FatigueNCT05813613
Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training
An observational study of Fatigue, run by Beirut Arab University at 1 site in Lebanon.
Trial summary
The goal of this observational predicted study is to predict muscle fatigue using a specific AI algorithm in healthy vs post Covid-19 infected individuals. The main question it aims to answer is: Can Artificial Intelligence be used as a reliable source of predicting localized muscle fatigue in healthy vs post Covid-19… Read more about this study
- Who is it for?
- Ages 18 to 49 yearsHealthy volunteers can take part
- Conditions the study is for
- Fatigue
- What is being tested?
- Squatting with the aid of Kynapsis Virtual Training apparatus.
- How many people
- 90 participants
- Where
- 1 siteLebanon
- Study dates
- 15 Apr 2023to 7 Jun 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 49 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: The study population consisted of two groups. * Non-athletic healthy individuals that did not perform any intense activities for the past 3 days and had not contacted Covid-19 previously. * Non-athletic healthy individuals that did not perform any intense activities for the past 3 days but have a confirmed positive PCR test done within a 1 year interval.
Who may be able to join
- Non-athletic healthy individuals.
- Avoided intense activities in the past 3 days.
- Confirmed positive PCR test done within an interval of 1 year for Covid-19 group subjects.
Who cannot join
- Being old age geriatrics (more than 50 years old).
- Having any respiratory, cardiac, renal, neuromuscular, orthopedic, and musculoskeletal disorders.
- Smokers and some medicinal drug users must be taken into consideration because it affects the performance and increases the fatigue levels.
- Subjects not meeting any of the inclusion criteria.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training In Healthy And Post COVID19 Subjects
The goal of this observational predicted study is to predict muscle fatigue using a specific AI algorithm in healthy vs post Covid-19 infected individuals. The main question it aims to answer is:
Can Artificial Intelligence be used as a reliable source of predicting localized muscle fatigue in healthy vs post Covid-19 infected individuals?
Read more
Participants will be divided into two groups: A healthy group and a post Covid-19 group.
- Each group will undergo a familiarization process before the start of the exercises.
- Then, each group will perform squatting exercises guided by the kynpasis virtual reality apparatus.
- sEMG for the vastus lateralis and rectus femories, chest expansion, and goniometric measurements of the knee will be taken during different reported fatigue levels using the Biopac system.
- Groups will continue squatting while recording their subjective fatigue levels using the Borg scale.
- Data will then be run through machine learning processes to produce an AI algorithm capable of predicting isolated muscle fatigue.
Read the detailed description
Participants were divided into two groups, one consisting of healthy individuals and another consisting of Covid-19 subjects. Both groups received a familiarization training for the exercise to be performed with 15 minutes of rest afterwards, before the start of the data collection.
Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.
Additional variables were considered, including chest expansion, and the range of motion using an electric goniometer, all being measured and recorded using the Biopac (BIOPAC Systems, Inc., Santa Barbara, CA) that, according to evidence, possess a high-pass frequency filter and bipolar electrode system.
The muscles tested are the 3 heads of the QF muscle RF, VM, and VL. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.
The Borg (C-10) scale was explained to the participants and was present in front of them while performing the exercise as an outcome measure to assess the subjective muscle fatigue that once reached will end the exercise.
- Conditions
- Fatigue
- Being tested
- Squatting with the aid of Kynapsis Virtual Training apparatus.
- Keywords
- Electromyography, Fatigue, Squats, Borg scale, Musculoskeletal disorders
- Medical subject headings
- Fatigue, Musculoskeletal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Squatting with the aid of Kynapsis Virtual Training apparatus.Other
Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.
| Study group | Role | What they receive |
|---|---|---|
| Healthy Group | * Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached
* Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac. Receives: Other: Squatting with the aid of Kynapsis Virtual Training apparatus. | |
| Post Covid-19 Group | * Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached
* Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac. Receives: Other: Squatting with the aid of Kynapsis Virtual Training apparatus. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Surface electromyography
non-invasive technique where electrodes were placed on the vastus lateralis and rectus femoris heads of the quadriceps femoris muscle, assessing it's myoelectric output. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.
Measured: During the squatting exercise.
The Borg Rating of Perceived Exertion (RPE) scale
A tool for measuring an individual's effort and exertion, breathlessness and fatigue during physical work and so is highly relevant for occupational health and safety practice. It ranges from 6 as a minimum to 20 as a maximum with 6 signifying no exertion and 20 signifying extreme maximal exertion
Measured: During the squatting exercise.
