Using Mental Imagination to Prevent Excessive Gestational Weight Gain in Overweight and Obese Pregnant Women
Phase: NA
Conditions: Obesity, Weight Gain, Maternal
Sponsor: Ohio State University

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IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
08/06/2020 
Research Protocol 
 
 
Using mental imagination to prevent excessive gestational weight gain in 
overweight and obese pregnant women 
 
I. 
Objectives   
 
1. To determine feasibility of the goal-oriented intervention: recruitment, randomization, retention, and 
intervention implementation. 
 
2. To investigate the potential efficacy of the intervention on gestational weight gain (primary outcome) 
and maternal and birth outcomes (secondary outcomes: gestational diabetes, gestational hypertension, 
mode of delivery, length of labor, apgar score, new born body weight, and premature baby).  
 
Primary hypothesis. A higher proportion of intervention participants will have healthy gestational 
weight gain than the usual prenatal care participants 
 
Secondary hypothesis. A lower proportion of intervention participants will have gestational diabetes, 
gestational hypertension, cesarean delivery, premature birth, longer duration of labor, unhealthy birth 
weight (>4000 or < 2500 grams), premature birth, and APPGAR score ≤ 3 than the usual prenatal care 
participants.   
 
3. To investigate the potential impact of the intervention on lifestyle behaviors: diet (caloric, fat, sugary 
drink, fruit and vegetable intakes) and physical activity (walking steps and energy expenditure). 
 
Hypothesis 1. A higher proportion of intervention participants will eat healthier than the usual prenatal 
care participants. 
 
Hypothesis 2. A higher proportion of intervention participants will have more walking steps and higher 
energy expenditure than the usual prenatal care participants. 
 
4. To investigate the potential intervention effects on motivation (autonomous motivation, self-efficacy, 
consideration of future, happiness, and hope), emotion (emotion control, stress, and depressive 
symptoms), cognition (executive function and impulsiveness) and psychological eating (cognitive 
restraint eating, emotional eating, overeating and eating out of boredom)   
 
Hypothesis 1. A higher proportion of intervention participants will have higher motivation and self-
efficacy, better emotion, higher cognition, and lower psychological eating than the usual prenatal care 
participants. 
 
II. 
Background and Rationale 
A.1. Overweight or obese pregnant women. Nearly 56% of American women aged 20-39 are overweight or 
obese.1 These women are at least two times more likely than normal weight women (65-85% for overweight or 
obese vs. 34% for normal weight) to experience excessive gestational weight gain (EGWG),2-8 exceeding 
Institute of Medicine (IOM) pregnancy weight gain guidelines.9 EGWG is a serious public health problem 
because it negatively influences maternal and birth outcomes: gestational diabetes,10 gestational 
hypertension,11 cesarean delivery,12 and fetal macrosomia (birth weight > 4000 gm).12,13 EGWG is also a strong 
predictor of significant postpartum weight retention (retaining ≥ 10 lbs), which is associated with lifelong obesity 
in mothers14 and childhood obesity.15,16 Pregnancy is a teachable movement for weight management and 
obesity prevention.17 Therefore, it is imperative to help overweight or obese pregnant women prevent EGWG. 

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A.2. Previous lifestyle behavior intervention studies. Healthy lifestyle behaviors (healthy eating and 
physical activity, PA) can prevent EGWG in overweight or obese pregnant women.18-28 Efficacious strategies 
identified in prior studies involve personalized caloric restriction,21-25 frequent in-person meeting attendance22-24 
or phone counseling25,26 and frequent text messages requiring responses.26 Such interventions have limited 
practicality, scalability, and sustainability due to high participant burden and excessive cost for clinical practice. 
Also, adherence was problematic,22,26,29-31 perhaps because of educational materials and counseling that were 
prescriptive and not sufficiently flexible or tailored to individuals.32 Moreover, the prior studies paid little or no 
attention to motivation, emotion and executive function, all of which are crucial for healthy lifestyle behaviors.  
  
A.3. Conceptual framework (Figure 1). We use future time perspective (FTP) theory (the present 
anticipation of future goals or personal experience in the past, present and future33) as a guiding conceptual 
framework. FTP drives human motivation and behavior in everyday life34 and is crucial to motivate individuals 
to perform an activity.35 FTP concepts include motivation,36 emotion,34 and cognition.37 Implementation of FTP 
has focused on episodic future thinking (EFT).37 Our goal-oriented (GO)EFT intervention -- vivid imagination 
(visualization) of goal-relevant future events in the individual’s life38 -- is designed to improve motivation 
(autonomous motivation, AuM and self-efficacy, SE), emotion (emotion control, EC and stress), and cognition 
(executive function, ExF), all of which promote success in achieving goals for lifestyle behaviors. Key 
behaviors include healthy dietary intake (less caloric, fat and sugary drink intake and more fruit and vegetable 
intake) and PA (more walking steps and energy expenditure). Key health outcomes are gestational weight gain 
(GWG, primary outcome), gestational diabetes (GDM), gestational hypertension (GHT), cesarean delivery and 
fetal macrosomia (secondary outcomes).  
 
 
Connecting EFT to proposed mechanisms. GOEFT39 is a promising approach to improving 
motivation, emotion and cognition (the key proposed mechanisms connecting the intervention to the targeted 
lifestyle behaviors and health outcomes). Neuroimaging40-43 and fMRI44-46 studies have shown that EFT 
activates the common core network of brain regions associated with ExF during daily activity and brain regions 
associated with emotion regulation, decision-making and memory. Influences on motivation: AuM and SE. 
AuM, will to engage in a behavior because of personal value, interest or choice, is important for achieving 
one’s goals.47 SE refers to beliefs that one can successfully undertake an action.48 EFT increases motivation 
by facilitating the link between goals and actions and by enhancing the subjective likelihood and/or value of a 
goal.49 Influences on emotion: EC and stress. EC, one’s ability to manage emotional reactions using 
appropriate strategies, is associated with ability to cope with stress.50 EFT improves emotion44,51-53 because 
EFT is emotionally positive and effectively influences emotion.54 Influences on cognition: ExF. ExF enables 
individuals to coordinate thoughts, actions, and emotions to achieve healthy lifestyle behaviors55,56and positive 
health outcomes.56,57 ExF includes inhibitory control (important for controlling one’s emotions, staying focused 
and resisting temptation to overeat and over react57), memory, reasoning, problem-solving and planning.57 ExF 
enables individuals to take goal-directed action.55 EFT, especially GOEFT,39 effectively increases inhibitory 
control (including in overweight or obese women58-61), thus reducing energy intake39,59,61,62 and promoting 
weight management63 by shifting the time perspective of intertemporal decision making64 and activating brain 
areas associated with prospection.65 Also, EFT fosters detailed generation in memory, more relevant steps in 

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problem solving66 and detailed steps to attain a goal49 and increases prospective memory.67 Finally, EFT 
increases reasoning, problem-solving and planning.44,49,66,68,69 Thus, there are substantial reasons to expect 
EFT to influence the key proposed mechanisms. Yet, there are limitations of prior EFT health behavior studies. 
With the exception of one “web plus in-person” four-week intervention,63 all prior EFT studies have been 
conducted in lab settings where participants followed intensive and specific scripts to vividly imagine future 
events. Thus, the relevance and scalability of EFT for clinical practice remains speculative. Also prior EFT 
interventions have only focused on one form of EFT: episodic simulation (specific mental representation of the 
future).44  
Connecting proposed mechanisms to each other and to lifestyle behaviors and health 
outcomes. Motivation and emotion are inter-related components of self-regulation that enable individuals to 
adhere to healthy lifestyle behaviors and achieve positive health outcomes.70 Motivation. Increased AuM and 
SE are strongly associated with reducing stress in overweight or obese women71,72 and promoting cognitive 
performance73 (e.g., future thinking and problem solving74), and healthy lifestyle behaviors.71,75-80  Also, AuM 
predicts success in reaching goals33,35 and promotes weight management.79,81-83 Emotion influences 
cognition.84 Stress, which is highly prevalent in pregnant women,85,86 negatively affects diet (increased intake of 
energy-dense foods that are high in fat and added sugar, leading to weight gain87) and PA.88 Higher levels of 
stress are associated with lower levels of inhibitory control87 and interfere with cognitive performance,89 but 
reducing stress improves ExF.90 Cognition (ExF). Whereas low levels of inhibitory control have been 
associated with increased energy intake in overweight or obese women,58 high levels of inhibitory control have 
been associated with reduced consumption of total calories, percent calories from fat,39,61 snacking and food 
intake59,62 in women. ExF also predicts moderate-to-vigorous PA56 and maintenance of PA91 and weight loss.56 
ExF deficits are more likely to occur in overweight or obese than normal weight women92-95 and can be 
improved through training and practice.90 Thus, previous research supports the assumed associations among 
the key mechanisms and connects those mechanisms to lifestyle behaviors and health outcomes.  
A.4. Scientific premise. The proposed R21 builds on the strengths and addresses limitations of prior 
studies. Strengths. Lifestyle behavior interventions can prevent EGWG, and EFT improves motivation, 
emotion, ExF, lifestyle behaviors, and weight management. Limitations. Prior lifestyle interventions did not 
apply FTP, which focuses on motivation, emotion and ExF (critical concepts for promoting healthy lifestyle 
behaviors and health outcomes). Also, they were too prescriptive and time-consuming for participants (difficult 
to scale and sustain in practice). Prior EFT studies were mainly conducted in lab and only applied episodic 
simulation. We propose a 20-week self-directed, web-based intervention (35-40 min/week) and include three 
forms of EFT: episodic simulation, intention (goal setting), and planning (organization of steps for 
accomplishing a goal).96 Our intervention will efficiently apply GOEFT to address motivation, emotion, ExF, 
lifestyle behaviors and health outcomes (C.3.6). This proposed study will add scientific knowledge in design 
and delivery of lifestyle interventions aimed to prevent EGWG in overweight or obese pregnant women.  
 
