Comparative Effectiveness of Financial Incentives and Smartphone-based Nudges to Reduce Cellphone Use While Driving Among Auto Insurance Policy Holders With Usage Based Insurance Policies
Phase: NA
Conditions: Automobile Accident
Sponsor: University of Pennsylvania

Protocol 
 
This trial protocol has been provided by the authors to give readers additional information 
about their work. 
 
This supplement contains the following items: 
1. Original protocol including the statistical analysis plan  
2. Final statistical anaylsis plan and summary of changes 
 
 
 

Comparative Effectiveness of Financial Incentives and Nudges to Reduce Cellphone Use 
While Driving Among UBI Auto Policy Holders  
Acronym: Progressive-UPenn-TrueMotion Investigation into Telephone Distraction of 
Operators With Nudges (PUT IT DOWN) 
 
CLINICAL TRIAL PROTOCOL 
 
Version 2.0 
7/24/2019 
 
ClinialTrials.Gov Identifier: 
NCT03833219 
 
Principal Investigator: 
M Kit Delgado, MD, MS 
Assistant Professor of Emergency Medicine, University of Pennsylvania 
 
Co-Investigators:  
University of Pennsylvania: 
Ian Barnett, PhD 
Scott D. Halpern, MD, PhD 
Jessica Hemmons, MS 
Dina Abdel-Rahman, BS 
Catherine McDonald, PhD, RN 
Roy Rosin, MBA 
Dylan Small, PhD 
Kevin Volpp, MD, PhD 
Douglas Wiebe, PhD 
Flaura K. Winston, MD, PhD 
Aria Xiong, MS 
 
 
Progressive Insurance: 
William Everett 
Kristen Gaba 
 
Truemotion, Inc. 
Rafi Feingold 
Ben Kotrc 
Emma Radford 
 
Contact Address:  
Blockley Hall, 936  
423 Guardian Drive  
Philadelphia, PA 19104 
 
Phone:  
215-746-8256 
 
Email:  
Kit.Delgado@uphs.upenn.edu 
 
Funded By:  
Federal Highway Administration (FHWA), US Department of Transportation 
 
 

Table of Contents  
A. Original Protocol 
1. Background and Significance 
1.1. 
Scope of Problem 
1.2. 
Existing Interventions 
1.3. 
Behavioral Drivers of Vehicle Safety 
1.4. 
Rationale for behavioral and financial interventions 
2. TRIAL DESIGN 
2.1. 
Overview 
2.2. 
Setting 
2.3. 
Inclusion Criteria 
2.4. 
Participant Identification 
2.5. 
Consent Procedures 
2.6. 
Contact Information 
2.7. 
Baseline 
2.8. 
Baseline Assessment 
2.9. 
Allocation to Treatment 
2.10. Behavioral Interventions 
2.10.1. 
Intervention Arms 
2.10.2. 
Exit Survey 
2.11. Assessment Controls 
2.12. Outcomes Assessment 
2.13. Compensation 
2.14. Regulatory 
2.15. Contingencies and Participant Withdrawal 
2.16. Study Flow Diagram 
3. OUTCOMES 
3.1. 
Definitions 
3.1.1. 
Primary Outcome 
3.1.2. 
Secondary Outcome 1 
3.1.3. 
Secondary Outcome 2 
3.1.4. 
Secondary Outcome 3 
4. SAMPLE SIZE CALCULTION AND STATISTICAL ANALYSIS PLAN 
4.1. 
Analytic Methods 
4.2. 
Sample Size, Power, and Minimum Detectable Differences 
4.3. 
Subgroup Analyses and Effect Modification 
5. DATA COLLECTION AND MANAGEMENT 
5.1. 
Data Collection Process 
5.2. 
Variables 
5.3. 
Data Quality and Validity 
5.4. 
Data Storage and Security 
6. HUMAN SUBJECTS 
6.1. 
Risks to Human Subjects 
6.2. 
Adequacy of Protection Against Risk 
6.2.1. 
Recruitment and Informed Consent 
6.2.2. 
Protection against Risk 
6.2.3. 
Potential Benefits to Individual Participants 
7. MONITORING 
7.1. 
Institutional Review Board (IRB) 
7.2. 
Data Safety and Monitoring 
8. TIMELINE AND ENROLLMENT  
8.1. 
Timeline 
8.2. 
Screening and Enrollment 

9. 
FUNDING 
10. PUBLICATIONS 
11. DATA SHARING 
12. TASKS AND RESPONSIBILITIES 
12.1. 
Principal Investigator 
12.2. 
Co-Investigators 
13. REFERENCES 
 
B. Final Statistical Analysis Plan 
1. STATISTICAL ANALYSIS PLAN 
 
1.1. Analytic Methods 
1.2. Sample Size, Power, and Minimum Detectable Differences 
1.3. Subgroup Analyses, Effect Modification, and Mediation Analysis 
1.4. Sensitivity Analyses 
1.5. Post-Intervention Period Analysis 
C. Summary of Changes 
 
Appendices:  
I: Trial Variable Table 
II: Intake Survey 
III: Exit Survey 
 
 

TRIAL OVERVIEW Title  
Comparative Effectiveness of Financial Incentives and 
Nudges to Reduce Cellphone Use While Driving Among 
UBI Auto Policy Holders (PUT-IT-DOWN Study) 
Clinical Trials Number  
NCT03833219 
Sources of monetary or material support  
FHWA  
Study Sites  
National cohort of Progressive Snapshot Mobile auto 
insurance policy holders 
 
Conditions studied  
Distracted driving  
 
Interventions  
Push notification reminders, social comparison feedback, 
and financial incentives 
 
Comparator  
Self-monitoring (control) 
 
Inclusion criteria  
  
Progressive Snapshot Users with policy activated within 
recruitment period 
 
Progressive Snapshot policy holder in one of the 
following states and regions: MI, PA, TX, FL, TN, OR, 
GA, NE, OK, OH, MT, MO, NV, CT, WI, MD, KY, MN, 
NH, NJ, AZ, ME, LA, SC, CO, MS, IN, IA, AL, ND, UT, 
RI, WV, WY, IL, AR, DE, KS, SD, NM, VT, ID, and the 
District of Columbia 
 
Has an email address 
 
Exclusion criteria  
1. Progressive Snapshot Mobile App not updated to 
enable push notifications 
2. Customer’s residential address is in a state in which 
phone use while driving is factored into insurance rating 
3. Customer’s Snapshot Mobile App does not collect trip 
data with all sensors active 
4. Customer in Snapshot program for < 30 days or more 
> 70 days 
 
Study type  
Interventional  
Phase 1: Baseline – 30-70 days (usual care monitoring in 
Snapshot program) 
• Phase 2: Intervention - 50 days – delivered to 
customers who agree to participate 
• Phase 3: Post-intervention - remaining time until 
Snapshot rating complete (~30-60 days). 
 
Randomization Arms 
1. Control (monitor only) 
2  Social comparison feedback 
3. End of rating period incentive  
4. End of rating period incentive + social comparison  
feedback 
5. Weekly loss-framed incentive + social comparison 
feedback 

6. Larger weekly loss-framed incentive + social 
comparison feedback 
 
Target sample size  
3000 Participants  
500 participants per study arm. 
 
Primary outcome  
Percent of trip time engaged in active handheld phone 
use (also measured as seconds of handheld phone use 
while driving per hour of driving) 
 
