Applying Novel Technologies and Methods to Inform the Ontology of Self-Regulation: Binge Eating and Smoking
Phase: NA
Conditions: Binge Eating, Smoking, Tobacco, Behavior
Sponsor: Stanford University

 
 
 
 
 
 
 
 
 
 
 
 
 
Science of Behavior Change (SOBC) 
 
 
Applying Novel Technologies and Methods to 
Inform the Ontology of Self-Regulation 
 
 
Aim 4 Stanford Study Protocol 
 
 
Lead Investigator: Russell A. Poldrack, PhD, Stanford University 
 
Last Updated: September 25, 2018 
 
 
 

 
2 
Table of Contents 
Introduction .................................................................................................... 3 
Objective ........................................................................................................ 5 
Study Design ................................................................................................... 5 
Samples .......................................................................................................... 7 
Inclusion criteria: .................................................................................................................... 7 
Exclusion criteria: ................................................................................................................... 8 
Recruitment .................................................................................................. 10 
Compensation ............................................................................................... 10 
Study Assessments ........................................................................................ 11 
Data Monitoring ............................................................................................ 14 
Sample Size ................................................................................................... 15 
Statistical Analysis Plan ................................................................................. 15 
Power and Sample Size Justification ............................................................... 17 
Protection of Human Participants .................................................................. 18 
Potential Risks: ..................................................................................................................... 18 
Adequacy of Protection Against Risks .................................................................................. 19 
Potential Benefits of the Proposed Research to Human Subjects and Others ...................... 26 
Importance of the Knowledge to be Gained ......................................................................... 27 
Data and Safety Monitoring Plan ......................................................................................... 27 
Protocol Updates ........................................................................................... 29 
 

 
3 
Introduction 
Health risk behavior, including poor diet, physical inactivity, tobacco and other 
substance use, causes as much as 40% of the illness, suffering, and early death 
related to chronic diseases. Non-adherence to medical regimens is an important 
exemplar of the challenges in changing health behavior and its associated impact 
on health outcomes. Although an array of interventions has been shown to be 
effective in promoting initiation and maintenance of health behavior change, the 
mechanisms by which they actually work are infrequently systematically 
examined. One promising domain of mechanisms to be examined across many 
populations and types of health behavior is self-regulation. Self-regulation 
involves identifying one’s goals, and maintaining goal-directed behavior. A large 
scientific literature has identified the role of self-regulation as a potential causal 
mechanism in promoting health behavior.  
 
Advances in digital technologies have created unprecedented opportunities to 
assess and modify self-regulation and health behavior.  In this project, we plan to 
use a systematic, empirical process to integrate concepts across the divergent 
self-regulation literatures to identify putative mechanisms of behavior change to 
develop an overarching “ontology” of self-regulatory processes.  
 
This multi-year, multi-institution project aims to identify an array of putative 
psychological and behavioral targets within the self-regulation domain implicated 
in medical regimen adherence and health behavior. This is in service of 
developing an "ontology" of self-regulation that will provide structure and 
integrate concepts across diverse literatures. We aim to examine the relationship 
between various constructs within the self-regulation domain, the relationship 
among measures and constructs across multiple levels of analysis, and the extent 
to which these patterns transcend population and context. The project consists of 
four primary aims across two phases of funding (UH2 and UH3 phases). Note that 
Aims 1–3 were conducted under our prior UH2 phase, and we herein include the 
protocol for Aim 4 to be conducted in the UH3 phase: 
 
Aim 1. Identify an array of putative targets within the self-regulation 
domain implicated in medical regimen adherence and health behavior across 
these 3 levels of analysis. We will build on Multiple PI Poldrack’s pioneering 
“Cognitive Atlas” ontology to integrate concepts across divergent literatures to 
develop an “ontology” of self-regulatory processes. Our expert team will catalog 

 
4 
tasks in the self-regulation literature, implement tasks via online testing 
(Mechanical Turk) to rapidly obtain large datasets of self-regulatory function, 
assess the initial ontology via confirmatory factor analysis and structural equation 
modeling, and assess and revise the resulting ontology according to neural 
similarity patterns across tasks (to identify tasks for Aim 2). 
Aim 2. Evaluate the extent to which we can engage and manipulate 
putative targets within the self-regulation domain both within and outside of 
laboratory settings. 50 smokers and 50 overweight/obese persons with binge 
eating disorder will participate in a lab study (led by Poldrack) to complete the 
tasks identified under Aim 1. We will experimentally modulate engagement of 
targets (e.g., stimulus set of highly palatable foods images or tobacco-related 
images as well as self-regulation interventions). A comparable sampling of 100 
persons will participate in a non-lab study (led by Multiple PI Marsch) in which we 
will leverage our novel mobile-based behavioral assessment/intervention 
platform to modulate target engagement and collect data in real-world 
conditions. 
Aim 3. Identify or develop measures and methods to permit verification of 
target engagement within the self-regulation domain. Led by Co-I MacKinnon, we 
will examine cross-assay validity and cross-context and cross-sample reliability of 
assays. We will employ discriminant and divergent validation methods and 
Bayesian modeling to refine an empirically-based ontology of self-regulatory 
targets (to be used in Aim 4). 
Aim 4. We will evaluate the degree to which engaging targets produces a 
desired change in medical regimen adherence (across 4-week interventions) and 
health behavior among smokers (n=50 each at Dartmouth and Stanford) and 
overweight/obese persons with binge eating disorder (n=50 each at Dartmouth 
and Stanford) (objectively measured smoking in the former sample and physical 
activity in the latter sample). We will employ our novel mobile behavioral 
assessment/intervention platform to engage targets in these samples, given that 
(1) it offers self-regulation assessment and behavior change tools via an 
integrated platform to a wide array of populations, and (2) content within the 
platform can be quickly modified as needed to better impact targets. The 
proposed project is designed to identify valid and replicable assays of mechanisms 
of self-regulation across populations to inform an ontology of self-regulation that 
can ultimately inform development of health behavior interventions of maximal 
efficacy and potency. 
 

