Applying Novel Technologies and Methods to Inform the Ontology of Self-Regulation - Aim 4 Dartmouth Study: Using Mobile Technology to Improve Self-Regulation
Phase: NA
Conditions: Self-regulation, Binge Eating, Smoking
Sponsor: Dartmouth-Hitchcock Medical Center

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Table of Contents 
Introduction .................................................................................................... 3 
Objective ........................................................................................................ 4 
Study Design ................................................................................................... 5 
Samples .......................................................................................................... 7 
Inclusion criteria: .................................................................................................................... 7 
Exclusion criteria: ................................................................................................................... 8 
Recruitment .................................................................................................... 9 
Compensation ................................................................................................. 9 
Study Assessments ........................................................................................ 11 
Data Monitoring ............................................................................................ 13 
Sample Size ................................................................................................... 14 
Statistical Analysis Plan ................................................................................. 15 
Power and Sample Size Justification ............................................................... 16 
Protection of Human Participants .................................................................. 17 
Potential Risks: ..................................................................................................................... 17 
Adequacy of Protection Against Risks .................................................................................. 18 
Potential Benefits of the Proposed Research to Human Subjects and Others ...................... 24 
Importance of the Knowledge to be Gained ......................................................................... 24 
Data and Safety Monitoring Plan ......................................................................................... 25 
Protocol Updates ........................................................................................... 27 
 

 
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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. 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 
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 

 
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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. Fifty 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) (smoking in the former sample and binge eating 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. 
 
This protocol details the Aim 4 study at Dartmouth led by Multiple PI Marsch. 
 
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 

 
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among smokers (n=50) and overweight/obese persons with binge eating disorder 
(n=50) (smoking in the former sample and binge eating 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 Dartmouth’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 verified by phone and then scheduled for a study period 
of 28 consecutive days, with no in-person visit necessary. The study staff will 
determine whether possible participants are willing to consent to the study, and 
participants who consent will sign a consent form (electronically through The 
Experiment Factory via a checkbox, “next,” 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 the baseline 
assessments to take approximately 1.5–2 hours of participants’ time. Due to 
Experiment Factory’s requirements, participants must complete the baseline tasks 
and questionnaires using Google Chrome as their web browser. Participants will 
be asked to abstain from smoking (smoking sample only) and eating (eating 
sample only) for three hours prior to the start of the baseline assessments. 
 
During the study period (the details of which are below), participants will 
complete a series of questions related to self-regulation and contexts. The 

 
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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. These devices will be mailed to participants after they 
complete the baseline battery of tasks and surveys. 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 (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 6 

 
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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, and stationary time. 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. We will also track use 
of any other psychoactive medications used by participants in 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, which is expected to take 
approximately 1.5–2 hours of participants’ time. Due to Experiment Factory’s 
requirements, participants must complete the baseline tasks and questionnaires 
using Google Chrome as their web browser. Participants will be asked to abstain 
from smoking (smoking sample only) and eating (eating sample only) for three 
hours prior to the start of the follow-up assessments. 
 
Participants will be compensated in full at the end of their study period (after 
returning any borrowed research equipment) via an Amazon gift card (primary 
option) or a check. If participants request a check, they will be required to give 
their legal name, legal mailing address, and Social Security number. 
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 
• Access to a computer in a setting in which the participant is comfortable 

 
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providing sensitive information 
• Use a smartphone operating system compatible with Laddr 
 
• Additional inclusion criteria for binge eating sample: 
o 27 ≤ BMI ≤ 45 kg/m2 
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 an eating 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 
• 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 
• 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 

 
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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 Currently in therapy with a clinician for binge eating 
o Nickel allergy (because Fitbit band contains nickel) 
 
• Additional exclusion criteria for smoking sample: 
o Currently in therapy with a clinician for smoking 
o Binge eating behavior according to QEWP-5 (“yes” to questions 8 and 
9 and for question 10, at least one episode per week for three 
months). 
 
Recruitment 
Study advertisements will be posted online (e.g., Craigslist, Facebook, Google 
AdWords). 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, 21% non-White race, and 16% 
Hispanic/Latino ethnicity. 
 
