Optimizing Self-monitoring in a Digital Health Intervention for Weight Loss
Phase: NA
Conditions: Obesity, Overweight, Weight Loss, Health Behavior
Sponsor: Stanford University

 
Study Protocol for the Spark Trial 
 
Grant Title: 
Optimizing self-monitoring in a digital health intervention for 
weight loss 
Principal Investigator: 
Michele L. Patel, PhD (michele.patel@stanford.edu) 
Sponsor: 
Stanford University 
Co-Investigators: 
Abby C. King, PhD 
Lisa G. Rosas, PhD, MPH  
Gary G. Bennett, PhD  
Consultants: 
John A. Gallis, ScM (biostatistician)  
Linda M. Collins, PhD (methodologist) 
Trial Registration: 
ClinicalTrials.gov Identifier NCT05249465 
IRB eProtocol Number: 
64716 
NIH Grant Number: 
K23DK129805 
Funder: 
NIH / National Institute of Diabetes and Digestive and Kidney 
Diseases (NIDDK) 
Role of Funder: 
The study funder (NIH/NIDDK) had no role in the study design, 
collection, management, analysis, and interpretation of data; 
writing of the report; and the decision to submit the report for 
publication. The content is solely the responsibility of the authors 
and does not necessarily represent the official views of the NIH. 
Recruitment Dates: 
September 22, 2023 to November 18, 2024 
End of Primary Data 
Collection: 
June 15, 2025 
End of Study Data 
Collection: 
June 23, 2025 
Initial Protocol Date: 
March 25, 2022 (eProtocol approved by Stanford University IRB) 
Current Version Date: 
June 24, 2025 (v1.7) 
 
 
 
 
 
 
 

Spark Trial Protocol | 2 
Statement of Compliance 
The trial will be conducted in accordance with International Council on Harmonisation 
Good Clinical Practice (ICH GCP), applicable United States (US) Code of Federal 
Regulations (CFR), and the National Institute of Diabetes and Digestive and Kidney 
Diseases (NIDDK)  Terms and Conditions of Award. The Principal Investigator will 
assure that no deviation from, or changes to the protocol will take place without prior 
agreement from the funding agency and documented approval from the Institutional 
Review Board (IRB), and the Investigational New Drug (IND) or Investigational Device 
Exemption (IDE) sponsor, if applicable, except where necessary to eliminate an 
immediate hazard(s) to the trial participants. All personnel involved in the conduct of 
this study have completed Human Subjects Protection and ICH GCP Training. 
The protocol, informed consent form(s), recruitment materials, and all participant 
materials will be submitted to the IRB for review and approval. Approval of both the 
protocol and the consent form(s) must be obtained before any participant is consented. 
Any amendment to the protocol will require review and approval by the IRB before the 
changes are implemented to the study. All changes to the consent form(s) will be IRB 
approved; a determination will be made regarding whether a new consent needs to be 
obtained from participants who provided consent, using a previously approved consent 
form. 
The signature below constitutes the approval of this protocol and provides the necessary 
assurances that this study will be conducted according to all stipulations of the protocol, 
including all statements regarding confidentiality, and according to local legal and 
regulatory requirements and applicable US federal regulations and ICH guidelines, as 
described in the Statement of Compliance above. 
 
Principal Investigator:  Michele L. Patel 
Signed: 
 
Date 
9/22/23 
 
Name: Michele L. Patel 
 
Instructor, Stanford Prevention Research Center 
Stanford University School of Medicine 
 
Principal Investigator Contact Information: 
Address: 3180 Porter Drive, Palo Alto, CA 94304 
Telephone: 650-549-7047 
Email: Michele.Patel@stanford.edu 
 
 
 

Spark Trial Protocol | 3 
Table of Contents 
1. Synopsis​
5 
2. Purpose, Background and Rationale​
6 
2.1. Brief Summary​
6 
2.2 Objective​
6 
2.3 Aims​
7 
2.4 Background and Significance​
7 
2.4.1 Obesity​
7 
2.4.2 Digital Interventions​
7 
2.4.3 Self-Monitoring​
7 
2.5  Theoretical Framework​
8 
2.6 The Multiphase Optimization Strategy (MOST)​
8 
2.7 Gaps & Advancing Knowledge in the Field​
9 
3. Research Plan and Design​
10 
3.1 Overview​
10 
3.2 Study Design​
10 
3.3 Study Population​
11 
3.3.1 Recruitment​
11 
3.3.2 Retention​
12 
3.3.3 Participant Eligibility Criteria​
12 
3.4 Study Procedures​
13 
3.4.1 Screening and Orientation Session​
13 
3.4.2 Baseline Visit and Randomization​
13 
3.4.3 Screen Failures​
14 
3.4.4 Withdrawal/Termination Criteria​
14 
3.5 Core Intervention​
15 
3.6 Experimental Intervention Components​
15 
3.7 Participant Requirements​
16 
3.8 Outcomes​
17 
3.8.1 Primary Outcome​
17 
3.8.2 Secondary and Exploratory Outcomes​
17 
3.9 Data Collection​
20 
3.9.1 Masking​
20 
3.9.2  Quality Assurance​
21 
3.9.3 Research Ethics Approval​
21 
3.9.4 Declaration of Interests​
21 
3.10 Sample Size and Statistical Power​
21 
3.11  Statistical Analysis​
21 
3.12 Data Management​
22 
3.13 Endpoint​
22 
4. Risks and Protection Against Risks​
23 
 

Spark Trial Protocol | 4 
4.1 Potential Risks​
23 
4.2 Safeguards to Minimize Risks​
23 
4.2.1 Participant Population and Screening​
23 
4.2.2 Minimizing Risks​
23 
4.2.3 Data and Safety Monitoring Plan​
24 
4.2.4 Adverse Events​
25 
4.2.5 Data Quality​
27 
4.3 Privacy​
27 
5 Dissemination​
28 
5.1 Data Sharing Plan​
28 
5.2 Access to Data​
28 
6. Protocol Amendment History​
29 
7. References​
30 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Spark Trial Protocol | 5 
1. Synopsis 
Grant Title: 
Optimizing self-monitoring in a digital health intervention for weight loss 
Study 
Description: 
This optimization-RCT will examine the unique and combined efficacy of 
three self-monitoring strategies for weight loss -- tracking dietary intake, 
tracking steps, and tracking body weight -- all delivered through digital 
health tools. A 2x2x2 factorial design will be used, resulting in 8 conditions. 
Objectives: 
 
To identify the optimal combination of self-monitoring strategies that 
maximizes weight loss in a 6-month fully digital intervention for adults with 
overweight or obesity. Aim 1: Test the unique and combined effects of the 
three self-monitoring strategies on weight change from baseline to 6 
months. 
Aim 2: Test the unique and combined effects of the three self-monitoring 
strategies on change in secondary outcomes from baseline to 6 months, 
including caloric intake, physical activity, BMI, and health-related quality of 
life, as well as weight change from baseline to 1 and 3 months, and 
proportion of participants who achieve ≥5% weight loss at 6 months. 
Endpoints: 
The behavioral intervention is 6 months in length and will not be 
terminated early based on efficacy. The study is expected to end after our 
target sample size is reached and the final participant has completed the 6 
month intervention and assessment. 
Study 
Population: 
The sample (N=176) will consist of adults ( ages 18+) with body mass index 
(BMI) 25.0 to 45.0 kg/m2 from a nationwide sample of the U.S. population.  
Phase or Stage: 
Multiphase Optimization Strategy: Optimization Phase 
Description of 
Sites: 
All study procedures, including recruitment, intervention delivery, and 
assessments, will take place remotely.  
Description of 
Study 
Intervention/ 
Experimental 
Manipulation: 
All participants will receive a 6-month behavioral weight loss intervention 
that is delivered in a fully digital context without human counseling. Core 
components include a 10% weight loss goal and weekly behavioral lessons 
and action plans. Participants will be randomized to 1 of 8 conditions that 
vary in their receipt of 3 self-monitoring strategies. Participants randomized 
to track their dietary intake will be instructed to do so daily via the Fitbit 
app, and will receive a tailored calorie goal. Those randomized to track their 
steps will be instructed to do so daily via an activity tracker (Fitbit Inspire 
3), and will receive an adaptive step goal. Participants randomized to track 
their body weight will be instructed to do so daily via an e-scale (Fitbit Aria 
Air scale) and will receive a weekly weight loss goal. Weekly tailored 
feedback on self-monitoring and goals will be provided. 
Study 
Duration: 
The intervention will last 6 months. We expect the study will require 
approximately 18-24 months for recruitment, intervention and data 
collection.  
Participant 
Duration: 
Individual participants will complete study related tasks within 6 months 
after randomization. 
 

Spark Trial Protocol | 6 
2. Purpose, Background and Rationale 
2.1. Brief Summary  
This optimization randomized clinical trial (i.e., “optimization-RCT”) will examine three 
self-monitoring strategies for weight loss -- tracking dietary intake, tracking steps, and 
tracking body weight -- all delivered through digital health tools. The purpose of the 
study is to evaluate the combination of these strategies that maximizes 6-month weight 
loss in the context of a fully digital health intervention for adults with overweight or 
obesity. 
 
