, Eckles, 
and Imbens (2015). 
 
b. Heterogeneous Effects 
 
In order to identify heterogeneous effects in participation, we will interact 
baseline characteristics with our treatment effect estimates. We will estimate 
the following equation: 
 
!" = $ + &',)*",',) + +,-*",,- + b',)*",',)×/" + b,-*",,-×/" + Γ/" + 0" 
 
We will also test for heterogeneity under the more flexible specification: 

20 
 
 
!"
=
$ + &'*",' + &)*",) + +,-*",,- + b'*",'×/"
+b)*",)×/" + b,-*",,-×/" + Γ/" + 0"
 
 
Our estimates of heterogeneity in the case of short-run health and productivity 
effects will be obtained as follows in reduced form: 
 
 8" = $ + 9*" + g*"×/" + Γ/" + 0". 
 
We will also estimate the following, more flexible specifications: 
 
8" = $ + 93*",3 + 9'*",' + 9)*",) + 9:-*",:- + 9,-*",,- + g3*",3×/"
+ g'*",'×/" + g)*",)×/" + g:-*",:-×/" + g,-*",,-×/" + Γ/"
+ 0" 
 
and 
 
8" = $ + 93:-*",3,:- + 93,,-*",3,,- + 9',:-*",',:- + 9',,-*",',,- + 9),:-*",),:-
+ 9),,-*",),,- + g3:-*",3,:-×/" + g3,-*",3,,-×/" + g':-*",',:-×/"
+ g',-*",',,-×/" + g):-*",),:-×/" + g),-*",),,-×/" + Γ/" + 0". 
 
Likewise, we will estimate heterogeneous 2SLS estimates: 
 
8"
=
$ + +GH3/JKLMMNOP@/QRSTTU" + +VMWWNMXXYTZZUT[[" + Γ/"
+bGH3/JKLMMNOP@/QRSTTU"×/" + bVMWWNMXXYTZZUT[["×/" + 0"
 
 
where YTZZUT[[" will either be an indicator for taking at least one wellness 
activity, a flexible set of indicators for either a fall or spring wellness activity, or 
the count of completed wellness activities.  
 
One of our interaction terms, the deviation between expected biometrics 
screening results and actual screening results, is measured conditional on taking 
the biometric screening, and, furthermore, could be influenced by treatment 
assignment. We will first test for significant differences in this measure across 
treatment groups, and will only include it if the measure does not systematically 
vary with treatment group. 
 
c. Multiple Outcomes, Standardized Treatment Effects, Multiple Inference 
 
Many of our domains of interest, such as “baseline health”, can be measured by 
several different variables. When reporting estimates for individual outcomes 
within a domain, we will report both regular p-values and p-values adjusted to 
account for multiple outcomes (Westfall and Young 1993). We will follow the 

21 
 
methodology employed by Finkelstein et al. (2012) in all cases. 
 
d. Missing Data and Questions with limited Variation 
 
If the outcome variable is missing for a substantial fraction of the sample, we will 
not conduct regression analysis using that outcome. If that happens, we will note 
it in our write-up. Also, we will not report results if the outcome variable is 
identical for 90% or more of the sample. If a control variable is missing for a 
significant fraction of the population, we will omit it from our regressions. For 
control variables that are missing for a minor share of respondents, we will use a 
dummy variable to indicate a missing value, and retain the control variable. 
Decisions to include or exclude control variables on this basis will be done prior 
to treatment assignment and observation of outcome variables.  
 
e. Inconsistent responses 
 
The baseline survey asks respondents to report their age and their gender. These 
data are also available from our administrative HR dataset, so we will be able to 
assess whether or not these data are consistent with answers on the baseline 
survey. We will define a respondent as having “inconsistent data” if they report a 
different age or gender than what is recorded in HR data. We will report as a 
robustness test results omitting those participants with inconsistent answers.  If 
omitting these individuals significantly affects our results, then we will remove 
them from our preferred specification. 
 
f. Follow-Up Survey Attrition 
 
Our follow-up analysis can only be conducted among employees that remain 
employed at the university and furthermore, among employees that agree to 
complete the follow-up survey and biometric screening. We will first examine If 
treatment assignment is related to attrition at these various levels at a 
statistically significant level. If not, then we will conduct analysis ignoring 
attrition. However, if we do find a significant difference in attrition, we will 
implement bounds on our treatment effect estimates, using “Lee bounds” in the 
case of differential employment turnover, and using sharper bounds to address 
differential survey and/or biometric screening response, which rely on variation 
in follow-up incentives to calculate the bounds. We will implement methods 
outlined in Behaghel et al. (2009) and Behaghel et al. (2015). 
 
6. Contingency Plans 
a. Low Baseline Survey Response 
 
If we receive less than 2,000 responses in the 7 days following the launch of the 
baseline survey, then we will increase the Amazon.com gift card amount from 

22 
 
$30 to $50 to encourage a larger response rate. If that happens, we will also 
award an extra $20 to participants who already responded in the first 7 days. 
 
b. Low Baseline Screening Rates 
 
If rates of participation in the biometric screening are slightly lower than 
projected, we may assign individuals from the control group to treatment groups 
A, B, and C in order to increase statistical power. The number of additional 
individuals invited will depend on the budget and a projected response rate 
based on early participation in Groups A, B, and C. This decision will be made 
prior to observation of any outcome variables from the screening rate portion of 
the experiment. 
 
If rates of participation in the biometric screening are much lower than 
projected, we may add another treatment arm, Group D, comprised of 
individuals from the control group. Group D will receive the same treatment as 
Groups A, B, and C, except that the screening incentive will be $400. The number 
of individuals invited to Group D will depend on the budget and a projected 
response rate based on early participation in Groups A, B, and C. Determination 
of whether to add another treatment arm will be done prior to observation of 
any outcome variables from the screening rate portion of the experiment.