 
1. Background and Significance  
1.1 Scope of Problem 
The proportion of U.S. drivers witnessed visibly manipulating hand-held devices while driving has 
doubled since 2010 (NHTSA 2014), despite the fact that 97% of Americans know this is dangerous (AT&T 
2012) and texting while driving is illegal in all but four states. Current policies to curb cellphone use while 
driving have had limited impact because they do not address the underlying impulsivity (Hayashi, 2015) 
that causes drivers to continue to engage in this behavior despite knowing it is against their best interests 
(Delgado 2016).  
1.2 Existing Interventions 
In November 2016, NHTSA released guidelines recommending the use of operating systems, 
aftermarket applications, and devices to enable a “driver mode” on smartphones to limit use while driving 
(Department of Transportation 2016). Akin to the federally mandated use of an “airplane mode” intended to 
limit cellular transmissions during air travel, a driver mode can be implemented on cellphones automatically 
limiting handheld cellphone use over certain speed thresholds consistent with driving based on native 
phone sensors or Bluetooth pairing with in-vehicle devices. These settings and applications have 
customizable configurations that can be programmed to activate when the car is moving, including locking 
the phone screen, silencing notifications, blocking incoming calls and text messages, and sending 
automated responses to incoming text messages. Research on these applications supports the efficacy of 
blocking technology on significantly reducing phone calls and text messaging while driving compared to 
control groups (Creaser et al. 2015; Ebel et al. 2015; Funkhouser and Sayer 2013). Third-party 
smartphone applications that enable a driver mode are also available for download (“Using Technology” 
2014), but adoption and use have been limited. Though reasons for limited adoption have not been 
examined in the peer-review literature, media reports suggest that this is due to limited consumer 
acceptability (Richtel 2016). A recent study examining the adoption of similar smartphone apps to monitor 
and provide feedback on driving behavior among young drivers showed that perceived gains (e.g., financial 
incentives) and social norms are the biggest motivating factors in adoption of these applications. June 5, 
2017, Apple (Cupertino, CA) announced a “do not disturb while driving mode” that became available as an 
iPhone operating system feature in Fall 2017 that can be turned on to lock the phone screen and silence 
and send automated responses to incoming text messages while driving (Sinder 2017). 
Applications and settings aimed at limiting cellphone use while driving will only be effective from a 
population health standpoint if widely adopted and sustainably used. A promising approach to increased 
adoption of these applications and settings to sustainably change behavior is to increase perceived gains 
of these actions by providing financial incentives (Kervick et al. 2015; Musicant and Lotan 2016). This 
could be brought to scale through usage-based auto insurance programs that use mobile telematic 
smartphone apps to provide insurance discounts based on monitored driving and phone use behavior 
(Delgado et al. 2016). The effectiveness of financial incentive strategies in usage-based insurance 
programs could be enhanced by leveraging insights from behavioral economics, as has been effective for 

health behavior change (Delgado et al. 2016). This includes increasing the frequency and salience     of 
incentives, to be more averse to losses than to gains. (Asch and Rosin 2016; Loewenstein et al. 2007, 
2013). Despite the promise of redesigning usage based insurance programs with insights from behavioral 
economics, such strategies have not been tested previously.   
 
1.3 Behavioral Drivers of Vehicle Safety 
Conventional behavior change models assume individuals form behavioral change intentions based 
on the rational assessment of the costs and benefits and consistently act in their own interest. By contrast, 
behavioral economics applies psychological insights to understand the many natural decisions people 
make that routinely deviate from their best interests. Through dozens of studies, we have demonstrated 
how a variety of common decision errors such as status quo bias, present bias (tendency to overweight 
immediate costs and benefits over future), loss aversion, regret aversion, and social norms, can be used to 
develop effective, high impact interventions for health behavior change. Our work has led to the 
development of a new health plan (Humana Simplicity), the enrollment process for automatic medication 
refills at CVS Health, financial incentives and smoking cessation programs among employees at General 
Electric and CVS. Further translating our deep expertise in behavioral economics to reducing cellphone 
use while driving could lead to a transformational paradigm shift in the approach to behavior change for 
this emerging cause of injury and risky driving in general. 
1.4 Rationale for financial incentive and social comparison interventions 
We have designed the interventions to be tested in our field experiment with several elements 
based on behavioral economic theory (Lowenstein, Volpp 2007), including: (1) promoting restraint from 
using a cellphone while driving by providing a substitute reinforcer in the form of incentives; (2) delivering 
incentives on a weekly basis to offset a heightened tendency to favor immediate gratification over delayed 
benefits among those who engage in texting while driving; (3) providing feedback via smartphones 
regarding accrued losses from non-adherence to maximize regret aversion; (4) framing reward-based 
incentives as losses to augment motivation through loss aversion; leveraging; and (5) social network theory 
and evidence that behavior change programs may be more effective when individuals are compared to 
those similar to them. 
 
 
2. TRIAL DESIGN 
2.1 Overview 
We propose to conduct a randomized, control trial evaluating the comparative effectiveness of 
social comparison and financial incentive interventions with customers of Progressive’s mobile 
phone usage-based insurance policy.  A total of 3,000 adult participants will be enrolled with 500 
participants per arm. We expect an attrition rate of 17% based on historical customer data.  
2.2 Setting 
The study will be conducted by Progressive Insurance and their mobile technology vendor 
TrueMotion. Progressive will be responsible for recruiting participants from their newly enrolled 
group of Snapshot customers. TrueMotion will be responsible for conducting study procedures 
including interventions, data collection, and participant payments. Penn researchers will be 
responsible for study design and data analysis. 
2.3 Inclusion Criteria 
Inclusion Criteria: 
1. Progressive Snapshot Users with policy activated within recruitment period 
 

2. Progressive Snapshot policy holder in one of the following states and regions: MI, PA, TX, FL, 
TN, OR, GA, NE, OK, OH, MT, MO, NV, CT, WI, MD, KY, MN, NH, NJ, AZ, ME, LA, SC, CO, MS, 
IN, IA, AL, ND, UT, RI, WV, WY, IL, AR, DE, KS, SD, NM, VT, ID, and the District of Columbia 
 
3. Has an email address 
 
 
Exclusion Criteria: 
1. Progressive Snapshot Mobile App not updated to enable push notifications 
 
2. Customer’s residential address is in a state in which phone use while driving is factored into 
insurance rating 
 
3. Customer’s Snapshot Mobile App does not collect trip data with all sensors active 
 
4. Customer in Snapshot program for < 30 days or more > 70 days 
 
2.4 Participant Identification 
Progressive will designate a cohort of customers eligible, based on inclusion and exclusion criteria, 
to invite to participate based on being in the Snapshot program on Day 30 to 70 at the date of 
study invitation.  
2.5. Consent Procedures 
Progressive Snapshot users will be sent an email from Progressive about participating in research. 
Those interested will follow a link that takes them to a website hosted by TrueMotion with information about 
participating in the study. Those that wish to participate will opt-in to enroll online through the website 
hosted by TrueMotion and complete an intake survey.  
2.6. Contact Information 
 
The research team and technology team at the University of Pennsylvania and TrueMotion (TMO), 
respectively, will not have access to participant information. Progressive will send invites with a Globally 
Unique Identifier (GUID) attached to the enrollment link. Progressive will send a list of GUIDs to TMO who 
will use these GUIDs to identify participants. TrueMotion will pair those GUIDs with participant emails that 
are given by the participant during the enrollment process. When participants enroll in the study, on the 
website hosted by TrueMotion, they will have to provide their e-mail address to receive their compensation. 
TrueMotion will use the email addresses provided by participants to compensate those that complete study 
surveys or are in a treatment arm that receives monetary incentives. Participants that have any issues or 
concerns during the study will be prompted to contact the study team via email at 
drivingstudy@gotruemotion.com.  
2.7. Baseline 
All new Progressive Snapshot customers will be monitored for 30-70 days during the baseline period. 
Progressive will designate a cohort of customers to invite into the study who will be on day 30-70 of their 
Snapshot monitoring period on the date of invitation. Study participants will be those that opt-in after the 
baseline period.  
2.8. Baseline Assessment 
Baseline Data  

For Snapshot customers that opt-in to the study, Progressive will extract demographic data on the 
participant’s age, marital status, gender, occupation, highest educational level, residential ZIP code, and 
urban, suburban, rural classification of the residential ZIP code (Kolko 2015). 
Survey 
After participants opt-in to the study, they will be prompted to complete an intake survey hosted by 
Truemotion. This will collect data on their vehicle characteristics (e.g. Bluetooth connectivity, GPS 
navigation), driving history (e.g. length of licensure), self-reported phone use while driving, perceptions of 
level phone use relative to others like them, use of settings to limit distracted driving (e.g. Do Not Disturb 
While Driving), and general behavioral characteristics (e.g. 5-trial delay discounting scale).  
2.9. Allocation to Treatment 
Progressive Snapshot users that opt-in to participate will be added to a study list tracked by TrueMotion.  
3,000 participants have enrolled into the study, they will complete an intake survey, collecting information 
about driving history, demographics, and behavioral characteristics. Simple individual-level randomization 
will be used. TrueMotion will use a random number generator to randomize each user that opts in to 1 of 
the 6 arms: (Arm 1) control, (Arm 2) social comparison feedback, (Arm 3) end of rating period incentive, 
(Arm 4) end of rating period incentive and social comparison feedback (Arm 5) weekly loss framed 
incentive and social comparison feedback, (Arm 6) larger weekly loss framed incentive and social 
comparison feedback. Intervention will be delivered by TrueMotion through the Snapshot mobile app to the 
study participants. Additionally, iPhone users in all arms will get push notifications prompting them to 
activate the Do Not Disturb While Driving setting on iPhones.   
2.10. Behavioral Interventions 
2.10.1. Intervention Arms 
Intervention 
Arm 
Notifications 
Incentive 
Timing  
Size 
Driver Mode 
Nudge (all arms, 
iPhone users 
only) 
3 times over 3 weeks: Prompt to enable Do Not 
Disturb While Driving.  Linked to website with 
instructions and frequently asked questions: 
www.besafir.org/do-not-disturb.   
 