 
5 
This protocol details the Aim 4 study at Stanford led by Multiple PI Poldrack. 
 
Objective 
We will evaluate the degree to which engaging targets produces a desired change 
in medical regimen adherence (across 4-week interventions) and health behavior 
among smokers (n=50) and overweight/obese persons with binge eating disorder 
(n=50) (objectively measured smoking in the former sample and physical activity 
in the latter sample). We will employ our novel mobile behavioral 
assessment/intervention platform to engage targets in these samples, given that 
(1) it offers self-regulation assessment and behavior change tools via an 
integrated platform to a wide array of populations, and (2) content within the 
platform can be quickly modified as needed to better impact targets.  
 
Study Design 
This phase of the study takes what we learned about self-regulation in the first 
three phases and applies it in two samples that are exemplary for “lapses” in self-
regulation: individuals who smoke and overweight/obese individuals with binge 
eating disorder. We learned in Aim 2 that many real-world conditions (e.g., 
temptation, negative affect) may decrease self-regulation, whereas training 
through the mobile intervention described below may increase self-regulation. 
The primary purpose of this Aim 4 study is to target self-regulation to impact 
health behaviors. 
 
Based on information provided in the study ads, interested individuals will be 
directed to an eligibility screening questionnaire through Stanford’s Research 
Electronic Data Capture (REDCap) system and may call or email our study staff 
with questions. Those who meet the eligibility criteria for the study based on the 
online screening will be scheduled for an in-person introductory session at our 
research center. The study staff will determine whether possible participants are 
willing to consent to the study, and participants who consent will sign a consent 
form (electronic through REDCap via a checkbox or “agree” button) and be 
offered an electronic or paper copy. Consenting participants will complete 
baseline tasks and questionnaires, as described in the assessments below. We 
expect this introductory session to take 30 minutes of participants’ time. 
Participants will then undergo a 90-minute neuroimaging session on a separate 
date. The tasks will be administered in the scanner. The first neuroimaging session 

 
6 
is expected to take 2.5 hours (approximately 1.5 hours of scanning and one hour 
of practice, setup, and assessments outside the scanner). 
 
During the study period (the details of which are below), participants will 
complete a series of questions related to self-regulation and contexts. The 
questions, including the momentary self-regulation questionnaire, were used in 
Aim 2.  Participants will be asked to complete items in response to queries using 
mobile ecological momentary assessment (EMA); i.e., questions/questionnaires 
asked at a given time point outside of a laboratory setting via a mobile device.  
We will assess antecedent conditions prior to health risk behavior: smoking or 
binge eating among our smoking and binge eating samples, respectively. These 
antecedent conditions include mood, companionship, location, and temptation. 
We will offer the resources of Laddr® (a science-based behavior change 
intervention delivered via an interactive, self-directed mobile platform) to 
individuals and assess the effect of this mobile intervention system on putative 
targets of self-regulatory function.  Laddr will also be used to deliver the EMAs to 
participants. Participants will use their own smartphones. 
 
We will ask participants to respond to EMAs and to engage with the mobile 
intervention for 28 consecutive days.  We will prompt them four times daily to 
inquire about health risk behavior and ask them to complete measures of self-
regulation and potentially important contexts.  We will send the four prompts at 
random times within time windows (e.g., 8–11:30 AM, 11:30 AM–3 PM, 3–6:30 
PM, 6:30–10 PM). The time windows may vary based on participants’ waking 
hours, and we will program through the software at least an hour between 
prompts (e.g., if a participant receives a prompt at 11 AM, he/she cannot receive 
another prompt until 12 PM). We will ask them to use Laddr daily to, at a 
minimum, update progress toward goals and complete various activities on the 
application that we expect will improve their self-regulation.  Participants will be 
recommended priority therapy guides (binge eating for the binge eating sample; 
smoking for the smoking sample) and can also choose to engage with other 
guides, such as depression, anxiety, and substance use. 
 
Participants will also be asked to use devices that allow for objective measuring of 
behaviors of interest. We will ask participants in the smoking sample to provide 
carbon monoxide (CO) samples via a breath CO meter (iCO™ Smokerlyzer®, 
Bedfont® Scientific Ltd.). The monitor measures breath CO in parts per million 

 
7 
(ppm) based on the conversion of CO to CO2 over a catalytically active electrode. 
We will ask smoking participants to provide one CO sample in the same time 
window each day. Smoking participants will have a goal of smoking abstinence by 
the end of the study, as evidenced by a CO reading of less than 4 ppm. We will ask 
participants in the binge eating sample to wear a physical activity tracking device 
(Fitbit Flex 2™), which tracks steps, distance, calories burned, active minutes, 
hourly activity, stationary time, and sleep. Objective measures of eating are in a 
nascent phase, and all participants in the binge eating sample are overweight or 
obese, making physical activity a suitable behavior to measure. We will ask binge 
eating participants to wear the activity tracker for at least 12 hours per day. We 
will encourage participants to use a changing criterion design to set stepped 
goals, in which participants set a 7-day activity criterion goal and are encouraged 
to increase their criterion goals in each subsequent 7-day block if they achieve the 
goal on at least 4 of the 7 days. 
 
Participants in the smoking sample may use nicotine replacement medications, 
and we will track which participants use these medications. Potential participants 
taking medications for psychiatric reasons will be excluded from both samples. 
 
All participants will be asked to complete a battery of follow-up tasks and surveys 
at the end of their four-week study period. Participants will be asked to return in 
person to the research center to undergo a 90-minute neuroimaging session. The 
tasks and some of the questionnaires will be administered in the scanner. Like the 
baseline visit, the follow-up visit is expected to take 2.5 hours (approximately 1.5 
hours of scanning and one hour of practice, setup, and assessments outside the 
scanner). Participants will be compensated in full at the end of their study period. 
 
