Pelvic PainWorkoutCPPNCT07810218
Personalized Exercise Recommendations for Chronic Pelvic Pain Using Reinforcement Learning
A study testing Reinforcement Learning (RL)-Based Personalized Exercise Recommendations and Generic Exercise Recommendation in people with Pelvic Pain and Endometriosis, run by Icahn School of Medicine at Mount Sinai at 1 site in United States.
Trial summary
WorkoutCPP is a pilot study evaluating the feasibility of a personalized exercise recommendation system for individuals with chronic pelvic pain disorders (CPPDs). The study uses reinforcement learning (RL), a type of artificial intelligence that adapts recommendations over time based on each participant's reported… Read more about this study
- Who is it for?
- Ages 18 to 55 years, women only
- Conditions the study is for
- Pelvic Pain, Endometriosis, Chronic Pelvic Pain
- What is being tested?
- Reinforcement Learning (RL)-Based Personalized Exercise Recommendations and Generic Exercise Recommendation
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 45 participants
- Where
- 1 siteUnited States
- Study dates
- 6 Feb 2026to Aug 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
- Women only
- No healthy volunteers
- Adult
Who may be able to join
- Self-reported CPPD (e.g., endometriosis, adenomyosis, fibroids, etc.) based on clinician diagnosis
- Aged 18-55 years.
- Ownership of an iOS or Android smartphone.
- Willingness to self-track daily symptoms, exercise activities, and self-management behaviors using a smartphone research app.
- Willingness to wear an activity tracker for the study duration.
- Willingness to follow exercise recommendations from a smartphone research app, provided no adverse symptoms occur.
- Ability to read and write in English sufficient to understand study materials and communications.
- At least intermittently physically active (e.g., ≥30 minutes of walking twice per week).
Who cannot join
- Absolute contraindications to PA (e.g., recent myocardial infarction, complete heart block, acute congestive heart failure, unstable angina, or uncontrolled severe hypertension, BP ≥180/110 mm Hg).
- More than two "Yes" responses on the Physical Activity Readiness Questionnaire (PAR-Q) (16) without physician clearance.
- Major life events expected during the next 10 weeks (e.g., pregnancy, planned surgery, or extended travel likely to interfere with participation).
- Current or planned pregnancy within the next 6 months.
- Having given birth in the past 6 months or currently nursing.
- Inability to wear an activity tracker or use the app for the study duration.
- Complete inactivity (i.e., <60 minutes of moderate-intensity PA per week).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Icahn School of Medicine at Mount Sinai
New York, New York, 10029
Ipek Ensari, PhD, 631-565-1829, ipek.ensari@mssm.edu
Gerard M Ona, MD, 347-835-8115, GerardAnneAprilOna@mssm.edu
About this study
Official title: WorkoutCPP: A Pilot Series of N-of-1 Trials Evaluating RL-Generated Adaptive Exercise Recommendations for Pelvic Pain Management
WorkoutCPP is a pilot study evaluating the feasibility of a personalized exercise recommendation system for individuals with chronic pelvic pain disorders (CPPDs). The study uses reinforcement learning (RL), a type of artificial intelligence that adapts recommendations over time based on each participant's reported pain levels, symptom burden, and exercise compliance. Participants receive daily exercise recommendations that alternate between standard, non-personalized guidance and personalized, RL-generated recommendations across four 2-week phases, allowing within-person comparison of outcomes under each condition. The primary hypothesis is that an RL-based adaptive recommendation system is feasible to deliver in a CPPD population.