Sample messages: 
Arms 1-2: “Reduce annoying notifications when 
driving (while keeping the urgent ones!). Enable 
Do Not Disturb While Driving. Tap here to learn 
how!” 
 
Arms 3-6: “Increase your chance to earn $ and 
reduce annoying notifications when driving (while 
keeping the urgent ones!). Enable Do Not Disturb 
While Driving. Tap here to learn how!” 
 
 
N/A 
N/A 
1. Control 
(monitor only) 
Continue in monitoring only mode 
 
N/A 
N/A 
2. Social 
comparison 
feedback 
Initial push notification: Told they will be 
compared to drivers like them each week and 
that they will receive up to 3 stars.   
 
N/A 
N/A 

 
Weekly push notification: 
a. Compare phone use % relative to 
Progressive Snapshot customers like them 
based (age, sex, marital status, 
urban/suburban/rural residence). Told can 
receive up to three stars.   
 
1 star (if <50th percentile) 
 
2 stars (if > 50th percentile, <90th 
percentile) 
 
3 stars if > 90th percentile (top performer) 
 
Sample message: 
“⭐⭐ out of 3 stars. Almost there! You're better 
than at least half of our drivers at staying off your 
phone.  A little less swiping, typing, and holding 
the phone and you'll be one of our best drivers! ” 
 
 
3. End of rating 
period incentive  
Initial notification:  
a. Financial Incentive: Tell them they have a 
chance to win $50 in 50 days for keeping 
handheld phone use to a minimum.  
 
If <50th percentile), then paid $0 
 
If > 50th percentile, <90th percentile), then 
paid $25 
 
If > 90th percentile (top performer), then 
paid $50 
 
Redeemed 
at end of 
rating 
period 
Max: 
$50 
($1/day) 
4. End of rating 
period incentive 
+ social 
comparison  
feedback 
Initial notifications:  
a. Social comparison feedback: Tell them they 
will be compared to drivers like them each 
week and that they will receive up to 3 
stars.   
 
b. Financial Incentive: Tell them they have a 
chance to win $50 in 50 days for keeping 
handheld phone use to a minimum.  
 
If get 1 star (<50th percentile), then paid 
$0 
 
If get 2 stars (> 50th percentile, <90th 
percentile), then paid $25 
 
If get 3 stars > 90th percentile [top 
performer]), then paid $50 
 
Weekly push notifications: 
a. Social Comparison - Compare to distribution 
for Progressive driver cohort (age, geographic 
area) 
 
1 star (if <50th percentile) 
 
2 stars (if > 50th percentile, <90th 
percentile) 
 
3 stars if > 90th percentile (top performer) 
Redeemed 
at end of 
rating 
period 
Max: 
$50 
($1/day) 

 
e.g. “⭐ out of 3 stars. Not bad, but more than half 
of our drivers used their phone less than you this 
week. Less swiping, typing, and holding your 
phone and you can move up the ranks!” 
  
 
b.  Financial Incentive: Reminder regarding the 
potential to earn incentive at end of intervention 
period. 
 
e.g. “Remember, you need to use your phone 
less than half our other drivers to earn $ at the 
end of the study! Safety pays!” 
 
c. Post- trip 0 seconds distraction 
notification (throttled): Each week, 
participant will receive a gain-framed 
message after the first driving trip with 0 
seconds of phone use. 
 
e.g. “Hey Now! You didn't swipe and type your 
phone while driving. Awesome! Keep up the 
great work to stay safe and be one our best 
drivers!” 
 
 
 
5. Weekly loss-
framed incentive 
+ social 
comparison 
feedback 
Initial push notifications:  
a. Social comparison feedback: Tell them they 
will be compared to drivers like them each 
week and that they will receive up to 3 
stars.   
 
b. Financial incentive: Tell participants they have 
$1/day per coming to them over the next 50 days 
and that they will be paid each week.  Told that 
they need to not use their phone while driving to 
keep from losing any money.  
 
Weekly push notifications:  
a. Social Comparison - Compare to distribution 
for Progressive driver cohort (age, geographic 
area) 
1 star (if <50th percentile) 
2 stars (if > 50th percentile, <90th percentile) 
3 stars if > 90th percentile (top performer) 
 
 
Weekly push notifications: 
b. Social Comparison - Compare to distribution 
for Progressive driver cohort (age, geographic 
area) 
Amount 
not lost for 
week 
delivered 
weekly 
 
 
Max: 
$50 
($1/day) 

1 star (if <50th percentile) 
2 stars (if > 50th percentile, <90th percentile) 
3 stars if > 90th percentile (top performer) 
 
e.g. “⭐ out of 3 stars. Not bad, but more than half 
of our drivers used their phone less than you this 
week. Less swiping, typing, and holding your 
phone and you can move up the ranks!” 
 
b. Financial incentive: Amount of incentive 
received this week + total amount of money lost 
during study = [MONEYLOST]. 
 
 
If get 1 star (if <50th percentile), then paid 
$0 
 
If get 2 stars (if > 50th percentile, <90th 
percentile), then get paid $3.58 
 
If get 3 stars (if > 90th percentile [top 
performer]), then get paid $7.15 
 
e.g. “You missed out on your weekly payment 
this week and a total of $[MONEYLOST] so far 
this study. No worries, fresh start! Use your 
phone less to get ⭐⭐ and $ next week! Safety 
pays!” 
 
 
c. Post- trip 0 seconds distraction notification 
(throttled): Each week, participant will receive a 
gain-framed message after the first driving trip 
with 0 seconds of phone use. 
e.g. “Hey Now! You didn't swipe and type your 
phone while driving. Awesome! Keep up the 
great work to stay safe and be one our best 
drivers!” 
 
 
6. Larger weekly 
loss-framed 
incentive + 
social 
comparison 
feedback 
Initial push notifications:  
a. Social comparison feedback: Tell them they 
will be compared to drivers like them each 
week and that they will receive up to 3 
stars.   
 
b. Financial incentive: Tell participants they have 
$2/day per coming to them over the next 50 
days and that they will be paid each week.  
Told that they need to not use their phone 
while driving to keep from losing any money.  
 
Weekly push notifications:  
a. Social Comparison - Compare to distribution 
for Progressive driver cohort (age, geographic 
area) 
Amount 
not lost for 
week 
delivered 
weekly 
Max: 
$100 
($2/day) 

 
1 star (if <50th percentile) 
 
2 stars (if > 50th percentile, <90th 
percentile) 
 
3 stars if > 90th percentile (top performer) 
 
 
e.g. “⭐ out of 3 stars. Not bad, but more than half 
of our drivers used their phone less than you this 
week. Less swiping, typing, and holding your 
phone and you can move up the ranks!” 
 
b. Financial incentive: Amount of incentive 
received this week + total amount of money lost 
during study = [MONEYLOST]. 
 
If get 1 star (if <50th percentile), then paid 
$0 
 
If get 2 stars (if > 50th percentile, <90th 
percentile), then get paid $7.15 
 
If get 3 stars (if > 90th percentile [top 
performer]), then get paid $14.29 
 
e.g. “You missed out on your weekly payment 
this week and a total of $[MONEYLOST] so far 
this study. No worries, fresh start! Use your 
phone less to get ⭐⭐ and $ next week! Safety 
pays!” 
 
 
c. Post- trip 0 seconds distraction notification 
(throttled): Each week, participant will receive a 
gain-framed message after the first driving trip 
with 0 seconds of phone use. 
e.g. “Hey Now! You didn't swipe and type your 
phone while driving. Awesome! Keep up the 
great work to stay safe and be one our best 
drivers!” 
 
