SleepSENSENCT06670287
The Use of Multiple Sensors to Track Sleep in Nightshift Workers
A study testing Single-Sensor Tracking (In-Lab), Multi-Sensor Sleep Tracking (In-Lab) and Multi-Sensor Sleep Tracking (At-Home) in people with Sleep and Nightshift Work, run by Henry Ford Health System at 1 site in United States.
Trial summary
Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Sleep, Nightshift Work
- What is being tested?
- Single-Sensor Tracking (In-Lab), Multi-Sensor Sleep Tracking (In-Lab) and Multi-Sensor Sleep Tracking (At-Home)
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteUnited States
- Study dates
- 23 Feb 2026to 30 Jun 2031
- 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Participants must be working a fixed nightshift schedule, operationalized as: a) working at least three night shifts a week, b) shifts must begin between 18:00 and 02:00, and last between 8 to 12 hours, and c) must also plan to maintain the nightshift schedule for the duration of the study
- Participants must have worked the nightshift for at least six months
- Must plan to maintain the nightshift schedule for the duration of the study
- Participants must be at least 18 years old
Who cannot join
- Termination of nightshift schedule or planned travel during the study period
- Does not have at least an average of 8-hour time bed opportunity per 24-hour period
- Unwilling to integrate the study smart sensors in their bedroom environment
- Illicit drug use via self-report and urine drug screen
- History of neurological disorders
- Alcohol use disorder
- Pregnancy
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
Henry Ford Columbus Medical Center
Novi, Michigan, 48377
Philip Cheng, PhD, 248-344-7361, pcheng1@hfhs.org
Elle M Wernette, PhD, 2483442409, ewernet1@hfhs.org
About this study
Official title: A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard legacy algorithms only correctly identify 50.3% of daytime sleep. This significantly reduces the validity for nightshift workers. The purpose of this study is to test a novel method to expand actigraphy by using 1) a multi-sensor approach that 2) uses machine learning (ML) algorithms to increase the accuracy of detecting daytime sleep.
Read the detailed description
The first aim of this study is to establish an open-source machine learning algorithm for sleep tracking that outperforms legacy actigraphy algorithms in detecting daytime sleep periods. The second aim is to enhance tracking of sleep continuity variables by adding multiple sensors. The final aim is to identify facilitators and barriers of at-home implementation of multi-sensor sleep tracking. Our central hypothesis is that a multi-sensor ML approach will outperform legacy algorithms against gold-standard polysomnography (PSG).
This study will be type I hybrid effectiveness-implementation trial that 1) validates the proposed multi-sensor ML approach using in-lab polysomnography, and 2) examines implementation of the multi-sensor ML approach in an ecologically valid setting via an at-home implementation for four weeks. A sample of nightshift workers will be enrolled in the in-lab validation portion of the study and will be hooked-up to PSG with continuous data collection for the duration of the lab visit to capture five planned sleep opportunities at varying lengths (4 hr, 2 hr, 1.5 hr, and two 30-minute naps; 8 hrs total). For each participant, sensor data will be processed using two separate methods. For the legacy actigraphy algorithm method, only raw accelerometer data will be processed. For the multi-sensor machine learning method, accelerometer data from the watch along with additional sensors will be processed using a machine learning algorithm. Some participants who complete the in-lab portion of the study will be asked to complete the at-home portion of the study, which includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Participants will receive the sensor kit and will have an at-home appointment with study staff to aid with sensor set-up, which will then be collected again at the end of the 4-week period. Daily sleep diaries will also be collected during the 4 weeks to enable data quality check.
- Conditions
- Sleep, Nightshift Work
- Being tested
- Single-Sensor Tracking (In-Lab), Multi-Sensor Sleep Tracking (In-Lab), Multi-Sensor Sleep Tracking (At-Home)
- Keywords
- Sleep tracking, Actigraphy, Nightshift work, Sleep, machine learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Single-Sensor Tracking (In-Lab)Other
In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms.
Multi-Sensor Sleep Tracking (In-Lab)Other
Also called Multi-sensor ML, Multi-Sensor Machine Learning
In-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
Multi-Sensor Sleep Tracking (At-Home)Other
At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning.
| Study group | Role | What they receive |
|---|---|---|
| Single vs Multi-Sensor Sleep Tracking In-Lab | Experimental | In Part 1 of the study, all participants' data will undergo two separate methods for analyzing sleep.
The legacy actigraphy algorithm methods will use only raw accelerometer data from a single sensor collected and processed using legacy actigraphy algorithms. The legacy algorithm is comprised first of reducing accelerometer data into activity counts per epoch, which will then be categorized into sleep or wake in accordance with the Cole-Kripke algorithm.
