Myasthenia GravisNCT04590716
Help Build an A.I. Model to Predict Myasthenia Gravis Symptom Patterns and Flares
An observational study of Myasthenia Gravis, run by doc.ai inc at 1 site in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Myasthenia Gravis
- What is being tested?
- Data Collection
- How many people
- 113 participants
- Where
- 1 siteUnited States
- Study dates
- 2 Oct 2020to 26 Jul 2021
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with myasthenia gravis (MG) who meet the inclusion criteria will be invited to join this digital health trial. There will be a web pre-screening link where potential participants will self-screen to see if they meet the basic eligibility criteria for this study. The recruitment tool for this trial is developed for diversity, fairness, and inclusion. With the aim to ensure diversity in the demographics of the trial to better understand the health needs of different populations. So, while some interested potential participants do qualify, they may not be invited into the trial due to these diversity requirements.
Who may be able to join
- Must have a documented diagnosis of Myasthenia Gravis
- Must have ocular (eye drooping) and/or bulbar (speech) symptoms
- Must be over the age of 18
- Must reside in the US for the duration of the study
- Must be able to read, understand, and write in English
- Must have a smartphone supported by the doc.ai research app (iOS and Android)
Who cannot join
- None
Trial locations and contacts
1 site in 1 country.
About this study
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
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.
Read the detailed description
Using their smartphones, potential participants will download the doc.ai research mobile app. There will be a web pre-screening link where potential participants will self-screen to see if they meet the basic eligibility criteria for this study. The recruitment tool for this trial is developed for diversity, fairness, and inclusion. With the aim to ensure diversity in the demographics of the trial to better understand the health needs of different populations. So, while some interested potential participants do qualify, they may not be invited into the trial due to these diversity requirements.
Patients with myasthenia gravis (MG) who meet the inclusion criteria will be invited to join this digital health trial. Participants will sign the e-consent and self-enroll into the study. Once their eligibility is confirmed by the study team (to ensure eligibility criteria and validity of participant i.e. eliminate robo sign-ins) they will be asked to take a selfie, provide documented proof of MG diagnosis, respond to a series of survey questions regarding their demographics, current health, medical history, and other MG related information.
Enrolled participants will have a daily brief check-in, 2 weekly check-ins and a weekly check-in which will include an audiovisual check-in, and will maintain an audiovisual diary to keep track of their symptoms, connect data, record their voice (to detect vocal symptoms: weakness, nasality) and take videos of their face (to detect facial symptoms: ocular, mouth droop) on a daily to weekly basis through the various data collecting modules in the doc.ai research app for the duration of their study participation. doc.ai's digital health trial platform will be leveraged to collect this data.
The study aims to enroll approximately 200 participants in approximately 9 months. It is expected that a minimum of 100 participants will be included in the final analysis as at any given time there will be a lag between potential participants expressing interest in the study, their eligibility being assessed by the PI, and participants completing all required study procedures.
At the end of their participation, participants will be asked to complete a questionnaire. After the participant has completed their final survey, they will be able to connect to a link redeemable as an Amazon.com gift card worth $250. All participants will also receive an end-of-trial-summary of the data that they had collected during the study. No medical advice or direction will be given based on this study.
In addition, in the final survey participants will be asked if they would be willing to complete a usability interview after their participation in this trial has ended. This subset of participants invited to be part of a follow-up usability interview will include those who complete all study required procedures and some who may not have completed all study required procedures, in order to assess usability experience of the app for the duration of their participation. Participants will be contacted at their end of their period of participation until a total of 10-15 participants successfully complete the usability interview. Participants who successfully complete the usability interview will receive a link for a $50 in Amazon.com gift card via the app.
For this study the data and, audio and video recordings will be captured directly on the doc.ai research app and securely stored on a HIPAA compliant cloud provider (Google Cloud Platform).
This data will be used to understand the patterns of symptoms and triggers in order to better characterize factors such as the length and timing of flares and any unique symptom patterns in order to create more objective measures of MG symptoms. Ultimately this data would be used to build a machine learning model that could predict MG symptom flares.
Primary Objective:
Use a collection of digital health modules on the smartphone to collect myasthenia gravis (MG) symptoms and triggers to better characterize symptom patterns and flares.
Secondary Objective:
Use the data collected to develop an A.I. model to detect and/or predict symptom flares.
- Conditions
- Myasthenia Gravis
- Being tested
- Data Collection
- Medical subject headings
- Myasthenia Gravis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Data CollectionOther
This is a non-interventional study conducted on the participant's smartphones to record MG related symptoms and conditions.
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: After enrollment, 3 months with in-app twice a week audiovisual recording of symptoms.