Read the detailed description
Chronic pelvic pain disorders (CPPDs) are associated with high symptom burden and reduced quality of life. Physical activity (PA) and exercise have emerged as a promising non-pharmacological approach for symptom management. However, optimal exercise type, intensity, and timing for pain management vary substantially across individuals, supporting the need for personalized adaptive approaches (Ensari et al., 2022, Krasny-Pacini et al., 2017). This study will enroll participants will a CPPD diagnosis into a remote, 9-week study to evaluate the feasibility of RL-based personalized exercise to non-personalized, standard recommendations. Enrollment is rolling, with participants entering the study on a continuous basis. Each participant's start date, and their 9-week intervention period, is determined by their baseline interview date. A baseline interview upon enrollment is scheduled with an exercise physiologist to review the participant's initial exercise list and provide exercise safety information, as well as overview use of the study App. Participants can choose to stay in the study for 2 additional weeks to make up any weeks with inadequate adherence. Study outcomes are measured daily over the course of the intervention period. Daily App-based tracking items assess pain and other symptoms, exercise behavior, perceived effect and feedback to the recommendation, menstrual status, and recommendation compliance. Fitbit trackers simultaneously track participants' objectively-estimated PA. A reinforcement learning (RL) agent implemented in Meier et al. 2023 as the middleware platform generates daily personalized exercise recommendations delivered via a research mobile phone application (Hirten et al., 2023, Meier et al., 2023). Participant-reported perceived effect of each exercise recommendation is used by the RL agent to calculate reward. Participants serve as their own controls, allowing for within-person comparison under the two conditions (Krasny-Pacini et al., 2017). Primary outcomes for the study include standard study feasibility metrics (e.g., adherence, retention). Secondary outcomes focus on RL agent performance and learning over time. Participant safety will be monitored throughout the study, in accordance with the institutional review board.
This work was supported by the Digital Health Partnership (DHP), a collaboration between the Hasso Plattner Institute, Data4Life, the Windreich Department of Artificial Intelligence and Human Health, the Hasso Plattner Institute for Digital Health at Mount Sinai, and The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai.
- Conditions
- Pelvic Pain, Endometriosis, Chronic Pelvic Pain
- Being tested
- Reinforcement Learning (RL)-Based Personalized Exercise Recommendations, Generic Exercise Recommendation
- Keywords
- Mobile health, Reinforcement learning, Physical activity, N-of-1 trial, exercise, personalized, intervention
- Medical subject headings
- Pelvic Pain, Endometriosis, Motor Activity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Reinforcement Learning (RL)-Based Personalized Exercise RecommendationsBehavioural
Daily exercise recommendations (using type, intensity, and duration) are generated by a contextual bandit reinforcement learning agent, based on the implementation described in Meier et al. 2023. Recommendations are personalized using each participant's initially generated list of exercises based on their physical ability and resources available, as well as contextual daily factors including pain symptoms, prior exercise compliance, and their feedback to the previous exercise recommendation.
Generic Exercise RecommendationBehavioural
Participants receive exercise recommendations from a standardized, set list of exercise recommendations that are based on USDHHS physical activity guidelines (Piercy et al., 2020). Recommendations are not personalized based on participant contextual information and do not adapt over the course of the study.
| Study group | Role | What they receive |
|---|---|---|
| RL-based personalized phase | Experimental | Participants will receive RL-generated personalized exercise recommendations, which are generated using the list from the initial participant intake form indicating their capacity and resources for carrying out various modalities and intensities of physical activity. The RL agent learns from the participant feedback to update the update the subsequent recommendations. Receives: Behavioral: Reinforcement Learning (RL)-Based Personalized Exercise Recommendations |
| Standard (Generic) Exercise Arm | Active comparator | Participants will receive standardized, non-personalized exercise recommendations based on the U.S. Physical Activity Guidelines, in 2-week blocks. This comparison will serve as the "active control" arm to which the experimental RL arm will be compared. This type of control condition was selected to provide a more rigorous test of the experimental condition. Receives: Behavioral: Generic Exercise Recommendation |
What is being measured
The primary outcome is the main question the study is designed to answer.
Exercise Recommendation Adherence Rate
Exercise recommendation adherence rate is calculated by the proportion of daily exercise recommendations completed over the course of the intervention. A higher exercise adherence rate indicates that participants are completing their given exercise recommendations at higher frequencies.
Measured: At 9 weeks at study completion
Participant Retention Rate
Participant retention rate is the proportion of enrolled participants completing study participation until the end of intervention. A higher retention rate indicates that participants complete the 9-week intervention period at higher frequencies.
Measured: At 9 weeks at study completion
Secondary outcomes (1)
Reinforcement Learning Agent Action Entropy Over Time
Entropy of the reinforcement learning (RL) agent's action probability distributions is calculated at each decision point throughout the study period. Entropy ranges from a minimum of 0 (the agent selects a single action with certainty) to a maximum of log(K), where K is the number of distinct recommendation actions available to the agent at that decision point (maximum entropy occurs when the agent assigns equal probability to all available actions). Decreasing entropy over time indicates increasing model confidence and more consistent personalized recommendation patterns. Higher entropy indicates greater uncertainty in the agent's decision making. This outcome will be evaluated for the entire duration of the intervention based on the daily data.