A. 
Research Design 
This proposed pilot randomized controlled trial (RCT) aims to (1) determine feasibility of the GOEFT 
intervention and investigate the potential efficacy of the intervention on (2) GWG and maternal and birth 
outcomes, (3) lifestyle behaviors, and (4) motivation, emotion, and cognition. We will enroll 90 overweight or 
obese pregnant women (50% White, 50% minority). All measures will be assessed at baseline (T1, ≤15 week-
gestation), at 24-27 week-gestation (T2) and at 35-37 week-gestation (T3) 
 
B. 
Sample 
Inclusion criteria. Participants must be pregnant women ≤ 13 week-gestation with a single fetus as assessed 
by ultrasound (Research staff or Dr. Schaffir, Co-I, a board-certified Ob/Gyn at a collaborating clinic will verify 
eligibility from the patient’s Electronic Health Record, EHR). Participants must also have self-reported (1) pre-
pregnancy body mass index (BMI) of 25.0-45.0 kg/m2 and height (we will use height and weight to compute 
body mass index).  (2) ability to read and speak English, (3) age of 18-45 years, (4) access to a working smart 
phone with unlimited text messaging (89 to 94% of American adults aged 18 to 49 years own a smart phone)97 
and access to internet and (5) receipt of prenatal care and from our collaborating clinics and plan to deliver the 
baby at The Ohio State University (OSU) Wexner Medical Center, 6) a resident of Franklin Count, and (7) 
committed to the 20-week intervention.   

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Exclusion criteria. Self-reported (1) history of ≥ 3 miscarriages, (2) planned termination of the pregnancy, (3) 
diagnosed hypertension and type 1 or 2 diabetes, (4) history of or current diagnosis of an eating disorder, (5) 
serious current physical disease (e.g., renal disease or cancer), (6) past bariatric surgery, (7) current or history 
of substance abuse in the past 6 months, (8) current treatment for a serious psychological disorder (e.g., 
schizophrenia and bipolar disorder) or (9) contraindications to walking. Consented women will become not 
eligible to participate in the study if they are not randomized by 16-week 6 days gestation (see D. Detailed 
Study Procedure: Recruitment and enrollment)—this is because the study intervention starts ≤ 17 weeks 
gestation. Also, women will become not ineligible for participation if they did not complete the baseline data 
(T1): online survey via REDCap, two 24-hour dietary recall and wear the Actigraph monitor for at least 4 
consecutive days with 6 hours per day. 
 
 
Sample size/power. Our primary outcome variable is weekly gestational weight gain (GWG). A final 
sample size of 72 women (36 per group, after accounting for 20% attrition from the 90 enrolled) will have 80% 
power to detect a time-averaged between-group difference in weekly GWG with a medium effect size 
(standardized between-group difference of 0.6). This effect size is translated to a between-group difference of 
0.33 lb in weekly GWG, assuming a common standard deviation of 0.55 lb (or 0.25 kg) based on previous 
research.27 The power analysis was conducted using mixed-effect linear modeling with a two-sided 
significance level of 0.05, assuming a correlation of 0.7 between the two weekly GWG measures. Due to the 
pilot nature of the study, our sample size is not powered to detect smaller effect sizes (<0.6). Nevertheless, we 
will report point estimates, effect sizes, and 95% confidence intervals. These estimates along with clinical 
significance will guide the results interpretation and sample size determination for a future full-scale RCT 
(R01).    
 
C. 
Measurement/ Instrumentation 
All participants will be assessed at  16 week-gestation (T1), 24-27 week-gestation (T2) and 35-37 week-
gestation (T3). Survey data will be collected online using password-protected security-ensured Research 
Electronic Data Capture (REDCap), a secure web application for building and managing online surveys and 
databases. Primary and secondary outcomes will be extracted from electronic health record. An incentive of 
$40 will be provided for participation in each point of data collection for all measurements. 
 
Feasibility. We will use our tracking records to assess recruitment, randomization and retention. To assess 
intervention implementation, we will extract data from our study web site that will track and capture details 
about all activities (e.g., amount of and type of activities completed and % of participants used type in box). We 
will record the attendance of individual coaching session. Intervention participants will report their motivation 
and barriers preventing them from engaging in the intervention activities and evaluate the usefulness of with 
each EFT intervention component.  We will also use semi-structure interview questions (up to 20 minutes/an 
individual interview via zoom) to ask participants to evaluate the intervention. We will use website tracking 
data, individual coaching session, and results of phone interview to revise the intervention contents for a future 
large scale intervention study.  
 
Primary outcome: GWG (in lbs).  Body weight or gestational weight gain will be extracted from participants’ 
electronic health record. 
To compute weekly GWG, we will subtract the measured weights between two adjacent time points (T2 vs. T1; 
T3 vs. T2) then divide by the number of weeks between the two time points. The IOM recommends that 
overweight women (BMI 25.0-29.9) be limited to total GWG of 15-25 lbs and obese women (BMI  30.0) to 11-
20 lbs.9 To compute the total GWG, we will subtract the self-reported pre-pregnancy weight from weight 
measured at T3. A woman will be identified as having excessive gestational weight gain if her total gestational 
weight gain exceeds the Institute of Medicine’s criteria.   
 

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     Version:  
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Secondary outcomes: gestational diabetes, gestational hypertension, mode of delivery, length of labor,  
apgar score, new born body weight, and premature baby will be accessed by Dr. Schaffir (Co-I) or research 
staff from the participants’ electronic health record.  
 
Lifestyle behaviors. Dietary intake will be assessed using the NCI Automated Self-Administered 24-hour 
recall (ASA24). Participants will complete 24-hour recalls on two random days over a week.99,100 The variables 
of interest include calories, fat, sugary drinks, fruit and vegetable intakes. Physical activity will be assessed 
using Actigraph (GT3x), an objective measurement of walking steps and energy expenditure. We will distribute 
the Actigraph in person (T1, C.3.2) and mail it to participants’ home (T2 and T3) to wear at the waist for  five 
consecutive days (≥ 10 hours/per day) except showers/baths and water activities. Then, they will return the 
Actigraph with steps recorded when we measure their weight at T1, T2 and T3. Psychological eating. We will 
use the Modified Three-Factor Eating Survey (18 items) to assess emotional eating (3 items), overeating (9 items) and 
restrained eating (6 items)101 to measure psychological eating.  Eating out of boredom.  We will use the modified 
emotional eating scale (8 items).102  
 
Concepts. Motivation. Autonomous motivation will be measured using Treatment Self-Regulation 
Questionnaire (18 items) that asks why the respondent does a behavior.103 Self-efficacy will be measured 
using a 10-item survey for coping self-efficacy,104 an 8-item survey for healthy eating self-efficacy,105 and a 10-
item survey for physical activity self-efficacy104 that ask participants’ confidence in performing the specific 
activity. Consideration of future will be measured using consideration of future consequences 14 scale (14 
items: 7 items measuring future consequences and 7 items measuring immediate consequences).106 
Happiness will be measured using the subjective happiness survey (4 items).107  Hope will be measured using 
Snyder Hope Survey (12 items).108 Prenatal anxiety will be measured using the revised prenatal distress 
questionnaire (17 items).109 Food insecurity will be measured using USDA Food Insecurity Survey (18 
items).110  
 
 
Emotion. Emotion control will be measured using the Emotion Regulation Questionnaire (10 items) that assess 
emotion regulatory process using reappraisal, suppression and regulating negative emotion.111 Stress will be 
measured using The Perceived Stress Scale (10 items)112 that measures the degree to which situations in 
one’s life are appraised as stressful.  Depressive symptoms will be measured using the 10-item Edinburgh 
Postnatal Depression Scale113 
 
Cognition. Executive function will be measured using The Behavior Rating Inventory of Executive Function-
Adults (BRIEF-A,75 items).114 This survey measures an adult's executive function in her everyday 
environment: for example, inhibitory control, self-monitoring, plan/organization, and organization of materials. 
BRIEF-A has been used in prior RCTs and is sensitive to detect changes in executive function over time.115-117  
Impulsiveness will be measured using Barratt Impulsiveness Scale (30 items).118 
 
Process evaluation. All participants will report receipt of lifestyle behavior counseling from their clinicians, 
midwifes and dietitians and joining other programs.  
 