Secondary outcomes (1)
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
Measured: Approximately 10 minutes each week for 3 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Oct 2020
- Study start2 Oct 2020
- Primary completion26 Jul 2021
- Study completion26 Jul 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (11)Papers the sponsor has linked to this study.
- Other trials for Myasthenia GravisStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 113 participants (actual)
Sponsor and investigators
- Lead sponsor
- doc.ai inc
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- UCB Biopharma SRL
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04590716
- Sponsor's protocol ID
- DOC-005-2020
- Status last verified
- Jul 2021
- First submitted
- 2 Oct 2020
- Last updated
- 29 Jul 2021
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Data will be reviewed, and analysis will be done by personnel of doc.ai, and the medical experts. Population-level results of the data analysis in the form of a presentation/report, as well as the resulting proof-of-concept predictive AI model, will be shared with UCB Biopharma (SRL) (who are funding this study). No participant PII or PHI will be shared with UCB Biopharma (SRL) or any other 3rd parties.
Publications
Kent RD, Kent JF, Rosenbek JC. Maximum performance tests of speech production. J Speech Hear Disord. 1987 Nov;52(4):367-87. doi: 10.1044/jshd.5204.367.
PubMed 3312817Konopka 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.
PubMed 30138442Zhou 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: 10.21037/atm.2019.08.63.
PubMed 32042812Kang 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.
PubMed 23741561Borza D, Darabant AS, Danescu R. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors (Basel). 2016 Jul 16;16(7):1105. doi: 10.3390/s16071105.
PubMed 27438838Hegde 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.
PubMed 30316551Duffy, 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.
T. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency,
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
Steyaert S, Lootus M, Sarabu C, Framroze Z, Dickinson H, Lewis E, Steels JC, Rinaldo F. A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones. Front Neurol. 2023 Aug 1;14:1144183. doi: 10.3389/fneur.2023.1144183. eCollection 2023.
PubMed 37588667
Study at a glance
From the registry record, last updated by the sponsor on 29 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04590716
- Acronym / sponsor ID
- DOC-005-2020
- Conditions
- Myasthenia Gravis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 113 participants (actual)
- Lead sponsor
- doc.ai incIndustry (pharma, biotech, devices)
- Collaborators
- UCB Biopharma SRL
- Study start
- 2 Oct 2020 (actual)
- Primary completion
- 26 Jul 2021 (actual)
- Study completion
- 26 Jul 2021 (actual)
- First posted
- 19 Oct 2020 (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.
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.
Detailed description
Using their smartphones, potential participants will download the doc.ai research mobile app. There will be a web pre-screening link where potential participants will self-screen to see if they meet the basic eligibility criteria for this study. The recruitment tool for this trial is developed for diversity, fairness, and inclusion. With the aim to ensure diversity in the demographics of the trial to better understand the health needs of different populations. So, while some interested potential participants do qualify, they may not be invited into the trial due to these diversity requirements.
Patients with myasthenia gravis (MG) who meet the inclusion criteria will be invited to join this digital health trial. Participants will sign the e-consent and self-enroll into the study. Once their eligibility is confirmed by the study team (to ensure eligibility criteria and validity of participant i.e. eliminate robo sign-ins) they will be asked to take a selfie, provide documented proof of MG diagnosis, respond to a series of survey questions regarding their demographics, current health, medical history, and other MG related information.
Enrolled participants will have a daily brief check-in, 2 weekly check-ins and a weekly check-in which will include an audiovisual check-in, and will maintain an audiovisual diary to keep track of their symptoms, connect data, record their voice (to detect vocal symptoms: weakness, nasality) and take videos of their face (to detect facial symptoms: ocular, mouth droop) on a daily to weekly basis through the various data collecting modules in the doc.ai research app for the duration of their study participation. doc.ai's digital health trial platform will be leveraged to collect this data.
The study aims to enroll approximately 200 participants in approximately 9 months. It is expected that a minimum of 100 participants will be included in the final analysis as at any given time there will be a lag between potential participants expressing interest in the study, their eligibility being assessed by the PI, and participants completing all required study procedures.
At the end of their participation, participants will be asked to complete a questionnaire. After the participant has completed their final survey, they will be able to connect to a link redeemable as an Amazon.com gift card worth $250. All participants will also receive an end-of-trial-summary of the data that they had collected during the study. No medical advice or direction will be given based on this study.
In addition, in the final survey participants will be asked if they would be willing to complete a usability interview after their participation in this trial has ended. This subset of participants invited to be part of a follow-up usability interview will include those who complete all study required procedures and some who may not have completed all study required procedures, in order to assess usability experience of the app for the duration of their participation. Participants will be contacted at their end of their period of participation until a total of 10-15 participants successfully complete the usability interview. Participants who successfully complete the usability interview will receive a link for a $50 in Amazon.com gift card via the app.