Secondary outcomes (2)
Chest Expansion.
Using a respiration transducer wrapped around the subject's chest using a velcro strap that transmits expansion data to the main receiver module of the Biopac, that will be recorded on the computer.
Measured: During the squatting exercise.
Range of motion.
Using an electric goniometer wired on the subject's knee that will transmit signals of range of motion to the receiver module of the Biopac that will be recorded on the computer.
Measured: During the squatting exercise.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Apr 2023
- Study start15 Apr 2023
- Primary completion1 Jun 2023
- Study completion7 Jun 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (24)Papers the sponsor has linked to this study.
- Other trials for FatigueStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 90 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beirut Arab University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Rami Abbas, Beirut Arab University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05813613
- Sponsor's protocol ID
- AI in Prediciting Fatigue
- Status last verified
- Jun 2023
- First submitted
- 3 Apr 2023
- Last updated
- 9 Jun 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Wan JJ, Qin Z, Wang PY, Sun Y, Liu X. Muscle fatigue: general understanding and treatment. Exp Mol Med. 2017 Oct 6;49(10):e384. doi: 10.1038/emm.2017.194.
PubMed 28983090A narrative review of immersive virtual reality's ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload Souchet, A.D., Lourdeaux, D., Pagani, A. et al. A narrative review of immersive virtual reality's ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload. Virtual Reality (2022). https://doi.org/10.1007/s10055-022-00672-0
Donatelli, R.A. (2007) Sports-specific rehabilitation. St. Louis, MO: Churchill Livingstone.
Hall, J. E., & Hall, M. E. (2020). Guyton and Hall textbook of medical physiology e-Book. Elsevier Health Sciences.
Paneroni M, Simonelli C, Saleri M, Bertacchini L, Venturelli M, Troosters T, Ambrosino N, Vitacca M. Muscle Strength and Physical Performance in Patients Without Previous Disabilities Recovering From COVID-19 Pneumonia. Am J Phys Med Rehabil. 2021 Feb 1;100(2):105-109. doi: 10.1097/PHM.0000000000001641.
PubMed 33181531Schoenfeld BJ. Squatting kinematics and kinetics and their application to exercise performance. J Strength Cond Res. 2010 Dec;24(12):3497-506. doi: 10.1519/JSC.0b013e3181bac2d7.
PubMed 20182386Kubo K, Ikebukuro T, Yata H. Effects of squat training with different depths on lower limb muscle volumes. Eur J Appl Physiol. 2019 Sep;119(9):1933-1942. doi: 10.1007/s00421-019-04181-y. Epub 2019 Jun 22.
PubMed 31230110Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
PubMed 31363513Tack C. Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy. Musculoskelet Sci Pract. 2019 Feb;39:164-169. doi: 10.1016/j.msksp.2018.11.012. Epub 2018 Nov 23.
PubMed 30502096Luna A, Casertano L, Timmerberg J, O'Neil M, Machowsky J, Leu CS, Lin J, Fang Z, Douglas W, Agrawal S. Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial. Sci Rep. 2021 Sep 13;11(1):18109. doi: 10.1038/s41598-021-97343-y.
PubMed 34518568Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists' Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J Med Internet Res. 2022 Oct 20;24(10):e39565. doi: 10.2196/39565.
PubMed 36264614Qian J, McDonough DJ, Gao Z. The Effectiveness of Virtual Reality Exercise on Individual's Physiological, Psychological and Rehabilitative Outcomes: A Systematic Review. Int J Environ Res Public Health. 2020 Jun 10;17(11):4133. doi: 10.3390/ijerph17114133.
PubMed 32531906Sun J, Liu G, Sun Y, Lin K, Zhou Z, Cai J. Application of Surface Electromyography in Exercise Fatigue: A Review. Front Syst Neurosci. 2022 Aug 11;16:893275. doi: 10.3389/fnsys.2022.893275. eCollection 2022.
PubMed 36032326Al-Mulla MR, Sepulveda F, Colley M. An autonomous wearable system for predicting and detecting localised muscle fatigue. Sensors (Basel). 2011;11(2):1542-57. doi: 10.3390/s110201542. Epub 2011 Jan 27.
PubMed 22319367Calder KM, Stashuk DW, McLean L. Physiological characteristics of motor units in the brachioradialis muscle across fatiguing low-level isometric contractions. J Electromyogr Kinesiol. 2008 Feb;18(1):2-15. doi: 10.1016/j.jelekin.2006.08.012. Epub 2006 Nov 20.