Sent messages to participants 
We will email and text participants to complete study activates. Please note the sequence listed below 
corresponding to the sequence listed on the file called “All Email and Text Messages.” 
 
Activities 
Email Text 
Notes 
#1 Attend information session (first zoom meeting) 
Yes 
Yes 
Up to 3 times  
#2 Consent to full participation (Consent and HIPAA 
Authorization) 
Yes 
NO 
One time 
#2-A. REDCap for electronic signature 
Yes 
Yes 
One time 
#3. Delivery and pick up Actigraph 
Yes 
Yes 
Up to 3 times 
#4 Attend second zoom meeting 
Yes 
Yes 
Up to 3 times  

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Intervention Phase 
 
 
 
1A: Complete Part I intervention: becoming a better 
me 
Yes 
Yes 
Need to send both email and text (at 
the same time) because of including 
web link—participants can complete 
via smart phone or computer internet 
access. Up to 3 times for each 
2A. Complete Part II intervention: safe care booster 
Yes 
Yes 
Need to send both email and text (at 
the same time) because of including 
web link. Up to 3 times  
3A. Join the individual coaching via zoom 
Yes 
Yes 
Up to 3 times 
Throughout the project 
 
 
 
1A. 24-hour dietary recall 
Yes 
Yes 
Need to send both email and text (at 
the same time) because of including 
web link. Up to 6 times (3 
times/dietary recall) 
2A. Wear Actigraph 
No 
Yes 
Up to 7 times (1/day) when 
participants wear the Actigraph 
3A. Fill out online survey 
Yes 
Yes 
Need to send both email and text (at 
the same time) because of including 
web link. Up to 3 times  
 
4A. Cohort retention 
 
Yes 
 
Yes 
 
Monthly 
5A. Notify Incentives in Email 
No 
Yes 
Up to 3 times 
 
 
D. 
Detailed study procedures 
Recruitment and enrollment. We will use our previously successful strategies119 to get clinical care 
providers (e.g., Ob/Gyn and Midwives, hereafter providers) at five collaborating OSU prenatal care clinics to 
refer first trimester pregnant women to the study. Drs. Chang (PI) and Schaffir (Co-I) will meet with them to 
present the study purposes and requirements and demonstrate the web-based intervention. Study flyers will be 
posted in high-traffic and waiting areas at the clinics and in other locations near our study sites, e.g., pediatric 
clinics. The medical assistants and/or receptionists at each clinic will distribute the study flyer to participants. 
Potential participants who are interested in the study will ask their providers about the study or the providers 
will initiate the conversation about the study with the potential participants. Providers will put the potential 
participants’ chart into IHIS Inbox of trained study staff for screening if the potential participants expressed 
interest in learning more about the study. Next, the trained research staff will log into IHIS to make an initial 
screening (for example, gestational age based on ultrasound record and body mass index [if available]). If they 
are potentially qualified to participate, we will perform “an initial contact of potential participants “(Described 
below). 
Initial Contact of Potential Participants -  Research staff will contact potential participants by phone 
to further determine eligibility. We will obtain verbal consent prior to screening and obtaining demographic 
information.  Collecting demographic data will help us revise or plan for recruitment strategies for a future R01.  
If eligible, participants will provide up to 3 telephone numbers (at least 1 capable of receiving text messages), 
email address, and physical address as contact information. We will ask if we can leave a message via phone 
(Yes/No).  Next, the Research staff will schedule a zoom meeting (individual information session, described 
below) within the five business days with the qualified participants. Then, the trained research staff will send 
the full consent form to the participants for review (via email) prior to the first scheduled zoom meeting. 
Participants will be informed the zoom meeting will be either audio or video recorded per their preferences.  
First zoom meeting (information session lead by research staff). Participants will use their  
personal devise, for example, computer or smartphone  to join the zoom meeting, which will take up to 60 
minutes. First, the trained research staff will ask participants if they have questions and answer questions 
accordingly. Next, they will review key summary of incentive and intervention requirements (using “share” 
function in zoom) with the participants and answer questions that they may have.  Also, research staff will ask 

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the potential participants to think through their current and anticipated responsibilities and life situations before 
providing verbal consent for participation. After that, the research assistant will electronic  consent via REDCap 
follow by showing them how to complete data collection activities (online survey, 24 hour-dietary recall and 
Actigraph) and requirements. Then, participants will be asked to self-generate reminders to complete data 
collection activities. Finally, participants will be informed about the purpose of the second zoom meeting. They 
will also be informed that participants who complete all data collection activities must join the second zoom 
meeting by 16 weeks and 6 days gestation. Otherwise, they will become not eligible to participate in the study.  
This is because our intervention must start at or prior to 17 weeks gestation.  
Second zoom meeting (either audio or video recorded per participant preferences): Randomization. 
Participants will be randomly assigned to an intervention or usual care group (1:1 ratio).  
Usual care group. We will thank women in the usual care group and end the zoom meeting.  
Intervention group.  Intervention participants will be asked to self-generate 3-5 text messages to remind them 
to complete the intervention activities and join brief individual coaching via zoom. Next, they will receive a link 
to complete part I intervention activities, which will take up to 30 minutes to complete. Participants will use their 
first and last name, and birthday and own device (e.g., smart phone) to log into the intervention website and 
complete activities, while the research staff still on Zoom to answer women’s questions if they have.  After 
completion of the Part I intervention activities, the research staff will schedule an individual coaching session 
via zoom with the trained interventionist within the next two days. Participants will be informed that each 
coaching session will be recorded (either audio or video per their preferences). The recording will be 
transcribed and be analyzed to help us revise the individual coaching sessions for future studies. We will send 
a zoom link to participants to join the individual coaching session.  
 
Cohort retention. We will apply our previously successful retention strategies. The RA will make a 
monthly retention call to maintain relationships and ask for updated contact information and pregnancy status.. 
We will allow temporary lapses as needed (e.g., partial data collection) or extend the time window for data 
collection. We will monitor the retention rate monthly and keep retention logs by asking participants over the 
phone about their reasons for dropout and any adjustments that could keep them in the study.  
 
 
Randomization. Dr. Tan (Co-I, biostatistician) will generate a randomization schedule. The Project 
Director will use the randomization schedule to randomize participants to an intervention or usual care group. 
Our randomization protocol will utilize a stratified permuted-block algorithm.121,122 Specifically, women in each 
race stratum (White vs. minority) will be randomly allocated 1:1 to intervention or usual care using permuted 
block randomization with varying block sizes of 2 or 4. 
 
 
Usual care. All study participants will receive usual prenatal care from their obstetrician or midwife. The 
usual prenatal care visit occurs monthly until 28 week-gestation, every other week from 28-36 week-gestation 
and weekly from 36-week until delivery. At our collaborating clinics, pregnant women are weighed at each 
prenatal visit and will receive additional healthy eating counseling, e.g., if they have GDM. To improve retention 
of women randomly assigned to the usual care group, we will email study newsletters every other month with 
general information about pregnancy-related health (e.g., over-the-counter medication). 
 