The investigators will recruit 176 total participants to the trial. Recruitment will occur 
through remote channels. Interested individuals will be directed to an online eligibility 
screen; those who are eligible will then be invited to attend an initial remote baseline 
session with study staff to ensure interest and eligibility in the study. The weight loss 
intervention will last 6 months, and all participants will receive a "core" intervention 
consisting of goal setting, behavioral lessons, and action plans - all of which will be 
delivered remotely. Using a 2x2x2 factorial design, participants will be randomized to 
receive 0-3 of the self-monitoring strategies. All study tasks will occur remotely, thus, 
participants never need to come in-person for any intervention or assessment tasks. 
 
Investigators will use the Multiphase Optimization Strategy (MOST) framework to 
identify the most effective combination of self-monitoring strategies. The factorial 
design will allow the research team to determine the unique and combined impact of 
each self-monitoring component on outcomes. The primary outcome is weight change 
from baseline to 6 months. To complement the main trial, the research team will also 
randomize half of participants to receive an interactive orientation session, given prior 
to the consent process, in order to assess its impact on trial retention at 6 months. 
Overall, the information gathered from this trial will enable the construction of an 
optimized digital health intervention for weight loss that can be delivered remotely, 
which, if found to be effective, could have high potential for public health impact.​ 
2.2 Objective 
The overall goal is to identify the optimal combination of self-monitoring strategies that 
maximizes weight loss in a 6-month fully digital intervention for adults with overweight 
or obesity.​
 
2.3 Aims 
Aim 1: Test the unique and combined effects of the three self-monitoring strategies on 
 

Spark Trial Protocol | 7 
weight change from baseline to 6 months (primary outcome). 
Aim 2: Test the unique and combined effects of the three self-monitoring strategies on 
change in secondary outcomes from baseline to 6 months, including caloric intake, 
physical activity, BMI, and health-related quality of life, as well as weight change from 
baseline to 1 and 3 months, and the proportion of participants who achieve ≥5% weight 
loss at 6 months. 
2.4 Background and Significance 
2.4.1 Obesity 
Obesity is a significant public health problem. Obesity has become a pervasive health 
concern in the United States, with rates of 42% among adults1 and another 30+% with 
overweight.2 Excess weight heightens risk for chronic diseases,3 including type 2 
diabetes, and can magnify psychological distress as a result of weight stigma.4 
Behavioral weight loss interventions that involve frequent in-person meetings, diet and 
exercise goals, and behavioral strategies are the gold standard for treating overweight 
and obesity.5 These traditional treatments are effective in producing weight loss up to 
10%,6 but often lack scalability.  
2.4.2 Digital Interventions 
Remotely-delivered interventions show promise, but need enhanced efficacy. Digital 
health interventions that deliver treatment remotely seek to minimize burden, decrease 
costs, and broaden reach, compared to in-person interventions. With widespread 
penetration of home broadband, regular phones, and smartphones among U.S. adults 
(77%,7 97%, and 85%,8 respectively), digital health tools are increasingly being used in 
weight loss interventions, with promising efficacy.9-13 Compared to traditional 
interventions with counseling, fully standalone digital interventions offer greater 
scalability given lower personnel costs. However, weight loss tends to be modest14-15; 
thus, more work is needed to enhance the impact of standalone digital approaches. 
2.4.3 Self-Monitoring 
Self-monitoring via digital health is effective at facilitating weight loss. Self-monitoring 
is a well-established method of facilitating weight loss for adults with overweight or 
obesity.16-18 It involves tracking a behavior (e.g., diet, exercise) or behavioral outcome 
(e.g., body weight) and allows individuals to pay attention to their behaviors and gain 
feedback on how specific actions impact their weight. ​​
​
​
 
 

Spark Trial Protocol | 8 
2.5  Theoretical Framework 
Self-regulation theories, including Social Cognitive Theory and Control Theory, posit 
that behavior change occurs through comparison of one’s behavior to one’s goals or past 
performance.19-20 Self-monitoring is one of the strongest predictors of behavior change21 
and weight loss22 and is thought to work by increasing self-regulatory skills and 
self-efficacy for making healthy changes. 
 
However, self-monitoring engagement tends to decline over time with common barriers 
related to time demands, perceived burden, accessibility challenges, waning novelty, 
and literacy/numeracy constraints.23,24 Fortunately, there are ways to promote 
self-monitoring engagement by using digital rather than paper based methods of 
self-monitoring,16 and by incorporating theory-informed and empirically-supported 
self-regulation strategies such as goal setting, tailored feedback, and action planning. 
Meta-analyses have found that the effects of self-monitoring are magnified when adding 
these strategies.25-27 Advantages of using digital platforms for self-monitoring include 
immediate personalized feedback, reduced tracking time with automated monitoring via 
devices like fitness trackers and e-scales, and high portability of mHealth tools, which 
increases the likelihood of use while reducing retrospective errors. A systematic review 
of 39 randomized trials examined digital self-monitoring strategies used in weight loss 
interventions and concluded that greater self-monitoring was positively associated with 
weight loss in over 70% of the reported associations.16 This pattern was demonstrated 
across a range of digital health modalities and self-monitored behaviors. In sum, 
self-monitoring is a theory-informed and evidence-based approach for weight loss that 
can be delivered via digital tools.  ​ ​
 ​
​
 
 2.6 The Multiphase Optimization Strategy (MOST) 
It is important to have interventions that are both efficient and potent, with no 
extraneous components. Factorial designs can identify active ingredients of an 
intervention that contribute added impact as well as inactive components that are 
unnecessarily burdensome and potentially even detrimental. As such, a factorial design 
can be used to address which combination of self-monitoring strategies maximizes 
weight loss, and whether any synergistic or antagonistic effects exist, which occur when 
the combined effect of two or more intervention components is more or less favorable, 
respectively, than would be expected based solely on main effects. 
 
MOST is an engineering-inspired framework that provides an opportunity for 
researchers to design effective and efficient multicomponent behavioral interventions.28 
MOST involves three phases: Preparation, Optimization, and Evaluation. First, the 
Preparation Phase consists of understanding gaps in the field, selecting intervention 
 

Spark Trial Protocol | 9 
components to test, and developing a conceptual model. Second, the Optimization 
Phase consists of conducting a fully powered optimization-RCT to rigorously test the 
intervention components’ unique and combined effects on the outcome of interest. 
Third, a subsequent Evaluation Phase involves testing the newly optimized intervention 
versus a comparator in a traditional two-arm evaluation-RCT. 
2.7 Gaps & Advancing Knowledge in the Field 
No study has examined the optimal combination of self-monitoring strategies. While the 
value of self-monitoring has been demonstrated repeatedly, what remains unknown is 
which self-monitoring domains actually contribute the greatest amount to weight loss, 
and whether any combinations diminish weight loss and create undue burden. 
Examining the unique and combined impact of three popular self-monitoring strategies 
(diet, steps, and body weight) will allow an evidence-based intervention to be 
constructed that maximizes weight loss while minimizing undue patient burden. 
 
 

Spark Trial Protocol | 10 
3. Research Plan and Design​ 
The study purpose is to conduct a 6-month optimization-RCT (N=176) using a factorial 
design to evaluate three self-monitoring components in a remotely-delivered weight loss 
intervention for adults with overweight/obesity. As shown in the conceptual model 
(Figure 1, below), participants will be randomized to one level of each of the three 
self-monitoring components: self-monitoring diet (yes, no); self-monitoring steps (yes, 
no); self-monitoring body weight (yes, no). Each component has an expected 
mechanism of action (i.e., increase self-monitoring engagement for its respective 
domain), which, in turn, is expected to enhance self-efficacy and self-regulatory skills, 
based on Social Cognitive Theory.19 Together, these putative mediators are expected to 
improve healthy behaviors and decrease caloric intake, leading to weight loss.  
 
Figure 1. Conceptual Model of the Spark Trial.
  
3.1 Overview 
All participants will receive a core intervention and will be randomized to receive 0-3 
self-monitoring components. The intervention is fully digital without human counseling 
and will last 6 months, with remote assessments at baseline, 1, 3, and 6 months. 
3.2 Study Design 
 This optimization-RCT will use a 23 full factorial design (i.e., 2x2x2) to test three self- 
monitoring components. As shown in Figure 2, below, for each self-monitoring 
component, half of participants will be assigned to receive it (“Yes”) while the other half 
will not (“No”). In total, participants will be randomized to 1 of 8 experimental 
 

Spark Trial Protocol | 11 
conditions. A factorial trial compares means across combinations of conditions in order 
to test main effects of components and their interactions.28 For example, the main effect 
of ‘Tracking Diet’ is the mean weight change for the participants who receive this 
component (i.e., the “Yes’s” in experimental conditions 5-8) versus the mean weight 
change of those who do not receive it (i.e., the “Nos” in conditions 1-4). 
 
Figure 2. Factorial Design in the Spark Trial. 
 
3.3 Study Population 
3.3.1 Recruitment 
Participants will be recruited through several remote channels across the United States 
and in the San Francisco Bay Area, which may include the following: a) social media 
posts (Twitter and Facebook), b) research registries (ResearchMatch.org, a national 
registry of health research volunteers; ClinicalTrials.gov registry; the Stanford Diabetes 
Research Center’s registry comprised of adults with pre-diabetes or type 2 diabetes, as 
well as healthy individuals), c) community advertisements (Nextdoor, Craigslist), and d) 
university-affiliated research websites. The recruitment phase is expected to span up to 
15 months and will accumulate a total of 176 participants. Briefly, eligibility criteria 
include: adults ≥18 years, body mass index (BMI) 25-45 kg/m2, smartphone ownership, 
no recent weight loss ≥10 lbs., and no condition that contraindicates weight loss (see 
section 3.3.3 for more details). 
  