PubMed 17113787
Study at a glance
From the registry record, last updated by the sponsor on 9 Jun 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Role of Artificial Intelligence in Predicting Muscle Fatigue Using Virtual Reality Training In Healthy And Post COVID19 Subjects
- ClinicalTrials.gov ID
- NCT05813613
- Acronym / sponsor ID
- AI in Prediciting Fatigue
- Conditions
- Fatigue
- Keywords
- Electromyography, Fatigue, Squats, Borg scale, Musculoskeletal disorders
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 90 participants (actual)
- Lead sponsor
- Beirut Arab UniversityUniversity, hospital or other
- Study start
- 15 Apr 2023 (actual)
- Primary completion
- 1 Jun 2023 (actual)
- Study completion
- 7 Jun 2023 (actual)
- First posted
- 14 Apr 2023 (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.
The goal of this observational predicted study is to predict muscle fatigue using a specific AI algorithm in healthy vs post Covid-19 infected individuals. The main question it aims to answer is:
Can Artificial Intelligence be used as a reliable source of predicting localized muscle fatigue in healthy vs post Covid-19 infected individuals?
Participants will be divided into two groups: A healthy group and a post Covid-19 group.
- Each group will undergo a familiarization process before the start of the exercises.
- Then, each group will perform squatting exercises guided by the kynpasis virtual reality apparatus.
- sEMG for the vastus lateralis and rectus femories, chest expansion, and goniometric measurements of the knee will be taken during different reported fatigue levels using the Biopac system.
- Groups will continue squatting while recording their subjective fatigue levels using the Borg scale.
- Data will then be run through machine learning processes to produce an AI algorithm capable of predicting isolated muscle fatigue.
Detailed description
Participants were divided into two groups, one consisting of healthy individuals and another consisting of Covid-19 subjects. Both groups received a familiarization training for the exercise to be performed with 15 minutes of rest afterwards, before the start of the data collection.
Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine.
Additional variables were considered, including chest expansion, and the range of motion using an electric goniometer, all being measured and recorded using the Biopac (BIOPAC Systems, Inc., Santa Barbara, CA) that, according to evidence, possess a high-pass frequency filter and bipolar electrode system.
The muscles tested are the 3 heads of the QF muscle RF, VM, and VL. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.
The Borg (C-10) scale was explained to the participants and was present in front of them while performing the exercise as an outcome measure to assess the subjective muscle fatigue that once reached will end the exercise.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Healthy Group | – | Other: Squatting with the aid of Kynapsis Virtual Training apparatus.* Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached * Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac. |
| Post Covid-19 Group | – | Other: Squatting with the aid of Kynapsis Virtual Training apparatus.* Will perform squatting exercise while reporting subjective muscle fatigue levels periodically, until maximal subjective fatigue is reached * Will have sEMG for vastus lateralis and rectus femoris, chest expansion, goniometry for the knee recording using the Biopac. |
Who may qualify
- Ages
- 18 to 49 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- The study population consisted of two groups. * Non-athletic healthy individuals that did not perform any intense activities for the past 3 days and had not contacted Covid-19 previously. * Non-athletic healthy individuals that did not perform any intense activities for the past 3 days but have a confirmed positive PCR test done within a 1 year interval.
Key inclusion criteria (3 listed)
- Non-athletic healthy individuals.
- Avoided intense activities in the past 3 days.
- Confirmed positive PCR test done within an interval of 1 year for Covid-19 group subjects.
Key exclusion criteria (4 listed)
- Being old age geriatrics (more than 50 years old).
- Having any respiratory, cardiac, renal, neuromuscular, orthopedic, and musculoskeletal disorders.
- Smokers and some medicinal drug users must be taken into consideration because it affects the performance and increases the fatigue levels.
- Subjects not meeting any of the inclusion criteria.