Samples 
This study consists of samples from two target populations: (1) persons who 
smoke, and (2) overweight/obese persons with binge eating disorder. The specific 
eligibility criteria are as follows: 
 
Inclusion criteria: 
• Age 18–50 years 
• Understand English sufficiently to provide informed consent 
• Use a smartphone operating system compatible with Laddr (Android 

 
8 
version 5.1 or higher or iOS version 10 or higher) 
• Right-handed 
• Normal or corrected-to-normal vision and no color blindness 
• Interest in participating in an intervention to change behavior 
 
• Additional inclusion criteria for binge eating sample: 
o 27 ≤ BMI ≤ 45 kg/m2 
o Weight limit of 350 lbs 
o Have binge eating disorder according to DSM-5 criteria 
o Non-smoking (defined as no cigarettes in past 12 months—this 
includes former and never smokers) 
o Confirmed interest in a physical activity intervention 
o Use a smartphone compatible with Fitbit 
 
• Additional inclusion criteria for smoking sample: 
o Smoke 5 or more tobacco cigarettes/day for past year 
o 17 ≤ BMI < 27 kg/m2 
o Confirmed interest in a smoking quit attempt 
o Use a smartphone compatible with the iCO Smokerlyzer 
  
Exclusion criteria: 
• Enrolled in Aim 2 study 
• Significant medical illness 
o Have had heart attack (MI), stroke, coronary heart disease, 
congestive heart failure, or angina 
o Have had coronary artery bypass surgery or cardiac catheterization 
such as percutaneous transluminal coronary angioplasty (PTCA), cath 
or stent placement 
o Have moderate to severe asthma, or chronic obstructive pulmonary 
disorder (also called emphysema or chronic bronchitis) 
o Had cancer within the past 5 years (except non-melanoma skin 
cancer) 
o Currently under medical care for digestive issues, gastrointestinal 
distress, abdominal pain, or diarrhea 
o Had an organ transplant 
o Have an immunodeficiency disorder 
• History of head trauma with loss of consciousness, cerebrovascular 

 
9 
accident, seizures, neurosurgical intervention, or brain tumor 
• Current use of any medication for psychiatric reasons (including stimulants 
and mood stabilizers) 
• Any additional contraindication for MRI (including body metal [non-
removable piercings, braces]) 
o https://cni.stanford.edu/cniwiki/images/5/59/CNI_Screening_form.p
df  
• Any current substance use disorder 
o Will not exclude based on use of substances 
• Currently pregnant or plans to become pregnant in next 3 months 
• Lifetime history of mental disorder due to a medical condition 
• Lifetime history of major psychotic disorders (including schizophrenia and 
bipolar disorder) 
• Current use of prescription pain medications (e.g., Vicodin, oxycodone) 
• Current use of any medication for smoking (e.g., Wellbutrin, varenicline) 
o Exceptions: will not screen out for nicotine replacement therapy 
(e.g., patch, gum, lozenge, nasal spray, inhaler) 
• Current use of any medication for weight loss 
• Current  
• Have undergone weight-loss surgery (e.g., gastric bypass, lap band) 
• Current nighttime shift work or obstructive sleep apnea 
• NOTE: We will not exclude based on e-cigarette use. 
 
• Additional exclusion criteria for binge eating sample: 
o Compensatory behavior (e.g., purging, excessive exercise, fasting) 
§ Already excluded as part of the DSM-5 binge eating disorder 
criteria 
o Lost weight in recent past (>10 pounds in past 6 months) 
o Currently in a weight-loss program (e.g., Weight Watchers, Jenny 
Craig) 
§ Will ask about, but won’t exclude on, online/mobile app 
weight-loss programs as part of the screener 
o Currently on a special diet for a serious health condition 
o Current engagement in psychotherapy for binge eating disorder 
 
• Additional exclusion criteria for smoking sample: 
o Binge eating behavior according to QEWP-5 (“yes” to questions 8 and 

 
10 
9 and for question 10, at least one episode per week for three 
months). 
§ QEWP-5 #8: During the past three months, did you ever eat in 
a short period of time (for example, a two-hour period) what 
most people would think was an unusually large amount of 
food? [yes or no] 
§ QEWP-5 #9: During the times when you ate an unusually large 
amount of food, did you ever feel you could not stop eating or 
control what or how much you were eating? [yes or no] 
§ QEWP-5 #10: During the past three months, how often, on 
average, did you have episodes like this? That is, eating large 
amounts of food plus the feeling that your eating was out of 
control? (There may have been some weeks when this did not 
happen. Just average those in.) [less than one episode per 
week, five response options for 1 or more episodes per week] 
o Current engagement in psychotherapy for smoking behavior 
 
Recruitment 
Study advertisements will be posted online (e.g., Craigslist, Facebook, Google 
AdWords) and in the community (e.g., local newspapers, flyers at community 
centers).  Additional participants may be identified from co-investigators’ 
participant records at Stanford (Drs. Bohon and Prochaska).  Our team has had 
great success with similar recruitment strategies in previous research studies, 
including in Aim 2 of this project.  We expect the samples to include 
approximately 50% women, 39% non-White race, and 26% Hispanic/Latino 
ethnicity. 
 
Compensation 
Compensating participants for active study participation will help ensure 
exposure to the intervention intended to manipulate targets.  
 
Participants will be compensated up to $350 per participant for all baseline tasks, 
surveys, and neuroimaging (required to complete full battery of tasks, surveys, 
and neuroimaging to receive $350). This full compensation schedule breaks down 
as follows: 
 

 
11 
$20 for completion of a 30-minute introductory session with baseline 
surveys and consent form signing 
 
$50 for completion of the first neuroimaging session 
 
Up to $230 (see below for detail) for completion of the four-week EMA 
period: 
 
$1 per EMA * 4 EMAs per day * 28 days = up to $112 
 
$2 per day for Laddr activities (at least 5 minutes of engagement) * 
28 days = up to $56 
 
$2 per day for wearing the wrist sensor for a minimum of 12 hours 
per day and inputting activity data into Laddr (binge eating sample) 
or using the CO monitor daily and inputting CO reading into Laddr 
(smoking sample) * 28 days = up to $56 
 
$6 for data plan reimbursement (required to complete minimum of 
12 EMAs) 
 
$50 for completion of the second neuroimaging session 
 
Note that these schedules are based on the maximum compensation possible. 
Participants may receive partial compensation for certain activities. Participants 
will be compensated with their full payment at the end of their study period after 
completing the follow-up tasks, surveys, and neuroimaging session (and for binge 
eating participants, after returning the wrist sensor). The research team may 
deduct up to $50 from a binge eating participant’s compensation for failure to 
return, or for damage to, the wrist sensor. Smoking participants are able to keep 
the carbon monoxide monitor as this device is intended for use by a single 
person. 
 
Study Assessments 
At baseline and follow-up we will use the highest-loading subscale for each of the 
12 factors identified in the survey factor analytic space from prior aims of the 
project. We will also include the additional surveys listed below. Some of the 

 
12 
additional surveys have been well defined in the self-regulation literature, and 
others apply specifically to smoking or eating behavior. 
 