The multi-sensor machine learning (ML) method will use raw accelerometer data in addition to data from additional sensors from the watch, phone, and other smart sensors in the sleeping environment. These data will be processed using a machine learning algorithm. Receives: Other: Single-Sensor Tracking (In-Lab); Other: Multi-Sensor Sleep Tracking (In-Lab) |
| Multi-Sensor Sleep Tracking At-Home | Other | This condition includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Daily sleep diaries will also be collected to enable data quality check. Once collected, all data will be processed with the same machine learning algorithm used in the in-lab experimental condition. Receives: Other: Multi-Sensor Sleep Tracking (At-Home) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Sleep Continuity- Time in Bed
The amount of time (in minutes) a participant spends in bed from lights out to their final awakening time. All PSG variables will use standard American Academy of Sleep Medicine (AASM) sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Measured: Throughout study completion, up to 6 weeks
Sleep Continuity- Sleep Onset Latency
The amount of time (in minutes) a participant takes to fall asleep, from the time of lights out, or the amount of time spent awake but attempting sleep from lights out. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, EEG scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable including dim lights or darkness with lux near zero, presence in bed, rare/interspersed motion from phone and watch.
Measured: Throughout study completion, up to 6 weeks
Sleep Continuity- Wake After Sleep Onset
The amount of time (in minutes) a participant spends awake from the time they initially falling asleep, and excluding their final wake up. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, electroencephalography (EEG) scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Measured: Throughout study completion, up to 6 weeks
Sleep Continuity- Sleep Efficiency
The proportion of the total amount of time a participant is asleep of the total amount of time in bed [(Total Sleep Time in minutes) / (Time in Bed in minutes)]. All PSG variables will use standard AASM sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable, including dim lights or darkness, presence in bed, prolonged low motion from phone and watch, breathing rate changes, and heart rate (sleep staging).
Measured: Throughout study completion, up to 6 weeks
Wake
The amount of time (in minutes) a participant is awake [or the absence of any type of sleep- Stage 1 (N1), Stage 2 (N2), Stage 3 (N3), Rapid Eye Movement (REM)]. All PSG variables will use standard AASM sleep scoring rules; represented on PSG by activities prior to "lights out" marker or video monitoring (eg, video monitoring showing scrolling on social media in bed). Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform these sleep continuity variables including motion, lights on, high heart rate.
Measured: Throughout study completion, up to 6 weeks
Detection of Daytime Sleep Periods
Any sleep periods between 6a and 6p will be designated as daytime sleep. A daytime sleep period from the Apple Watch will be considered successfully detected if it falls within ±30 minutes of the PSG start and end times, and is at least 50% the length of the actual sleep period.
Measured: Throughout study completion, up to 6 weeks
User experience
This will be indexed with the User Experience Questionnaire (UEQ) that has been validated for evaluation of new products and has clear and well-established benchmarks. The UEQ includes items along six domains: 1) Attractiveness (overall likability or appeal), 2) Perspicuity (learning curve and ease of use), 3) Efficiency (speed and efficiency of interactions), 4) Dependability (predictability of system behaviors), 5) Stimulation (how exciting and motivating the product is), 6) Novelty (innovation and creativity of the product).
Measured: Within two days of the at-home intervention
Secondary outcomes (2)
Interviews
These interviews will be semi-structured using the Consolidated Framework for Implementation Research (CFIR). The moderator guide will solicit in-depth feedback on participants' experiences, challenges, and suggestions for improvement. Key themes to be explored in the interviews include ease of use, comfort, perceived accuracy, and any barriers to regular use.
Measured: Within one month of the at-home intervention
Digital health technology literacy
This will be measured using the Digital Health Technology Literacy scale (DHTL). This validated scale assesses degree of experience and skills in using digital health technology and services. The DHTL has strong internal consistency (Cronbach's α = 0.95) and strong validity with completion of ten digital tasks such as connecting a device to Wi-Fi and Bluetooth, downloading and installing an app, and entering weight data into an app.
Measured: During screening before the in-lab intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Nov 2024
- Study start23 Feb 2026
- Primary completion30 Nov 2029 (estimated)
- Study completion30 Jun 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SleepStudies of the same condition, open and under way.
- More trials run by Henry Ford Health SystemThe 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
- Non-randomised
- Intervention model
- Sequential
- Model description
- Type I hybrid effectiveness-implementation trial with two steps. Step 1: In-lab trial (with a sample of participants balanced on degree of technological literacy) comparing data processing using legacy actigraphy to multi-sensor machine learning. Step 2: At-home implementation of multi-sensor machine learning approach (balanced by technological literacy).
