Myasthenia Gravis Clinical Trial
Official title:
A Digital Health Trial That Assesses Participant-driven Data Collection Using Smartphone Modules to Characterize Myasthenia Gravis Symptoms and Develop an A.I. Model to Predict Flares
NCT number | NCT04590716 |
Other study ID # | DOC-005-2020 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | October 2, 2020 |
Est. completion date | July 26, 2021 |
Verified date | July 2021 |
Source | doc.ai inc |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
There are limited objective measurements of MG symptoms as well as a dearth of data at a granular level of MG (myasthenia gravis) symptoms and triggers occurring longitudinally. This study is designed to use the strengths of mobile smartphones which enable participant-driven real time capture of data manually and through augmented sensors such as video and audio, in order to better characterize MG symptoms and flares. The study aims to enroll approximately 200 participants for approximately 9 months until analyzable data is available from at least 100 participants. Participants will complete in-app surveys for 3 months with, audiovisual recording of symptoms. This will take approximately 35 minutes per week after the initial survey.
Status | Completed |
Enrollment | 113 |
Est. completion date | July 26, 2021 |
Est. primary completion date | July 26, 2021 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: 1. Must have a documented diagnosis of Myasthenia Gravis 2. Must have ocular (eye drooping) and/or bulbar (speech) symptoms 3. Must be over the age of 18 4. Must reside in the US for the duration of the study 5. Must be able to read, understand, and write in English 6. Must have a smartphone supported by the doc.ai research app (iOS and Android) Exclusion Criteria: None |
Country | Name | City | State |
---|---|---|---|
United States | Doc.Ai Mobile Based | Palo Alto | California |
Lead Sponsor | Collaborator |
---|---|
doc.ai inc | UCB Biopharma SRL |
United States,
Borza D, Darabant AS, Danescu R. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors (Basel). 2016 Jul 16;16(7). pii: E1105. doi: 10.3390/s16071105. — View Citation
Duffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (1st ed). St. Louis, 1995, Mosby.
Duffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (2nd ed). New York, 2005, Elsevier Health Sciences.
Hegde S, Shetty S, Rai S, Dodderi T. A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. J Voice. 2019 Nov;33(6):947.e11-947.e33. doi: 10.1016/j.jvoice.2018.07.014. Epub 2018 Oct 11. Review. — View Citation
Kang H. The prevention and handling of the missing data. Korean J Anesthesiol. 2013 May;64(5):402-6. doi: 10.4097/kjae.2013.64.5.402. Epub 2013 May 24. — View Citation
Kent RD, Kent JF, Rosenbek JC. Maximum performance tests of speech production. J Speech Hear Disord. 1987 Nov;52(4):367-87. Review. — View Citation
Konopka BM, Lwow F, Owczarz M, Laczmanski L. Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data. PLoS One. 2018 Aug 23;13(8):e0201950. doi: 10.1371/journal.pone.0201950. eCollection 2018. — View Citation
Panayotov V., Chen G., Povey D., Khudanpur S. (2015). Librispeech: an ASR corpus based on public domain audio books, in Proceedings of the ICASSP (South Brisbane, QLD:), 5206-5210
T. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency,
Zhou ZR, Wang WW, Li Y, Jin KR, Wang XY, Wang ZW, Chen YS, Wang SJ, Hu J, Zhang HN, Huang P, Zhao GZ, Chen XX, Li B, Zhang TS. In-depth mining of clinical data: the construction of clinical prediction model with R. Ann Transl Med. 2019 Dec;7(23):796. doi: — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Audiovisual recording of voice exercises to detect patterns and changes in voice and facial symptoms | participants to complete the audio and visual data modules designed to capture patient MG symptoms (especially ocular and voice).