All tasks and surveys/subscales below were used in Aim 2 of this project at 
Dartmouth and/or Stanford. The numbers in parentheses indicate the number of 
items included. 
 
We will also include a limited number of tasks; namely, the same 3 putative self-
regulation tasks used in the Aim 2 imaging study: (1) two stop-signal tasks that 
involve responding to imperative go stimuli except when a subsequent stop signal 
occurs, and which maps onto four of the factors related to the drift-diffusion 
model that emerged in our task factor space in previous aims of our project, and 
(2) a delay discounting task that requires choosing between hypothetical smaller-
sooner or larger-later monetary rewards, which mapped onto the fifth and final 
factor in the task factor space from prior project work. 
 
We will also use a movie-watching task that includes embedded food and smoking 
stimuli to evaluate more naturalistic responses to these stimuli. Finally, we will 
include a manipulation task in the scanner that includes now and later cues to 
food [binge eating sample], smoking [smoking sample], and neutral stimuli. This 
will assess the degree to which subjects can modulate their cravings in response 
to external cues.  
 
Subscales at baseline and follow-up representing the 12 factors identified in the 
survey factor analytic space:  
1. Reward sensitivity – BIS/BAS: BAS Fun Seeking (4) 
2. Sensation seeking – Zuckerman Sensation Seeking Scale: Thrill and 
Adventure Seeking (10) 
3. Financial risk-taking – DOSPERT Risk-taking Survey: Financial (6) 
4. Social risk-taking – DOSPERT Risk-taking Survey: Social (6) 
5. Ethical risk-taking – DOSPERT Expected Benefits Survey: Ethical (6) 
6. Eating control – Three-Factor Eating Questionnaire-R18: Uncontrolled 
Eating (9) 
7. Impulsivity – UPPS+P Impulsivity Survey: Lack of Premeditation (11) 
8. Emotional control – Ten-Item Personality Inventory: Emotional Stability (2) 
9. Mindfulness – Five Facet Mindfulness Questionnaire: Act with Awareness 
(8) 

 
13 
10. Goal directedness – Selection-Optimization-Compensation: Loss-based 
Selection (12) 
11. Agreeableness – Ten-Item Personality Inventory: Agreeableness (2) 
12. Risk perception – DOSPERT Risk Perceptions Survey: Health Safety (6) 
 
Additional surveys at baseline and follow-up: 
• Brief Self-Control Scale (13) 
• Ten-Item Personality Inventory (6 [10 minus 4 above]) 
• Three-Factor Eating Questionnaire–R18 (9 [18 minus 9 above]) 
• Reward-based Eating Drive Scale (RED-13) (13) 
• Stanford Leisure-Time Activity Categorical Item (L-Cat 2.2) (1) 
• Alcohol, smoking, and drug questionnaire from Aim 1 (36) 
• Demographic questionnaire from Aim 1 (24 [28 minus 4 at screening]) 
o Baseline only 
 
• Additional for eating sample: 
o QEWP-5 (11) 
§ At screening in addition to baseline and follow-up (but baseline 
and follow-up will ask about past month instead of past 3 
months) 
 
• Additional for smoking sample: 
o Select set of smoking questions, including from the PROMIS Smoking 
Initiative (6) 
o Fagerström Test for Nicotine Dependence (5 [6 minus 1 already 
included in alcohol, smoking, and drug questionnaire]) 
 
Tasks at baseline and follow-up: 
• Stop Signal Task 
• Stimulus Selective Stop Signal Task 
• Delay Discounting Titration Task 
• Movie-watching Task 
• Manipulation Task (with now and later cues to food [binge eating sample], 
smoking [smoking sample], and neutral stimuli) 
 
Ecological momentary assessment (EMA) during study period: 
• Momentary self-regulation questionnaire 

 
14 
• Self-reported behavior of interest (binge eating for the binge eating sample; 
smoking for the smoking sample) 
• Questions assessing daily intents and barriers 
• Questions assessing contexts 
 
The proposed baseline battery of surveys includes approximately 200 items, and 
the proposed follow-up battery includes approximately 175 items. The Aim 2 
baseline battery included 165 items and took roughly 20 minutes to complete. We 
expect the Aim 4 baseline and follow-up survey batteries to take no more than 30 
minutes each. The proposed baseline and follow-up tasks are estimated to take 
approximately 30 minutes. The tasks will be completed within the scanner and 
take 60-90 minutes.  
 
Each EMA is designed to be completed in less than five minutes. The average EMA 
completion time in Aim 2 at Dartmouth was less than five minutes, and we plan to 
administer the same EMAs in Aim 4. 
 
Data Monitoring 
The research assistant plans to monitor data quality on a daily basis and report 
any issues to the project manager. Our study team will use an initiation 
application for Laddr, developed by the same team that developed Laddr, to 
initiate participants into the EMA portion of the study. The team will download 
password-protected raw data files from a website created by the Laddr 
developers to monitor EMA completion and Laddr usage. These files will also 
allow the study team to track compensation amounts for each participant. Data 
are uploaded to the server when the smartphone is connected to the Internet 
(either via cellular or WiFi). If participants do not have access to a connection, the 
data are stored locally on the smartphone and uploaded to the server once a 
connection is established. 
 
For the overweight/obese participants with binge eating disorder, our study team 
will use Fitabase, a data management platform designed specifically for Fitbit 
data. As long as participants maintain a wireless connection (either cellular or 
WiFi), the sensor data are uploaded to the Fitabase server up to every 15 minutes, 
allowing for monitoring throughout the day. If participants do not have access to 
a connection, the data are stored locally on the smartphone and uploaded to the 

 
15 
server once a connection is established. Fitabase allows for near-real-time data 
monitoring. 
 
For the smoking participants, our study team will be able to monitor whether 
participants have input their daily CO readings into Laddr via the same password-
protected raw data files noted above regarding EMA completion and Laddr usage. 
 
Poor compliance regarding any data source will first be addressed through 
reminders sent to the participant’s smartphone (text message or app notification) 
or email address. If poor study compliance persists, the participant may be 
withdrawn from the study, and an additional participant will be enrolled. 
 
Sample Size 
We propose a sample size of up to 154 participants (77 individuals who smoke 
and 77 overweight/obese individuals with binge eating disorder) recruited from 
the United States, including U.S. districts and territories. 
 
