Trial Title:
Piloting a Reinforcement Learning Tool for Individually Tailoring Just-in-time Adaptive Interventions
NCT ID:
NCT05751993
Condition:
Overweight and Obesity
Overweight
Obesity
Conditions: Official terms:
Obesity
Overweight
Conditions: Keywords:
cancer
risk factor
just-in-time adaptive intervention (JITAI)
Study type:
Interventional
Study phase:
N/A
Overall status:
Not yet recruiting
Study design:
Allocation:
N/A
Intervention model:
Single Group Assignment
Primary purpose:
Health Services Research
Masking:
None (Open Label)
Intervention:
Intervention type:
Behavioral
Intervention name:
ADAPT
Description:
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.
Arm group label:
ADAPT intervention
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). 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.
Criteria for eligibility:
Criteria:
Inclusion Criteria:
1. Age 18-55 years
2. Body Mass Index of 25-40 kg/m2
3. English-speaking and writing
4. Has a smartphone with a data and text messaging plan
Exclusion Criteria:
1. Currently participating in a weight loss, nutrition, or physical activity study or
program or other study that would interfere with this study
2. 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)
3. Previous surgical procedure for weight loss or planned weight loss surgery in the
next year
4. Currently pregnant or planning pregnancy in the next 4 months
5. Lost 10 or more pounds and kept it off in the last 6 months
6. 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
7. 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
8. Type 1 diabetes or currently receiving medical treatment for Type 2 diabetes
9. 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
10. 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
11. Report a past diagnosis of or receiving treatment for a DSM-5-TR eating disorder
(anorexia nervosa, bulimia nervosa, or other diagnosis)
12. Moving out of the area in the next 4 months
13. Out of town for a week or more during the study period
14. Another member of the household is a participant or staff member in this trial
15. Not willing to attend two study visits
16. Not willing to wear a Fitbit every day
17. Reason to suspect that the participant would not adhere to the study intervention
18. Have participated in another study conducted by the UNC Weight Research Program
within the past 12 months
Gender:
All
Minimum age:
18 Years
Maximum age:
55 Years
Healthy volunteers:
Accepts Healthy Volunteers
Locations:
Facility:
Name:
University of North Carolina at Chapel Hill
Address:
City:
Chapel Hill
Zip:
27514
Country:
United States
Contact:
Last name:
Brooke Nezami, PhD, MA
Phone:
919-966-5852
Email:
bnezami@unc.edu
Contact backup:
Last name:
Karen Hatley, MPH, RD
Phone:
919-966-5852
Email:
keericks@email.unc.edu
Investigator:
Last name:
Brooke Nezami, PhD, MA
Email:
Principal Investigator
Investigator:
Last name:
Nisha Gottfredson O'Shea, PhD
Email:
Sub-Investigator
Start date:
December 2024
Completion date:
February 2025
Lead sponsor:
Agency:
UNC Lineberger Comprehensive Cancer Center
Agency class:
Other
Collaborator:
Agency:
Duke University
Agency class:
Other
Collaborator:
Agency:
RTI International
Agency class:
Other
Collaborator:
Agency:
National Cancer Institute (NCI)
Agency class:
NIH
Source:
UNC Lineberger Comprehensive Cancer Center
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05751993
http://unclineberger.org/patientcare/clinical-trials/clinical-trials