 
2.10.2 Exit Survey 
Using Study ID#, Progressive will send an email to study participants with a link to an exit survey hosted by 
TrueMotion approximately 3-7 days before the end of the intervention period. This survey will elicit 
feedback from participants about their experience in the study and how the intervention impacted their 
behavior surrounding phone use while driving. 
2.11. Assessment Controls 
Participants allocated to the control arm that have iPhones will receive push notifications prompting them to 
enable Do Not Disturb While Driving once a week for 3 weeks, without receiving any feedback or goal 
support for the intervention period, 50 days.  
2.12. Outcomes Assessment and Blinding 

The primary outcome will be seconds of active handheld, non-call use per hour of driving. Research 
associates and investigators will be blinded to the allocation until outcome evaluation. It is not possible to 
blind subjects given the nature of the intervention. We do not expect any scenarios where emergency 
unblinding will be necessary. 
2.13. Compensation 
Participants will be compensated $10 for completing the intake survey and $10 for completing the End of 
Study Survey. Arms 3-6 will receive compensation based on their driving scores. Participants will be 
scored based on historical demographic data that has been split into 3 buckets: 1) low performers (<50%); 
2) average performs 50-89%; 3) top performers >90%. 
2.14. Regulatory 
This study is IRB exempt. The Penn research team will not receive any PHI or identifiable information and 
all trial procedures and participant payments will be conducted and dispersed by TrueMotion. The trial is 
minimal risk to participants, given that the only possible risk will be from potential breach of confidentiality. 
TrueMotion will not have identifiable information of participants other than email address for communication 
and study payments. Regarding user driving data, Progressive will send a global unique identifier (GUID) 
to TrueMotion. TrueMotion will pair those GUIDs with those that have enrolled into the study. This way, 
TrueMotion will not have name/demographic data of participants connected with their Progressive 
Snapshot driving data. TrueMotion will send the list of enrolled GUIDS to Progressive. Only Progressive 
will have demographic information regarding which of their participants have enrolled into the study. 
Participants will voluntary provide their email address to TrueMotion to receive compensation.  
The study has been registered on clinicaltrials.gov under NCT03833219 
2.15. Contingencies and Participant Withdrawal 
If a participant withdraws from the study, further communication will be stopped. Data collected prior to 
withdrawal will be maintained but additional data will not be collected.  

2.16. Study Flow Diagram 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3. OUTCOMES 
3.1. Definitions 
3.1.1. Primary Outcome 
Seconds of active handheld phone use per hour of driving.  This is a composite outcomes that 
measures the proportion of total trip time in which the customer is engaged in handheld phone call use or 
non-call handheld use (e.g. texting, swiping, and typing) as measured by the Snapshot mobile application.  
Several studies (e.g. Klauer, NEJM, 2014) have demonstrated the association between handheld phone 
use, such reaching for phone, typing, swiping, dialing, and increased crash risk. This outcome is also 
known as the active phone use percentage. Passive phone use (e.g. phone is streaming GPS navigation 
directions or music without any typing, swiping, and handheld holding of the phone) is not included in this 
outcome.  Customers are able to view their active phone use percentage for each trip in the Snapshot 
Mobile app.  Trips are only counted if the Snapshot Mobile app predicts the customer was a driver for the 
trip based on the phone sensor data. In the Snapshot Mobile program, customers can open the Snapshot 
app and reclassify trips from driver trips to passenger trips and vice versa for up to 5 days.  This outcome 
is currently not used to calculate future auto-insurance rates and discounts among eligible study 
participants.   
 
Baseline 
Period from 
start of 
Snapshot 
policy 
(30-60) 
Days) 
Randomization 
Arm 1: Control (monitor only) 
 
Arm 2: Social comparison feedback 
Arm 3: End of rating period incentive 
(max $50) 
Arm 4: End of rating period incentive + 
social comparison feedback (max $50)  
Arm 5: Weekly loss-framed incentive + 
social comparison feedback (max $50) 
Arm 6: Larger weekly loss-framed 
incentive + social comparison feedback 
(max $100) 
Eligibility:  
New 
Progressive 
Snapshot Policy 
Holders  
During intervention period iPhone users in all arms will 
receive notifications to enable the Do Not Disturb While 
Driving feature 
Intervention Period (50 Days) 
Follow to 
end of 
rating 
period. 

3.1.2. Secondary Outcomes - Effectiveness 
Seconds of active handheld call use per hour of driving.  We will analyze the components of the 
primary outcome separately.   
Seconds of active handheld non-call use per hour of driving.  We will analyze the components of the 
primary outcome separately.   
Hard breaking events per 100 miles.  This is measured by the Snapshot Mobile app, reported for each 
trip, and used to calculate future auto-insurance rates and discounters.   
Fast acceleration events per 100 miles.  This is measured by the Snapshot Mobile app, reported for 
each trip, and used to calculate future auto-insurance rates and discounts.   
 
3.1.3. Secondary Outcome 2 – Cost Effectiveness 
Incremental Cost-Effectiveness Ratio. We will measure the incremental cost-effectiveness of the 
intervention arms relative to the control arm and to each other.  This will be done by calculating incremental 
cost-effectiveness ratios as follows: 
[(Incentive Costs in Intervention Arm) – (Incentive Costs in Comparison Arm)] / [(Seconds of active 
handheld use / hour of driving in Intervention Arm) – (Seconds of active handheld use / hour of driving in 
Comparison Arm)] 
 
3.1.4. Secondary Outcome 3 - Acceptability 
Acceptability of interventions will be measured using a mixed-methods approach: 
Snapshot Mobile app notifications disabled.  Given the eligible participants will have notifications 
enabled, if a customer disables notifications, this could indicate a lack of acceptability of push notification 
interventions.   
Net promoter score. In the exit survey, participants will be asked “How likely is it that you would 
recommend the intervention you received to a friend or colleague?”  on a 0 to 10 scale. 
Responses to open-ended questions in exit survey.  Participants will be asked the following two open-
ended questions: 1) “Please tell us what you liked about participating in this study;” 2) “Please tell us 
what didn’t like about participating in this study and how we should improve the experience in the future.”  
These qualitative responses will be coded for content related to intervention acceptability and emerging 
themes will be identified by randomization arm.   
 
4. STATISTICAL ANALYSIS PLAN 
4.1. Analytic Methods 
The primary outcome is seconds of active handheld phone use per hour of driving, which can also be 
expressed as active phone use %.  We will use logistic regression as our primary analytic model to assess 
effectiveness between and among the 5 interventions. Specifically, we will evaluate these 10 predefined 
contrasts: 
1. Social Comparison Feedback versus Control (monitor only) 
2. End of Rating Period Incentive versus Control (monitor only) 

3. End of Rating period Incentive + Social Comparison Feedback versus Control (monitor only) 
4. Weekly Loss-Framed Incentive + Social Comparison Feedback versus Control (monitor only) 
5. Larger Weekly Loss-Framed Incentive + Social Comparison Feedback versus Control (monitor 
only) 
6. End of Rating Period Incentive versus Social Comparison Feedback 
7. End of Rating Period Incentive + Social Comparison Feedback versus Social Comparison 
Feedback 
8. End of Rating Period Incentive + Social Comparison Feedback versus End of Rating Period 
Incentive 
9. Weekly Loss-Framed Incentive + Social Comparison Feedback versus End of Rating Period 
Incentive + Social Comparison Feedback 
10. Larger Weekly Loss-Framed Incentive + Social Comparison Feedback versus Weekly Loss-Framed 
Incentive + Social Comparison Feedback 
Although longitudinal models such as generalized estimating equations might be considered in light of the 
repeated measures among individual participants, logistic regression is preferred in this case because the 
primary outcome collapses to a number between 0 and 1. The model will be adjusted for: 1) length of time 
in the Snaphshot program program during the Baseline period; 2) and observed primary outcome during 
the Baseline period.   
Due to the multiple pre-planned contrasts, we will adjust for multiple comparisons using the Holm method.  
First, the 10 contrasts will be ranked (lowest to highest) based on the raw p-value from the logistic 
regression. For a contrast to be significant using the Holm threshold, the raw p-value must be below the 
threshold for each row calculated as (0.05 / [n remaining constrasts]).  Correspondingly, for clarity of 
presentation, adjusted p values will be reported by multiplying the raw p value by the number of remaining 
contrasts. For example, the smallest p-value is multiplied by 10 to obtain the adjusted p-value.  
4.2. Sample Size, Power, and Minimum Detectable Differences 
To detect a clinically meaningful reduction of 60 seconds/hour of driving (i.e. an absolute reduction in 
phone use % of 1.67%), the minimum sample size is 2,406 (401 per arm). 
With a target sample size of 3,000 and power of 0.80, we should be able to detect a difference between 
treatment arms of 52 seconds/hour of driving (i.e. an absolute reduction in phone use % of 1.44%).  We 
would have a power of 0.94 to detect a difference of 60 seconds/hour of driving.   
These estimates should be conservative as adding the rich set of baseline covariates (e.g. demographics, 
Apple car play, etc) to the model should increase the precision of the model estimates.   
 