We may selectively recruit and enroll based on gender, race and/or ethnicity to 
form samples that are representative of our populations of interest. We may do 
so by targeting ads based on gender, race, and/or ethnicity and by selectively 
enrolling (based on gender, race, and/or ethnicity) those who have screened into 
the study. 
 
Compensation 
Compensating participants for active study participation will help ensure 
exposure to the intervention intended to manipulate targets.  
 
Participants will be compensated according to the following schedule: 
 
$21 per participant for all baseline tasks and surveys (required to complete 

 
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full battery of tasks and surveys to receive this payment) 
 
$0.50 per EMA * 4 EMAs per day * 28 days = up to $56 
 
$10 bonus per week for completing minimum of 25 EMAS (~90% of 28 total 
EMAs in the seven-day period) * 4 weeks = up to $40 
 
$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 
 
$21 per participant for all follow-up tasks and surveys (required to complete 
full battery of tasks and surveys to receive this payment) 
 
Total: up to $250 over four-week study period 
 
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 and surveys (and for binge eating participants, 
after returning the research equipment; for smoking participants, after returning 
the headphone jack adapter if they borrowed one). 
 
The research team may deduct up to $55 from a binge eating participant’s 
compensation for failure to return, or for damage to, the wrist sensor and band, 
charging cable, or charging adapter (cube that plugs into wall). The research team 
may also deduct up to an additional $10 if the participant needs a replacement 
prepaid envelope to return the equipment. The research team may withhold all 
payment until the wrist sensor, band, charging cable, and charging adapter have 
been returned. 
 
Smoking participants are able to keep the carbon monoxide monitor as this device 
is intended for use by a single person. The research team may deduct up to $10 

 
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from a smoking participant’s compensation for failure to return, or for damage to, 
the headphone jack adapter if they borrowed one (required for carbon monoxide 
device using smartphones without 3.5 mm headphone jacks, including iPhone 7 or 
newer). The research team may also deduct up to an additional $10 if the 
participant needs a replacement prepaid envelope to return the adapter. The 
research team may withhold all payment until the adapter has been returned. 
 
The opportunity for compensation expires six months after the end of the 
participant’s study period. Each participant who is unable to be reached (e.g., 
after three tries) for compensation will be notified of this expiration date. This 
expiration is implemented to account for the end of study funding from the 
National Institutes of Health. 
 
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 
additional surveys have been well defined in the self-regulation literature, and 
others apply specifically to smoking or eating behavior. 
 
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. 
 
All tasks and surveys/subscales below were used in Aims 1 and/or 2 of this project 
at Dartmouth and/or Stanford. The numbers in parentheses indicate the number 
of items included in the surveys/subscales. 
 
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 

 
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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) 
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) 
• Theories of Willpower Scale (12) 
• Alcohol, smoking, and drug questionnaire from Aim 1 (36) 
• Patient Health Questionnaire (PHQ-8) (8) 
• Kessler Psychological Distress Scale (K6+) (14) 
• Demographic questionnaire from Aim 1 (30) 
o Baseline only 
o Revised from Aim 1 
• Other follow-up questions (3) 
o Follow-up 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) 
o Questions assessing last consumption of food prior to baseline and 

 
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follow-up assessments (3) 
 
• Additional for smoking sample: 
o Select set of smoking questions, including from the PROMIS Smoking 
Initiative (6) 
o Fagerström Test for Nicotine Dependence (6) 
o Questions assessing last consumption of nicotine prior to baseline 
and follow-up assessments (up to 10) 
 
Tasks at baseline and follow-up: 
• Stop-Signal Task 
• Motor-Selective Stop-Signal Task 
• Delay Discounting Titration Task 
 
Ecological momentary assessment (EMA) during study period: 
• Momentary self-regulation questionnaire 
• 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 
 
We expect the entire baseline and follow-up batteries to each take approximately 
1.5–2 hours.  
 
Each EMA is designed to be completed in less than five minutes. The average EMA 
completion time in Aim 2 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 

 
14
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 
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. 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. 