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Henry Ford Health System
- Sponsor type
- University, hospital or other
- Collaborators
- Michigan State University
- Responsible party
- Principal investigator, Philip Cheng, Henry Ford Health System
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06670287
- Sponsor's protocol ID
- 17505
- Acronym
- SENSE
- Status last verified
- Mar 2026
- First submitted
- 15 Oct 2024
- Last updated
- 18 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Request for data sharing will be evaluated on a case-by-case basis
Study at a glance
From the registry record, last updated by the sponsor on 18 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-Sensor Machine Learning Approach to Precision Sleep Tracking for Nightshift Workers
- ClinicalTrials.gov ID
- NCT06670287
- Acronym / sponsor ID
- SENSE / 17505
- Conditions
- Sleep, Nightshift Work
- Keywords
- Sleep tracking, Actigraphy, Nightshift work, Sleep, machine learning
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Henry Ford Health SystemUniversity, hospital or other
- Collaborators
- Michigan State University
- Study start
- 23 Feb 2026 (actual)
- Primary completion
- 30 Nov 2029 (estimated)
- Study completion
- 30 Jun 2031 (estimated)
- First posted
- 1 Nov 2024 (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.
Sleep is often a challenge for nightshift workers because their work and sleep schedules are inverted. Sleep is commonly measured using actigraphy, which is the standard measure of objective sleep in the general population; however, this method has substantial limitations for nightshift workers because the standard legacy algorithms only correctly identify 50.3% of daytime sleep. This significantly reduces the validity for nightshift workers. The purpose of this study is to test a novel method to expand actigraphy by using 1) a multi-sensor approach that 2) uses machine learning (ML) algorithms to increase the accuracy of detecting daytime sleep.
Detailed description
The first aim of this study is to establish an open-source machine learning algorithm for sleep tracking that outperforms legacy actigraphy algorithms in detecting daytime sleep periods. The second aim is to enhance tracking of sleep continuity variables by adding multiple sensors. The final aim is to identify facilitators and barriers of at-home implementation of multi-sensor sleep tracking. Our central hypothesis is that a multi-sensor ML approach will outperform legacy algorithms against gold-standard polysomnography (PSG).
This study will be type I hybrid effectiveness-implementation trial that 1) validates the proposed multi-sensor ML approach using in-lab polysomnography, and 2) examines implementation of the multi-sensor ML approach in an ecologically valid setting via an at-home implementation for four weeks. A sample of nightshift workers will be enrolled in the in-lab validation portion of the study and will be hooked-up to PSG with continuous data collection for the duration of the lab visit to capture five planned sleep opportunities at varying lengths (4 hr, 2 hr, 1.5 hr, and two 30-minute naps; 8 hrs total). For each participant, sensor data will be processed using two separate methods. For the legacy actigraphy algorithm method, only raw accelerometer data will be processed. For the multi-sensor machine learning method, accelerometer data from the watch along with additional sensors will be processed using a machine learning algorithm. Some participants who complete the in-lab portion of the study will be asked to complete the at-home portion of the study, which includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Participants will receive the sensor kit and will have an at-home appointment with study staff to aid with sensor set-up, which will then be collected again at the end of the 4-week period. Daily sleep diaries will also be collected during the 4 weeks to enable data quality check.
Study design
- Primary purpose
- Other
- Allocation
- Non-randomised
- Intervention model
- Sequential
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
- Model description
- Type I hybrid effectiveness-implementation trial with two steps. Step 1: In-lab trial (with a sample of participants balanced on degree of technological literacy) comparing data processing using legacy actigraphy to multi-sensor machine learning. Step 2: At-home implementation of multi-sensor machine learning approach (balanced by technological literacy).
| Arm | Type | Receives |
|---|---|---|
| Single vs Multi-Sensor Sleep Tracking In-Lab | Experimental | Other: Single-Sensor Tracking (In-Lab), Other: Multi-Sensor Sleep Tracking (In-Lab)In Part 1 of the study, all participants' data will undergo two separate methods for analyzing sleep. The legacy actigraphy algorithm methods will use only raw accelerometer data from a single sensor collected and processed using legacy actigraphy algorithms. The legacy algorithm is comprised first of reducing accelerometer data into activity counts per epoch, which will then be categorized into sleep or wake in accordance with the Cole-Kripke algorithm. The multi-sensor machine learning (ML) method will use raw accelerometer data in addition to data from additional sensors from the watch, phone, and other smart sensors in the sleeping environment. These data will be processed using a machine learning algorithm. |
| Multi-Sensor Sleep Tracking At-Home | Other | Other: Multi-Sensor Sleep Tracking (At-Home)This condition includes 4 weeks of at-home sleep tracking using the multi-sensor approach. Daily sleep diaries will also be collected to enable data quality check. Once collected, all data will be processed with the same machine learning algorithm used in the in-lab experimental condition. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Participants must be working a fixed nightshift schedule, operationalized as: a) working at least three night shifts a week, b) shifts must begin between 18:00 and 02:00, and last between 8 to 12 hours, and c) must also plan to maintain the nightshift schedule for the duration of the study