Recruitment efforts will target at least 50% racial/ethnic minority group enrollment 
given that these groups are under-represented in behavioral obesity treatment 
research.29-30 Further, populations who identify as Black and/or Hispanic have 
disproportionately higher rates of overweight/obesity.2,31 
 

Spark Trial Protocol | 12 
3.3.2 Retention  
Retention will be maximized by reimbursing participants for their time spent on the 3- 
and 6-month remote assessments ($20 and $30 e-gift card, respectively) and by 
providing an additional $10 e-gift card for completion of all 4 dietary recalls (2 at 
baseline, 2 at 6 months). Study staff will ask permission to collect multiple phone 
numbers, email addresses, and family or close friend contact information, and assess 
preferences for contact methods. Reminder text messages/emails will be sent prior to 
the remote assessments and follow up with participants via various channels (text, 
phone, email) if they have yet to complete assessments. Other tactics that we will use to 
help retain participants include staff available to troubleshoot, personalization of 
materials, use of technology for self-monitoring, use of a multiple-component 
intervention (e.g., self-monitoring plus feedback plus goals), and flexibility in 
participants’ completion of remote assessments. 
3.3.3 Participant Eligibility Criteria 
Table 1. Inclusion and Exclusion Criteria in the Spark Trial. 
Inclusion 
Criteria 
●​ adults (ages 18+ years) 
●​ body mass index (BMI) 25.0 to 45.0 kg/m2 
●​ smartphone ownership 
●​ willingness to install a mobile app (Fitbit)  on their phone 
●​ access to a personal email account 
●​ English language proficiency 
●​ interest in losing weight through behavioral strategies 
Exclusion 
Criteria 
●​ concurrent enrollment in another weight management 
intervention 
●​ loss of ≥10 lbs. in the past 6 months 
●​ current use of a weight loss medication 
●​ prior or planned bariatric surgery 
●​ current or planned pregnancy in the trial period 
●​ currently breastfeeding 
●​ living with someone else participating in the study 
●​ hospitalization for a mental health condition in the past 12 months 
●​ inability to engage in moderate forms of physical activity akin to 
brisk walking  
●​ if weight loss is contraindicated or might be impacted by a 
condition (e.g., end stage renal disease, cancer, schizophrenia, 
dementia) or  medication (e.g., steroids, anti-psychotics) 
●​ if an individual would be better suited for a more intensive or 
different type of intervention based on a health condition (e.g., 
 

Spark Trial Protocol | 13 
individuals with history of an eating disorder or cardiovascular 
event, uncontrolled hypertension, or uncontrolled diabetes 
mellitus) 
●​ investigator discretion for safety reasons 
3.4 Study Procedures 
3.4.1 Screening and Orientation Session 
All study procedures will occur through remote means. Recruitment materials will direct 
interested individuals to an online screening questionnaire (via REDCap) that describes 
the study and assesses initial eligibility. As part of an embedded experiment called a 
Study-Within-A-Trial (“SWAT”),32 eligible candidates are then randomized 1:1 to either 
receive a self-directed orientation session or not receive it. Those assigned to receive it 
will be prompted to complete a 20-25 minute web-based orientation session consisting 
of video content and interactive activities designed to set expectations, explain the study 
rationale and details, visualize the time commitment, and ensure the trial is a good fit 
for their interests. At the end of the orientation session, those individuals will have the 
option to sign up for a baseline visit (see preregistration repository: protocol SWAT 
211)33. The orientation session is modeled after the Methods-Motivational Interviewing 
approach,34 which was developed to increase clinical trial retention. 
3.4.2 Baseline Visit and Randomization  
Individuals not randomized to receive the orientation session will receive an email 
invitation to sign up for a baseline visit. All participants will attend a baseline visit, 
regardless of orientation session assignment. At this baseline visit (~1-1.5 hours), 
trained study staff will confirm eligibility and describe study procedures. If individuals 
are interested in participating, study staff will obtain informed consent to assure that 
they fully understand the demands and purpose of the study before enrolling. Next, 
study staff will help participants create a Fitbit account and download the Fitbit app. 
Study staff will then administer a web-based dietary recall, an oral health literacy 
assessment, and a web-based baseline survey. Once the survey is completed, study staff 
will mail all participants an e-scale to obtain baseline weight measurement. Once the 
e-scale is delivered, participants will receive an email that provides information on 
syncing their e-scale with the Fitbit app, and will be prompted to weigh themselves the 
following morning using a standardized protocol. Within the week after the baseline 
visit, a 2nd unannounced web-based dietary recall will be administered, with reminders 
sent if it is not completed. 
Once the weight assessment is completed, participants will be randomized to 1 of 8 
experimental conditions via an algorithm on REDCap. Study staff will mail an activity 
 

Spark Trial Protocol | 14 
tracker (Fitbit Inspire 3) within 2 business days to those randomized to the conditions 
with self-monitoring of steps (i.e., conditions 3, 4, 7, 8; Figure 2, above). Once the 
tracker is delivered, participants will receive instructions for syncing the tracker with the 
Fitbit app. Study staff will be available to troubleshoot with device syncing, as needed. 
Then, the weight loss intervention will begin -- participants will receive an automated 
email describing their assigned intervention. 
 
Following recommendations for factorial designs,35 we will randomize participants to 
the 8 conditions using restricted randomization. We will stratify by sex assigned at birth 
and use permuted block randomization with a block size of 8. The allocation sequence 
was generated using SAS by the study’s statistician (JAG) and stored in REDCap. Study 
staff will use REDCap to implement the random allocation sequence (i.e., randomize a 
participant) once both the baseline weight and web-based baseline surveys are 
submitted.  
3.4.3 Screen Failures 
Participants who consent but then become ineligible prior to randomization will be 
asked to withdraw from the study and will not be randomized. Examples would include 
if a participant’s baseline weight collected via the study-issued e-scale (after consenting) 
puts them out of range on the BMI criterion (i.e.,  no longer being in the 25.0-45.0 
kg/m2 range), which may occur when their initial BMI that was calculated using 
self-reported weight put them on the edges of that range), or if a participant reports a 
pregnancy or exclusionary medical condition in the period between consent and 
randomization. All other reports of changes to eligibility criteria will be decided upon by 
study investigators prior to randomization. 
3.4.4 Withdrawal/Termination Criteria 
If after randomization and during the study period the participant no longer meets 
certain eligibility criteria, they will be asked to withdraw from the study. This includes 
the following: if a participant becomes pregnant during the study;  finding out that 
certain criteria were falsified or not reported (e.g., initial age, initial BMI, concurrent 
enrollment in another weight management intervention, a health condition that 
contraindicates weight loss or requires more intensive treatment), or  investigator 
discretion for safety reasons. 
 
Participants who become pregnant during the study period will be asked to leave the 
study. Changes in health behaviors and weight may not be healthy for a baby during 
pregnancy. Note that if a participant begins use of a weight loss medication / antiobesity 
medication during the study, they will not be asked to withdraw from the study. 
 

Spark Trial Protocol | 15 
3.5 Core Intervention 
All participants will receive a 6-month core intervention consisting of the following 
empirically- and theoretically- supported components: 
 
Fitbit mobile app: Instead of designing our own app, we will leverage an existing 
state-of-the-art commercial app from Fitbit that is freely available on iPhone and 
Android platforms. At baseline, participants will install the app. The app will be set up 
by study staff so that it reflects only the self-monitoring components (tracking diet, 
steps, and/or weight) to which each participant is assigned. In-app graphical feedback 
allows participants to view their self-monitoring progress in real time. 
 
Weight loss goal: All participants will receive a goal to achieve 10% weight loss by 6 
months, which is consistent with obesity treatment guidelines5 and equates to 0.5-2 lbs 
weight loss/week.  
 
Tailored feedback: Each week, participants in conditions 2-8 will receive an email with 
tailored feedback pertaining to their progress on their assigned self-monitoring goals 
(described below). Feedback will be automatically generated using Fitbit data (retrieved 
from Fitabase) and constructed via Microsoft Office’s Mail Merge feature.  
 