 
Intervention: A self-directed, web-based GOEFT lifestyle behavior intervention (tailored to participants’ 
needs). Intervention mode. Intervention participants will receive all aspects of usual care plus a self-directed, 
web-based GOEFT lifestyle behavior intervention. Previous self-directed, web-based interventions have 
effectively promoted weight management in overweight or obese adults.123-125  Also, EFT has been delivered 
on the web and shown promise for promoting weight management.63,126 Intervention duration, dosage and 
topics. The intervention will last 20 weeks (start 17 weeks gestation), a duration effectively preventing EGWG 
in overweight or obese women.20-28 Intervention participants will complete weekly online activities (35-40 
min/week) via their smart phone or internet access from any location. The intervention includes three topics: 
stress management, healthy eating, and PA (Figure 2). Intervention (long-term) goals. Participants are 
strongly encouraged to (1) daily manage stress and emotional reactions using positive strategies, (2) daily eat 
a diet low in fat and consume less sugary drinks, (3) daily eat a diet high in fruits and vegetables, and (4) walk 
at a brisk pace for 30 min most days a week.127 

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Figure 2. Topics for the 20-week intervention. SM = stress management, HE = healthy eating. SM includes three subtopics (e.g., 
better ways to handle everyday life) and 13 short-term goals (e.g., have a better relationship with family). HE includes four subtopics 
(e.g., effective ways to reduce junk food intake) and 11 short-term goals (e.g., daily eat less junk food and be mindful what I eat). PA 
has one subtopic and three short-term goals (e.g., being more physically active outdoors). Our intervention has greater emphasis on 
SM and HE than PA, because stress impairs ExF57 and affects dietary intake,88,128 which is a strong predictor of EGWG.27 
 
1 
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4 
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7 
8 
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10 
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20 
Weekly web (30-35 min/week) 
S 
H 
PA 
S 
H 
PA 
H 
S 
H 
H 
S 
H 
PA 
S 
H 
PA 
H 
S 
H 
H 
Individual coaching via zoom 
(15 min/call, 10 calls) 
x 
x 
x 
x 
 
x 
 
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x 
 
 
Intervention development based on preliminary work. Informal interviews with stakeholders. We 
informally met with several clinicians who provided prenatal care to the target audience to inform our mode of 
intervention delivery. They suggested a self-directed, web-based intervention because of its easy 
implementation and future scalability to overcome clinicians’ time constraints to providing additional information 
on stress management, healthy eating and PA to help women manage their weight. Study one. We conducted 
seven focus group discussions with overweight or obese pregnant women (N = 96) to identify their critical 
needs in stress management, healthy eating, and PA. Women reported, for example, poor relationships with 
significant others, feeling emotional, eating foods for comfort, and lack of motivation to be physically active.129 
Results of this study were used to develop the pre-written short-term goals for the participants (Figure 2) 
because most women had challenges in goal setting. Study two. Below, we present lessons learned (LL) from 
our prior NIH-NIDDK R18 intervention study of overweight or obese women of child-bearing age130 to develop 
the two parts of the GOEFT intervention (Figure 3). Part I. Motivation. LL: Personal values and interest (AuM) 
motivated women to make positive lifestyle behavior changes. Many women had low commitment and 
confidence (SE) to implement plans/steps to achieve personal values and make positive changes. Emotion 
and cognition. LL: Realizing the importance (i.e., the potential benefits) of accomplishing personal goals and 
responding to open-ended questions (e.g., WHAT and WHY) helped women aware of current life 
situations/challenges and motived them to make positive changes. Yet, most women faced challenges in 
setting goals and identifying specific steps to accomplish the goals. Also, many challenges (e.g., lack of 
willpower, time, or energy) prevented them from implementing their plans. Including explicit planning and 
(HOW) material for how to overcome challenges should buttress the effectiveness of the current intervention. 
Part II: Evaluation of goal progress with feedback. LL: women were often unaware what strategies helped them 
accomplish their goals. They often gave up when unaware of the progress toward their goals or the benefits 
received from making positive changes. Based on the conceptual framework (Figure 1) and results of the 
preliminary work, Drs. Chang (an expert in healthy lifestyle behavior interventions including stress 
management) and Wegener (Co-I, an expert in psychological emotion and cognition research) worked with five 
peers of the target audience to develop the proposed self-directed, web-based GOEFT intervention (Figure 3). 
After developing the draft intervention, we used feedback from several additional peers of the target audience 
to review and finalize the intervention. 
 

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 Intervention implementation (Figure 3). After randomization and while intervention participants are 
still at the central study clinic (Visit 2), the Project Director will be present and provide the intervention web link 
and instructions on completing the weekly intervention activities online. Intervention participants will then use 
their smart phone, first and last name, and birthday to log into the website to complete the Part I intervention 
activities for week one. The day of the week that the participants complete the Part I intervention activities at 
WIC/HS will count as their weekly day one. The Project Director will answer questions and provide technical 
support as needed. S/he will observe and record the extent to which participants need assistance from the 
study office to complete the EFT intervention activity throughout the 20-week intervention. Participants who 
need substantial assistance will provide the best time to call so 
a research assistant can help them complete the intervention 
activities (e.g., read the responses to them over the phone). All 
participants will be given the study office number to call for 
questions and technical problems. Participants will use their 
own device to complete Part II intervention activities for week 
one and the additional 19 weeks of the intervention at 
convenient times and locations. We will send the web link to 
participants weekly via email and text with an “intervention 
adherence” text message reminder (generated by the 
participants) to log in and complete the intervention activities. 
We will lend hotspot connections to intervention participants 
who lose their internet connection and are unable to access the 
intervention website outside their home.  
 
Part I (weekly days 1-4, 30-35 min/week): Motivation, 
emotion and cognition. Motivation. Participants will first be 
asked to visualize, then use a dropdown menu to select their 
responses (or type in a box) for the following: their personal 
values and ways to help them commit to and increase 
confidence in achieving their personal values. Emotion and 
cognition. First, participants will select a subtopic from the 
week’s designated topic (Figure 2) followed by selecting a pre-
written short-term goal (or typing in a box) under the chosen 
subtopic that meets their need for that week’s focus (Women can select the same short-term goal up to two 
times during weeks 1-10 and two times during weeks 11-20.) Then, they will visualize and describe WHAT the 
week’s goal is, WHY it is important, WHEN, WHERE, and with WHOM it will take place, and HOW it can be 
accomplished, all of which enhance prospective memory, thus enabling individuals to carry out the plan to 
reach the goal.131 Related to HOW, they will be asked to view an example with three specific detailed steps to 
achieve their chosen goal. Step I. Use open-ended questions to ask themselves, thus to raise awareness of 
their current life situations/challenges (e.g., How often do I eat junk foods?). Step II. Take specific steps to 
overcome the challenges to achieve the chosen goal (e.g., pay attention to foods I eat and how much I eat). 
Step III. Record ways to reward themselves without using foods (e.g., smile and tell myself, “Wow, I am proud 
of myself of eating less junk food and being mindful what I eat, each time I follow through my plans”). After that, 
participants will visualize and describe their three steps (by typing) to accomplish the chosen goal. Next, they 
will repeat the same process for a second short-term goal for the week. Motivation. they will visualize and use 
the dropdown menus to select (or type in a box) (1) their three most important challenges (e.g., I don’t have the 
willpower) in implementing their steps to accomplish each of the two chosen goals for the week, (2) three 
potential solutions to overcome each chosen challenge and (3) benefits of overcoming the challenges. Phase I 
concludes with a summary of the participant’s motivation, emotion and cognition. Participants will be 
encouraged to accomplish their two chosen goals within the next few days and mentally rehearse their 
“identified steps” two times daily because rehearsal increases effectiveness of GOEFT on the chosen goals.132  
 
 
Part II (weekly days 5-7, 5 min/week): Evaluation of goal progress with feedback. After 
implementing steps to achieve both chosen goals, they will log into the intervention website and use the 

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dropdown menus to evaluate their progress on accomplishing their short-term goals, identify tips that proved 
helpful, recognize short- and long-term benefits of accomplishing the chosen goals, and rate progress on the 
four long-term intervention goals and three chosen personal values. They will receive feedback to their 
response for each evaluation component (e.g., short-term goal). Part II concludes with a summary of goal 
progress with feedback.   
 
 
Individual coaching session via zoom (15 min/call, 10 calls). Participants will receive a call within 1-
2 days after they complete the Part I intervention activities. All coaching session will be either video or audio 
recorded (per participants’ preference) with participants’ permission. Participants will be informed that the 
recording will be transcribed and be used to refine the individual coaching session for future studies. During 
each call, the research will listen empathetically and use open-ended questions asking participants to visualize 
and describe how the week’s goals fit with their personal values, thereby supporting their motivation 
(autonomous motivation).133 Next, participants will be asked to visualize and describe how they will 
accomplish the goal(s) – what specific steps they will take. Then, the research staff will assess the specificity 
of the steps and reinforce or help modify the plans (emotion and cognition). Finally, participants will be asked 
to visualize and describe barriers to implement plans and strategies to overcome barriers. The research staff 
will assist with problem solving as needed (self-efficacy). We will keep IPC attendance records. Fidelity. Dr. 
Chang and each research will listen to a random 25% of the audio recordings monthly and use the fidelity 
checklist to assess protocol adherence, strengths, and reasons for deviations. 
 