- Participants must have worked the nightshift for at least six months
- Must plan to maintain the nightshift schedule for the duration of the study
- Participants must be at least 18 years old
Key exclusion criteria (7 listed)
- Termination of nightshift schedule or planned travel during the study period
- Does not have at least an average of 8-hour time bed opportunity per 24-hour period
- Unwilling to integrate the study smart sensors in their bedroom environment
- Illicit drug use via self-report and urine drug screen
- History of neurological disorders
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 |
|---|---|---|
| Single-Sensor Tracking (In-Lab)In-lab sleep tracking using only raw accelerometer data from a single sensor collected and processed with legacy actigraphy algorithms. | Other | Single vs Multi-Sensor Sleep Tracking In-Lab |
| Multi-Sensor Sleep Tracking (In-Lab)Also: Multi-sensor ML, Multi-Sensor Machine LearningIn-lab sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning. | Other | Single vs Multi-Sensor Sleep Tracking In-Lab |
| Multi-Sensor Sleep Tracking (At-Home)At-home sleep tracking using raw accelerometer data and additional sensors collected and processed with machine learning. | Other | Multi-Sensor Sleep Tracking At-Home |
Primary outcome
Sleep Continuity- Time in Bed
Time frame: Throughout study completion, up to 6 weeks
The amount of time (in minutes) a participant spends in bed from lights out to their final awakening time. All PSG variables will use standard American Academy of Sleep Medicine (AASM) sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Sleep Continuity- Sleep Onset Latency
Time frame: Throughout study completion, up to 6 weeks
The amount of time (in minutes) a participant takes to fall asleep, from the time of lights out, or the amount of time spent awake but attempting sleep from lights out. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, EEG scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable including dim lights or darkness with lux near zero, presence in bed, rare/interspersed motion from phone and watch.
Sleep Continuity- Wake After Sleep Onset
Time frame: Throughout study completion, up to 6 weeks
The amount of time (in minutes) a participant spends awake from the time they initially falling asleep, and excluding their final wake up. All PSG variables will use standard AASM sleep scoring rules; indicated with "lights out" marker on a PSG, electroencephalography (EEG) scored as wake, accompanied with a prototypical sleep posture (e.g. supine) with eyes closed. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable.
Sleep Continuity- Sleep Efficiency
Time frame: Throughout study completion, up to 6 weeks
The proportion of the total amount of time a participant is asleep of the total amount of time in bed [(Total Sleep Time in minutes) / (Time in Bed in minutes)]. All PSG variables will use standard AASM sleep scoring rules. Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform this sleep continuity variable, including dim lights or darkness, presence in bed, prolonged low motion from phone and watch, breathing rate changes, and heart rate (sleep staging).
Wake
Time frame: Throughout study completion, up to 6 weeks
The amount of time (in minutes) a participant is awake [or the absence of any type of sleep- Stage 1 (N1), Stage 2 (N2), Stage 3 (N3), Rapid Eye Movement (REM)]. All PSG variables will use standard AASM sleep scoring rules; represented on PSG by activities prior to "lights out" marker or video monitoring (eg, video monitoring showing scrolling on social media in bed). Data from the Apple Watch will have non-PSG inputs from the multi-sensor system to inform these sleep continuity variables including motion, lights on, high heart rate.
Detection of Daytime Sleep Periods
Time frame: Throughout study completion, up to 6 weeks
Any sleep periods between 6a and 6p will be designated as daytime sleep. A daytime sleep period from the Apple Watch will be considered successfully detected if it falls within ±30 minutes of the PSG start and end times, and is at least 50% the length of the actual sleep period.
User experience
Time frame: Within two days of the at-home intervention
This will be indexed with the User Experience Questionnaire (UEQ) that has been validated for evaluation of new products and has clear and well-established benchmarks. The UEQ includes items along six domains: 1) Attractiveness (overall likability or appeal), 2) Perspicuity (learning curve and ease of use), 3) Efficiency (speed and efficiency of interactions), 4) Dependability (predictability of system behaviors), 5) Stimulation (how exciting and motivating the product is), 6) Novelty (innovation and creativity of the product).
2 secondary outcomes
Interviews
Time frame: Within one month of the at-home intervention
Digital health technology literacy
Time frame: During screening before the in-lab intervention
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 18 Mar 2026.
- Individual participant dataSharing planned: No. Request for data sharing will be evaluated on a case-by-case basis
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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.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Single-Sensor Tracking (In-Lab), Multi-Sensor Sleep Tracking (In-Lab) and Multi-Sensor Sleep Tracking (At-Home) in people with Sleep and Nightshift Work, run by Henry Ford Health System 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 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 18 Mar 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 Philip Cheng, PhD (248-344-7361, pcheng1@hfhs.org), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sleep Continuity- Time in Bed (time frame: Throughout study completion, up to 6 weeks). The study lists 7 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 18 Mar 2026.