We propose the sample sizes above to reach 50 participants in each sample with 
at least 10% EMA response percentage over the duration of the four-week study 
(at least 12/112 EMAs). We calculated the percentage of participants who 
completed at least 10% of their EMAs in Aim 2 at Dartmouth. We then estimated 
a target sample size to reach at least 50 participants in each sample with 
adequate EMA participation (at least 10% completion). The proposed sample sizes 
account for participants who sign consent but participate minimally, and the 
target sample sizes of 50 for each sample with at least 10% EMA completion were 
determined through power calculations in Aim 2. 
 
Statistical Analysis Plan 
To evaluate the degree to which changing self-regulation is associated with 
changes in medical regimen adherence (measured as daily smoking abstinence in 
smokers and daily physical activity in overweight/obese individuals with binge 
eating behavior), we will fit several multilevel models (e.g., linear mixed-effects 
models) in addition to an overall model examining mechanistic effects. Details are 
provided in the following text. At the momentary level, there two relationships to 
be examined to understand the role of self-regulation in behavior change: the 
relationship between context and self-regulation, and the relationship between 

 
16 
self-regulation and medical regimen adherence. We will examine each 
relationship separately prior to examining the full mechanistic model. To examine 
whether context is associated with level of momentary self-regulation, we will fit 
several multilevel models with momentary context as a predictor of momentary 
self-regulation measure. In Aim 2, we have identified several challenging contexts 
associated with worse momentary self-regulation, and we will confirm these 
relationships in this treatment-seeking sample of participants. The multilevel 
models will include a random individual-level effect to account for the non-
independence of repeated observations over the 28-day period within an 
individual. Additionally, the models will also include a fixed effect of time to 
account for changes in momentary self-regulation over the treatment period. We 
will also consider a random individual-level slope term if there is significant 
variability in this change over time between individuals. Such a change in self-
regulation over time may be due to the cumulative effect of Laddr use over the 
treatment period. We will also test whether including an interaction between 
context and recent Laddr usage is significant indicating Laddr use affects the 
association between context and momentary self-regulation. These models will 
identify contextual factors associated with increased or decreased momentary 
self-regulation. 
 
The next model will examine the relationship between level of momentary self-
regulation and daily medical regimen adherence. One model for each subscale of 
the momentary self-regulation will be fit. In each model, momentary self-
regulation will be the predictor and daily medical regimen adherence (smoking 
abstinence for smokers and daily physical activity for overweight/obese 
participants with binge eating disorder) will be the outcome. These models will 
identify the influence of momentary self-regulation on medical regimen 
adherence. Together with the previous set of models, the components of a 
mechanistic effect of momentary self-regulation on medical regimen adherence 
will be estimated. If each relationship is significant, we will fit a full multi-level 
structural equation model to examine the indirect effect of momentary context 
on medical regimen adherence as well as the direct effect of context on medical 
regiment adherence, independent of momentary self-regulation. Bootstrapping 
will be used in estimating the indirect effect in the mediation model. 
Bootstrapping involves taking numerous pseudo-replicate samples from the 
dataset and using the variability in the statistic from sample to sample to 
construct an interval estimate conveying the direction, magnitude, and precision 

 
17 
of an indirect effect. This mediation model will allow us to estimate the impact of 
self-regulation as a mechanism for decreased medical regimen adherence in 
challenging contexts. 
 
At the individual level, we wish to examine whether Laddr can influence 
momentary self-regulation in a positive direction and, in turn, increase medical 
regimen adherence. To test this hypothesis, we will examine both the effect of 
time in the study on momentary self-regulation via multilevel models (e.g. linear 
mixed-effects models) as well as the effect of various Laddr engagement metrics 
on momentary self-regulation. Since Laddr use is offered as an intervention 
during the 28-day intervention period, time in study serves as a proxy for the 
cumulative effect of Laddr use. Since individuals will differ in their use of Laddr 
during the course of the intervention period, we will also examine Laddr 
engagement metrics (e.g., guides read, goals tracked) in addition to time. If Laddr 
usage appears to influence momentary self-regulation in a positive direction, we 
will again fit a multi-level structural equation model to examine both the indirect 
and direct effects of Laddr use on medical regimen adherence.   
 
Additionally, we will evaluate whether Laddr influences task behavior in our four 
tasks as well as fMRI activity and connectivity by comparing the pre and post scan 
results. 
 
Power and Sample Size Justification 
With 77 participants in each sample with up to 4 EMA responses per day for 28 
days, the maximum number of observations in the momentary dataset is 8,624. 
However, EMA response rates are not expected to be 100%, and some 
participants may withdraw from the study. With 10% study drop-out and 75% 
EMA completion rate, the number of observations in a momentary dataset may 
be closer to 5,821. Even with this sample size, there will be adequate power to 
detect momentary relationships between contextual factors and momentary self-
regulation. For example, with intraclass correlations ranging from 0.3 to 0.6, and 
prevalence of contexts of interest (e.g., recent exposure to smoking cue, easy 
food access) between 10% and 30%, there would be at least 80% power to detect 
differences in momentary self-regulation measures between context between 
0.05-0.10 times an SD. These are small effect sizes. In the pilot data, the average 
SD of a momentary self-regulation measure was between 0.5 and 0.9, and the 
stated standardized effect sizes are equivalent to detectable mean differences 

 
18 
between contexts of 0.03-0.09 points on the momentary self-regulation 
subscales, which range from 1 to 5. Well-developed formulae for determining 
power to detect indirect effects within a multilevel structural equation model of 
intensively collected data do not exist. However, simulation studies of the 
bootstrap approach at the individual-level indicate that with a sample size of 71, 
an indirect effect with medium-sized component effects (β = .39) can be detected 
with 80% power.1 
 
1Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. Psychol Sci 2007. 
Mar; 18(3):233-239. 
 
Protection of Human Participants 
 
Potential Risks: 
The potential risks associated with the data collected are low. 
 