Intervention adherence. Each week, participants will receive up to three prescheduled text reminders 
via their smart phone to engage in the week’s intervention activities (until they complete). If women have not 
completed all activities after seven days, the RA will call and ask them to complete the activities that they have 
missed and ask reasons for nonadherence. When a woman expresses interest in quitting some aspects of the 
intervention activities, we will assess barriers to adherence, brainstorm strategies to overcome barriers, and 
offer options to reduce intervention adherence burden. We will keep intervention adherence log. 
 
E. 
Internal Validity 
 
Feasibility of recruitment, retention, intervention adherence and acceptability (Aim 1). We already plan 
to track recruitment and retention activities. Intervention adherence. The web will track and capture details 
about all activities (e.g., number of logins and amount of and type of contents used). We will also ask 
intervention participants about motivation and barriers preventing them from using the web. Acceptability. We 
will assess acceptability by asking participants to evaluate the usefulness of with each intervention component, 
e.g., personal values, using 5 Ws and H, and rehearsal. Lessons learned and results of this aim will be used to 
refine our future R01, e.g., recruitment and intervention. 
 
Measures (See above and File Name: Study Survey. shown above). Dr. Chang will use the NHANES 
anthropometric manual134 to train the data collectors (unaware of participants’ group assignment) on measuring 
BW until we reach inter-rater reliability of  95%. Self-reported data will be collected online using password-
protected security-ensured Research Electronic Data Capture (REDCAP).  
 
F. 
Statistical analysis 
 
 Statistical analysis-need to consistent with hypothesis. We will use descriptive statistics to examine 
variable distributions, check for outliers, and summarize sample characteristics. Bivariate tests (T-test and Chi-
square) will be used to compare sample characteristics between the two study groups. Congruent with the 
RCT nature of the design, we will conduct intent-to-treat analysis. Aim 1. We will (1) conduct content analysis 
to analyze recruitment/enrollment, retention and intervention adherence logs to identify successful strategies 
used, (2) review quality of steps generated to achieve goals (using a scoring system), and (3) perform 
descriptive statistics. We will use the following criteria to determine feasibility: 30% of women screened will 
meet the study criteria and 8-9 women enroll monthly (recruitment); 75% of consented women will enroll 
(randomization); 80% women will have measured body weight at T3 (retention). Also, 85% of women will 

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complete  12 weekly intervention activity with  80% weekly activities completed; 75% of women will be able 
to generate good quality of steps to achieve their chosen goals; 80% will use the pre-written goals instead of 
type in (intervention implementation). Aim 2, we will first use descriptive statistics and trend plots to summarize 
and visually compare the weekly gestation weight gain over time. Mixed-effects linear modeling will be used to 
model the weekly GWG as a linear function of treatment (intervention vs. usual prenatal care care), time, and 
treatment by time interaction. From the model, we can derive estimates of the time-averaged weekly 
gestational weight gain for each group, the between-group difference in weekly gestational weight gain at each 
time point and adjust for within-subject clustering from repeated measures and covariates (e.g., race). We will 
use logistic regression to estimate the between-group (intervention vs. usual care) difference in the probability 
of having a binary outcome (for example, excessive gestational weight gain, gestational hypertension, new 
born body weight), adjusting for covariates (e.g., race). Aims 3 and 4, mixed-effects linear modeling will be 
used to model each continuous outcome (dietary intakes, PA, and psychological eating for Aim 3; motivation, 
emotion and cognition for Aim 4) as a linear function of treatment, time, and the treatment by time interaction. 
The intervention effects will be estimated by between-group comparisons of change in the outcome (e.g., 
healthy lifestyle behaviors) from baseline. Again, we will adjust for within-subject clustering from repeated 
measures and covariates (e.g., race) in the mixed-effects regression models. Missing data. We will carefully 
examine the pattern of missing data and conduct appropriate multiple imputation if missing at random is 
indicated. The mixed-effects modeling allows for missing at random. If missing not at random exists, pattern 
mixture modeling will be used. Sensitivity analysis will evaluate the robustness of study findings without 
multiple imputation vs. those with imputation or from pattern-mixture modeling. 
 
 
Zoom IRB Boilerplate: 
Zoom is a secure, user-friendly, cloud-based enterprise videoconferencing service that Ohio State University 
implemented in 2018. Zoom is accessible to faculty and staff at all Ohio State campuses via 
https://carmenzoom.osu.edu . 
This multifaceted video and audio conferencing system supports video and audio conferencing across multiple 
platforms, including room systems, mobile devices, desktops and telephones. 
The Zoom platform at Ohio State University has two main features: Zoom meetings and Zoom webinars. 
Designed to support collaboration, Zoom meetings support up to 300 video participants. By default, any 
participant in a meeting can share their video and audio and utilize the chat feature to exchange messages with 
participants. The meeting host controls all meeting features, which include mute/unmute participants, screen 
sharing, recording options, video sharing, remote screen control and participant annotation. Annotation allows 
participants to draw and highlight on the screen share. Zoom webinar provides access for up to 300 view-only 
attendees and features live question-and-answer, polling, registration and post-webinar reporting. 
Ohio State University Zoom offers both local recording and cloud recording and has a storage capacity of 270 
days for recordings. Cloud recording includes an option to produce an audio transcript for a meeting or webinar. 
The transcript is saved to the cloud as a separate .vtt text file, and the host can elect to display the transcript text 
within the video itself, similar to a closed-caption display. 
Zoom is accessed via Ohio State’s single sign-on solution, which provides an environment in which users can 
authenticate/log in at one time to a central server and connect with web-based services.  Meeting security best 
practices such as waiting lobby, inability to join before host, disabling of sharing for participants, and disabling 
annotation by participants by default have been implemented.  

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III. Bibliography 
 
1. 
Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass 
index among US adults, 1999-2010. JAMA. 2012;307(5):491-497. 
2. 
Endres LK, Straub H, McKinney C, et al. Postpartum weight retention risk factors and relationship to obesity at 1 
year. Obstet Gynecol. 2015;125(1):144-152. 
3. 
Stotland NE, Cheng YW, Hopkins LM, Caughey AB. Gestational weight gain and adverse neonatal outcome 
among term infants. Obstet Gynecol. 2006;108(3 Pt 1):635-643. 
4. 
Olson CM, Strawderman MS, Hinton PS, Pearson TA. Gestational weight gain and postpartum behaviors 
associated with weight change from early pregnancy to 1 y postpartum. Int J Obes Relat Metab Disord. 
2003;27(1):117-127. 
5. 
Wells CS, Schwalberg R, Noonan G, Gabor V. Factors influencing inadequate and excessive weight gain in 
pregnancy: Colorado, 2000-2002. Matern Child Health J. 2006;10(1):55-62. 
6. 
Kraschnewski JL, Chuang CH, Downs DS, et al. Association of prenatal physical activity and gestational weight 
gain: results from the first baby study. Womens Health Issues. 2013;23(4):e233-238. 
7. 
Ferrari RM, Siega-Riz AM. Provider advice about pregnancy weight gain and adequacy of weight gain. Matern 
Child Health J. 2013;17(2):256-264. 
8. 
Gould Rothberg BE, Magriples U, Kershaw TS, Rising SS, Ickovics JR. Gestational weight gain and subsequent 
postpartum weight loss among young, low-income, ethnic minority women. Am J Obstet Gynecol. 2011;204(1):52 
e51-11. 
9. 
Medicine Io. Weight gain during pregnancy: Reexaming the guidelines. 
http://nationalacademies.org/hmd/reports/2009/weight-gain-during-pregnancy-reexamining-the-guidelines.aspx. 
Published 2009. AccessedJuly, 2009. 
10. 
Hedderson MM, Gunderson EP, Ferrara A. Gestational weight gain and risk of gestational diabetes mellitus. 
Obstet Gynecol. 2010;115(3):597-604. 
11. 
Ren M, Li H, Cai W, et al. Excessive gestational weight gain in accordance with the IOM criteria and the risk of 
hypertensive disorders of pregnancy: a meta-analysis. BMC Pregnancy Childbirth. 2018;18(1):281. 
12. 
Goldstein RF, Abell SK, Ranasinha S, et al. Association of Gestational Weight Gain With Maternal and Infant 
Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017;317(21):2207-2225. 
13. 
Johansson S, Villamor E, Altman M, Bonamy AK, Granath F, Cnattingius S. Maternal overweight and obesity in 
early pregnancy and risk of infant mortality: a population based cohort study in Sweden. BMJ. 2014;349:g6572. 
14. 
Rooney BL, Schauberger CW. Excess pregnancy weight gain and long-term obesity: one decade later. Obstet 
Gynecol. 2002;100(2):245-252. 
15. 
Lau EY, Liu J, Archer E, McDonald SM, Liu J. Maternal Weight Gain in Pregnancy and Risk of Obesity among 
Offspring: A Systematic Review. Journal of Obesity. 2014. 
16. 
Mamun AA, Mannan M, Doi SA. Gestational weight gain in relation to offspring obesity over the life course: a 
systematic review and bias-adjusted meta-analysis. Obes Rev. 2014;15(4):338-347. 
17. 
Phelan S. Pregnancy: a "teachable moment" for weight control and obesity prevention. Am J Obstet Gynecol. 
2010;202(2):135 e131-138. 
18. 
Shieh C, Cullen DL, Pike C, Pressler SJ. Intervention strategies for preventing excessive gestational weight gain: 
systematic review and meta-analysis. Obes Rev. 2018;19(8):1093-1109. 
19. 
Peaceman AM, Clifton RG, Phelan S, et al. Lifestyle Interventions Limit Gestational Weight Gain in Women 
with Overweight or Obesity: LIFE-Moms Prospective Meta-Analysis. Obesity (Silver Spring). 2018;26(9):1396-
1404. 
20. 
Guelinckx I, Devlieger R, Mullie P, Vansant G. Effect of lifestyle intervention on dietary habits, physical activity, 
and gestational weight gain in obese pregnant women: a randomized controlled trial. Am J Clin Nutr. 
2010;91(2):373-380. 
21. 
Thornton YS, Smarkola C, Kopacz SM, Ishoof SB. Perinatal outcomes in nutritionally monitored obese pregnant 
women: a randomized clinical trial. J Natl Med Assoc. 2009;101(6):569-577. 
22. 
Vesco KK, Karanja N, King JC, et al. Efficacy of a group-based dietary intervention for limiting gestational 
weight gain among obese women: a randomized trial. Obesity (Silver Spring). 2014;22(9):1989-1996. 