Behavioral skills training: Each week, participants will receive an email with 
theory-informed skills training materials that include structured behavioral lessons on 
nutrition and physical activity as well as corresponding action plans. These materials 
will be adapted from our recent trials36-37 as well as from gold standard weight loss 
curricula.38 Lessons include topics such as reading nutrition labels and promoting 
physical activity. Embedded in this email will be a link to a brief action plan survey 
(Qualtrics) that incorporates motivational interviewing39-40 and problem-solving 
strategies.41 Specifically, participants will be prompted each week to reflect on their 
current behaviors and areas for change, generate actionable steps to change, identify 
confidence in doing so, and brainstorm potential barriers and support people.  
3.6 Experimental Intervention Components 
Self-monitoring diet (Yes vs. No): Participants randomized to receive this component 
will be instructed to self-monitor their dietary intake daily via the Fitbit mobile app. 
This app allows users to track all foods and beverages consumed using a built-in 
nutritional database, barcode scanner, or manual entry of individual recipes, and to 
view graphically their change in caloric intake. Participants will receive a tailored daily 
calorie goal using data reported at baseline: age, sex, weight, and height. For safety, a 
minimum of 1200 Calories (kcal)/day is set for women and 1500 kcal/day for men.3 
 

Spark Trial Protocol | 16 
 
Self-monitoring steps (Yes vs. No): Participants randomized to receive this component 
will be instructed to self-monitor their step count daily via a wrist-worn activity tracker 
(Fitbit Inspire 3). In conjunction with the self-monitoring goal, a tailored daily step goal 
will be given (e.g., 7,000 steps) that will adapt based on progress. The initial week’s step 
goal will be based on the participant’s baseline scores on the Godin Leisure-Time 
Exercise Questionnaire (GLTEQ) leisure score index,42-43 with scores ranging from 0 to 
13 (interpreted as insufficiently active) assigned to a goal of 5000 steps per day, scores 
of 14 to 23 (moderately active) assigned to 7000 steps per day, and scores ≥ 24 (active) 
assigned to 10,000 steps per day. Starting in the second week of the intervention, an 
adaptive step goal will be given using an empirically tested algorithm44-45 whereby the 
60th percentile of the past week’s daily step counts, rounded up to the nearest multiple 
of 50, is assigned as the subsequent week’s daily step goal. For example, a week with 
daily steps of 5000, 5100, 6000, 6500, 7000, 8200, and 8500 would result in a daily 
step goal of 6800 for the subsequent week. The new step goal will appear in each week’s 
progress report. The Fitbit activity tracker will be synced with the Fitbit app to allow 
participants to view their progress towards the step goal. 
 
Self-monitoring weight (Yes vs. No): Participants randomized to receive this 
component will be instructed to self-monitor their body weight daily via a Bluetooth 
enabled e-scale (the Fitbit Aria Air scale). These participants will receive a weekly 
weight loss goal of 0.5 to 2.0 lbs. (0.23 kg to 0.91 kg) per week, calculated as the 
magnitude of weight loss that is required to achieve 10% weight loss at 6 months. The 
e-scale will be synced with the Fitbit mobile app, providing graphical feedback. 
3.7 Participant Requirements 
Table 2. Participant Time Involvement. 
- 10 minute online eligibility screen 
- orientation video (~20-25 minutes), if randomized to view it 
- baseline visit / informed consent: ~60-90 minutes 
-  active participation in the intervention: 6 months (~20 minutes of daily tracking of 
weight-related behaviors, and ~20-30 minutes/week of review of lessons and 
completion of action plans) 
- 4 remote assessments: baseline, 1 month, 3 months, 6 months. The 1-month 
assessment is expected to be ~5 minutes. The 3 and 6-month assessments are 
expected to be 30-60 minutes each 
 

Spark Trial Protocol | 17 
3.8 Outcomes 
All measures collected are listed in Table 3, below.  
3.8.1 Primary Outcome 
The primary outcome is weight change from baseline to 6 months (in kg), which will be 
assessed objectively via e-scale (Fitbit Aria Air) that will be mailed to participants. At 
each assessment, participants will receive an automated email in the morning 
instructing them to weigh themselves on the e-scale and follow best-practices46: place 
the scale on a hard, flat surface; remove all articles of clothing and accessories; weigh on 
the scale in the morning before eating or drinking and after emptying their bladder; step 
on the scale and record the value; and repeat it 2 more times for a total of 3 weight 
measurements per time point. Weights will sync from the e-scale to the Fitbit app via 
Bluetooth; as a preventive measure in case of syncing errors, participants will also be 
asked to input their weight values on a web-based weight check-in form. There is high 
concordance between weights measured from commercial e-scales and those from 
scales used in a clinical setting.47-48 
3.8.2 Secondary and Exploratory Outcomes 
Secondary outcomes include the following: 6-month change in caloric intake, physical 
activity, BMI, and health-related quality of life; 1- and 3-month weight change; and the 
proportion of participants achieving clinically significant weight loss (≥5% at 6 months). 
We will also examine additional outcomes: self-monitoring engagement rates and their 
relation to 6-month weight change; 6-month changes in diet quality; 6-month changes 
in step count among those assigned to self-monitor steps; the impact of potential 
moderators on 6-month weight change to determine for whom each self-monitoring 
strategy works best; and the impact of the orientation session on 6-month retention. In 
exploratory analyses, 6-month weight change data will be disaggregated by sex.  
 
Caloric intake will be assessed via the Automated Self-Administered 24-hour (ASA24) 
Dietary Assessment Tool (version 2022 and 2024- beginning in April 2025), which is a 
free web-based tool developed by the National Cancer Institute.49 Participants will be 
asked to complete a total of four 24-hour dietary recalls, including two at baseline and 
two at 6 months. At both time points, we will request one weekday recall and one 
weekend day recall. We will send up to 4 reminders per time point. We will exclude from 
our analyses any recalls with outliers of daily caloric intake reported as a minimum of 
<800 kcal for everyone, and a maximum of >4400 kcal for women and >5700 kcal for 
men, in accordance with ASA24 recommended procedures.50 To compute caloric intake 
at each time point, we will calculate the mean of the weekday recall and the 
weekend-day recall; if only one recall is available at a given time point, we will use that 
 

Spark Trial Protocol | 18 
value. Diet quality will be assessed using the Healthy-Eating Index-2020,51 calculated 
from ASA24 data. 
Physical activity will be assessed via the GLTEQ, a self-report measure that assesses the 
past week’s frequency of different types of exercise (strenuous, moderate, and mild or 
light) that were engaged in for more than 15 minutes during one’s free time.42-43 
Strenuous activities are described as those where one’s “heart beats rapidly” (e.g., 
running, jogging, or swimming), moderate activities are described as “not exhausting” 
(e.g., fast walking or tennis), and mild activities are described as requiring “minimal 
effort” (e.g., yoga or easy walking). A leisure score index will be created using the 
following formula: (strenuous ×9) + (moderate ×5) + (light ×3), with higher scores 
indicating more frequent exercise. To assess weekly moderate-to-vigorous physical 
activity (MVPA), a composite score will be created using the same procedures but 
excluding the light activities; from this MVPA score index, scores of ≥ 24 units will be 
interpreted as active and scores < 24 will be considered insufficiently active.52 We will 
objectively assess step count using the Fitbit Inspire 3 activity tracker, among those 
assigned to track steps. We will operationalize the week 1 step count as the average of 
the first 7 days of the intervention and 6-month step count as the average of the last 7 
days (week 26), so long as ≥ 3 valid days (≥1000 steps/day) per week are reported.53  
 
Health-related quality of life (HRQoL) will be assessed using the 36-item Short Form 
Health Survey (SF-36),54 with scores provided for both the physical component and 
mental component. It assesses 8 domains: physical functioning, role-physical, bodily 
pain, general health, vitality, social functioning, role-emotional, and mental health. 
 
Self-monitoring engagement will be assessed objectively for all 182 days (6 months) of 
the intervention using data collected from the Fitbit digital tools. Engagement will be 
operationalized as the percent of days in the intervention that participants self-monitor 
dietary intake, steps, and weight (each reported separately). For self-monitoring of diet, 
we will count days as valid if they have ≥ 800 kcal recorded, which is a common 
threshold. For self-monitoring of steps, we will count days as valid only if ≥ 1000 steps 
are recorded; this minimum threshold is used to minimize the possibility of inaccurately 
counting days with high amounts of non-wear time.55 We will also assess engagement in 
other intervention components, including the percentage of action plans completed, 
which is objectively assessed via Qualtrics, with 100% indicating completion of all 19 
action plans. Via self-report at 6 months, we will assess which of the 19 lessons were 
read and the frequency with which progress reports were reviewed, with options of 
weekly, less than 1 time per week, less than 1 time per month, and never.  
 
Putative moderators will be assessed via self-report survey measures administered at 
baseline, including demographic (e.g., sex, age, education), psychosocial (e.g., stress, 
 

Spark Trial Protocol | 19 
self-efficacy), behavioral (e.g., pretreatment caloric intake and physical activity), and 
clinical (e.g., type 2 diabetes diagnosis) characteristics. A health literacy questionnaire, 
the Newest Vital Sign, will be administered orally by study staff at the baseline visit.56 
 
Table 3. Assessment Schedule in the Spark Trial.     
  