For this study the data and, audio and video recordings will be captured directly on the doc.ai research app and securely stored on a HIPAA compliant cloud provider (Google Cloud Platform).
This data will be used to understand the patterns of symptoms and triggers in order to better characterize factors such as the length and timing of flares and any unique symptom patterns in order to create more objective measures of MG symptoms. Ultimately this data would be used to build a machine learning model that could predict MG symptom flares.
Primary Objective:
Use a collection of digital health modules on the smartphone to collect myasthenia gravis (MG) symptoms and triggers to better characterize symptom patterns and flares.
Secondary Objective:
Use the data collected to develop an A.I. model to detect and/or predict symptom flares.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with myasthenia gravis (MG) who meet the inclusion criteria will be invited to join this digital health trial. There will be a web pre-screening link where potential participants will self-screen to see if they meet the basic eligibility criteria for this study. The recruitment tool for this trial is developed for diversity, fairness, and inclusion. With the aim to ensure diversity in the demographics of the trial to better understand the health needs of different populations. So, while some interested potential participants do qualify, they may not be invited into the trial due to these diversity requirements.
Key inclusion criteria (6 listed)
- Must have a documented diagnosis of Myasthenia Gravis
- Must have ocular (eye drooping) and/or bulbar (speech) symptoms
- Must be over the age of 18
- Must reside in the US for the duration of the study
- Must be able to read, understand, and write in English
Key exclusion criteria (1 listed)
- None
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 |
|---|---|---|
| Data CollectionThis is a non-interventional study conducted on the participant's smartphones to record MG related symptoms and conditions. | Other | – |
Primary outcome
Audiovisual recording of voice exercises to detect patterns and changes in voice and facial symptoms
Time frame: After enrollment, 3 months with in-app twice a week audiovisual recording of 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.
1 secondary outcome
Completion of MG-Quality of Life assessment
Time frame: Approximately 10 minutes each week for 3 months.
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 29 Jul 2021.
- Results publication: PubMed 3312817Kent RD, Kent JF, Rosenbek JC. Maximum performance tests of speech production. J Speech Hear Disord. 1987 Nov;52(4):367-87. doi: 10.1044/jshd.5204.367.
- Results publication: PubMed 30138442Konopka 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.
- Results publication: PubMed 32042812Zhou 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: 10.21037/atm.2019.08.63.
- Results publication: PubMed 23741561Kang 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.
- Results publication: PubMed 27438838Borza D, Darabant AS, Danescu R. Real-Time Detection and Measurement of Eye Features from Color Images. Sensors (Basel). 2016 Jul 16;16(7):1105. doi: 10.3390/s16071105.
- Results publication: PubMed 30316551Hegde 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.
- Results publicationDuffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (1st ed). St. Louis, 1995, Mosby.
- Results publicationDuffy, JR: Motor Speech Disorders. Substrates, Differential Diagnosis and Management (2nd ed). New York, 2005, Elsevier Health Sciences.
- Results publicationT. Baltrusaitis, A. Zadeh, Y. C. Lim and L. Morency,
- Results publicationPanayotov 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
- Derived from this study: PubMed 37588667Steyaert S, Lootus M, Sarabu C, Framroze Z, Dickinson H, Lewis E, Steels JC, Rinaldo F. A decentralized, prospective, observational study to collect real-world data from patients with myasthenia gravis using smartphones. Front Neurol. 2023 Aug 1;14:1144183. doi: 10.3389/fneur.2023.1144183. eCollection 2023.
- "Myasthenia gravis - Genetics Home Reference - NIH."Link provided by the sponsor.
- Assessment Instruments for Patients with Myasthenia Gravis (MG)Link provided by the sponsor.
- Individual participant dataSharing planned: Yes. Data will be reviewed, and analysis will be done by personnel of doc.ai, and the medical experts. Population-level results of the data analysis in the form of a presentation/report, as well as the resulting proof-of-concept predictive AI model, will be shared with UCB Biopharma (SRL) (who are funding this study). No participant PII or PHI will be shared with UCB Biopharma (SRL) or any other 3rd parties.
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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 Myasthenia Gravis, run by doc.ai inc 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 6 inclusion and 1 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 29 Jul 2021. 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?
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?
Audiovisual recording of voice exercises to detect patterns and changes in voice and facial symptoms (time frame: After enrollment, 3 months with in-app twice a week audiovisual recording of symptoms.). 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 NCT04590716, which also lists 11 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 29 Jul 2021.