Risks from behavioral testing:  The types of data we plan to collect (assessment, 
self-report questionnaires, cognitive tasks, behavioral testing) will not harm the 
participants’ financial standing, employability, or reputation, or expose the 
participant to civil or criminal liability.  The types of risk associated with the data 
collected include possible fatigue, frustration, or the discussion of sensitive or 
personal information.  If participants do not wish to answer particular mobile 
surveys, they may elect not to do so, and may continue in the remainder of the 
study without penalty. (Participants must complete the entire baseline and 
follow-up task and survey batteries to continue in the study.) Additionally, 
participants may be concerned about confidentiality risk when using the Internet 
to access Laddr, even on secured, encrypted connections.  They may also be 
worried about prompts they receive on a mobile device designed to remind them 
to complete Laddr activities, such as updating goals. 
 
Risks from MRI: There are no known risks of the magnetic resonance imaging 
scanning procedures proposed in this application. Participants may experience 
increased anxiety before MRI scanning, just as for any interview or medical 
examination. The strong magnetic field can be dangerous if the participant has an 
implanted electronic device (such as a pacemaker), the participant has implanted 
metal in his/her body (such as an aneurysm clip), or the participant enters the 
field with magnetic objects on or in his/her body. Some participants may feel 

 
19 
claustrophobia inside the bore of the scanner, some participants who enter or 
exit the bore too quickly may feel dizzy or nauseous, and the scanner is noisy. 
Participants for whom it is not safe to enter the scanner, or who report 
claustrophobia, will not be included in the study. 
 
Risks from mobile sensing of physical activity (binge eating participants only):  
This project does not involve any risks beyond those ordinarily encountered in 
daily life or the performance of routine tests.  Participants may experience slight 
initial discomfort while wearing the wrist sensors, such as minor skin irritations.  
As with any electrical device, the sensors can theoretically cause electric shocks.  
Electrical shocks can be a health concern with certain health conditions (e.g., 
heart conditions that require a pacemaker).  Additionally, participants could have 
privacy concerns regarding mobile sensing. 
 
Risks from carbon monoxide monitor (smoking participants only):  The carbon 
monoxide (CO) monitor requires participants to hold their breath for 
approximately 15 seconds and then to exhale for 15–20 seconds, which may 
cause slight discomfort for some participants. Additionally, the CO monitor carries 
the risk of spreading illnesses. 
 
Risks from electronic databases:  There may be risks to participants by virtue of 
their representation in electronic databases, principally involving the risk that 
privacy or confidentiality might be compromised if there were lapses in security of 
the information contained in these databases.  
 
Risks from physical activity intervention (binge eating participants only): There 
may be physical health risks associated with increases in physical activity. 
 
Risks from smoking quit attempts (smoking participants only): There may be 
psychological discomfort experienced as a result of making a smoking quit 
attempt. 
 
 
Adequacy of Protection Against Risks 
A. 
Informed Consent: 
All descriptions in the informed consent form are written at the 8th-grade reading 
level. The consent document includes descriptions about: background of the 

 
20 
study, study procedures, risks and discomforts, benefits, payment for 
participation, voluntary nature of participation, privacy and confidentiality, and 
contact information for the research team. The consent form will be presented to 
prospective study participants online.  
 
Individuals who wish to participate in the study will be asked to carefully read the 
consent form or to have the consent form read to them. If they have any 
questions about the study, they can ask the study staff before consenting and at 
any time afterward. Individuals who provide consent for participation in the study 
will be offered an electronic or paper copy of the form. Their screening 
information, agreement to participate, contact information, and baseline data will 
be saved in REDCap. Study participants will be informed that they can withdraw 
from the study for any reason at any time. 
 
 
B. 
Protections Against Risk: 
 
Protection against risks from behavioral testing:  To protect against the possible 
risks associated with behavioral testing, which are fatigue and frustration, 
participants will receive EMA prompts four times daily and will not be asked to 
initiate use on their own.  It will be made clear to participants during the informed 
consent process that they are free to discontinue their participation at any point 
without penalty.  Any participant that experiences significant discomfort during 
the four-week period will be able to stop procedures immediately.  
 
To protect against concerns that others may see when a participant receives a 
prompt from the Laddr system (the mobile behavior change intervention), the 
content of the prompts sent will be intentionally vague.  While they will be 
designed to be meaningful to individual participants, they will not include specific 
references to study participation.  
 
Protection against risks from MRI: There are no known risks of the magnetic 
resonance imaging scanning procedures proposed in this application. All scans will 
be conducted on an MRI system that is FDA approved, has been approved for 
research use, and uses sequences that are within FDA guidelines for relevant 
safety parameters. The risks associated with implanted electronic devices, 
implanted metal objects, and metallic objects being brought into the scanning 
room are minimized by an extensive set of safety procedures that are used to 

 
21 
screen participants for implants and ferromagnetic materials on or in their bodies 
prior to scanning, which include detailed questionnaires about medical/surgical 
and occupational histories. In addition, all participants are screened with a metal 
detector prior to scanning and security prevents unauthorized individuals from 
having access to the imaging suite. In addition, all users of the Center for 
Neurobiological Imaging at Stanford are highly trained in MRI safety and safety 
training is renewed annually. 
 
To protect against the increased anxiety that is experienced prior to MRI 
scanning, participants receive information beforehand about what to expect, 
discuss with staff the methods for remaining still and feeling comfortable in the 
scanner, and receive training in the Simulator, all of which help to increase their 
familiarity with the scanning environment. Staff members are also highly 
experienced in the scan environment and help participants feel more comfortable 
in the scanner; however, if the discomfort cannot be reduced to an appropriate 
level, participants are told that they can discontinue the procedure at any time 
without penalty. To protect against risks associated with feeling uncomfortable in 
the scanner, and noise associated with the scanner, we use audio and video 
inputs, which decrease the feelings of being in an enclosed space for many 
participants. All participants will also be provided with earplugs and headphones 
to protect their hearing. Participants receive instructions before scanning about 
communicating with staff and scan operators, which can be done either by a 
squeeze bulb attached to the participant’s headphone cord, or by voice through a 
microphone mounted inside the scanner. All participants will be told during 
consent and prior to the scan session that they can discontinue the procedure at 
any time without penalty. 
 
Protection against risks from mobile sensing (binge eating participants only):  
Previous studies with wristband sensors have indicated that after a brief 
adjustment period, the majority of the participants adjusted to wearing the bands 
and did not find them to be intrusive or restraining.  Although slight discomfort is 
possible from wearing the wrist sensors, Fitbit sensors have been used in over 600 
studies.  Therefore, we assess the severity of the discomfort and irritation of 
wearing the wrist sensors to be minimal.  If irritation persists to a point where the 
participant no longer wishes to participate, any irritation is entirely reversible 
once the participant removes the wrist sensors. 
 