Measured: At 9 weeks at study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2026
- Study start6 Feb 2026
- Primary completionAug 2027 (estimated)
- Study completionAug 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for Pelvic PainStudies of the same condition, open and under way.
- More trials run by Icahn School of Medicine at Mount SinaiThe sponsor's other registered studies.
- 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
- Other
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- N-of-1 randomized crossover design in which each participant serves as their own control, randomized 1:1 to one of two intervention sequences (ABAB or BABA), alternating between RL-generated personalized exercise recommendations (active arm) and standard generic recommendations (control arm) across multiple 2-week blocks within the 9-week study period. First week is treated as a baseline week where participants get accommodated to the study App and procedures, as well as RL agent warm-up.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 45 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Icahn School of Medicine at Mount Sinai
- Sponsor type
- University, hospital or other
- Collaborators
- Hasso Plattner Institute, Potsdam, Germany
- Responsible party
- Principal investigator, Ipek Ensari, Icahn School of Medicine at Mount Sinai
- Investigators
- Ipek Ensari, PhD, Principal investigator, Icahn School of Medicine at Mount SinaiStefan Konigorski, PhD, Principal investigator, Department of Computational Precision Nutrition, German Institute of Human Nutrition
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07810218
- Sponsor's protocol ID
- STUDY-23-00721
- Acronym
- WorkoutCPP
- Status last verified
- Sep 2026
- First submitted
- 3 Sep 2026
- Last updated
- 9 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared, in order to protect participant privacy and confidentiality, consistent with the IRB guidelines and informed consent form under which participants were enrolled.
Publications
Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open. 2022 Jul 18;12(7):e059280. doi: 10.1136/bmjopen-2021-059280.
PubMed 35851021Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The Physical Activity Guidelines for Americans. JAMA. 2018 Nov 20;320(19):2020-2028. doi: 10.1001/jama.2018.14854.
PubMed 30418471Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Orlov G, Rodrigues J, Alleva E, Ensari I, Bottinger E, Nadkarni GN, Fuchs TJ, Fayad ZA. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Res Protoc. 2023 Nov 16;12:e49204. doi: 10.2196/49204.
PubMed 37971801Krasny-Pacini A, Evans J. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med. 2018 May;61(3):164-179. doi: 10.1016/j.rehab.2017.12.002. Epub 2017 Dec 15.
PubMed 29253607Rabbi M, Aung MS, Gay G, Reid MC, Choudhury T. Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. J Med Internet Res. 2018 Oct 26;20(10):e10147. doi: 10.2196/10147.
PubMed 30368433
Study at a glance
From the registry record, last updated by the sponsor on 9 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- WorkoutCPP: A Pilot Series of N-of-1 Trials Evaluating RL-Generated Adaptive Exercise Recommendations for Pelvic Pain Management
- ClinicalTrials.gov ID
- NCT07810218
- Acronym / sponsor ID
- WorkoutCPP / STUDY-23-00721
- Conditions
- Pelvic Pain, Endometriosis, Chronic Pelvic Pain
- Keywords
- Mobile health, Reinforcement learning, Physical activity, N-of-1 trial, exercise, personalized, intervention
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 45 participants (estimated)
- Lead sponsor
- Icahn School of Medicine at Mount SinaiUniversity, hospital or other
- Collaborators
- Hasso Plattner Institute, Potsdam, Germany
- Study start
- 6 Feb 2026 (actual)
- Primary completion
- Aug 2027 (estimated)
- Study completion
- Aug 2027 (estimated)
- First posted
- 9 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
WorkoutCPP is a pilot study evaluating the feasibility of a personalized exercise recommendation system for individuals with chronic pelvic pain disorders (CPPDs). The study uses reinforcement learning (RL), a type of artificial intelligence that adapts recommendations over time based on each participant's reported pain levels, symptom burden, and exercise compliance. Participants receive daily exercise recommendations that alternate between standard, non-personalized guidance and personalized, RL-generated recommendations across four 2-week phases, allowing within-person comparison of outcomes under each condition. The primary hypothesis is that an RL-based adaptive recommendation system is feasible to deliver in a CPPD population.