  
Time Point (months) 
Construct 
Measure or assessment tool 
0 
1 
3 
6 
Anthropometric, behavioral, and demographic measures 
Weight (primary 
outcome) 
E-scale (Fitbit Aria Air)46-48 
• 
• 
• 
• 
Height 
Self-reported57 
• 
  
  
  
Caloric intake 
ASA24 dietary recall (1 weekday, 1 weekend day)49 
• 
  
  
• 
Diet quality 
Healthy Eating Index,51 measured by ASA24 
• 
  
  
  
Physical activity 
GLTEQ42-43 
• 
• 
• 
• 
Physical activity 
guideline adherence 
Stanford Leisure-Time Activity Categorical Item 
(L-Cat)58 
• 
  
• 
• 
Step counta 
Activity tracker (Fitbit Inspire 3) 
••••••••••••••••••••••••• 
History of 
self-monitoring 
Self-reported use of self-monitoring of diet, steps, 
and weight in the month prior to study enrollment59 
• 
  
  
  
Sociodemographic and 
clinical 
characteristics 
Self-reported age, race/ethnicity, sex, gender, 
sexual identity, birth country, marital status, 
education, employment, income, household size, # 
children in home, prediabetes, type II diabetes, 
hypertension, smoking 
• 
  
  
  
Engagement metrics 
  
  
  
  
% days of 
self-monitoring (diet, 
steps, weight) 
Digital tools (Fitbit app, Fitbit Inspire 3 activity 
tracker, Fitbit Aria Air e-scale, respectively) 
••••••••••••••••••••••••• 
% action plans 
completed 
Action plans in Qualtrics 
••••••••••••••••••••••••• 
% lessons read 
Self-reported frequency of reading weekly lessons 
 
 
 
• 
Progress reports viewed 
Self-reported frequency of viewing weekly progress 
reports 
 
 
 
• 
Psychosocial measures 
Health-related quality of 
life 
SF-3654 
• 
  
  
• 
Self-efficacy for dietary 
change 
Weight Efficacy Lifestyle Questionnaire 
Short-Form (WEL-SF)60 
• 
  
• 
• 
Self-efficacy for exercise 
Exercise Self-Efficacy Scale61 
• 
  
• 
• 
Self-regulation for eating 
Three Factor Eating Questionnaire (TFEQ-R18)62 
• 
  
• 
• 
Sleep 
Pittsburgh Sleep Quality Index (PSQI)63 
• 
  
• 
• 
Motivation 
Treatment Self-Regulation Questionnaire (TSRQ)64 
• 
  
• 
  
Mastery for 
self-monitoringb 
Self-Report Habit Index (SRHI) Automaticity 
subscale65-66 
  
• 
  
  
 

Spark Trial Protocol | 20 
  
  
Time Point (months) 
Construct 
Measure or assessment tool 
0 
1 
3 
6 
Self-efficacy for 
self-monitoringb 
Adapted from Bandura’s self-efficacy measure67 to 
assess self-efficacy for self-monitoring diet, 
steps, and weight 
• 
• 
  
  
Outcome expectations/ 
realizations 
Outcome Expectations68 
• 
  
  
• 
Confidence in 
participating fully 
Investigator-designed 1-item measure to capture 
confidence in participating in weight loss 
intervention over next 6 months 
• 
  
  
  
Health literacy 
Newest Vital Sign (NVS)56 via interview 
• 
  
  
  
Weight bias 
internalization 
Weight Bias Internalization Scale (WBIS)69 
• 
  
  
  
Stress 
Perceived Stress Scale (PSS-10)70 
• 
  
  
  
Negative life events 
Occurrence of negative life events over past 12 
months 
• 
  
  
  
Stages of change 
Stages of change questionnaire in weight 
management (S-Weight)71 
• 
  
  
  
Social support 
Social Support for Diet & Exercise72 
• 
  
  
  
a This outcome will be collected only in the conditions that were asked to track steps (conditions 3, 4, 7, 8). 
b At the 1-month assessment, these measures will be specific to what domain is being self-monitored (i.e., diet, steps, 
weight). Only participants in the conditions that are asked to track a given domain will be administered those 
measures. 
3.9 Data Collection 
Data will be collected at baseline, 1, 3, and 6 months. All data collection procedures will 
occur through remote means. Weights will be collected via e-scale, self-monitoring data 
will be collected objectively by the digital tools, health literacy will be assessed orally, 
and all other measures will be collected via web-based questionnaire (REDCap and a 
dietary recall tool49). Fitabase software (Small Steps Lab, LLC) will be used to retrieve 
the objective self-monitoring data via Fitbit’s API. At the time of each assessment, 
participants will be emailed instructions for weighing themselves on their e-scales, and 
sent a link to the web-based survey to complete measures. Reminders will be sent if 
weights are not reported or surveys not completed.  
3.9.1 Masking 
Participants will not be masked to treatment assignment, by design, as they will be 
informed before enrollment about the three types of self-monitoring to which they could 
be assigned. Study staff will not be masked to treatment assignment due to logistical 
limitations and the need for quality assurance. However, all surveys will be sent via 
REDCap and study staff will not have access to the allocation sequence. Once all data 
are collected, the study statistician (JAG) will analyze the primary outcome (weight 
change) in a masked fashion such that treatment assignment will not be revealed. 
 

Spark Trial Protocol | 21 
3.9.2  Quality Assurance 
Participants enrolled in this study may not concurrently participate in other research 
studies where weight loss is targeted. In addition, participants will be asked to refrain 
from self-monitoring behaviors that are not assigned to them and refrain from using any 
other weight-related apps or activity trackers for the duration of the trial. In the first 2 
weeks of the intervention, study staff will verify whether participants are 
self-monitoring correctly by checking their Fitbit data. If a deviation is found, staff will 
flag it in REDCap, and the participant will receive an automated email reminder 
indicating what they are supposed to self-monitor or not self-monitor. By explaining the 
rationale of the study prior to enrollment, we hope to minimize contamination between 
conditions. It is common practice for control group participants to still have their 
weights assessed for study evaluation purposes even if they are not instructed to weigh 
regularly.  In a prior trial testing a daily self-weighing intervention, all participants 
received a scale at baseline, and control participants were instructed to use the scale 
only at the assessment time points73; these procedures (which will be replicated here) 
were successfully enacted with no contamination found and negligible impact on 
outcomes. 
3.9.3 Research Ethics Approval 
The Stanford University Institutional Review Board (IRB) approved our study protocol 
on March 28, 2022. Protocol modifications will be communicated to all study 
investigators and approved by the IRB prior to implementation, and the trial registry 
will be updated accordingly.  
3.9.4 Declaration of Interests 
The investigators report no competing interests. 
3.10 Sample Size and Statistical Power 
See Statistical Analysis Plan for the Spark Trial in a separate document. 
3.11  Statistical Analysis 
See Statistical Analysis Plan for the Spark Trial in a separate document. 
3.12 Data Management 
REDCap will be used for direct data entry, randomization, and tracking participants’ 
status in the study. REDCap is a secure, web-based software platform for building and 
managing data, with a particular emphasis on those from clinical research studies. 
 

Spark Trial Protocol | 22 
There is a version hosted at Stanford University. Data will be stored on Stanford 
Medicine Box, which is a secure, cloud-based document management platform.   ​ 
3.13 Endpoint 
The behavioral intervention is 6 months in length and will not be terminated early 
based on efficacy. The study is expected to end after we reach our target sample size 
and the final participant has completed the 6 month intervention and assessment. 
 
 

Spark Trial Protocol | 23 
4. Risks and Protection Against Risks​
​
​
​
 
4.1 Potential Risks   
Potential risks include: 1) breach of confidentiality; 2) psychological distress associated 
with answering questions about psychological or behavioral content (e.g., perceived 
stress level, weight bias, body weight) or engaging in the intervention; 3) medical risks 
associated with participating in a behavioral weight loss treatment. These risks are 
consistent with typical risks associated with research participation in behavioral weight 
loss studies and are expected to be minimal. There are no invasive procedures planned. 
We will monitor the occurrence of adverse events and serious adverse events as required 
for these types of intervention studies. 
4.2 Safeguards to Minimize Risks 
4.2.1 Participant Population and Screening 
All participants will be adults with overweight/obesity who are otherwise healthy. 
Individuals who are pregnant or who have a medical condition that would limit 
participation in a behavioral weight loss intervention or be better suited for a more 
intensive or different type of intervention are excluded from our trial (e.g., individuals 
with a history of an eating disorder or cardiovascular event, uncontrolled diabetes 
mellitus or hypertension, cancer, dementia). Risks to participants are anticipated to be 
low given that we are examining evidence-based intervention strategies and promoting 
weight loss through lifestyle change (e.g., decrease intake of unhealthy foods, increase 
steps). We will not be including a vulnerable population (e.g., children, pregnant 
women) as participants. 
 
To ensure participant safety in this study, a set of screening procedures that are 
common in behavioral weight loss interventions, such as ensuring participants are safe 
to engage in moderate intensity physical activity and ensuring participants do not have a 
medical condition that is contraindicated in a remote weight loss program will be 
employed.  
4.2.2 Minimizing Risks 
This study is expected to impose minimal risk for participants. The content of the weight 
loss intervention – including moderate-intensity physical activity and dietary change 
through reduction of unhealthy foods and increase of healthy foods – is commensurate 
with the current national guidelines for behavioral obesity treatment.  
 

Spark Trial Protocol | 24 
 
Communications about the study, confirmation of eligibility, and obtaining informed 
consent will be conducted via 1:1 phone or Zoom with a member of the study team (see 
ClinicalTrials.gov for the informed consent form). These communications will be 
conducted by a study staff member who is in a private room. Participants will be 
requested to complete these sessions in a private room when possible to protect their 
privacy. Only study team members will have access to the email account and telephone 
line used to communicate with participants. Participant data obtained during the study 
will be gathered by researchers who are HIPAA and CITI certified. Responses will be 
entered into a computer database that is password protected. Data will be stored on a 
remote server (not on a local hard drive) and will be encrypted and accessible only to 
select study investigators. Confidentiality will be enhanced by assigning a unique 
participant ID number to each participant. Identifying information will be removed 
from these datasets. 
 
