Overweight and ObesityNCT05751993
Piloting a Reinforcement Learning Tool for Individually Tailoring Just-in-time Adaptive Interventions
A study testing ADAPT in people with Overweight and Obesity and Overweight, run by UNC Lineberger Comprehensive Cancer Center at 1 site in United States.
Trial summary
The purpose of this pilot study is to conduct a 12-week pilot feasibility study testing usability of a reinforcement learning model (AdaptRL) in a weight loss intervention (ADAPT study). Read more about this study
- Who is it for?
- Ages 18 to 55 yearsHealthy volunteers can take part
- Conditions the study is for
- Overweight and Obesity, Overweight, Obesity
- What is being tested?
- ADAPT
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 19 participants
- Where
- 1 siteUnited States
- Study dates
- 12 Apr 2025to 18 Aug 2025
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 55 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- Age 18-55 years
- Body Mass Index of 25-40 kg/m2
- English-speaking and writing
- Has a smartphone with a data and text messaging plan
Who cannot join
- Currently participating in a weight loss, nutrition, or physical activity study or program or other study that would interfere with this study
- Currently using prescription medications with known effects on appetite or weight (e.g., oral steroids, weight loss medications), with the exception of individuals on a stable dose of SSRIs for 3 months)
- Previous surgical procedure for weight loss or planned weight loss surgery in the next year
- Currently pregnant or planning pregnancy in the next 4 months
- Lost 10 or more pounds and kept it off in the last 6 months
- Report a heart condition, chest pain during periods of activity or rest, or loss of consciousness on the Physical Activity Readiness Questionnaire (PAR-Q; items 1-4). Individuals endorsing joint problems, prescription medication usage, or other medical conditions that could limit exercise will be required to obtain written physician consent to participate
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- Pre-existing medical condition(s) that preclude adherence to an unsupervised exercise program, diabetes treated with insulin, history of heart attack or stroke, current treatment for cancer, or inability to walk for exercise
- Type 1 diabetes or currently receiving medical treatment for Type 2 diabetes
- Other health problems which may influence the ability to walk for physical activity or be associated with unintentional weight change, including cancer treatment within the past 5 years or tuberculosis
- Health or psychological diagnoses that preclude participation in a prescribed dietary and exercise program, including history of or diagnosis of schizophrenia or bipolar disorder, hospitalization for a psychiatric diagnosis in the past year, a current diagnosis of alcohol or substance abuse
- Report a past diagnosis of or receiving treatment for a DSM-5-TR eating disorder (anorexia nervosa, bulimia nervosa, or other diagnosis)
- Another member of the household is a participant or staff member in this trial
- Not willing to attend one study visit
- Not willing to wear a Fitbit every day
- Reason to suspect that the participant would not adhere to the study intervention
- Have participated in another study conducted by the UNC Weight Research Program within the past 12 months
Trial locations and contacts
1 site in 1 country.
United States1 site
University of North Carolina at Chapel Hill
Chapel Hill, North Carolina, 27514
About this study
The purpose of this pilot study is to conduct a 12-week pilot feasibility study testing usability of a reinforcement learning model (AdaptRL) in a weight loss intervention (ADAPT study). Building upon a previous just-in-time adaptive intervention (JITAI), a reinforcement learning model will generate decision rules unique to each individual that are intended to improve the tailoring of brief intervention messages (e.g., what behavior to message about, what behavior change techniques to include), improve achievement of daily behavioral goals, and improve weight loss in a sample of 20 adults.
Read the detailed description
Reinforcement Learning (RL), a type of machine learning, holds promise for addressing the limitations of previous approaches to implementing JITAIs. Adaptive RL applications work by updating information about expected "rewards" (i.e., proximal outcomes) based on the results of sequentially randomized trials. To realize the potential of adaptive interventions to reduce health disparities in cancer prevention and control, mHealth interventionists first need to identify methods of using digital health participant data to continually adapt decision rules guiding highly tailored intervention delivery. This research team has developed a reinforcement learning model (AdaptRL) that reads in and analyzes user data (e.g., calories, weight, and activity data from Fitbit) in real-time, uses RL to efficiently determine which message a participant should receive up to 3 times per day, and creates a JITAI tailored to optimize daily behavioral goal achievement and weight loss for each participant. The objective of this study is to test the feasibility of using this reinforcement learning model in a pilot weight loss study.