4.3. Subgroup Analyses and Effect Modification 
For the 10 pre-defined contrasts, we will also assess the effect modification of having an iPhone versus 
Android phone.  This will enable us to estimate the effectiveness of push notifications to enable automated 
activation of Do Not Disturb While Driving. These notifications only get sent to iPhones. 
 
5. DATA COLLECTION AND MANAGEMENT 
5.1. Data Collection Process 
All study data, including survey data and driving data will be the collected by TrueMotion and demographic 
data will be collected by Progressive. All baseline and outcome variables will be obtained prospectively 

from elective surveys or through the Snapshot App maintained by TrueMotion. Penn researchers will not 
collect any study data and will receive de-identified data to analyze outcomes. 
5.2. Variables 
Measure library is included in the Appendix  
5.3. Data Quality and Validity 
Data quality and validity will be optimized by using a detailed data dictionary which will be distributed to 
TrueMotion. Data quality will be monitored by the TrueMotion development and tech team.  
5.4 Transfer of Data 
Study data, including survey data and driving data will be de-identified (email address removed) and 
transferred to the UPenn research via an encrypted .csv file uploaded to a secure, cloud based data 
storage system hosted on PMACs servers. The file will be password protected and the password will be 
provided to the research team via telephone. 
Demographic data from Progressive will be transferred in the same manner.  
Demographic data will be matched with study data using a study ID # assigned to each participant. 
5.5. Data Storage and Security 
Any datasets and computer files are referred to by study ID. The study ID is also used on all analytical files. 
The Penn Medicine Academic Computing Services (PMACS) will store all data securely sent to the Penn 
research team. The data center is housed in Information Systems and Computing at 3401 Walnut Street. 
All data are stored in a single relational database, allowing researchers to correct mistakes. The same 
unique study ID is used to link these outside data to the participants. All data for this project will be stored 
on the secure/firewalled servers of the PMACS Data Center, in data files that will be protected by multiple 
password layers. These data servers are maintained in a guarded facility behind several locked doors, with 
very limited physical access rights. They are also cyber-protected by extensive firewalls and multiple layers 
of communication encryption. Electronic access rights are carefully controlled by UPenn system managers.  
This multi-layer system of data security, identical to the system protecting the University of Pennsylvania 
Health Systems medical records, greatly minimizes the risk of loss of privacy. 
 
6. HUMAN SUBJECTS 
6.1. Risks to Human Subjects 
This project proposes minimal risk. There is a risk of breach of confidentiality for participants receiving 
study information from TrueMotion. For the Penn research team project is primarily a data assessment of 
an existing dataset, and all information used was gathered previously by Progressive and TrueMotion and 
is de-identified.  
6.2. Adequacy of Protection Against Risk 
Participants are Progressive Insurance Snapshot® customers that have the Snapshot® app downloaded 
onto their mobile devices. Customers have agreed to have data sharing and location services turned on. 
These users have previously agreed to Snapshot® terms of service 
(https://www.progressive.com/support/legal/snapshot-app-terms-conditions/?theme=neutral) and privacy 
statement (https://www.progressive.com/support/legal/snapshot-privacy-statement/). Progressive will 
assign a Research ID to each Study Participant. Progressive and TrueMotion will not disclose to the 

University the key for the association of Study Participant to Research ID. Data will be transferred between 
research partners using encryption and password protection, as described in section 5.4. 
The University of Pennsylvania research team will not have access or hold any PHI for any of these users. 
All user names have been de-identified. Prior to sharing the data with us TrueMotion will remove identifiers 
(email address).  
6.2.1. Recruitment and Informed Consent 
Progressive will designate a cohort of customers to invite to study on Day 30 to 70 of their Snapshot Mobile 
enrollment. This will be the “Eligible Cohort.” 
Progressive will send an email to a subset of its customers who use the App with an invitation to participate 
in the Research by providing their express consent after the baseline period. The email will contain a link to 
a TrueMotion landing page specific to the Research and will contain a GUID. Once such a Progressive 
customer consents to participate, that customer will be considered a Study Participant for purposes of this 
study, until such time as a Study Participant elects to opt-out of participation. Progressive will assign the 
Study ID to each Study Participant. TrueMotion will provide Progressive with the GUID for each Study 
Participant and Progressive will then provide to TrueMotion the Study ID associated with such GUID.  
Those interested will follow a link that takes them to a website hosted by TrueMotion with information about 
participating in the study. Those that wish to participate will opt-in to enroll online through the website 
hosted by TrueMotion. 
6.2.2. Protection against Risk 
In order to protect study participants form potential risks related to the loss of confidentiality and due to any 
discomfort that they may experience in answering any questions, the following steps will be taken: 1) 
Participants will be told that they can withdraw from the study at any time by contacting us. 2) All 
information provided by the participant will be referenced to a subject ID#. The participant’s ID# can only 
be connected to their identifiable information only through GUID mapping file held only by Progressive. The 
Penn research team will have no access to PHI.  All data and files will be entered on computers protected 
by passwords and stored in a locked office. 3) All research staff will be trained in the importance of 
maintaining confidentiality and will undergo annual mandatory training of human subjects’ research. 4) All 
participant data will be presented in aggregate and no individuals will be identified individually.  
 
6.2.3. Potential Benefits to Individual Participants 
This information could allow the research team and Progressive to gain insight about effective usage 
based insurance (UBI) program strategies for reducing distraction from phone use while driving. 
 
7. MONITORING 
7.1. Institutional Review Board (IRB) 
This study has been approved as an IRB exempt protocol. 
7.2. Data Safety and Monitoring 
The limited data collected by TrueMotion through the Snapshot app and will be de-identified and manually 
uploaded by members of the TrueMotion team through secure .CSV files. Data will not be analyzed in real 
time. It will be securely stored on the study investigator's computers on a Penn Medicine secure network 
server using password protection and encryption. All data received will be de-identified and will remain de-
identified. All data obtained in the study will be used exclusively for the purposes of the proposed research. 

Users of this platform have been assigned a Study ID# and no identifiers will be contained in this file. This 
data  will be  linked  by a  programmer utilizing  coded  study IDs and operating  behind  a  University  
firewall.  The dataset is blinded of all personally identifiable information. (Variables in Appendix) 
 
8. TIMELINE AND ENROLLMENT  
8.1. Timeline 
Study period is approximately 150 days. 
 
Phase 1: Baseline - 30 to 70 days (usual care monitoring in Snapshot program) 
 
Phase 2: Intervention - 50 days – delivered to opt-in Participants 
 
Phase 3: Post-intervention - remaining time until Snapshot rating complete (~30-60 days). 
 
8.2. Screening and Enrollment 
Progressive will designate a cohort of customers to invite to study on Day 30-70 of their Snapshot Mobile 
enrollment. This will be the “Eligible Cohort.” 
Progressive Snapshot users will be sent an email from Progressive about participating in research between 
day 30-70 of baseline. Those interested will follow a link that takes them to a website hosted by TrueMotion 
with information about participating in the study. Those that wish to participate will opt-in to enroll online 
through the website hosted by TrueMotion. 
Once the participant is randomized, they will receive an email about their arm assignment. The email will 
provide details about the study (i.e. “What do I need to do?” and “How will I get paid?”) and contact 
information for any questions. 
Once all 3,000 participants have been enrolled, the intervention period will begin (up to 2 weeks after 
enrollment). 
9. FUNDING 
Funding for the present trial is provided by FHWA. The funding agencies have no role in the design and 
conduct of the study, collection, management, analysis, and interpretation of the data, preparation, review, 
or approval of the manuscript, or the decision to submit the manuscript for publication.  
10. PUBLICATIONS 
We plan to publish the findings in conference proceedings and/or peer-reviewed journals. 
11. DATA SHARING 
The investigator has a collaborative research agreement between Progressive, TrueMotion, and recipient 
researchers (University of Pennsylvania research staff) before sharing a limited dataset (driving trip 
summaries, phone usage, rating, etc.).  
12. TASKS AND RESPONSIBILITIES 
Principal investigator: Overall responsibility for protocol development, intervention development, budget 
overview, data dictionary development, ethical approval, trial registration, trial oversight, and the data and 
safety monitoring board, assessment of overall recruitments, potential, data analysis, and dissemination 
and presentation of results.  
 
Co-Investigators: Also protocol development, data dictionary development, trial oversight, dissemination of 
results.  