Detailed description
Chronic pelvic pain disorders (CPPDs) are associated with high symptom burden and reduced quality of life. Physical activity (PA) and exercise have emerged as a promising non-pharmacological approach for symptom management. However, optimal exercise type, intensity, and timing for pain management vary substantially across individuals, supporting the need for personalized adaptive approaches (Ensari et al., 2022, Krasny-Pacini et al., 2017). This study will enroll participants will a CPPD diagnosis into a remote, 9-week study to evaluate the feasibility of RL-based personalized exercise to non-personalized, standard recommendations. Enrollment is rolling, with participants entering the study on a continuous basis. Each participant's start date, and their 9-week intervention period, is determined by their baseline interview date. A baseline interview upon enrollment is scheduled with an exercise physiologist to review the participant's initial exercise list and provide exercise safety information, as well as overview use of the study App. Participants can choose to stay in the study for 2 additional weeks to make up any weeks with inadequate adherence. Study outcomes are measured daily over the course of the intervention period. Daily App-based tracking items assess pain and other symptoms, exercise behavior, perceived effect and feedback to the recommendation, menstrual status, and recommendation compliance. Fitbit trackers simultaneously track participants' objectively-estimated PA. A reinforcement learning (RL) agent implemented in Meier et al. 2023 as the middleware platform generates daily personalized exercise recommendations delivered via a research mobile phone application (Hirten et al., 2023, Meier et al., 2023). Participant-reported perceived effect of each exercise recommendation is used by the RL agent to calculate reward. Participants serve as their own controls, allowing for within-person comparison under the two conditions (Krasny-Pacini et al., 2017). Primary outcomes for the study include standard study feasibility metrics (e.g., adherence, retention). Secondary outcomes focus on RL agent performance and learning over time. Participant safety will be monitored throughout the study, in accordance with the institutional review board.
This work was supported by the Digital Health Partnership (DHP), a collaboration between the Hasso Plattner Institute, Data4Life, the Windreich Department of Artificial Intelligence and Human Health, the Hasso Plattner Institute for Digital Health at Mount Sinai, and The Charles Bronfman Institute for Personalized Medicine at the Icahn School of Medicine at Mount Sinai.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- N-of-1 randomized crossover design in which each participant serves as their own control, randomized 1:1 to one of two intervention sequences (ABAB or BABA), alternating between RL-generated personalized exercise recommendations (active arm) and standard generic recommendations (control arm) across multiple 2-week blocks within the 9-week study period. First week is treated as a baseline week where participants get accommodated to the study App and procedures, as well as RL agent warm-up.
| Arm | Type | Receives |
|---|---|---|
| RL-based personalized phase | Experimental | Behavioral: Reinforcement Learning (RL)-Based Personalized Exercise RecommendationsParticipants will receive RL-generated personalized exercise recommendations, which are generated using the list from the initial participant intake form indicating their capacity and resources for carrying out various modalities and intensities of physical activity. The RL agent learns from the participant feedback to update the update the subsequent recommendations. |
| Standard (Generic) Exercise Arm | Active comparator | Behavioral: Generic Exercise RecommendationParticipants will receive standardized, non-personalized exercise recommendations based on the U.S. Physical Activity Guidelines, in 2-week blocks. This comparison will serve as the "active control" arm to which the experimental RL arm will be compared. This type of control condition was selected to provide a more rigorous test of the experimental condition. |
Who may qualify
- Ages
- 18 to 55 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult
Key inclusion criteria (8 listed)
- Self-reported CPPD (e.g., endometriosis, adenomyosis, fibroids, etc.) based on clinician diagnosis
- Aged 18-55 years.
- Ownership of an iOS or Android smartphone.
- Willingness to self-track daily symptoms, exercise activities, and self-management behaviors using a smartphone research app.
- Willingness to wear an activity tracker for the study duration.
Key exclusion criteria (7 listed)
- Absolute contraindications to PA (e.g., recent myocardial infarction, complete heart block, acute congestive heart failure, unstable angina, or uncontrolled severe hypertension, BP ≥180/110 mm Hg).
