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Existing patients at MassGeneral Hospital's MG clinic, ages 18-80, with autoimmune, AChR or MuSK antibody positive generalized myasthenia gravis.

Participants will wear a pendant sensor for 7 days and then participate in an on-site examination with using a tablet designed to evaluate weakness in MG.

Wearable sensor

Carina C Stafstrom, BS

Carina Stafstrom, BS

Amanda C Guidon, MD, MPH

The goal of this observational study is to evaluate the feasibility of using wearable sensor and digital technologies to measure motor and speech function in adults with autoimmune Myasthenia Gravis (MG).

The main question\[s\] it aims to answer are:

* To measure the correlation of sensor-based measures of motor function with existing outcome measures including the MG-ADL, MGQOL15r, QMG, MGComposite, and Neuro-QOL Fatigue scales.
* To develop and validate tablet-based digital assessments of speech and facial expression and to compare with existing outcome measures.

Participants will wear a pendant sensor for 7 days and then participate in tablet-based and in-person myasthenia-specific physical examinations. This will be performed in concert with routine care in the Massachusetts General Hospital MG clinic.

Myasthenia gravis (MG) is a chronic autoimmune neuromuscular disease characterized by fluctuating muscle weakness and symptoms that interfere with activities of daily living and negatively impacts quality of life. MG symptoms are currently assessed in person through a careful history and physical exam by a neuromuscular disease expert. Such in-clinic assessments are time-consuming, subjective, only provide a snapshot of a patient's disease and do not adequately reflect the spectrum of fluctuating weakness and symptoms. In 2019, NIH funded a rare disease clinical research consortium called MGNet. The consortium is focused on improved characterization of clinical phenotypes, discovery of biomarkers, and advancing clinical trial readiness for MG, which would enhance the development of more effective and personalized treatments. In this Fast Track SBIR project, BioSensics will collaborate with Massachusetts General Hospital - one of the key Consortium sites for MGNet - to develop and validate a wearable sensor solution (MGWear) for continuous remote monitoring of motor symptoms and function in MG patients and also a secure mobile application (MG app) for automatic assessment of speech and facial characteristics. The mobile application will also enable the transfer of data from the wearable device to BioSensics HIPAA-compliant backend cloud called BioDigit Cloud.

The objective of the study is to evaluate the feasibility of using wearable sensor and digital technologies to measure motor and speech function as well as developing metrics to measure ocular and facial expression to monitor disease activity and fluctuating muscle weakness in people with Myasthenia Gravis (MG).To achieve this objective, the investigators will conduct an observational study is to investigate the correlation between outcomes as measured by the PAMSys sensor and digital health technologies with the total score and sub-scores of MG specific outcome measures.

A key application and market for the proposed solution technology is pharmaceutical clinical trials. Wearable sensors and digital technologies like the technology proposed here could allow drug developers to test and iterate faster, providing a valuable new method for evaluating efficacy. Clinically, the proposed solution could be used to predict an individual's response to immunosuppressive drugs and to improve medication titration. Such solutions can enable detecting subtle changes in movement-based and digital biomarkers and provide insight into the phase of clinical disease onset. Additionally, the growing use of telemedicine to expand access and potentially reduce costs of high-quality care will require remote assessment strategies. 20% of states in the US (10 out of 50 states) have no specialized care for MG. Travel time, distance and cost may limit patients' access to expert care, even in states with identified MG specialists. The proposed remote monitoring technologies have potential to lessen barriers to quality care access for MG patients.

Physical Activity Monitoring in Myasthenia Gravis

Wearable Sensor and Digital Technologies for Quantitative Assessment and Remote Monitoring of Symptoms in Myasthenia Gravis

To measure the correlation of sensor-based measures of motor function to the MG-ADL, QMG, MGC, and Neuro-QOL Fatigue.

To develop and validate digital assessment of speech and facial expression compared to rating of MG-ADL, QMG, MGC, and Neuro-QOL Fatigue.

BioSensics

Massachusetts General Hospital

Assistant Professor of Neurology/Harvard Medical School

Peripheral Nervous System Diseases

Subjects will be receiving sensor-based interactive exercise program (game-based exercise) intervention twice a week for 6 weeks.

Subjects will be receiving non-technology based foot and ankle exercise twice a week for 6 weeks

This intervention includes interactive game-based balance training including repetitive weight shifting and virtual obstacle crossing tasks. Wearable sensors will provide real-time visual/auditory feedback from foot and ankle position and allowed perception of motor-errors during each motor-action.

sensor-based interactive exercise (game-based exercise)

Subjects are asked to perform non-technology based foot and ankle exercises, which include body weight shifting and obstacle crossing tasks.

Intervention without game-based exercise

Cancer patients with chemotherapy-induced peripheral neuropathy (CIPN) have deficits in sensory and motor skills leading to inappropriate proprioceptive feedback, impaired postural control and high fall risk. This study will investigate the acceptability and effect of an interactive motor adaptation balance training program based on wearable sensors for improving balance in older cancer patients with CIPN. Cancer patients with confirmed CIPN will be recruited and will be randomized to either intervention (IG) or control (CG) group and followed for 6 months. The intervention group will take part in a 6-week balance training program twice per week in either their home or in clinic (based on subject preference) under the supervision of a qualified research staff member. This intervention includes interactive game-based balance training including repetitive weight shifting and virtual obstacle crossing tasks. Wearable sensors will provide real-time visual/auditory feedback from foot and ankle position and allowed perception of motor-errors during each motor-action. The control group will be instructed to complete a supervised foot and ankle exercise without using sensor technology. Changes in balance, gait, and physical activity, and number of falls will be compared pre- and post-intervention, as well as 3 and 6 month post intervention. Investigators hypothesize that patients receiving sensor-based exercise training will benefit more compared to group receiving conventional non-technology home-based training in terms of improving functional performance and reducing falls.