To minimize risks related to experiencing psychological discomfort, participants will be 
informed upfront of types of survey questions and assessment measures to expect, and 
will be made aware they can skip any questions they do not feel comfortable answering 
with no impact on their participation or compensation. Any participants, including 
those who experience discomfort as a result of completing intervention components, are 
able to discontinue participation from the study at any time. We expect minimal adverse 
psychological effects. 
4.2.3 Data and Safety Monitoring Plan  
The Principal Investigator (Dr. Patel) and Co-Investigator (Dr. King) will be responsible 
for regular monitoring of data and safety in conjunction with an Independent Safety 
Officer: David J. Maron, MD, Chief of the Stanford Prevention Research Center in the 
Stanford University School of Medicine. Dr. Maron is board certified in internal 
medicine, cardiovascular disease, and clinical lipidology and has extensive expertise in 
leading clinical trials. He will be responsible for reviewing clinical trial data, with 
particular emphasis on adverse events or clinical safety. As the Independent Safety 
Officer, Dr. Maron will not be involved in the study’s design and conduct nor have any 
conflict of interests related to the trial. Based on his review of the data, he may propose 
modifications to the study. 
 
Information that will be monitored by the PI include: (1) adverse events and serious 
adverse events (SAE) occurring during the study period for events both related and 
unrelated to the study procedures; (2) feedback from participants about discomfort they 
are experiencing as a result of completing intervention components; (3) feedback from 
participants about discomfort related to survey questions or other study procedures; (4) 
unexpectedly high weight change over time; and (5) any breach of confidentiality. 
 

Spark Trial Protocol | 25 
 
Dr. Patel will provide at least yearly updates to the investigator team regarding 
recruitment rates, enrollment rates, participant demographics, retention rates, adverse 
events, and any other substantive trial-related issues. All team members, including 
investigators, study staff, and students, will be listed on the IRB protocol and will have 
up-to-date ethics training (CITI certification). All study procedures involving participant 
contact will be done by trained research staff who have completed formal human 
subjects training (e.g., CITI Certification) to ensure understanding of methods and 
procedures to protect personal health information, and the importance of maintaining 
the confidentiality of study participants. Dr. Patel and her study team (Project 
Coordinator, Research Assistants) will meet weekly during the clinical trial period and 
will be able meet more frequently if needed.  
4.2.4 Adverse Events 
Definitions: 
-​
Serious Adverse Event (SAE): Any adverse event temporally associated with the 
subject's participation in research that meets any of the following criteria:  
-​
Results in death  
-​
Is life-threatening (places the subject at immediate risk of death from the 
event as it occurred)  
-​
Requires inpatient hospitalization or prolongation of existing 
hospitalization  
-​
Results in a persistent or significant disability/incapacity  
-​
Results in a congenital anomaly/birth defect  
-​
Any other adverse event that, based upon appropriate medical judgment, 
may jeopardize the subject's health and may require medical or surgical 
intervention to prevent one of the other outcomes listed in this definition   
-​
Examples: chest pain; myocardial infarction (heart attack); stroke; kidney 
stones; knee replacement; car accident resulting in hospitalization; a fall 
resulting in hospitalization; fracture    
-​
Unanticipated problem (UP) involving risks to subjects or others: Any incident, 
experience, or outcome that meets all the following criteria:  
-​
Unexpected (in terms of nature, severity, or frequency) given (a) the 
research procedures that are described in the protocol-related documents, 
such as the IRB-approved research protocol and informed consent 
document; and (b) the characteristics of the subject population being 
studied  
-​
Related or possibly related to a subject's participation in the research  
 

Spark Trial Protocol | 26 
-​
Suggests that the research places subjects or others at a greater risk of 
harm (including physical, psychological, economic, or social harm) related 
to the research than was previously known or recognized  
-​
a regular Adverse Event (AE): Any untoward or unfavorable medical occurrence 
in a human subject, including any abnormal sign (for example, abnormal physical 
exam or laboratory finding), symptom, or disease, temporally associated with the 
subject's participation in the research, whether or not considered related to the 
subject's participation in the research  
-​
 Examples: Skin irritation on wrist from wearing accelerometer wristband; 
migraine; back pain; knee pain; COVID-19 diagnosis; a cold 
Information will be collected on all potential types of adverse events, including 
musculo-skeletal soreness and injury, as well as major medical events including injuries 
or conditions that result in health care provider visits or hospitalization. Serious adverse 
events will be reported promptly in writing to the NIH and Stanford IRB, and all adverse 
events reported to the appropriate entities as required throughout the study. 
 
Participants will be provided with contact information for the study team, including a 
study phone number and study email address that will be checked daily on business 
days by study staff and/or the PI. This contact information will be provided to 
participants on multiple occasions, including in the informed consent form, in weekly 
intervention-related emails, and in the study assessment emails sent at baseline, 1 
month, 3 months, and 6 months. In addition, participants will speak to study staff via 
phone/video conference at the beginning of the study (before randomization) to ensure 
eligibility, interest, and understanding of study procedures and assigned intervention 
components. During this call, participants will be encouraged to report any adverse 
events, serious adverse events, or discomforts that arise during the trial to the study 
team. 
 
Study staff will be the primary source of contact with participants (e.g., sending 
intervention materials and confirming completion of assessments). Study staff will alert 
the PI of any negative participant feedback. Based on participant feedback, the PI will 
revise the intervention and study procedures as necessary to ensure that risks of injury 
or discomfort are minimized and will seek IRB approval before doing so. Study staff will 
flag any unexpectedly high changes in weight and will notify the PI and Co-Investigator 
of this occurrence. The Independent Safety Officer will then be asked to review these 
outcomes and help decide next steps, such as whether a participant will be asked to drop 
out of the trial for safety reasons. Study staff will present all serious adverse events 
(SAE) and Unexpected Events to the PI  by telephone or electronically within 24 hours 
of the time they are reported, except for life-threatening events where the expectation is 
that the PI will be alerted immediately. An internal project-specific report will be 
 

Spark Trial Protocol | 27 
generated in REDCap for each event reported. This information will be provided to the 
Stanford University IRB and NIDDK in a timely manner, in accordance with a protocol 
approved by the IRB and NIDDK. The study team will collect several channels of contact 
information for each participant (with their permission), including phone number(s) 
and e-mail address(es), along with those of a close friend or family member. In addition 
to standard study purposes, this contact information will be used to reach participants if 
additional information about an adverse event or SAE is needed. All study staff will have 
access to each other’s contact information, including an email address and phone 
number, in a centralized database to facilitate easy communication. 
4.2.5 Data Quality 
Data quality will be assured by using standardized procedures via comprehensive 
intervention and assessment protocols. Dr. Patel will also closely monitor the data and 
will use IRB-approved documentation strategies of research procedures. Any updates to 
the protocol will first receive approval from the IRB before implementing. 
4.3 Privacy 
Confidentiality of participant data will be maintained by: 1) handling individual data by 
participant ID number, rather than by name; 2) storing all individual data in password 
protected files and electronic hard drives, and in encrypted data servers at Stanford; 3) 
not disclosing individual data to anyone other than trained study staff; 4) using the 
Stanford protected version of Qualtrics and REDCap, allowing encrypted transmission 
of all survey data; 5) presenting study findings in aggregate in publications, 
presentations, and all other dissemination efforts. 
 
Investigators will be sensitive to issues surrounding confidentiality and other forms of 
participant risk, including discomfort surrounding talking about personal health issues 
and behaviors.  The study team's onboarding meeting will include a discussion of 
confidentiality and how this is to be maintained throughout the course of the study. All 
staff will have taken the required online human subjects course on this topic (e.g., CITI 
training). In addition, this topic will be regularly addressed during weekly staff meetings 
where staff will be encouraged to ask questions pertaining to this issue.  
 
If data sharing is required, we will transfer data electronically in accordance with the 
following guidelines: Transmission of data will be done using File Transfer Protocol 
(FTP) and encrypted through the use of 128-bit SSL or other industry acceptable 
methods. Wireless communication will be encrypted using Wi-Fi Protected Access 
(WPA), VPN, or 128-bit SSL.
 
 

Spark Trial Protocol | 28 
5 Dissemination 
5.1 Data Sharing Plan 
The clinical trial is registered at ClinicalTrials.gov (NCT05249465) and information 
about the results will be submitted there for public posting. Results information will be 
submitted no later than one year after the trial’s primary completion date. Consent 
documents for this trial will include a specific statement relating to posting of clinical 
trial information at ClinicalTrials.gov. 
 
The Stanford University School of Medicine has an internal policy in place to ensure that 
clinical trials registration and results reporting occur in compliance with policy 
requirements. Specifically, the policy states that the PI will be the Responsible Party for 
registering, updating, and reporting results from the clinical trial in a timely manner. 
Registration was completed via the Stanford ClinicalTrials.gov Protocol Registration 
System (PRS).  
 
Commensurate with current requirements, once the study data have undergone final 
data cleaning, analysis, and planned publications, we will make the final data collected 
as part of the proposed research available in electronic form to researchers who request 
them. We will request that researchers submit a data request in writing to the project 
Principal Investigator so that the requested data can be made available while protecting 
the confidentiality of study participants and ensuring that the proposed research 
questions do not overlap with planned publication development by the project research 
team or other groups. The requested dataset will be free of identifiers that would permit 
linkages to individual research participants and we will remove protected health 
information in compliance with the HIPAA Privacy Rule. The requestor must agree to 
maintain the privacy and confidentiality of the data and must indicate how they plan to 
store the data to demonstrate compliance with secure computing. NIH guidelines, 
institutional policies, IRB rules, and regulatory policies will be taken into account. Per 
NIH guidelines, all publications that emerge from this award will be publicly available 
via PubMed Central. 
Study results will be shared through presentations at national conferences of 
professional organizations (e.g., Society of Behavioral Medicine; The Obesity Society), 
articles in peer-reviewed journals, and postings on social media channels. 
5.2 Access to Data 
The principal investigator will retain access to the final trial dataset.
 