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 |
|---|---|---|
| Squatting with the aid of Kynapsis Virtual Training apparatus.Squatting exercise was performed using a virtual reality (VR) machine (kynapsis) for guidance in both groups. Squats were performed while the hands were kept in front of the bodies and the knees flexed to 90 degrees following a rhythm of two seconds for descent, two second ascent mimicking the movement done on the VR machine. | Other | Healthy Group, Post Covid-19 Group |
Primary outcome
Surface electromyography
Time frame: During the squatting exercise.
non-invasive technique where electrodes were placed on the vastus lateralis and rectus femoris heads of the quadriceps femoris muscle, assessing it's myoelectric output. Their areas were cleaned using alcohol and shaved to reduce resistance of electrodes. Three disposable sEMG surface electrodes were placed, two of them on the muscle belly with 2.5cm distance between them, and one control electrode placed on the agonist side, the participant was asked to extend their knee and flex it against resistance to locate the lateral and medial vasti. sEMG electrodes were placed on the subdivisions of the QF muscle during the exercise. The extracted data is then run through an AI algorithm that will analyze and predict muscle fatigue.
The Borg Rating of Perceived Exertion (RPE) scale
Time frame: During the squatting exercise.
A tool for measuring an individual's effort and exertion, breathlessness and fatigue during physical work and so is highly relevant for occupational health and safety practice. It ranges from 6 as a minimum to 20 as a maximum with 6 signifying no exertion and 20 signifying extreme maximal exertion
2 secondary outcomes
Chest Expansion.
Time frame: During the squatting exercise.
Range of motion.
Time frame: During the squatting exercise.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jun 2023.
- Background: PubMed 28983090Wan JJ, Qin Z, Wang PY, Sun Y, Liu X. Muscle fatigue: general understanding and treatment. Exp Mol Med. 2017 Oct 6;49(10):e384. doi: 10.1038/emm.2017.194.
- BackgroundA narrative review of immersive virtual reality's ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload Souchet, A.D., Lourdeaux, D., Pagani, A. et al. A narrative review of immersive virtual reality's ergonomics and risks at the workplace: cybersickness, visual fatigue, muscular fatigue, acute stress, and mental overload. Virtual Reality (2022). https://doi.org/10.1007/s10055-022-00672-0
- BackgroundDonatelli, R.A. (2007) Sports-specific rehabilitation. St. Louis, MO: Churchill Livingstone.
- BackgroundHall, J. E., & Hall, M. E. (2020). Guyton and Hall textbook of medical physiology e-Book. Elsevier Health Sciences.
- Background: PubMed 33181531Paneroni M, Simonelli C, Saleri M, Bertacchini L, Venturelli M, Troosters T, Ambrosino N, Vitacca M. Muscle Strength and Physical Performance in Patients Without Previous Disabilities Recovering From COVID-19 Pneumonia. Am J Phys Med Rehabil. 2021 Feb 1;100(2):105-109. doi: 10.1097/PHM.0000000000001641.
- Background: PubMed 20182386Schoenfeld BJ. Squatting kinematics and kinetics and their application to exercise performance. J Strength Cond Res. 2010 Dec;24(12):3497-506. doi: 10.1519/JSC.0b013e3181bac2d7.
- Background: PubMed 31230110Kubo K, Ikebukuro T, Yata H. Effects of squat training with different depths on lower limb muscle volumes. Eur J Appl Physiol. 2019 Sep;119(9):1933-1942. doi: 10.1007/s00421-019-04181-y. Epub 2019 Jun 22.
- Background: PubMed 31363513Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
- Background: PubMed 30502096Tack C. Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy. Musculoskelet Sci Pract. 2019 Feb;39:164-169. doi: 10.1016/j.msksp.2018.11.012. Epub 2018 Nov 23.
- Background: PubMed 34518568Luna A, Casertano L, Timmerberg J, O'Neil M, Machowsky J, Leu CS, Lin J, Fang Z, Douglas W, Agrawal S. Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial. Sci Rep. 2021 Sep 13;11(1):18109. doi: 10.1038/s41598-021-97343-y.
- Background: PubMed 36264614Alsobhi M, Khan F, Chevidikunnan MF, Basuodan R, Shawli L, Neamatallah Z. Physical Therapists' Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J Med Internet Res. 2022 Oct 20;24(10):e39565. doi: 10.2196/39565.
- Background: PubMed 32531906Qian J, McDonough DJ, Gao Z. The Effectiveness of Virtual Reality Exercise on Individual's Physiological, Psychological and Rehabilitative Outcomes: A Systematic Review. Int J Environ Res Public Health. 2020 Jun 10;17(11):4133. doi: 10.3390/ijerph17114133.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Fatigue, run by Beirut Arab University at 1 site in Lebanon.
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 3 inclusion and 4 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 9 Jun 2023. 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 Lebanon. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Surface electromyography (time frame: During the squatting exercise.). 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 NCT05813613, which also lists 24 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 Jun 2023.