- More than two "Yes" responses on the Physical Activity Readiness Questionnaire (PAR-Q) (16) without physician clearance.
- Major life events expected during the next 10 weeks (e.g., pregnancy, planned surgery, or extended travel likely to interfere with participation).
- Current or planned pregnancy within the next 6 months.
- Having given birth in the past 6 months or currently nursing.
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 |
|---|---|---|
| Reinforcement Learning (RL)-Based Personalized Exercise RecommendationsDaily exercise recommendations (using type, intensity, and duration) are generated by a contextual bandit reinforcement learning agent, based on the implementation described in Meier et al. 2023. Recommendations are personalized using each participant's initially generated list of exercises based on their physical ability and resources available, as well as contextual daily factors including pain symptoms, prior exercise compliance, and their feedback to the previous exercise recommendation. | Behavioural | RL-based personalized phase |
| Generic Exercise RecommendationParticipants receive exercise recommendations from a standardized, set list of exercise recommendations that are based on USDHHS physical activity guidelines (Piercy et al., 2020). Recommendations are not personalized based on participant contextual information and do not adapt over the course of the study. | Behavioural | Standard (Generic) Exercise Arm |
Primary outcome
Exercise Recommendation Adherence Rate
Time frame: At 9 weeks at study completion
Exercise recommendation adherence rate is calculated by the proportion of daily exercise recommendations completed over the course of the intervention. A higher exercise adherence rate indicates that participants are completing their given exercise recommendations at higher frequencies.
Participant Retention Rate
Time frame: At 9 weeks at study completion
Participant retention rate is the proportion of enrolled participants completing study participation until the end of intervention. A higher retention rate indicates that participants complete the 9-week intervention period at higher frequencies.
1 secondary outcome
Reinforcement Learning Agent Action Entropy Over Time
Time frame: At 9 weeks at study completion
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Sep 2026.
- Background: PubMed 35851021Ensari I, Lipsky-Gorman S, Horan EN, Bakken S, Elhadad N. Associations between physical exercise patterns and pain symptoms in individuals with endometriosis: a cross-sectional mHealth-based investigation. BMJ Open. 2022 Jul 18;12(7):e059280. doi: 10.1136/bmjopen-2021-059280.
- Background: PubMed 30418471Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The Physical Activity Guidelines for Americans. JAMA. 2018 Nov 20;320(19):2020-2028. doi: 10.1001/jama.2018.14854.
- Background: PubMed 37971801Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Orlov G, Rodrigues J, Alleva E, Ensari I, Bottinger E, Nadkarni GN, Fuchs TJ, Fayad ZA. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Res Protoc. 2023 Nov 16;12:e49204. doi: 10.2196/49204.
- Background: PubMed 29253607Krasny-Pacini A, Evans J. Single-case experimental designs to assess intervention effectiveness in rehabilitation: A practical guide. Ann Phys Rehabil Med. 2018 May;61(3):164-179. doi: 10.1016/j.rehab.2017.12.002. Epub 2017 Dec 15.
- Background: PubMed 30368433Rabbi M, Aung MS, Gay G, Reid MC, Choudhury T. Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. J Med Internet Res. 2018 Oct 26;20(10):e10147. doi: 10.2196/10147.
- Study protocol registry on the Open Science Framework (OSF)Link provided by the sponsor.
- Individual participant dataSharing planned: No. Individual participant data will not be shared, in order to protect participant privacy and confidentiality, consistent with the IRB guidelines and informed consent form under which participants were enrolled.
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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.
Ipek Ensari, PhD principal investigator
Icahn School of Medicine at Mount Sinai
Stefan Konigorski, PhD principal investigator
Department of Computational Precision Nutrition, German Institute of Human Nutrition
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Reinforcement Learning (RL)-Based Personalized Exercise Recommendations and Generic Exercise Recommendation in people with Pelvic Pain and Endometriosis, run by Icahn School of Medicine at Mount Sinai 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 8 inclusion and 7 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: looking for participants now, as of the sponsor's last update on 9 Sep 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ipek Ensari, PhD (631-565-1829, ipek.ensari@mssm.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Exercise Recommendation Adherence Rate (time frame: At 9 weeks at study completion). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07810218, which also lists 5 linked publications.
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 9 Sep 2026.