Using smartphone recordings to assess symptoms of Myasthenia Gravis

Capturing Key Symptoms Using Smartphone Recordings in Patients With Myasthenia Gravis (CAPTURE-MG)

This study is testing if smartphone recordings can help track symptoms like speech problems and fatigue in people with Myasthenia Gravis.

Quick facts

• Study type: Observational
• Enrollment: 225 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: Leiden University Medical Center (other)
• Locations: 2 sites (Leiden, South Holland and 1 other locations)
• Trial ID: NCT06743490 on ClinicalTrials.gov

What this trial studies

This observational study aims to quantitatively assess key symptoms of Myasthenia Gravis (MG) using smartphone recordings from both MG patients and non-MG participants. Participants will visit the Leiden University Medical Center once, where baseline data, including demographics and clinical history, will be collected alongside symptom-specific assessments. The study will explore the applicability of machine learning algorithms to analyze the recorded data, focusing on symptoms such as dysarthria and fatigue. The study is considered low risk, primarily involving questionnaire-based assessments and digital data collection.

Who should consider this trial

Good fit: Ideal candidates include adults aged 18 and older with a clinical diagnosis of Myasthenia Gravis and specific symptoms like dysarthria or fatigue.

Not a fit: Patients without a diagnosis of Myasthenia Gravis or those who do not exhibit the symptoms of interest may not benefit from this study.

Why it matters

Potential benefit: If successful, this study could enhance the understanding and measurement of Myasthenia Gravis symptoms, leading to improved patient management.

How similar studies have performed: While the use of smartphone technology in symptom assessment is gaining traction, this specific approach combining machine learning with MG symptom evaluation is relatively novel.

Eligibility criteria

Inclusion Criteria:

1. Age ≥ 18 years
2. Ability to understand the requirements of the study and provide written informed consent.

Inclusion Criteria for MG participants only:

1. A clinical diagnosis of myasthenia gravis (ocular or generalized) as defined by the Dutch national guideline (category "definite" or "probable" MG).
2. MGFA Clinical Classification of disease severity I-IV.
3. Subjects have at least one of the symptoms of interest (namely dysarthria, dysphonia, proximal arm fatigue and/or ptosis).

Inclusion Criteria for non-MG participants only

1. Subjects are not diagnosed with and have no clinical suspicion of MG.
2. Subjects do not have a medical history of any of the symptoms of interest (namely dysarthria, dysphonia, proximal arm fatigue and/or ptosis).

Exclusion Criteria:

1. Not willing to be audio-recorded for the study assessments.
2. Not willing to be video-recorded for the study assessments.
3. Subjects currently taking part in a clinical trial of an Investigational Medicinal Product.
4. Subjects who have used an immediate release pyridostigmine-based medication in the 12 hours prior to their participation and participants on prolonged release pyridostigmine.
5. Subjects have cognitive or physical limitations that, in the opinion of the investigator, limits the subject's ability to complete study procedures/

Exclusion Criteria for MG participants only:

1. Subjects with an upper-limb amputation or who are non-verbal.
2. Subjects with a diagnosed neurological disease resulting in muscle weakness, other than MG.

Exclusion Criteria for non-MG participants only:

1\. Limitation of upper limb mobility or speech impairment of any cause.

Where this trial is running

Leiden, South Holland and 1 other locations

• Leiden University Medical Center — Leiden, South Holland, Netherlands (NOT_YET_RECRUITING)
• Leiden University Medical Center — Leiden, Netherlands (RECRUITING)

Study contacts

• Principal investigator: Martijn R. Tannemaat, MD, PhD — Leiden University Medical Center
• Study coordinator: Martijn R Tannemaat, MD, PhD

Conditions: Myasthenia Gravis, Fatigue, Digital features, Machine Learning Algorithms, Dysarthria, Dysphonia, Proximal arm fatigue, Ptosis