 
15
 
Statistical Analysis Plan 
To evaluate the degree to which changing self-regulation is associated with 
changes in health behavior (measured as daily smoking abstinence in smokers and 
daily binge eating 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 are 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 
self-regulation and health behavior. 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 the 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 health behavior. One model for each subscale of the 
momentary self-regulation measure will be fit. In each model, momentary self-
regulation will be the predictor and daily health behavior (daily smoking 
abstinence for smokers and daily binge eating for overweight/obese participants 
with binge eating disorder) will be the outcome. These models will identify the 
influence of momentary self-regulation on health behavior. Together with the 
previous set of models, the components of a mechanistic effect of momentary 

 
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self-regulation on health behavior 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 health behavior as well as the direct 
effect of context on health behavior, 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 
of an indirect effect. This mediation model will allow us to estimate the impact of 
self-regulation as a mechanism for poor health behavior 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 health 
behavior. 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 health behavior.  
 
Power and Sample Size Justification 
With up to 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 

 
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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 
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 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. 

 
18
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 
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 

 
19
will be offered an electronic or paper copy of the form. Their screening 
information and contact information will be saved in REDCap, and their 
agreement to participate will be saved in The Experiment Factory. 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 mobile sensing of physical activity (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. 
 
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 

 
20
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 Dartmouth, 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 Dartmouth 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 unless participants voluntarily share their own identifying information, 
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 
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 

 
21
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 survey; (2) baseline and follow-up tasks and surveys; (3) EMA and Laddr 
(mobile intervention); (4) mobile sensing of physical activity (binge eating 
participants only); and (5) carbon monoxide monitor (smoking participants only). 
 
(1) 
We plan for the eligibility survey batteries and resulting data to be 
available and coordinated through Dartmouth’s Research Electronic Data 
Capture (REDCap) system. All eligibility survey data will be either acquired 

 
22
using REDCap forms or stored directly in REDCap upon acquisition. 
 
(2) 
Baseline and follow-up task and survey 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 
and survey 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. Coded data will be copied via an encrypted 
Internet connection from the Stanford site to the Dartmouth site. 
 
(3)  
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. 
 
Dartmouth research staff will use an application, developed by the 
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. 
 
(4)  
(Binge eating participants only) Mobile sensing data will be used to 

 
23
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. Dartmouth 
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 3 about Laddr. 
 
(5) 
(Smoking participants only) The CO monitors connect directly to 
participants’ smartphones via the headphone jack. The CO readings will be 
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 

 
24
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 Dartmouth 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. 
 
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 

 
25
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. Marsch) 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, 
she will immediately inform the Chairperson of the appropriate Institutional 
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. 
 

 
26
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.” 
 

 
27
Protocol Updates 
 
December 12, 2018 
• Moved baseline and follow-up surveys from REDCap to The Experiment 
Factory 
• Clarified language in the statistical analysis plan 
• Added details to compensation structure 
 
March 13, 2019 
• Consent saved in The Experiment Factory 
• Updated eligibility criteria 
o Include in binge eating sample based on confirmed interest in eating 
intervention rather than physical activity intervention 
o Exclude from binge eating sample based on current therapy with a 
clinician for binge eating and on nickel allergy (because of Fitbit) 
o Exclude from smoking sample based on current therapy with a 
clinician for smoking 
• Updated compensation structure and added further details 
• Updated list of tasks and surveys. Added: 
o Theories of Willpower Scale 
o Alcohol, smoking, and drug questionnaire from Aim 1 
o Patient Health Questionnaire (PHQ-8) 
o Kessler Psychological Distress Scale (K6+) 
o Demographic questionnaire from Aim 1 (revised) 
§ Baseline only 
o Motor-selective stop-signal task (and removed stimulus-selective 
stop-signal task) 
• Clarified that Small Steps Labs LLC will not receive identifying information 
unless participants voluntary share this information with the company 
 
July 11, 2019 
• Added three-hour abstinence period before baseline and follow-up 
assessments (smoking participants are asked to abstain from smoking; 
binge eating participants are asked to abstain from eating) 
o Added corresponding questions at baseline and follow-up asking 
about last use of nicotine (smoking sample) and last consumption of 
food (binge eating sample) 

 
28
• Added inclusion criterion based on computer access 
• Added requirement that participants use Google Chrome as their web 
browser when completing the baseline and follow-up batteries 
• Added to recruitment section that we may selectively recruit and enroll 
based on gender, race, and/or ethnicity to create samples that are 
representative of the populations of interest