e.g Vocal e.g.: Say "papapapa" for 4 seconds Say "tatatatata" for 4 seconds Say "kakakaka" 4 seconds Say "mamamama" 4 seconds Say "papapapa" 4 seconds Say "buttercup, buttercup, buttercup" 4 seconds Say "aaaahhh" and hold it as long as you can Counting e.g.: Look straight at the camera for 4 seconds Count as precisely as possible from 1 to 25 while looking up Look straight at the camera for 4 seconds The recordings will be used to detect change from baseline and any patterns that may occur. This will be used to analyze where and if different features are linked to see if a single or combined effect of the features is connected to flare frequency and/or severity. |
After enrollment, 3 months with in-app twice a week audiovisual recording of symptoms. | |
Secondary | Completion of MG-Quality of Life assessment | Participants complete MG activities of Daily living and MG-Quality of Life assessments weekly. This assessment has been adapted from www.myasthenia.org/healthprofessionals/educationalmaterials.aspx | Approximately 10 minutes each week for 3 months. |
Status | Clinical Trial | Phase | |
---|---|---|---|
Completed |
NCT05039190 -
Evaluate the Efficacy and Safety of HBM9161(HL161)Subcutaneous Injection in Patients With Generalized MG Patients
|
Phase 3 | |
Recruiting |
NCT04561557 -
Safety and Efficacy of CT103A Cells for Relapsed/Refractory Antibody-associated Inflammatory Diseases of the Nervous System
|
Early Phase 1 | |
Completed |
NCT01727193 -
The Efficacy and Safety of Leflunomide or Azathioprine Therapy in Myasthenia Gravis Patients After Expand Thymectomy
|
Phase 3 | |
Completed |
NCT00285350 -
Mycophenolate Mofetil in Myasthenia Gravis
|
Phase 3 | |
Recruiting |
NCT05890833 -
The Risk of Falls Index for Patients With Neuromuscular Disorders
|
||
Completed |
NCT05694234 -
Influences of Sugammadex on Postoperative Progress in Patients With Myasthenia Gravis Undergoing Video-assisted Thoracoscopic Thymectomy: Retrospective Study
|
||
Recruiting |
NCT05635266 -
Tissue Repository Providing Annotated Biospecimens for Approved Investigator-directed Biomedical Research Initiatives
|
||
Not yet recruiting |
NCT04965987 -
Oxaloacetate in Myasthenia Gravis
|
Phase 1 | |
Not yet recruiting |
NCT05095103 -
Immune Profiles in Myasthenia Gravis
|
||
Completed |
NCT02774239 -
A Pilot Trial To Assess The Feasibility And Efficacy Of SCIG In Patients With MG Exacerbation (SCIG-MG)
|
Phase 3 | |
Terminated |
NCT02102594 -
Therapy of Antibody-mediated Autoimmune Diseases by Bortezomib (TAVAB)
|
Phase 2 | |
Completed |
NCT02066519 -
Benefits and Tolerance of Exercise in Patients With Generalized and Stabilized Myasthenia Gravis
|
N/A | |
Completed |
NCT02118805 -
Innovative Measures of Speech and Swallowing Dysfunction in Neurological Disorders
|
||
Terminated |
NCT01828294 -
Subcutaneous Ig Maintenance Therapy for Myasthenia Gravis
|
Phase 1 | |
Terminated |
NCT00727194 -
Safety and Efficacy Study of Eculizumab in Patients With Refractory Generalized Myasthenia Gravis
|
Phase 2 | |
Recruiting |
NCT04837625 -
Study of Myasthenic Crisis in China
|
||
Not yet recruiting |
NCT01469858 -
Perception and Multisensory Integration in Neurological Patients Using fMRI
|
N/A | |
Completed |
NCT05408702 -
Exercise in Autoimmune Myasthenia Gravis and Myasthenic Syndromes
|
||
Completed |
NCT03205306 -
Myasthenia Gravis and Psyche
|
||
Recruiting |
NCT06106672 -
Evaluate the Safety, Tolerability, Pharmacodynamics and Efficacy of CNP-106 in Subjects With Myasthenia Gravis
|
Phase 1/Phase 2 |