- Conditions
- Overweight and Obesity, Overweight, Obesity
- Being tested
- ADAPT
- Keywords
- cancer, risk factor, just-in-time adaptive intervention (JITAI)
- Medical subject headings
- Overweight, Obesity, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ADAPTBehavioural
The intervention is testing the feasibility of a reinforcement learning model to pull in participants' behavioral data (calories, activity, and weight) and use this data along with participants' past behavioral goal achievements to deliver the type of message that should be most effective for a given participant at a given time. At each decision point (morning, midday, and evening on a daily basis), the system evaluates which behaviors a participant is eligible to receive a message about (eating, activity, self-weighing), which intervention options a participant is eligible to receive, and then chooses what type of behavioral message a participant should receive. Over time, the model uses participant data and response to interventions to better tailor message choice.
| Study group | Role | What they receive |
|---|---|---|
| ADAPT intervention | Experimental | Participants will receive a smart scale and a physical activity tracker and will have three daily goals: weigh daily, a daily personalized active minutes goal, and a daily calorie goal. For 12 weeks, participants will receive 0-3 text messages per day about their behaviors and progress towards their goals, along with weekly personalized feedback, progress graphs, and lessons and resources available on the website. Receives: Behavioral: ADAPT |
What is being measured
The primary outcome is the main question the study is designed to answer.
Feasibility: Messages Delivered
Feasibility as the success of using the AdaptRL model will be defined as the mean number of messages delivered per participant per day.
Measured: up to 12 weeks
Study Engagement: Percent of Days With Logins
Study engagement will be defined as the percent of days, averaged across participants, with logins to the web application.
Measured: up to 12 weeks
Self-monitoring Adherence
Self-monitoring adherence will be defined as the percent of days, averaged across participants, in which participants tracked at least one weight loss behavior (tracked calories, wore tracker, or self-weighed).
Measured: up to 12 weeks
Secondary outcomes (6)
Percent Weight Loss
Percent weight loss will be defined as weight change from baseline to 12 weeks calculated as a percent from baseline weight.
Measured: 12 weeks
Change in Moderate-to-vigorous Physical Activity Minutes
Moderate-to-vigorous physical activity will be defined as the change in self-reported weekly minutes of moderate-to-vigorous physical activity as measured by the Paffenbarger Activity Questionnaire from baseline to 12 weeks. The minimum is 0, no maximum. Higher numbers represent a greater increase in minutes of weekly moderate-to-vigorous physical activity.
Measured: Baseline, 12 weeks
Change in Dietary Intake
Change in dietary intake will be defined as the change in average daily calorie intake as measured by the Automated Self-Administered 24-hour (ASA 24-hour) dietary recalls from baseline to 12 weeks. Daily caloric intake is measured in kcals, with higher numbers indicating higher caloric intake. Negative change values reflect decreases in daily caloric intake from baseline to 12 weeks.
Measured: Baseline, 12 weeks
Adherence to Calorie Goal
Adherence to the calorie goal as the mean percent of days in which participants tracked their calories and stayed at or under their calorie goal will be measured by dietary self-monitoring data tracked in the Fitbit app and transmitted via Application Programming Interface (API) to study servers.
Measured: up to 12 weeks
Adherence to Daily Active Minutes Goal
Adherence to daily active minutes goal, the mean percent of days in which participants met their daily active minutes goal, will be measured by activity tracker data tracked in the Fitbit app and transmitted via Application Programming Interface (API) to study servers.
Measured: up to 12 weeks
Adherence to Daily Self-weighing
Adherence to daily self-weighing, the mean percent of days in which participants self-weighed will be measured by Fitbit smart scales and transmitted via Application Programming Interface (API) to study servers.
Measured: up to 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Mar 2023
- Study start12 Apr 2025
- Primary completion3 Aug 2025
- Study completion18 Aug 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Other trials for Overweight and ObesityStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 19 participants (actual)
Sponsor and investigators
- Lead sponsor
- UNC Lineberger Comprehensive Cancer Center
- Sponsor type
- University, hospital or other
- Collaborators
- RTI International, National Cancer Institute (NCI)
- Responsible party
- Sponsor
- Investigators
- Brooke Nezami, PhD, MA, Principal investigator, University of North Carolina, Chapel Hill
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05751993
- Sponsor's protocol ID
- 22-0149
- Other IDs
- R21CA260092
- Status last verified
- Jul 2026
- First submitted
- 20 Feb 2023
- Last updated
- 21 Aug 2026
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Piloting a Reinforcement Learning Tool for Individually Tailoring Just-in-time Adaptive Interventions
- ClinicalTrials.gov ID
- NCT05751993
- Acronym / sponsor ID
- 22-0149
- Conditions
- Overweight and Obesity, Overweight, Obesity
- Keywords
- cancer, risk factor, just-in-time adaptive intervention (JITAI)
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 19 participants (actual)
- Lead sponsor
- UNC Lineberger Comprehensive Cancer CenterUniversity, hospital or other
- Collaborators
- RTI International, National Cancer Institute (NCI)
- Study start
- 12 Apr 2025 (actual)
- Primary completion
- 3 Aug 2025 (actual)
- Study completion
- 18 Aug 2025 (actual)
- First posted
- 2 Mar 2023 (actual)
- Results
- Posted 21 Aug 2026
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The purpose of this pilot study is to conduct a 12-week pilot feasibility study testing usability of a reinforcement learning model (AdaptRL) in a weight loss intervention (ADAPT study). Building upon a previous just-in-time adaptive intervention (JITAI), a reinforcement learning model will generate decision rules unique to each individual that are intended to improve the tailoring of brief intervention messages (e.g., what behavior to message about, what behavior change techniques to include), improve achievement of daily behavioral goals, and improve weight loss in a sample of 20 adults.