Using Game-based Exercise to Improve Balance in Cancer Patients

Managing Chemotherapy Induced Neuropathy in Cancer Patients Using Game-based Exercise

Only de-identified data and associated results will be published in peer-review papers or scientific abstracts.

walking ability is quantified by gait speed.

Measuring fear of falling using Fall Efficacy Scale International (FES-I) questionnaire

National Institutes of Health (NIH)

Baylor College of Medicine

Principal Investigator

Infections

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Participants will be enrolled during induction therapy for ALL, before the first high-risk period of treatment, and will contribute data for two 5-day periods of continuous monitoring.

To develop predictive algorithms that identify early onset of sepsis in children being treated for ALL. This study will use a two-stage design. The initial stage (the feasibility phase) will evaluate the feasibility of data collection, and the second stage (the completion phase) will provide data for the design of predictive models.

Wearable Sensors

Joshua Wolf, MBBS,PhD

Joshua Wolf, MBBS, PhD

Acute lymphoblastic leukemia (ALL) is the most common cancer of childhood and long-term survival has risen to above 90%, but 1-4% of treated patients die from infections. Early detection and treatment of infection can improve these outcomes by preventing increased severity and death. This study aims to determine whether continuous analysis of information from wearable devices (Like a watch and sticky patch) that measure temperature, pulse rate, oxygen level, and other similar information can predict infection before it is apparent to the patient or caregiver. About 65 patients will be enrolled and will wear these devices for 10 days; during that time the information will be recorded, but not available. After completion, information collected immediately before infection will be compared to other times to identify features that predict infections.

Primary Objectives (Feasibility Phase) To determine the feasibility of non-invasive collection of continuous physiological data in children with acute lymphoblastic leukemia during outpatient treatment.

(Completion Phase) To develop competing sepsis prediction algorithms using continuous physiological data during outpatient treatment in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.

Secondary Objectives To estimate the PPV and NPV of competing sepsis prediction algorithms using continuous physiological data for prediction of fever and sepsis in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.

To estimate the frequency of undocumented fever episodes in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.

To estimate the time from detection of fever or sepsis by a wearable device to identification by standard practice in children with acute lymphoblastic leukemia during high-risk periods of induction therapy.

This study aims to determine whether continuous analysis of those biosignals in children treated for ALL can predict early onset of sepsis. To collect those biosignals, we combine two wearable sensors, namely TempTraq, an adhesive temperature sensor, and Empatica E4, a wearable physiological monitoring device.

Data will be collected during two selected high-risk 5-day periods of induction therapy for acute lymphoblastic leukemia. Statistical models will be applied and a series of validation methodologies will be developed to arrive at an optimal predictive model for subsequent external validation .

This study will use a two-stage design. The initial stage (the feasibility phase) will evaluate the feasibility of data collection, and the second stage (the completion phase) will provide sufficient data for the development of predictive models and the estimation of the sensitivity and specificity of these models .

Feasibility Phase

The feasibility phase of the study will comprise the first 10 study participants, including at least 3 in each age group (5 to \<10 years, and \>/= 10 years). The aim of this stage is to estimate the proportion of time that continuous monitoring data are available, and these results will determine whether the study progresses to the completion phase

Completion Phase

The Completion Phase will comprise approximately 55 participants. Enrollment will continue until:

1. A total of 15 participants have experienced a fever or sepsis event for the prediction model, and
2. At least 15 participants have completed the study without experiencing a fever or sepsis event.

Continuous Ambulatory Monitoring to Predict Elevated Risk of Infection in Children With Lymphoblastic Leukemia Undergoing Induction Chemotherapy (CAMPER-ALL)

The aim of this stage is to estimate the proportion of time that continuous monitoring data are available, and these results will determine whether the study progresses to the completion phase.

This phase will provide sufficient data to develop competing sepsis prediction algorithms using continuous physiologic data for cross-validation.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Physical Activity Monitoring in Myasthenia Gravis	NCT06277830	Myasthenia Grav...	Wearable sensor	18 Years - 80 Years	Massachusetts General Hospital
Using Game-based Exercise to Improve Balance in Cancer Patients	NCT02773329	Chemotherapy In... Cancer	sensor-based in... Intervention wi...	55 Years -	Baylor College of Medicine
Continuous Ambulatory Monitoring to Predict Elevated Risk of Infection in Children With Lymphoblastic Leukemia Undergoing Induction Chemotherapy (CAMPER-ALL)	NCT04322084	Acute Lymphobla...	Wearable Sensor...	5 Years - 25 Years	St. Jude Children's Research Hospital
Using Game-based Exercise to Improve Balance in Cancer Patients	NCT02773329	Chemotherapy In... Cancer	sensor-based in... Intervention wi...	55 Years -	Baylor College of Medicine
Continuous Ambulatory Monitoring to Predict Elevated Risk of Infection in Children With Lymphoblastic Leukemia Undergoing Induction Chemotherapy (CAMPER-ALL)	NCT04322084	Acute Lymphobla...	Wearable Sensor...	5 Years - 25 Years	St. Jude Children's Research Hospital
Physical Activity Monitoring in Myasthenia Gravis	NCT06277830	Myasthenia Grav...	Wearable sensor	18 Years - 80 Years	Massachusetts General Hospital
Using Game-based Exercise to Improve Balance in Cancer Patients	NCT02773329	Chemotherapy In... Cancer	sensor-based in... Intervention wi...	55 Years -	Baylor College of Medicine

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