 

Spark Trial Protocol | 29 
6. Protocol Amendment History 
The Stanford University Institutional Review Board (IRB) approved our study protocol 
on March 28, 2022. Additional details were added to the study protocol prior to the trial 
beginning on September 22, 2023. No substantive changes to the IRB protocol were 
made once the trial began. Only minor protocol changes were made after this date, 
including the following, in Table 4, below: 
 
Table 4. Protocol Amendments in the Spark Trial. 
Version 
Approval 
Date 
Description of Change 
Brief Rationale 
1.0 
Mar. 25, 2022 
None (original IRB approval) 
-- 
1.1 
June 28, 2023 
Changed Protocol Director to Michele Patel 
(from Abby King); added new team 
personnel; added David Maron as 
Independent Safety Officer; changed ASA24 
recall reimbursement from $12 to $10; 
modifications to eligibility screen and 
surveys; added recruitment flyers; updated 
orientation slides 
Dr. Patel submitted the IRB 
application as a postdoctoral 
fellow but is now an Instructor at 
Stanford; new study personnel 
joined the team; identified a 
Safety Officer without prior PI 
collaborations; modified 
incentives for ASA24 due to 
increased costs of shipping Fitbit 
devices; updated questions before 
trial deployment 
1.2 
Sept. 21, 2023 
added new team personnel 
New study personnel joined the 
team. 
Trial began on September 22, 2023 
1.3 
July 2, 2024 
added StudyPages as an online recruitment 
source; added a new vendor for purchasing 
Fitbit devices; updated office address on the 
informed consent form; updated the Google 
and Fitbit Privacy Policy and Terms of Service 
documents; added new team personnel 
Expand recruitment strategy; 
discontinuation of original Fitbit 
vendor; change in office address; 
change to Google and Fitbit legal 
documents; new study personnel 
joined the team. 
1.4 
Aug. 28, 2024 
added new team personnel 
New study personnel joined the 
team. 
1.5 
Dec. 13, 2024 
 added new team personnel 
New study personnel joined the 
team. 
1.6 
June 12, 2025 
Stylistic/typographical updates to the 
protocol and updated recruitment dates  and 
ClinicalTrials.gov status information; added 
new version of ASA24 
Improve readability of the 
document; provide actual 
recruitment dates; ASA24 retired 
version-2022 in April 2025 
1.7 
June 24, 2025 
Added date of final data collection; added 
primary data collection row in synopsis; 
finalized stylistic/typographical updates 
Spark trial has finished data 
collection; differentiate between 
primary data collection and 
completion of all other data 
collection 
 
[As of June 12, 2025] No changes were made to our trial registration on 
ClinicalTrials.gov once the trial began on September 22, 2023. 
 
 
 

Spark Trial Protocol | 30 
7. References 
 1.          ​ Prevalence of obesity and severe obesity among adults: United States, 2017–2018. 2020. 
https://stacks.cdc.gov/view/cdc/85451. 
2.          ​ National Center for Health Statistics: Obesity and Overweight. 2020; 
http://www.cdc.gov/nchs/fastats/obesity-overweight.htm. 
3.          ​ Jensen M, Ryan D, Apovian C, et al. 2013 AHA/ACC/TOS guideline for the management 
of overweight and obesity in adults: a report of the American College of 
Cardiology/American Heart Association Task Force on Practice Guidelines and The Obesity 
Society. Circulation. 2014;129(25 Suppl 2):S139-S140. 
4.          ​ Puhl RM, Heuer CA. Obesity stigma: important considerations for public health.  Am J 
Public Health. 2010;100(6):1019-1028. 
5.          ​ Curry SJ, Krist AH, Owens DK, et al. Behavioral weight loss interventions to prevent 
obesity-related morbidity and mortality in adults: US Preventive Services Task Force 
recommendation statement. JAMA. 2018;320(11):1163-1171. 
6.          ​ Butryn ML, Webb V, Wadden TA. Behavioral treatment of obesity. Psychiatric Clinics. 
2011;34(4):841-859. 
7.          ​ PEW. Mobile Technology and Home Broadband 2021. 2021; 
https://www.pewresearch.org/internet/2021/06/03/mobile-technology-and-home-broadb
and-2021/. 
8.          ​ PEW. Mobile Fact Sheet. Pew Research Center 2021; 
https://www.pewresearch.org/internet/fact-sheet/mobile/. 
9.          ​ Skinner R, Gonet V, Currie S, Hoddinott P, Dombrowski SU. A systematic review with 
meta-analyses of text message-delivered behaviour change interventions for weight loss and 
weight loss maintenance. Obesity Reviews. 2020;21(6):e12999. 
10.       ​ Podina IR, Fodor LA. Critical review and meta-analysis of multicomponent behavioral 
e-health interventions for weight loss. Health Psychology. 2018;37(6):501. 
11.        ​ Semper H, Povey R, Clark-Carter D. A systematic review of the effectiveness of 
smartphone applications that encourage dietary self-regulatory strategies for weight loss in 
overweight and obese adults. Obesity reviews. 2016;17(9):895-906. 
12.       ​ Teasdale N, Elhussein A, Butcher F, et al. Systematic review and meta-analysis of 
remotely delivered interventions using self-monitoring or tailored feedback to change 
dietary behavior. The American journal of clinical nutrition. 2018;107(2):247-256. 
13.       ​ Yen HY, Chiu HL. The effectiveness of wearable technologies as physical activity 
interventions in weight control: A systematic review and meta-analysis of randomized 
controlled trials. Obesity Reviews. 2019;20(10):1485-1493. 
14.       ​ Tang JC, Abraham C, Greaves CJ, Nikolaou V. Self-directed interventions to promote 
weight loss: a systematic review and meta-analysis. Health Psychol Rev. 2016:1-15. 
15.        ​ Schippers M, Adam P, Smolenski D, Wong H, Wit J. A meta-analysis of overall effects of 
weight loss interventions delivered via mobile phones and effect size differences according 
to delivery mode, personal contact, and intervention intensity and duration. Obesity 
Reviews. 2017;18(4):450-459. 
16.       ​ Patel ML, Wakayama LN, Bennett GG. Self-Monitoring via Digital Health in Weight Loss 
Interventions: A Systematic Review Among Adults with Overweight or Obesity. Obesity. 
2021;29(3):478-499. 
 

Spark Trial Protocol | 31 
17.        ​ Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the 
literature. Journal of the American Dietetic Association. 2011;111(1):92-102. 
18.       ​ Baker RC, Kirschenbaum DS. Self-monitoring may be necessary for successful weight 
control. Behavior Therapy. 1993;24(3):377-394. 
19.       ​ Bandura A. Social cognitive theory of self-regulation. Organizational behavior and 
human decision processes. 1991;50(2):248-287. 
20.      ​ Carver CS, Scheier MF. Control theory: A useful conceptual framework for 
personality–social, clinical, and health psychology. Psychological bulletin. 
1982;92(1):111-135. 
21.       ​ Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy 
eating and physical activity interventions: a meta-regression. Health Psychology. 
2009;28(6):690-701. 
22.       ​ Hartmann-Boyce J, Johns D, Jebb S, Aveyard P. Effect of behavioural techniques and 
delivery mode on effectiveness of weight management: systematic review, meta-analysis 
and meta-regression. Obesity Reviews. 2014;15(7):598-609. 
23.       ​ Burke LE, Swigart V, Warziski Turk M, Derro N, Ewing LJ. Experiences of 
self-monitoring: successes and struggles during treatment for weight loss. Qualitative 
health research. 2009;19(6):815-828. 
24.       ​ Turner-McGrievy GM, Yang C-H, Monroe C, Pellegrini C, West DS. Is burden always 
bad? Emerging low-burden approaches to mobile dietary self-monitoring and the role 
burden plays with engagement. Journal of Technology in Behavioral Science. 
2021;6(3):447-455. 
25.       ​ Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy 
eating and physical activity interventions: a meta-regression. Health Psychology. 
2009;28(6):690-701. 
26.       ​ Harkin B, Webb TL, Chang BP, et al. Does monitoring goal progress promote goal 
attainment? A meta-analysis of the experimental evidence. Psychol Bull. 
2016;142(2):198-229. 
27.       ​ Dombrowski SU, Sniehotta FF, Avenell A, Johnston M, MacLennan G, Araújo-Soares V. 
Identifying active ingredients in complex behavioural interventions for obese adults with 
obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic 
review. Health Psychol Rev. 2012;6(1):7-32. 
28.      ​ Collins LM. Optimization of behavioral, biobehavioral, and biomedical interventions. 
Springer; 2018. 
29.       ​ Rosenbaum D, Piers A, Schumacher L, Kase C, Butryn M. Racial and ethnic minority 
enrollment in randomized clinical trials of behavioural weight loss utilizing technology: a 
systematic review. Obesity Reviews. 2017;18(7):808-817. 
30.      ​ Haughton CF, Silfee VJ, Wang ML, et al. Racial/ethnic representation in lifestyle weight 
loss intervention studies in the United States: a systematic review. Preventive medicine 
reports. 2018;9:131-137. 
31.       ​ Hales CM, Fryar CD, Carroll MD, Freedman DS, Aoki Y, Ogden CL. Differences in 
obesity prevalence by demographic characteristics and urbanization level among adults in 
the United States, 2013-2016. Jama. 2018;319(23):2419-2429. 
 