Detailed description
Reinforcement Learning (RL), a type of machine learning, holds promise for addressing the limitations of previous approaches to implementing JITAIs. Adaptive RL applications work by updating information about expected "rewards" (i.e., proximal outcomes) based on the results of sequentially randomized trials. To realize the potential of adaptive interventions to reduce health disparities in cancer prevention and control, mHealth interventionists first need to identify methods of using digital health participant data to continually adapt decision rules guiding highly tailored intervention delivery. This research team has developed a reinforcement learning model (AdaptRL) that reads in and analyzes user data (e.g., calories, weight, and activity data from Fitbit) in real-time, uses RL to efficiently determine which message a participant should receive up to 3 times per day, and creates a JITAI tailored to optimize daily behavioral goal achievement and weight loss for each participant. The objective of this study is to test the feasibility of using this reinforcement learning model in a pilot weight loss study.
Study design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| ADAPT intervention | Experimental | Behavioral: ADAPTParticipants will receive a smart scale and a physical activity tracker and will have three daily goals: weigh daily, a daily personalized active minutes goal, and a daily calorie goal. For 12 weeks, participants will receive 0-3 text messages per day about their behaviors and progress towards their goals, along with weekly personalized feedback, progress graphs, and lessons and resources available on the website. |
Who may qualify
- Ages
- 18 to 55 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (4 listed)
- Age 18-55 years
- Body Mass Index of 25-40 kg/m2
- English-speaking and writing
- Has a smartphone with a data and text messaging plan
Key exclusion criteria (16 listed)
- Currently participating in a weight loss, nutrition, or physical activity study or program or other study that would interfere with this study
- Currently using prescription medications with known effects on appetite or weight (e.g., oral steroids, weight loss medications), with the exception of individuals on a stable dose of SSRIs for 3 months)
- Previous surgical procedure for weight loss or planned weight loss surgery in the next year
- Currently pregnant or planning pregnancy in the next 4 months
- Lost 10 or more pounds and kept it off in the last 6 months
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ADAPTThe intervention is testing the feasibility of a reinforcement learning model to pull in participants' behavioral data (calories, activity, and weight) and use this data along with participants' past behavioral goal achievements to deliver the type of message that should be most effective for a given participant at a given time. At each decision point (morning, midday, and evening on a daily basis), the system evaluates which behaviors a participant is eligible to receive a message about (eating, activity, self-weighing), which intervention options a participant is eligible to receive, and then chooses what type of behavioral message a participant should receive. Over time, the model uses participant data and response to interventions to better tailor message choice. | Behavioural | ADAPT intervention |
Primary outcome
Feasibility: Messages Delivered
Time frame: up to 12 weeks
Feasibility as the success of using the AdaptRL model will be defined as the mean number of messages delivered per participant per day.
Study Engagement: Percent of Days With Logins
Time frame: up to 12 weeks
Study engagement will be defined as the percent of days, averaged across participants, with logins to the web application.
Self-monitoring Adherence
Time frame: up to 12 weeks
Self-monitoring adherence will be defined as the percent of days, averaged across participants, in which participants tracked at least one weight loss behavior (tracked calories, wore tracker, or self-weighed).
6 secondary outcomes
Percent Weight Loss
Time frame: 12 weeks
Change in Moderate-to-vigorous Physical Activity Minutes
Time frame: Baseline, 12 weeks
Change in Dietary Intake
Time frame: Baseline, 12 weeks
Adherence to Calorie Goal
Time frame: up to 12 weeks
Adherence to Daily Active Minutes Goal
Time frame: up to 12 weeks
Adherence to Daily Self-weighing
Time frame: up to 12 weeks
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 21 Aug 2026.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- University of North Carolina Lineberger Comprehensive Cancer Center Clinical TrialsLink provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Brooke Nezami, PhD, MA principal investigator
University of North Carolina, Chapel Hill
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing ADAPT in people with Overweight and Obesity and Overweight, run by UNC Lineberger Comprehensive Cancer Center at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 16 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 21 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 21 Aug 2026). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Feasibility: Messages Delivered (time frame: up to 12 weeks). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 21 Aug 2026.