 
12.1. Principal Investigator 
Mucio Kit Delgado, MD, MS 
12.2. Co-Investigators 
University of Pennsylvania: 
Ian Barnett, PhD 
Scott D. Halpern, MD, PhD 
Jessica Hemmons, MS 
Dina Abdel-Rahman, BS 
Catherine McDonald, PhD, RN 
Roy Rosin, MBA 
Dylan Small, PhD 
Kevin Volpp, MD, PhD 
Douglas Wiebe, PhD 
Flaura K. Winston, MD, PhD 
Aria Xiong, MS 
 
Progressive Insurance: 
William Everett 
Kristen Gaba 
 
Truemotion, Inc. 
Rafi Feingold 
Ben Kotrc 
Emma Radford 
  
 
13. REFERENCES 
Asch DA, Rosin R. Engineering social incentives for health. N Engl J Med. 2016;375:2511–2513. 
AT&T. AT&T Teen Driver Survey: Executive Summary 2012. 
 https://www.att.com/Common/aboutus/txtingdriving/attteensurveyexecutive.pdf     
Creaser JI, Edwards CJ, Morris NL, Donath M. Are cellular phone blocking applications effective for 
novice teen drivers? J Safety Res. 2015;54: 75–78. 
Delgado MK, Wanner KJ, McDonald C. Adolescent cellphone use while driving: an overview of the 
literature and promising future directions for prevention. Media Commun. 2016;4(3):79–89. 
Department of Transportation, NHTSA. Visual–Manual NHTSA  Driver Distraction Guidelines for Portable  
and  Aftermarket  Devices. 2016. Docket No. NHTSA-2013-0137. Available at: 
https://www.nhtsa.gov/press-releases/us-dot-proposes-guidelines- address-driver-distraction-caused-
mobile-devices-vehicles. 
Ebel B, Boyle L, O’Connor S, et al. Randomized Trial of Cell Phone Blocking and In-Vehicle Camera To 
Reduce High-Risk Driving Events among Novice Drivers. San Diego, CA: Pediatric Academic Societies; 
2015. 
Funkhouser D, Sayer JR. Cellphone Filter/Blocker Techonology Field Test. Washington, DC: NHTSA; 
2013. Report No. HS 811 863. 

Hayashi Y, Russo CT, Wirth O. Texting while driving as impulsive choice: a behavioral economic analysis. 
Accident Analysis & Prevention. 2015;83:182-189. 
Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 1979 
6:65-70. 
Kervick AA, Hogan MJ, O’Hora D, Sarma KM. Testing a structural model of young driver willingness to uptake 
smartphone driver support systems. Accid Anal Prev. 2015;83:171–181. 
Klauer SG, Guo F, Simons-Morton BG, Ouimet MC, Lee SE, Dingus TA. Distracted driving and risk of road 
crashes among novice and experienced drivers. New England journal of medicine. 2014; 370(1)54-9. 
Kolko, Jed. “How Suburban Are Big American Cities?” FiveThirtyEight, 15 May 2015, 
fivethirtyeight.com/features/how-suburban-are-big-american-cities/. 
Loewenstein G, Asch D, Volpp K. Behavioral economics holds potential to deliver better results for patients, 
insurers, and employers. Health Aff (Millwood). 2013;32:1244–1250. 
Loewenstein G, Brennan T, Volpp KG. Asymmetric paternalism to improve health behaviors. JAMA. 
2007;298:2415–2417. 
Musicant O, Lotan T. Can novice drivers be motivated to use a smartphone based app that monitors their 
behavior? Transp Res Part F Traffic Psy- chol Behav. 2016;42:544–557. 
National Highway Traffic Safety Administration.  Distracted Driving 2014.  Traffic Safety Facts Research 
Note.  Available at: http://www.-nrd.nhtsa.dot.gov/CATS/index.aspx    
Richtel M. Phone makers could cut off drivers. So why don’t they? New York Times. September 24, 2016. 
Sinder M. iPhone will get “Do Not Disturb While Driving” mode in iOS 11 this fall. USA Today. June 5, 
2017. 
Using Technology to Fix the Texting-While-Driving Problem. National Public Radio. April 24, 2014. 
 
 
 
 

B. Final Statistical Analysis Plan 
1. STATISTICAL ANALYSIS PLAN 
1.1. Analytic Methods 
The primary outcome is seconds of active handheld phone use per hour of driving, which can 
also be expressed as active phone use %.  We will use fractional regression with a logit link 
function as our primary analytic model to assess effectiveness between and among the 5 
interventions. Specifically, we will evaluate these 10 predefined contrasts: 
1. Social Comparison Feedback versus Control (monitor only) 
2. End of Rating Period Incentive versus Control (monitor only) 
3. End of Rating period Incentive + Social Comparison Feedback versus Control (monitor only) 
4. Weekly Loss-Framed Incentive + Social Comparison Feedback versus Control (monitor only) 
5. Larger Weekly Loss-Framed Incentive + Social Comparison Feedback versus Control 
(monitor only) 
6. End of Rating Period Incentive versus Social Comparison Feedback 
7. End of Rating Period Incentive + Social Comparison Feedback versus Social Comparison 
Feedback 
8. End of Rating Period Incentive + Social Comparison Feedback versus End of Rating Period 
Incentive 
9. Weekly Loss-Framed Incentive + Social Comparison Feedback versus End of Rating Period 
Incentive + Social Comparison Feedback 
10. Larger Weekly Loss-Framed Incentive + Social Comparison Feedback versus Weekly Loss-
Framed Incentive + Social Comparison Feedback 
Although longitudinal models such as generalized estimating equations might be considered in light 
of the repeated trip measures among individual participants, fractional regression with a logit link is 
preferred in this case because the primary outcome collapses to a proportion between 0 and 1 that 
is weighted by travel time within and across trips. Furthermore, weekly feedback messages and 
incentives are delivered based on the weekly averages of seconds of active handheld phone use 
time divided by weekly total trip time.  The model will be adjusted for: 1) length of time in the 
Snaphshot program during the Baseline period; 2) and observed primary outcome during the 
Baseline period.  Other covariates to be included in the model include: 
 
1. Proposed baseline covariates to be included in primary outcome analysis model 
2. Baseline period active phone use % (i.e. total active handheld phone use seconds / total trip  
seconds) 
3. Proportion of period active phone use % due to handheld calls vs. handheld non-call use 
4. Mean hours of driving/week during baseline period 
5. Age 
6. Sex 
7. Marital status 
8. Urban-suburban-rural residence 
9. Residence in state with universal handheld ban 
10. Race/ethnicity 
11. Income 
12. Education level 
13. Phone type (iPhone vs. Android) 
14. Baseline use of automated Do Not Disturb While Driving setting (Y/N) 
15. Bluetooth/USB car connectivity (Y/N) 
16. Dashboard touchscreen (Y/N) 

17. Frequency of letting passenger use phone 
18. Frequency of riding as a passenger 
19. Number of prior traffic violations in prior 5 years 
20. Number of car crashes in prior 5 years 
 
Due to the multiple pre-planned contrasts, we will adjust for multiple comparisons using the Holm 
method.  First, the 10 contrasts will be ranked (lowest to highest) based on the raw p-value from the 
logistic regression. For a contrast to be significant using the Holm threshold, the raw p-value must 
be below the threshold for each row calculated as (0.05 / [n remaining contrasts]).  
Correspondingly, for clarity of presentation, adjusted p values will be reported by multiplying the raw 
p value by the number of remaining contrasts. For example, the smallest p-value is multiplied by 10 
to obtain the adjusted p-value. 
 
1.2. Sample Size, Power, and Minimum Detectable Differences 
To have power of 0.80 to detect a clinically meaningful reduction of a 2% difference in amount of 
trip time spent actively using a phone (72 seconds/hour of driving), the minimum sample size is 
1,842 (301 per arm). 
With a target sample size of 3,000 and power of 0.80, we should be able to detect a difference 
between treatment arms of 52 seconds/hour of driving (i.e. an absolute reduction in phone use % of 
1.44%).  We would have a power of 0.94 to detect a difference of 60 seconds/hour of driving.   
Proposed variables to be considered as effect modifiers in the primary analysis 
1. iPhone vs. Android.  Hypothesis: the relative reductions in active handheld phone use 
will be greater among those with iPhones due to the notification to encourage Do Not 
Disturb While Driving. 
2. Quartile of baseline period active phone use % (i.e. total active handheld phone use 
seconds / total trip seconds).  Hypothesis: the relative reductions in active hand held 
phone use while be greater among those in the highest quartile of baseline phone 
use due to greatest opportunity for improvement and least opportunity for 
improvement in the lowest quartiles.   
3. Proportion of period active phone use % due to handheld calls vs. handheld non-call 
use. Hypothesis: the relative reductions in active hand held phone use while be 
greater among those with a greater proportion of handheld phone use due handheld 
call use vs. non-call use due to multiple available technological means to switch to 
handsfree calls. 
4. Proposed variables to be considered as effect modifiers in hypothesis 
generating secondary analyses  
5. Baseline use of automated Do Not Disturb While Driving setting (Y/N) 
6. Baseline use of a phone mount 
7. Mean hours of driving/week during baseline period 
8. Age 
9. Sex 
10. Marital status 
11. Urban-suburban-rural residence 
12. Residence in state with universal handheld ban 
13. Income 
14. Education level 