13 
 
IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
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23. 
Vinter CA, Jensen DM, Ovesen P, Beck-Nielsen H, Jorgensen JS. The LiP (Lifestyle in Pregnancy) study: a 
randomized controlled trial of lifestyle intervention in 360 obese pregnant women. Diabetes Care. 
2011;34(12):2502-2507. 
24. 
Wolff S, Legarth J, Vangsgaard K, Toubro S, Astrup A. A randomized trial of the effects of dietary counseling on 
gestational weight gain and glucose metabolism in obese pregnant women. Int J Obes (Lond). 2008;32(3):495-
501. 
25. 
Redman LM, Gilmore LA, Breaux J, et al. Effectiveness of SmartMoms, a Novel eHealth Intervention for 
Management of Gestational Weight Gain: Randomized Controlled Pilot Trial. JMIR Mhealth Uhealth. 
2017;5(9):e133. 
26. 
Herring SJ, Cruice JF, Bennett GG, Rose MZ, Davey A, Foster GD. Preventing excessive gestational weight gain 
among African American women: A randomized clinical trial. Obesity (Silver Spring). 2016;24(1):30-36. 
27. 
Phelan S, Wing RR, Brannen A, et al. Randomized controlled clinical trial of behavioral lifestyle intervention 
with partial meal replacement to reduce excessive gestational weight gain. Am J Clin Nutr. 2018;107(2):183-194. 
28. 
Cahill AG, Haire-Joshu D, Cade WT, et al. Weight Control Program and Gestational Weight Gain in 
Disadvantaged Women with Overweight or Obesity: A Randomized Clinical Trial. Obesity (Silver Spring). 
2018;26(3):485-491. 
29. 
Olson CM, Strawderman MS, Reed RG. Efficacy of an intervention to prevent excessive gestational weight gain. 
Am J Obstet Gynecol. 2004;191(2):530-536. 
30. 
Thornton YS. Preventing excessive weight gain during pregnancy through dietary and lifestyle counseling: a 
randomized controlled trial. Obstet Gynecol. 2009;114(1):173; author reply 173-174. 
31. 
Shirazian T, Monteith S, Friedman F, Rebarber A. Lifestyle modification program decreases pregnancy weight 
gain in obese women. Am J Perinatol. 2010;27(5):411-414. 
32. 
Skouteris H, Hartley-Clark L, McCabe M, et al. Preventing excessive gestational weight gain: a systematic review 
of interventions. Obes Rev. 2010. 
33. 
Lens W, Paixao MP, Herrera D, Grobler A. Future time perspective as a motivational variable: Content and 
extension of future goals affect the quantity and quality of motivation. Japanese Psychological Research. 
2012;54(3):321-333. 
34. 
Andre L, van Vianen AEM, Peetsma TTD, Oort FJ. Motivational power of future time perspective: Meta-analyses 
in education, work, and health. PLoS One. 2018;13(1):e0190492. 
35. 
Simons J, Vansteenkiste M, Lens W. Placing Motivation and Future Time Perspective Theory in a Temporal 
Perspective. Educational Psychology Review. 2004;16(2):121-139. 
36. 
Kooij D, Kanfer R, Betts M, Rudolph CW. Future time perspective: A systematic review and meta-analysis. J 
Appl Psychol. 2018;103(8):867-893. 
37. 
Husman J., J.C H. Extending Future Time Perspective Theory through Episodic Future Thinking Research: A 
Multidisciplinary Approach to Thinking About the Future. In: Kostić A. CDe, ed. Time Perspective. London: 
Palgrave Macmillan; 2017:267-280. 
38. 
Schacter DL, Benoit RG, De Brigard F, Szpunar KK. Episodic future thinking and episodic counterfactual 
thinking: intersections between memory and decisions. Neurobiol Learn Mem. 2015;117:14-21. 
39. 
O'Donnell S., Oluyomi Daniel T., LH E. Does goal relevant episodic future thinking amplify the effect on delay 
discounting? Conscious Cogn 2017;51:10-16. 
40. 
Schacter DL, Addis DR, Buckner RL. Remembering the past to imagine the future: the prospective brain. Nat Rev 
Neurosci. 2007;8(9):657-661. 
41. 
Stawarczyk D, D'Argembeau A. Neural correlates of personal goal processing during episodic future thinking and 
mind-wandering: An ALE meta-analysis. Hum Brain Mapp. 2015;36:2929-2947. 
42. 
Benoit RG, Schacter DL. Specifying the core network supporting episodic simulation and episodic memory by 
activation likelihood estimation. Neuropsychologia. 2015;75:450-457. 
43. 
Takeuchi H, Taki Y, Sassa Y, et al. Brain structures associated with executive functions during everyday events in 
a non-clinical sample. Brain Struct Funct. 2013;218(4):1017-1032. 
44. 
Schacter DL, Benoit RG, Szpunar KK. Episodic Future Thinking: Mechanisms and Functions. Curr Opin Behav 
Sci. 2017;17:41-50. 
45. 
Palombo DJ, Hayes SM, Peterson KM, Keane MM, Verfaellie M. Medial Temporal Lobe Contributions to 
Episodic Future Thinking: Scene Construction or Future Projection? Cereb Cortex. 2018;28(2):447-458. 