Spark Trial Protocol | 32 
32.       ​ Treweek S, Bevan S, Bower P, et al. Trial forge guidance 1: what is a study within a trial 
(SWAT)? Trials. 2018;19(1):1-5. 
33.       ​ Patel ML. SWAT 211: Effects of a self-directed orientation session on retention in a 
digital weight loss study. The Northern Ireland Network for Trials Methodology Research 
2023; 
https://www.qub.ac.uk/sites/TheNorthernIrelandNetworkforTrialsMethodologyResearch/
FileStore/Filetoupload,1950833,en.pdf. 
34.       ​ Jake-Schoffman DE, Brown SD, Baiocchi M, et al. Methods-Motivational Interviewing 
Approach for enhanced retention and attendance. Am J Prev Med. 2021;61(4):606-617. 
doi:10.1016/j.amepre.2021.04.005 
35.       ​ Gallis JA, Bennett GG, Steinberg DM, Askew S, Turner EL. Randomization procedures 
for multicomponent behavioral intervention factorial trials in the multiphase optimization 
strategy framework: challenges and recommendations. Transl Behav Med. 
2019;9(6):1047-1056. 
36.       ​ Patel ML, Hopkins CM, Brooks TL, Bennett GG. Comparing self-monitoring strategies 
for weight loss in a smartphone app: Randomized controlled trial. Journal of medical 
Internet research. 2019;21(2). 
37.       ​ Bennett GG, Steinberg D, Askew S, et al. Effectiveness of an app and provider counseling 
for obesity treatment in primary care. American journal of preventive medicine. 
2018;55(6):777-786. 
38.      ​ National Diabetes Prevention Program: PreventT2 Curriculum and Handouts. 2018; 
https://www.cdc.gov/diabetes/prevention/lifestyle-program/curriculum.html. Accessed 
4/1/2018. 
39.       ​ Miller WR, Rollnick S. Motivational interviewing: Helping people change. Guilford 
press; 2012. 
40.      ​ Patel ML, Wakayama LN, Bass MB, Breland JY. Motivational interviewing in eHealth 
and telehealth interventions for weight loss: A systematic review. Prev Med. 
2019;126:105738. 
41.       ​ Perri MG, Nezu AM, McKelvey WF, Shermer RL, Renjilian DA, Viegener BJ. Relapse 
prevention training and problem-solving therapy in the long-term management of obesity. 
Journal of consulting and clinical psychology. 2001;69(4):722. 
42.       ​ Godin GS, R. Godin leisure-time exercise questionnaire. Med Sci Sports Exerc. 
1997;29(6):S36-S38. 
43.       ​ Godin G, Shephard R. A simple method to assess exercise behavior in the community. 
Can J Appl Sport Sci. 1985;10(3):141-146. 
44.       ​ Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E. An adaptive physical 
activity intervention for overweight adults: a randomized controlled trial. PloS one. 
2013;8(12):e82901. 
45.       ​ Adams MA, Todd M, Angadi SS, et al. Adaptive goals and reinforcement timing to 
increase physical activity in adults: a factorial randomized trial. Am J Prev Med. 
2022;62(2):e57-e68. 
 

Spark Trial Protocol | 33 
46.       ​ Krukowski RA, Ross KM. Measuring weight with e‐scales in clinical and research 
settings during the COVID‐19 pandemic. Obesity (Silver Spring). 2020;28(7):1182-1183. 
47.       ​ Ross KM, Wing RR. Concordance of in‐home ‘smart’scale measurement with body 
weight measured in‐person. Obesity science & practice. 2016;2(2):224-228. 
48.      ​ Pebley K, Klesges R, Talcott G, Kocak M, Krukowski R. E-scale measurements in 
comparison to clinic weight measurements. Obesity (Silver Spring). 2019;27:1107-1114. 
49.       ​ Subar AF, Kirkpatrick SI, Mittl B, et al. The Automated Self-Administered 24-hour 
Dietary Recall (ASA24): a resource for researchers, clinicians and educators from the 
National Cancer Institute. J Acad Nutr Diet. 2012;112(8):1134. 
50.       ​ Automated Self-Administered 24-Hour (ASA24) Dietary Assessment Tool. 2016; 
https://epi.grants.cancer.gov/asa24/. 
51.        ​ Shams-White MM, Pannucci TE, Lerman JL, et al. Healthy eating index-2020: review 
and update process to reflect the dietary guidelines for Americans, 2020-2025. J Acad Nutr 
Diet. 2023;123(9):1280-1288. 
52.       ​ Amireault S, Godin G. The Godin-Shephard leisure-time physical activity questionnaire: 
validity evidence supporting its use for classifying healthy adults into active and 
insufficiently active categories. Perceptual and motor skills. 2015;120(2):604-622. 
53.       ​ Yao J, Tan CS, Lim N, Tan J, Chen C, Müller-Riemenschneider F. Number of daily 
measurements needed to estimate habitual step count levels using wrist-worn trackers and 
smartphones in 212,048 adults. Scientific Reports. 2021;11(1):9633. 
54.       ​ Ware Jr JE. SF-36 health survey update. Spine. 2000;25(24):3130-3139. 
55.       ​ Orstad SL, Gerchow L, Patel NR, et al. Defining valid activity monitor data: a 
multimethod analysis of weight-loss intervention participants’ barriers to wear and first 100 
days of physical activity. Paper presented at: Informatics2021. 
56.       ​ Weiss BD, Mays MZ, Martz W, et al. Quick assessment of literacy in primary care: the 
newest vital sign. The Annals of Family Medicine. 2005;3(6):514-522. 
57.       ​ Cui Z, Stevens J, Truesdale KP, Zeng D, French S, Gordon-Larsen P. Prediction of body 
mass index using concurrently self-reported or previously measured height and weight. 
PloS one. 2016;11(11):e0167288. 
58.       ​ Kiernan M, Schoffman DE, Lee K, et al. The Stanford Leisure-Time Activity Categorical 
Item (L-Cat): a single categorical item sensitive to physical activity changes in 
overweight/obese women. Int J Obes (Lond). 2013;37(12):1597-1602. 
59.       ​ Linde JA, Rothman AJ, Baldwin AS, Jeffery RW. The impact of self-efficacy on behavior 
change and weight change among overweight participants in a weight loss trial. Health 
Psychol. 2006;25(3):282. 
60.      ​ Clark MM, Abrams DB, Niaura RS, Eaton CA, Rossi JS. Self-efficacy in weight 
management. J Consult  Clin Psychol. 1991;59(5):739-744. 
 

Spark Trial Protocol | 34 
61.       ​ Sallis JF, Pinski RB, Grossman RM, Patterson TL, Nader PR. The development of 
self-efficacy scales for health related diet and exercise behaviors. Health Educ Res. 
1988;3(3):283-292. 
62.       ​ Karlsson J, Persson L-O, Sjöström L, Sullivan M. Psychometric properties and factor 
structure of the Three-Factor Eating Questionnaire (TFEQ) in obese men and women. 
Results from the Swedish Obese Subjects (SOS) study. Int J Obes Relat Metab Disord. 
2000;24(12):1715-1725. 
63.       ​ Buysse DJ, Reynolds III CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep 
Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 
1989;28(2):193-213. 
64.       ​ Levesque CS, Williams GC, Elliot D, Pickering MA, Bodenhamer B, Finley PJ. Validating 
the theoretical structure of the Treatment Self-Regulation Questionnaire (TSRQ) across 
three different health behaviors. Health Educ Res. 2007;22(5):691-702. 
65.       ​ Verplanken B, Orbell S. Reflections on Past Behavior: A Self‐Report Index of Habit 
Strength. J Appl Soc Psychol. 2003;33(6):1313-1330. 
66.       ​ Gardner B, Abraham C, Lally P, de Bruijn G-J. Towards parsimony in habit 
measurement: Testing the convergent and predictive validity of an automaticity subscale of 
the Self-Report Habit Index. Int J Behav Nutr Phys Act. 2012;9(1):1. 
67.       ​ Bandura A. Guide for constructing self-efficacy scales. Self-efficacy beliefs of 
adolescents. 2006;5(1):307-337. 
68.      ​ Neff K, King A. Exercise program adherence in older adults: The importance of achieving 
one’s expected benefits. Med Exerc Nutr Health. 1995;4:355-362. 
69.       ​ Durso LE, Latner JD. Understanding self‐directed stigma: development of the weight 
bias internalization scale. Obesity. 2008;16(S2):S80-S86. 
70.       ​ Cohen S, Kamarck T, Mermelstein R. Perceived stress scale. Measuring stress: A guide 
for health and social scientists. 1994. 
71.        ​ Andrés A, Saldaña C, Gómez‐Benito J. Establishing the stages and processes of change 
for weight loss by consensus of experts. Obesity. 2009;17(9):1717-1723. 
72.       ​ Sallis JF, Grossman RM, Pinski RB, Patterson TL, Nader PR. The development of scales 
to measure social support for diet and exercise behaviors. Prev Med. 1987;16(6):825-836. 
73.       ​ Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. The efficacy 
of a daily self-weighing weight loss intervention using smart scales and e‐mail. Obesity. 
2013;21(9):1789-1797.