 
These estimates should be conservative as adding the rich set of baseline covariates (e.g. 
demographics, Apple Car Play, etc) to the model should increase the precision of the model 
estimates.   
1.3. Subgroup Analyses, Effect Modification, and Mediation Analysis 
 
For the 10 pre-defined contrasts, we will also assess the effect modification of having an 
iPhone versus Android phone.  This will enable us to estimate the effectiveness of push 
notifications to encourage the automated activation of Do Not Disturb While Driving. These 
notifications only get sent to iPhones. In the primary analysis, we will also determine the extent to 
which relative reductions in phone use are modified by the amount of baseline phone use.  
We will also conduct a mediation analysis to determine whether activation of an automated Do Not 
Disturb While Driving setting during the trial as measure on intake and exit surveys mediates the 
primary outcome by treatment arm and by phone type.   
4.4 Sensitivity Analyses 
The primary analysis will only include trip data classified as driver trips.  It is possible that the 
interventions may cause customers to reclassify some driver trips to passenger trips within the 
allowable window of 5 days within the Snapshot Program.  This may be done to: 1) appropriately to 
correct an incorrect classification by the Snapshot app; or 2) to inappropriately drop driver trips with 
high levels of phone distraction from being factored in the performance calculations in the 
intervention arms.  Therefore we will assess the robustness of the primary results to the inclusion 
and exclusion of driver trips that participants reclassified to passenger trips in both the baseline and 
intervention periods.   
4.5 Post-Intervention Period Analysis 
The primary outcome analysis will be repeated for the post-intervention period to determine 
whether any behavior change is sustained after the interventions are discontinued. 
C. Summary of Changes 
The purpose of this modification is clarify aspects of the statistical analysis plan prior to commencing the 
statistical analysis.   
1. We clarified the primary analysis statistical model will be a factional regression with a logit link 
rather than a logistic regression model. 
2. We pre-specified the covariates to be included in the primary analysis statistical model  
3. We further clarified the variables to be considered as effect modifiers and delineated those that will 
be considered in the primary analysis with hypothesized directions and the others to be considered 
in a separate hypothesis generating secondary analysis 
4. We described a mediation analysis of whether activating iPhone’s Do Not Disturb While Driving 
during the trial mediates differences in the primary outcome by phone type. 
15. Number of prior traffic violations in prior 5 years 
16. Number of car crashes in prior 5 years 
17. Self-reported frequency of types of handheld phone use while driving (e.g. phone call, 
text messaging, adjusting navigation app, changing music app, social media app) 
18. Willingness to give up phone use while driving scale 
19. Automaticity scale of phone use while driving 
20. Compulsive phone use scale 
21. Delay discounting rate 

 
Appendix I 
Data to be transferred to UPenn for Participants (Baseline, Intervention, and Post- 
Intervention Phases). 
Variables 
Progressive 
Demographic Data 
 
Research ID 
Age 
Sex 
Marital status 
Residential ZIP code 
Progressive data for 
each trip 
Research ID 
Trip date 
Trip start time 
Trip end time 
Total trip time 
Seconds of phone use (total) 
Seconds of handheld phone calls 
Seconds of handsfree calls 
Seconds of active non-call phone use 
Seconds of passive non-call phone use 
Number of handheld phone use events 
Number of hard breaking events 
Number of fast acceleration events 
Randomization arm (aka test cell) 
Study phase (baseline vs. intervention vs. 
post- intervention 
Phone System and 
Attrition data collected by 
TrueMotion 
Research ID 
Phone operating system (iPhone vs. Android) 
Date of App uninstall 
Date Notifications turned off 
Data collected via intake 
survey administered by 
TrueMotion 
Research ID 
 Responses to survey questions (see attached) 
 
 

 
 
 
Appendix II 
Intake Survey 
Safe Driving Study 
 
Safe Driving Study Survey Sign-up 
 
Hello and thanks for checking out this exclusive opportunity! Progressive has teamed up with researchers at 
the University of Pennsylvania and TrueMotion, a mobile technology company, to test if the Snapshot Mobile 
app can help reduce distracted driving. Participating is easy and you can help us learn how to promote safe 
driving behaviors and earn money doing   it! 
 
What’s in it for me? 
 
· You get a minimum of $20 in Amazon gift cards:  
· o   $10 for completing a 10-minute survey  below 
o   $10 for completing another 10-minute survey just before the end of the study (in early   June) 
 
· Plus, you will have a chance to get an additional$50 to $100 in Amazon gift cards based on what test group 
you’re placed in and your driving  behaviors! 
 
· If you sign up, you may begin to receive notifications once in a while from the Snapshot Mobile app 
 
How does this work? 
 
· Any money earned in this study is not part of your Progressive auto policy or   discount. 
 
· All driving information collected during the course of this study will be from the Progressive Snapshot 
Mobile app and protected as described in the Progressive Snapshot Mobile privacy policy. 
 
Enrolling is easy! 
 
· If you agree to participate, you will be immediately redirected to complete our intake survey to get your first 

$10. The survey should take approximately 10 minutes to complete. We’ll need your email address to send you 
any earned  money. 
 
· Feel free to email us at drivingstudy@gotruemotion.com if you have any questions about the study. 

* Do you agree to participate in this research  study? 
   Yes 
No 
 
* Please enter the email address where you would like to receive your gift   cards: 
 
 
Thanks for taking the time to complete this survey and providing us a little more information about 
yourself! 
 
The survey may take about 10 minutes to complete. The information collected from you will help the research 
team develop ways to make our roads safer in the   future. 
 
After completing this survey, you will be emailed a $10 Amazon gift code, within 1 week, to the email address 
you just provided. You need to finish the survey to get the   $10. 

 
 
 
* Which type of smartphone do you use with Snapshot Mobile? 
 
Apple iPhone Android 
Smartphone 
 
How often have you placed your phone in a mount or holder while driving in the past two   weeks? 
 
Never  
Some trips 
Most trips 
 
How often have you connected your phone to your car's audio using Bluetooth or a USB cable while driving in the past two 
weeks? 
Never  
Some trips 
Most trips 
 
Does your car allow for your phone to connect to a dashboard touchscreen that allows you to control phone apps and functions (e.g. 
Apple CarPlay, Android Auto, Ford Sync, GM MyLink, Toyota Entune   etc.)? 
Yes 
 No 

 
 
 
 
How often have you used the dashboard touchscreen instead of your phone screen to check text messages, control music, and 
use navigation apps while driving? 
Never  
Some trips 
Most trips 
 
How often have you let a passenger use your phone while you were driving in the last two   weeks? 
 
Never 
 
1 to 2 days 
 
3 days or more 
 
 
How often have you ridden as a passenger in a car (including a rideshare or taxi) and used your phone in the last two weeks? 
Never 
 
1 to 2 days 
 
3 days or more 

 
 
 
 
Do you currently use Do Not Disturb While Driving or a similar phone setting that silences and hides notifications while driving? 
Yes  
No
All contacts 
 
I do not know 
 
Is Do Not Disturb While Driving or the similar setting set to come on automatically when you start driving or do you have to manually 
turn it on when you want  to? 
Yes - comes on automatically No - 
have to turn it on manually 
 
Does Do Not Disturb While Driving auto-respond to incoming text messages for your list of Favorite contacts or to anyone who 
sends you a text  message? 
No one (auto-reply is disabled) 
Recents 
Favorites 

 
 
 
For the next few questions, think about how your driving behavior compares to others like you 
(similar age, gender, where you live..). 
But first, some basic demographic questions. What’s the highest level of education you have   completed? 
 
Less than a high school diploma 
 
High school degree or equivalent (e.g. GED) Some 
college, no degree 
College degree 
 
Some graduate-level courses, no degree 
Post-graduate degree 
 
What is your annual household  income? 
 