 
22 
While electrical devices do introduce the risk of electric shocks, the probability of 
a participant experiencing even minor electrical shocks is negligible.  High 
impedance circuitry is used to limit current flow, even in the case of external 
events (e.g., through physical breaking of the sensor board or shorting of the 
battery leads).  All sensors in the wristbands are commonly used in mobile phones 
and other activity monitors and pose minimal risk to participants. We expect that 
the wrist sensors, which have precedent of prior use in a research study or have 
otherwise been designed for everyday wear, will elicit similar acceptability among 
the participants in this study. 
 
Regarding privacy concerns, participants’ contact information will be linked to 
their study data via a code.  The key to this code will be available only to the 
research staff at Stanford, and will be secured separately from the rest of the 
study data that is transmitted to Fitabase.  Participants will be informed of their 
rights to terminate their participation in the study at any time.  Participants will 
also be informed of their rights to remove the wrist sensors if they so choose, if 
they do not wish to be tracked.  Participants will be given a summary of the 
incentive structure, and will be informed how their participation will affect their 
final incentive payment. 
 
All personnel that will be present for the research activities will be essential 
personnel in the conduct of this research.  All personnel who will interact with 
participants at Stanford are trained in human participants research. Moreover, all 
staff at Small Steps Labs LLC, which owns and operates Fitabase, will receive only 
coded data. No directly identifiable data will be provided to Small Steps Labs LLC 
staff, and the key linking participant codes to identifiers will not be shared under 
any circumstances. 
 
Study participants will be informed that this research is conducted with the use of 
Fitabase and that coded data, which will not directly identify them, will be 
maintained in a database owned and operated by Small Steps Labs LLC.   
 
Protection against risks from carbon monoxide monitor (smoking participants 
only):  While slight discomfort may result from participants holding their breath 
and exhaling, these carbon monoxide monitors have been used in several 
previous studies. Most participants are able to provide readings without issue. 
Participants will be informed of their rights to terminate their participation in the 

 
23 
study at any time if they experience excessive discomfort related to holding their 
breath or exhaling. 
 
To minimize the risk of any spread of illness from the use of the carbon monoxide 
monitors, participants will each be provided with their own CO monitor. The CO 
monitors will not be reused by subsequent participants who enroll in the studies. 
 
Protection against risks from physical activity intervention (binge eating 
participants only): Participants will be encouraged to set physical activity goals in 
line with their current health and fitness status. Participants who have concerns 
about increasing their physical activity will be encouraged to contact a medical 
professional. 
 
Protection against risks from smoking quit attempts (smoking participants only): 
While participants may experience psychological discomfort from making a 
smoking quit attempt, this discomfort is not beyond what participants would 
ordinarily encounter in daily life (e.g., cravings, disappointment from a lack of 
success). Laddr is designed to support participants through a smoking quit 
attempt. Participants experiencing psychological discomfort beyond that 
encountered in ordinary life will be withdrawn from the study and referred to the 
appropriate resources. 
 
Protection against risks from electronic databases:  To protect the privacy or 
confidentiality of participants’ data stored in electronic databases, every effort 
will be made to safeguard the confidentiality of research records, using data files 
free of information enabling individual identification of participants, lock-and-key 
access to paper records, and computer data files maintained with encryption, 
password protection, and behind firewalls. We will remove individual identifying 
information from data representations so that security failures would not put 
individual privacy and confidentiality at risk. Individual identifying information will 
only be maintained in a separate encrypted database with passwords known only 
to the PIs and specific members of the research team.  
 
Each of the following sources of data is explained in detail separately: (1) 
eligibility, baseline, and follow-up surveys; (2) baseline and follow-up tasks; (3) 
baseline and follow-up neuroimaging; (4) EMA and Laddr (mobile intervention); 
(5) mobile sensing of physical activity (binge eating participants only); and (6) 

 
24 
carbon monoxide monitor (smoking participants only). 
 
(1) 
We plan for the eligibility, baseline, and follow-up survey batteries 
and resulting data to be available and coordinated through Stanford’s 
Research Electronic Data Capture (REDCap) system.  All eligibility, baseline, 
and follow-up survey data will be either acquired using REDCap forms or 
stored directly in REDCap upon acquisition. 
 
(2) 
Baseline and follow-up task data will be acquired using The 
Experiment Factory, a platform for deploying behavioral experiments that 
our team created and implemented in Aim 1 of this project. All task data 
will be coded (i.e., a unique participant code will replace all other 
identifying information) and stored on the OAK storage system in 
Sherlock2, a supercomputing cluster at Stanford University. Only essential 
members of the research team will have access to a key linking each code 
to participant identifiers. 
 
(3) 
 Raw neuroimaging data will be stored on Flywheel, a state-of-the-art 
platform for storing neuroimaging data that reduces security risk. All raw 
imaging data will be promptly coded to eliminate privacy and 
confidentiality issues and stored on the OAK storage system in Sherlock2 
alongside task and summary imaging data. 
 
(4)  
All EMA and Laddr usage data provided by participants when using 
the web-based Laddr® intervention will be stored locally on the phone in an 
AES-256 encrypted database.  Data are stored in a key-value store residing 
on AES-256 encrypted solid state drives (encrypted at rest).  Data are sent 
to the server when the device is online over an encrypted connection using 
Secure Sockets Layer (SSL) over Hypertext Transfer Protocol Secure (HTTPS) 
and will not be accessible to anyone not affiliated with the research project.  
The server is located in a locked cabinet, in a card-key secured room, in a 
secure, monitored data center in Fremont, California.  The server is behind 
multiple firewalls with intrusion detection systems in place.  Security 
patches for system software are installed within days and most often within 
hours after their release.  All data stored on this server will be coded by 
participant ID number. 
 
Stanford research staff will use an application, developed by the 

 
25 
same team that developed Laddr, to initiate Laddr for each participant at 
the beginning of the participant’s study period. Throughout the study the 
team will download password-protected raw data files from a website 
created by the Laddr developers to monitor EMA completion and Laddr 
usage. These files will also allow the study team to track compensation 
amounts for each participant. Exported data will be identified via 
participant code and stored on password-protected computers. We 
successfully used this application, file download process, and similar 
procedures in the prior Aim 2. 
 