14 
 
IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
08/06/2020 
46. 
Peters J, Buchel C. Episodic future thinking reduces reward delay discounting through an enhancement of 
prefrontal-mediotemporal interactions. Neuron. 2010;66(1):138-148. 
47. 
Deci EL, Ryan RM. The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of 
Behavior. Psychological Inquiry. 2000;11(4):227-268. 
48. 
Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004;31(2):143-164. 
49. 
Rebetez MM, Barsics C, Rochat L, D'Argembeau A, Van der Linden M. Procrastination, consideration of future 
consequences, and episodic future thinking. Conscious Cogn. 2016;42:286-292. 
50. 
Bandura A. Social learning theory. Englewood Cliffs, N.J.: Prentice-Hall; 1977. 
51. 
MacLeod AD. Prospection, well-being and memory Memory Studies. 2016;9:266-274. 
52. 
Miloyan B, Bulley A, Suddendorf T. Episodic foresight and anxiety: Proximate and ultimate perspectives. Br J 
Clin Psychol. 2016;55(1):4-22. 
53. 
Wu JQ, Szpunar KK, Godovich SA, Schacter DL, Hofmann SG. Episodic future thinking in generalized anxiety 
disorder. J Anxiety Disord. 2015;36:1-8. 
54. 
Cole SN, Berntsen D. Do future thoughts reflect personal goals? Current concerns and mental time travel into the 
past and future Quarterly journal of experimental psychology. 2016;69(2):273-284. 
55. 
Dohle S, Diel K, Hofmann W. Executive functions and the self-regulation of eating behavior: A review. Appetite. 
2018;124:4-9. 
56. 
Butryn ML, Martinelli MK, Remmert JE, et al. Executive Functioning as a Predictor of Weight Loss and Physical 
Activity Outcomes. Ann Behav Med. 2019. 
57. 
Diamond A. Executive functions. Annu Rev Psychol. 2013;64:135-168. 
58. 
Appelhans BM, Waring ME, Schneider KL, et al. Delay discounting and intake of ready-to-eat and away-from-
home foods in overweight and obese women. Appetite. 2012;59(2):576-584. 
59. 
Dassen FC, Jansen A, Nederkoorn C, Houben K. Focus on the future: Episodic future thinking reduces discount 
rate and snacking. Appetite. 2016;96:327-332. 
60. 
Stein JS, Sze YY, Athamneh L, Koffarnus MN, Epstein LH, Bickel WK. Think fast: rapid assessment of the 
effects of episodic future thinking on delay discounting in overweight/obese participants. J Behav Med. 
2017;40(5):832-838. 
61. 
Daniel TO, Stanton CM, Epstein LH. The future is now: comparing the effect of episodic future thinking on 
impulsivity in lean and obese individuals. Appetite. 2013;71:120-125. 
62. 
Vartanian LR, Chen WH, Reily NM, Castel AD. The parallel impact of episodic memory and episodic future 
thinking on food intake. Appetite. 2016;101:31-36. 
63. 
Sze YY, Daniel TO, Kilanowski CK, Collins RL, Epstein LH. Web-Based and Mobile Delivery of an Episodic 
Future Thinking Intervention for Overweight and Obese Families: A Feasibility Study. JMIR Mhealth Uhealth. 
2015;3(4):e97. 
64. 
Lin H, Epstein LH. Living in the moment: effects of time perspective and emotional valence of episodic thinking 
on delay discounting. Behav Neurosci. 2014;128(1):12-19. 
65. 
D'Argembeau A, Stawarczyk D, Majerus S, et al. The neural basis of personal goal processing when envisioning 
future events. J Cogn Neurosci. 2010;22(8):1701-1713. 
66. 
McFarland CP, Primosch M, Maxson CM, Stewart BT. Enhancing memory and imagination improves problem 
solving among individuals with depression. Mem Cognit. 2017;45(6):932-939. 
67. 
Terrett G, Rose NS, Henry JD, et al. The relationship between prospective memory and episodic future thinking 
in younger and older adulthood. Q J Exp Psychol (Hove). 2016;69(2):310-323. 
68. 
Schacter DL. Adaptive constructive processes and the future of memory. Am Psychol. 2012;67(8):603-613. 
69. 
Thorstad R, Wolff P. A big data analysis of the relationship between future thinking and decision-making. Proc 
Natl Acad Sci U S A. 2018;115(8):E1740-E1748. 
70. 
Bandura A. The Primacy of Self‐Regulation in Health Promotion. Applied Psychology. 2005;54(2):245-254. 
71. 
Chang M, Robbins L, Ling J, Brown R, Wegener D. Mediators Affecting the Association between Intervention 
and Stress in Low-Income women with Overweight or Obesity. Journal of Health Psychology. 2019. 
72. 
Huang Y, Lv W, Wu J. Relationship Between Intrinsic Motivation and Undergraduate Students' Depression and 
Stress: The Moderating Effect of Interpersonal Conflict. Psychol Rep. 2016;119(2):527-538. 
73. 
Barch DM, Yodkovik N, Sypher-Locke H, Hanewinkel M. Intrinsic motivation in schizophrenia: relationships to 
cognitive function, depression, anxiety, and personality. J Abnorm Psychol. 2008;117(4):776-787. 

15 
 
IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
08/06/2020 
74. 
Brown AD, Kouri NA, Rahman N, Joscelyne A, Bryant RA, Marmar CR. Enhancing self-efficacy improves 
episodic future thinking and social-decision making in combat veterans with posttraumatic stress disorder. 
Psychiatry Res. 2016;242:19-25. 
75. 
Young MD, Plotnikoff RC, Collins CE, Callister R, Morgan PJ. Social cognitive theory and physical activity: a 
systematic review and meta-analysis. Obes Rev. 2014;15(12):983-995. 
76. 
Senecal C, Nouwen A, White D. Motivation and dietary self-care in adults with diabetes: are self-efficacy and 
autonomous self-regulation complementary or competing constructs? Health Psychol. 2000;19(5):452-457. 
77. 
Shaikh AR, Vinokur AD, Yaroch AL, Williams GC, Resnicow K. Direct and mediated effects of two theoretically 
based interventions to increase consumption of fruits and vegetables in the Healthy Body Healthy Spirit trial. 
Health Educ Behav. 2011;38(5):492-501. 
78. 
Silva MN, Vieira PN, Coutinho SR, et al. Using self-determination theory to promote physical activity and weight 
control: a randomized controlled trial in women. J Behav Med. 2010;33(2):110-122. 
79. 
Teixeira PJ, Carraca EV, Marques MM, et al. Successful behavior change in obesity interventions in adults: a 
systematic review of self-regulation mediators. BMC Med. 2015;13:84. 
80. 
Santos I, Ball K, Crawford D, Teixeira PJ. Motivation and Barriers for Leisure-Time Physical Activity in 
Socioeconomically Disadvantaged Women. PLoS One. 2016;11(1):e0147735. 
81. 
Silva MN, Markland D, Carraca EV, et al. Exercise autonomous motivation predicts 3-yr weight loss in women. 
Med Sci Sports Exerc. 2011;43(4):728-737. 
82. 
Santos I, Mata J, Silva MN, Sardinha LB, Teixeira PJ. Predicting long-term weight loss maintenance in 
previously overweight women: a signal detection approach. Obesity (Silver Spring). 2015;23(5):957-964. 
83. 
Teixeira PJ, Silva MN, Mata J, Palmeira AL, Markland D. Motivation, self-determination, and long-term weight 
control. Int J Behav Nutr Phys Act. 2012;9:22. 
84. 
Izard C. The Many Meanings/Aspects of Emotion: Definitions, Functions, Activation, and Regulation. Emotion 
Review 2010;2(4):363-370. 
85. 
Chang M, Tan A, Schaffir J. Relationships between stress, demographics and dietary intake behaviours among 
low-income pregnant women with overweight or obesity. Public Health Nutrition. 2019;22(6):1066-1074. 
86. 
Glasheen C, Colpe L, Hoffman V, Warren LK. Prevalence of serious psychological distress and mental health 
treatment in a national sample of pregnant and postpartum women. Matern Child Health J. 2015;19(1):204-216. 
87. 
Fields SA, Lange K, Ramos A, Thamotharan S, Rassu F. The relationship between stress and delay discounting: a 
meta-analytic review. Behav Pharmacol. 2014;25(5-6):434-444. 
88. 
Chang M, Nitzke S, Guilford E, Adair C, Hazard D. Motivators and barriers to healthful eating and physical 
activity among low-income overweight and obese mothers. J Am Diet Assoc. 2008;108(6):1023-1028. 
89. 
Muraven M, Baumeister RF. Self-regulation and depletion of limited resources: does self-control resemble a 
muscle? Psychol Bull. 2000;126(2):247-259. 
90. 
Diamond A, Ling DS. Conclusions about interventions, programs, and approaches for improving executive 
functions that appear justified and those that, despite much hype, do not. Dev Cogn Neurosci. 2016;18:34-48. 
91. 
Best JR, Nagamatsu LS, Liu-Ambrose T. Improvements to executive function during exercise training predict 
maintenance of physical activity over the following year. Front Hum Neurosci. 2014;8:353. 
92. 
Emery RL, Levine MD. Questionnaire and behavioral task measures of impulsivity are differentially associated 
with body mass index: A comprehensive meta-analysis. Psychol Bull. 2017;143(8):868-902. 
93. 
Yang Y, Shields GS, Guo C, Liu Y. Executive function performance in obesity and overweight individuals: A 
meta-analysis and review. Neurosci Biobehav Rev. 2018;84:225-244. 
94. 
Wu M, Brockmeyer T, Hartmann M, Skunde M, Herzog W, Friederich HC. Set-shifting ability across the 
spectrum of eating disorders and in overweight and obesity: a systematic review and meta-analysis. Psychol Med. 
2014;44(16):3365-3385. 
95. 
Weller RE, Cook EW, 3rd, Avsar KB, Cox JE. Obese women show greater delay discounting than healthy-weight 
women. Appetite. 2008;51(3):563-569. 
96. 
Szpunar KK, Spreng N, Schacter DL. A taxonomy of prospection: Introducing an organizational framework for 
future-oriented cognition. PNAS. 2014;111(52):18414-18421. 
97. 
Technology PRCI. Mobile Fact Sheet. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/. 
Published 2020. Accessed April 13, 2020. 
98. 
Headen I, Cohen AK, Mujahid M, Abrams B. The accuracy of self-reported pregnancy-related weight: a 
systematic review. Obes Rev. 2017;18(3):350-369. 