Under $20,000 
 
$20,000 - $29,999 
 
$30,000 - $39,999 
 
$40,000 - $49,999 
 
$50,000 - $59,999 
 
$60,000 - $74,999 
 
$75,000 - $99,999 
 
$100,000 - $124,999 
 
$125,000 - $149,999 
 
Over $150,000 
 
 
What is your race? (Select one or more  responses) 
 
White or Caucasian Black 
or African American 

American Indian or Alaska Native Asian or 
Asian American 
Native Hawaiian or other Pacific Islander Other 
(please specify) 
 
Are you Hispanic or Latino? 
   Yes 
No 
 
Compared to drivers like me, I think I use my phone while  driving: 
   More than others 
   About the same as others 
   Less than others 
 
Compared to drivers like me, if I were to type a reply to a text message while driving, I think I would be: 
   More likely to get into a crash than others 
 
   About as likely to get into a crash as others 
   Less likely to get into a crash than others 
 
Compared to drivers like me, I think my rate of hard braking events   is: 
   More than others 
   About the same as others 
   Less than others 
 
How many years have you had your driver's  license? 
   0 to 4 
   5 to 9 
   10 to 14 
   15 or more 

 
 
How many tickets for a traffic violation (like speeding or going through a red light) have you received in the last 5 years? Remember, 
this information is confidential and will not affect your insurance   rating. 
   0 
   1 
   2 
3 or more 

How many car accidents have you been involved in as a driver in the last 5 years (whether your fault or not)? Remember, this 
information is confidential and will not affect your insurance   rating. 
0 
 
1 
 
2 
 
3 or more 
 
 
Think about the last 2 weeks. How often did you engage in the following behaviors while you were driving and the car was in motion 
(i.e. not stopped at a red  light)? 
Never 
Sometimes (some trips) 
Often (most trips) 
 
Talked on your phone while 
it was in your hand 
Talked on the phone using 
hands-free technology 
(like Bluetooth) 
Read text messages and 
app notifications (SMS, 
email, Snapchat, Facebook 
messenger, etc.) while your 
phone was in your hand 
Typed messages (SMS, 
email, Snapchat, Facebook 
messenger, etc.) while your 
phone was in your hand 
Sent text messages using 
hands-free technology 
Checked social media 
notifications (Facebook, 
Twitter, Instagram, etc.) 
Touched your phone to 
change the music 
Touched your phone to 
interact with a navigation 
app 

How often do you find yourself checking your phone to view a text message, email, or push notification while driving and not 
realize you are doing  it? 
Never 
Rarely 
Sometimes 
Very Often 
Always 
 
Do you find it hard to stop yourself from checking an incoming text message, email, or push notification while driving? 
Not at all 
Slightly 
Moderately 
Very 
Extremely 
 
For the next few questions, think about how likely it would be for you to willingly give up the following behaviors while driving 
(again, while the car is in  motion). 
Not willing to give up 
Somewhat willing to give up 
Willing to give up 
 
Reading incoming texts 
Sending texts 
Reading or sending emails 
 
Making or 
receiving phone calls with 
the phone in your hand 
Using navigation apps such 
as Google or Apple Maps 
Using or checking/browsing 
social media apps like 
Instagram and Facebook 
Using/searching music apps 
such as Pandora or Spotify 

The next questions ask about behavior related to your mobile phone use. Read each statement and select how often each behavior 
describes you. Do Not spend too much time on each statement. Answer quickly and honestly. 
Strongly Agree 
Agree 
Neutral 
Disagree 
Strongly Disagree 
 
I often think about my 
mobile phone when I am 
not using it 
I often use my mobile 
phone for no particular 
reason 
Arguments have arisen 
with others because of 
my mobile phone use 
I interrupt whatever else I 
am doing when I am 
contacted on my mobile 
phone 
I feel connected to others 
when I use my mobile 
phone 
I lose track of how much I 
am using my mobile 
phone 
The thought of being 
without my mobile phone 
makes me feel distressed 
I have been unable to 
reduce my mobile phone 
use 
 
 

 
 
We're almost done! The final few questions ask you to make decision. Don't think too long about each answer. 
 
Which would you rather have? 
 
$1000 in 3 weeks 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 2 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 1 day 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 8 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 4 months 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 4 days 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 4 hours 
 
$500 now

 
Which would you rather have? 
 
$1000 in 18 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 4 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 8 months 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 2 months 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 1.5 weeks 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 2 days 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 9 hours 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 2 hours 
 
$500 now 

Which would you rather have? 
 
$1000 in 25 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 12 years 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 5 years 
 
$500 now 
Which would you rather have? 
 
$1000 in 3 years 
 
$500 now 
Which would you rather have? 
 
$1000 in 1 year 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 6 months 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 3 months 
 
$500 now 
Which would you rather have? 
 
$1000 in 1 month 
 
$500 now 
Which would you rather have? 
 
$1000 in 2 weeks 
 
$500 now 
 
 

 
Which would you rather have? 
 
$1000 in 1 week 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 3 days 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 1.5 days 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 12 hours 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 6 hours 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 3 hours 
 
$500 now 
 
Which would you rather have? 
 
$1000 in 1 hour 
 
$500 now 
 
End of the Survey 
 
 
Thank you for completing this survey! You can expect a gift card in your email within 7 days. 

Appendix III 
 
Safe Driving Study Exit Survey 
 
 
Thank you for taking the time to complete this survey, as the Safe Driving Study comes to an end 
this weekend. You should be able to complete this survey in less than 10  minutes. 
 
The information collected from you will help the research team develop ways to make our roads safer in the 
future. After completing this survey, you will be emailed a $10 Amazon gift code. You’ll be asked to provide your 
email address so you can receive this payment. 
 
If you have any questions, please contact the research manager at the University of Pennsylvania at: 
drivingstudy@gotruemotion.com 
* Email address for $10 gift code: 
 
 
 
For the next few questions, think about how your driving behavior compares to others like you (similar age, gender, where you live..). 
 
 
Compared to drivers like me, I think I use my cell phone while driving: 
 
More than others 
 
About the same as others Less 
than others 
 
Compared to drivers like me, if I were to type a reply to a text message on my phone while driving, I think I would be 
More likely to get into a crash than others  
About as likely to get into a crash as others  
Less likely to get into a crash than others 
 
Compared to drivers like me, I think my rate of hard braking is: 
 
Better than others 
 
About the same as others 
 Worse than others 
 
 
 

Do you currently use Do Not Disturb or similar phone setting that silences and hides notifications while driving? 
Yes  
No

 
 
 
 
Is Do Not Disturb While Driving or the similar setting set to come on automatically when you start driving or do you have to manually turn it on 
when you want to? 
Yes – comes on automatically No - 
have to turn it on  manually 
 
Does Do Not Disturb While Driving auto-respond to incoming text messages for your list of Favorite contacts or to anyone who 
sends you a text message? 
No one (auto reply is disabled) 
Recents 
Favorites 
All contacts  
Don’t know 
 

 
 
 
 
 
What, if anything, caused to you reduce your handheld phone use while driving after enrolling in this study? (select all that apply) 
Activating the Do Not Disturb While Driving setting or a similar setting  
Tracking my own phone use while driving 
Being more aware of how much I use my phone compared to drivers like me 
 
The possibility of getting additional money for reducing my phone use while driving  
Nothing caused me to reduce my phone use while driving 
Other (please specify) 

Which additional strategies did you adopt to help yourself use your phone less while driving? (Please check all that 
apply): 
Placing my phone out of reach Using 
more hands-free options Giving my 
phone to a passenger Turning my 
phone off 
Ignoring notifications 
Muting my phone None 
Other (please specify) 
 
How likely is it that you would recommend this program to a friend or colleague? 
NOT AT ALL LIKELY 
EXTREMELY LIKELY 
 
0 
10 
 
 
 
Did the app have any technical issues? (Please check all that apply): 
 
No 
 
Registered passenger trips as driver trips Registered 
driver trips as passenger  trips 
Phone use or driving measurements were inaccurate Other 
(please specify) 
 
If an insurance company sent weekly push notification feedback on your driving behaviors would you opt-out? 
   Absolutely Not 
   Somewhat likely will not 
   Neutral 
   Somewhat Likely 
   Absolutely 
 
 
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PUT IT DOWN Trial Protocol v.1.0; 5/7/19 
If an insurance company sent weekly incentives based on your driving behaviors would you opt-out? 
   Absolutely Not 
   Somewhat likely will not 
   Neutral 
   Somewhat Likely 
   Absolutely 
 
Please tell us what factors helped you reduce the amount of time you used your phone while driving while 
participating in this study. 
 
 
Please tell us what you liked about participating in this study. 
 
 
Please tell us what you didn’t like about participating in this study and how we should improve the experience in the 
future. 
 
 
Please give us your suggestions for how we could best encourage drivers to be safer on the roads with apps like 
Snapshot mobile. 

PUT IT DOWN Trial Protocol v.1.0; 5/7/19