(5)  
(Binge eating participants only) Mobile sensing data will be used to 
infer physical activity among this sample. These data will first be 
aggregated from the wearable sensors and will be stored on the 
participants’ smartphones. After participants configure the proper settings, 
aggregated wrist sensor data from the smartphone will be transmitted 
automatically to Fitabase, a data management platform designed 
specifically for Fitbit data.  All study data will be sent via a Secure Sockets 
Layer (SSL) connection, which encrypts all transmitted data. Stanford 
research staff will link mobile sensing data to a participant code so that 
participant identifiers (except for a study-specific code) are not included in 
the participant’s Fitbit.com account or Fitabase. 
 
Data are stored and indexed in the Fitabase SQL Server database. 
These database servers are IP firewalled and whitelisted such that they 
refuse any connection from IP addresses not preprogrammed by Small 
Steps Labs LLC, which owns and operates Fitabase. Fitabase databases are 
stored on the Microsoft Azure platform, which uses geographically 
dispersed data centers that comply with industry standards, such as ISO/IEC 
27001:2005, for security and reliability. Small Steps Labs LLC maintains 
snapshot archives of the databases, which are encrypted and password 
protected, for disaster recovery purposes. 
Participants may also be asked to input their activity data directly 
into Laddr as part of tracking their goals and progress. Activity data in Laddr 
will then be protected and transferred based on the same methods 
detailed above in section 4 about Laddr. 
 
(6) 
(Smoking participants only) The CO monitors connect directly to 
participants’ smartphones via the headphone jack. The CO readings will be 

 
26 
stored locally on participants’ smartphones in a mobile application 
designed for the CO reader. Participants will input their CO readings from 
the device app directly into Laddr. The CO data will then be protected and 
transferred based on the same methods detailed above in section 2 about 
Laddr. 
 
At the PIs’ discretion, with the approval of the relevant IRB, and while the study is 
ongoing, any coded data from this project may be shared with other research 
team members within the Marsch/Poldrack SOBC research team, and any de-
identified data (i.e., stripped of all codes or other information that could be linked 
back to an individual participant) from this project may be shared with other 
researchers outside of the Marsch/Poldrack SOBC research team. 
 
After completion of the study, a de-identified dataset (i.e., stripped of all codes or 
other information that could be linked back to an individual participant) will be 
generated and made available to the research community as a whole. Informed 
consent procedures will ensure that participants are aware that consenting to 
participate in the study means consenting to inclusion in this open dataset. 
 
All information that is collected from the participants will be the minimum 
necessary, and de-identified to the maximum extent possible, to conduct the 
research. Study participants will be informed that research data collected about 
them will be stored at Stanford until they are no longer useful. It is estimated that 
the data will possibly be useful for 10 years, but the data may be useful and may 
continue to be retained indefinitely.  Eligibility data from the screening 
questionnaires will be retained for those who participate but will be deleted at 
the end of study for those who screen out or choose not to participate. Data may 
be retained indefinitely for those who screen in and sign consent, regardless of 
further participation. 
 
Potential Benefits of the Proposed Research to Human Subjects and Others 
Participants may learn self-regulation skills through the Laddr mobile application, 
which may have a positive impact on their smoking (in the smoking sample) or 
binge eating behavior (in the binge eating sample). Additionally, the information 
to be gained from their participation may benefit others in the future. 
 

 
27 
Importance of the Knowledge to be Gained 
The proposed activities are designed to identify valid and replicable assays of 
mechanisms of self-regulation across populations to inform an ontology of self-
regulation that can ultimately inform the development/refinement of health 
behavior interventions of maximal efficacy and potency.  Because the need to 
alter health-related behavior is ubiquitous across medicine, understanding the 
extent to which the principles of effective health behavior change, and the 
mechanisms by which they work, are similar or different across health conditions 
and settings is a critically important area of scientific inquiry.  It may inform more 
efficient, cost-effective, and patient-centered care.  This line of research may 
ultimately allow us to make great strides in crafting “precision medicine” 
approaches for a wide array of populations.  Given the importance of the 
knowledge to be gained, the risks to the participants are reasonable, as the risks 
are minimal and plans for protection against these risks are in place. 
 
Data and Safety Monitoring Plan 
Consistent with best practices, the Principal Investigator (Dr. Poldrack) will 
oversee all data and safety monitoring functions (described above) to ensure the 
safety of participants in the proposed study and to ensure the validity and 
integrity of the data obtained in the study.  The Principal Investigator will also 
regularly meet with the Project Manager, Research Assistants, and Co-
Investigators to track study progress and review these monitoring procedures.  
The Principal Investigator will regularly oversee all aspects of the study, including 
participant recruitment, informed consent, data collection, data management, 
and data analysis procedures, as well as regularly assess the risk/benefit ratio 
associated with participation in the study. 
 
The Principal Investigator will train all project staff to recognize and report any 
adverse event immediately to them.  Adverse events involving human participants 
include, for example, physical injuries, worsened physical or mental health, 
suicidal ideation, panic attacks, and depression.  Other adverse events may also 
include the inadvertent disclosure by research staff of confidential research 
information to other persons and/or to staff of criminal justice or government 
agencies. 
 
In the event that such adverse events are reported to the Principal Investigator, 
he will immediately inform the Chairperson of the appropriate Institutional 

 
28 
Review Board, who will make a decision about whether the reported event is a 
Serious Adverse Event (SAE) that must be reported to the National Institute on 
Drug Abuse (NIDA).  If the Principal Investigator determines that there is sufficient 
evidence of an adverse event to necessitate suspension of data collection, further 
IRB review, modification of the protocol, or other changes, the Principal 
Investigator will immediately discuss this recommendation with the Chairperson 
of the IRB and reach a determination of whether to suspend data collection or to 
stop the study from proceeding.  Resumption shall be based on the concurrence 
of the Principal Investigator, the Chairperson of the IRB, and any other relevant 
parties.  NIDA will receive a written report within three days of any such 
suspension and/or resumption of data collection. 
 
The Principal Investigator will provide an annual summary report of all adverse 
events to the IRB as part of the annual review and to NIDA as part of the annual 
Progress Report.  If no adverse events have occurred, the report will state, “No 
adverse events affecting human participants have occurred during this project 
year.” 
 

 
29 
Protocol Updates