16 
 
IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
08/06/2020 
99. 
Stunkard AJ, Waxman M. Accuracy of self-reports of food intake. Journal of the American Dietetic Association. 
1981;79(5):547-551. 
100. 
Eck LH, Klesges RC, Hanson CL, Slawson D, Portis L, Lavasque ME. Measuring short-term dietary intake: 
development and testing of a 1-week food frequency questionnaire. Journal of the American Dietetic Association. 
1991;91(8):940-945. 
101. 
de Lauzon B, Romon M, Deschamps V, et al. The Three-Factor Eating Questionnaire-R18 is able to distinguish 
among different eating patterns in a general population. J Nutr. 2004;134(9):2372-2380. 
102. 
Koball AM, Meers MR, Storfer-Isser A, Domoff SE, Musher-Eizenman DR. Eating when bored: revision of the 
emotional eating scale with a focus on boredom. Health Psychol. 2012;31(4):521-524. 
103. 
Pelletier LG, Tuson KM, Haddad NK. Client Motivation for Therapy Scale: a measure of intrinsic motivation, 
extrinsic motivation, and amotivation for therapy. J Pers Assess. 1997;68(2):414-435. 
104. 
Chang M, Brown R, Nitzke S. Scale development: Factors affecting diet, exercise, and stress management 
(FADESM). BMC Public Health. 2008;8(76). 
105. 
Chang M, Nitzke S, Brown R, Baumann L, Oakley L. Development and validation of a self-efficacy measure for 
fat intake behaviors in low-income women. J Nutr Educ Behav. 2003;35(6):302-307. 
106. 
Joireman J, Shaffer MJ, Balliet D, Strathman A. Promotion orientation explains why future-oriented people 
exercise and eat healthy: evidence from the two-factor consideration of future consequences-14 scale. Pers Soc 
Psychol Bull. 2012;38(10):1272-1287. 
107. 
Lyubomirsky S, Lepper H. A measure of subjective happiness: Preliminary reliability and construct validation. 
Social Indicators Research. 1999;46:137-155. 
108. 
Snyder CR, Harris C, Anderson JR, et al. The will and the ways: development and validation of an individual-
differences measure of hope. J Pers Soc Psychol 1991;60(4):570-585. 
109. 
Lobel M, Cannella DL, Graham JE, DeVincent C, Schneider J, Meyer BA. Pregnancy-specific stress, prenatal 
health behaviors, and birth outcomes. Health Psychol. 2008;27(5):604-615. 
110. 
USDA. Survey Tools. USDA. https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-
us/survey-tools/. Published 2019. Accessed June 5, 2020. 
111. 
Gross JJ, John OP. Individual differences in two emotion regulation processes: implications for affect, 
relationships, and well-being. J Pers Soc Psychol. 2003;85(2):348-362. 
112. 
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983;24(4):385-
396. 
113. 
Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression. Development of the 10-item Edinburgh 
Postnatal Depression Scale. Br J Psychiatry. 1987;150:782-786. 
114. 
Rouel M, Raman J, Hay P, Smith E. Validation of the Behaviour Rating Inventory of Executive Function - Adult 
Version (BRIEF-A) in the obese with and without binge eating disorder. Eat Behav. 2016;23:58-65. 
115. 
Adler L, Tanaka Y, Williams D, et al. Executive function in adults with attention-deficit/hyperactivity disorder 
during treatment with atomoxetine in a randomized, placebo-controlled, withdrawal study. J Clin 
Psychopharmacol. 2014;34(4):461-466. 
116. 
Adler LA, Dirks B, Deas PF, et al. Lisdexamfetamine dimesylate in adults with attention-deficit/ hyperactivity 
disorder who report clinically significant impairment in executive function: results from a randomized, double-
blind, placebo-controlled study. J Clin Psychiatry. 2013;74(7):694-702. 
117. 
Adler LA, Clemow DB, Williams DW, Durell TM. Atomoxetine effects on executive function as measured by the 
BRIEF--a in young adults with ADHD: a randomized, double-blind, placebo-controlled study. PLoS One. 
2014;9(8):e104175. 
118. 
Barratt ES. Anxiety and impulsiveness related to psychomotor efficiency. Perceptual and Motor Skills. 
1959;9:191–198. 
119. 
Chang M, Nitzke, S., Brown, R., Egan, M., Bendekgey, C., Buist, D. Recruitment Challenges and Enrollment 
Observations from a Community Based Intervention (Mothers In Motion) for Low-Income Overweight and Obese 
Women. Contemporary Clinical Trials Communication 2017;5(26-33). 
120. 
Brinol P, McCaslin MJ, Petty RE. Self-generated persuasion: effects of the target and direction of arguments. J 
Pers Soc Psychol. 2012;102(5):925-940. 
121. 
Chow SC, &  Liu, J. P. Design and analysis of clinical trials: concepts and methodologies. Hoboken, NJ.: John 
Wiley and Sons Inc.; 2004. 

17 
 
IRB Protocol Number: 
2019H0441 
IRB Approval date:  
 
     Version:  
08/06/2020 
122. 
Matts JPL, J. M. Properties of permuted-block randomization in clinical trials. Control Clin Trials. 
1988;9(4):327-344. 
123. 
Tang JC, Abraham C, Greaves CJ, Nikolaou V. Self-directed interventions to promote weight loss: a systematic 
review and meta-analysis. Health Psychol Rev. 2016;10(3):358-372. 
124. 
Wieland LS, Falzon L, Sciamanna CN, et al. Interactive computer-based interventions for weight loss or weight 
maintenance in overweight or obese people. Cochrane Database Syst Rev. 2012(8):CD007675. 
125. 
Neve M, Morgan PJ, Jones PR, Collins CE. Effectiveness of web-based interventions in achieving weight loss 
and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obes Rev. 
2010;11(4):306-321. 
126. 
Sze YY, Stein JS, Bickel WK, Paluch RA, Epstein LH. Bleak Present, Bright Future: Online Episodic Future 
Thinking, Scarcity, Delay Discounting, and Food Demand. Clin Psychol Sci. 2017;5(4):683-697. 
127. 
Practice CoO. Exercise during pregnancy and postpartum period. https://www.acog.org/Clinical-Guidance-and-
Publications/Committee-Opinions/Committee-on-Obstetric-Practice/Physical-Activity-and-Exercise-During-
Pregnancy-and-the-Postpartum-Period. Published 2017. Accessed August 15, 2018. 
128. 
George GC, Milani TJ, Hanss-Nuss H, Freeland-Graves JH. Compliance with dietary guidelines and relationship 
to psychosocial factors in low-income women in late postpartum. J Am Diet Assoc. 2005;105(6):916-926. 
129. 
Chang MW, Nitzke S, Buist D, Cain D, Horning S, Eghtedary K. I am pregnant and want to do better but i can't: 
focus groups with low-income overweight and obese pregnant women. Matern Child Health J. 2015;19(5):1060-
1070. 
130. 
Chang MW, Brown R, Nitzke S. Results and lessons learned from a prevention of weight gain program for low-
income overweight and obese young mothers: Mothers In Motion. BMC Public Health. 2017;17(1):182. 
131. 
Gollwitzer PM. Implementation intentions: Strong effects of simple plans. American Psychologist. 
1999;54(7):493-503. 
132. 
Adams C, Rennie L, Uskul AK, Appleton KM. Visualising future behaviour: Effects for snacking on biscuit bars, 
but no effects for snacking on fruit. J Health Psychol. 2015;20(8):1037-1048. 
133. 
Deci EL, Ryan RM. The “What” and “Why” of Goal Pursuits:Human Needs and the Self-Determination of 
Behavior. Psychological Inquiry. 2000;11(4). 
134. 
CDC. National Health and Nutrition Survey: Anthropometric procedure manual. 
http://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/manual_an.pdf. Accessed December 24, 2014, 2014.