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Muscular Dystrophies clinical trials

View clinical trials related to Muscular Dystrophies.

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NCT ID: NCT04607824 Completed - Clinical trials for Duchenne Muscular Dystrophy

Heart Rate Variability in Duchenne Muscular Dystrophy During Computer Task

Start date: March 2014
Phase:
Study type: Observational

HRV is attained using a Polar RS800CX. Then, evaluated through linear, non-linear and chaotic global techniques (CGT). Forty-five male subjects were included in the DMD group and age-matched with forty-five in the healthy Typical Development (TD) control group. They were assessed for twenty minutes at rest sitting, and then five minutes whilst performing the maze task on a computer.

NCT ID: NCT04587908 Recruiting - Clinical trials for Duchenne Muscular Dystrophy

A Phase 3 Study of TAS-205 in Patients With Duchenne Muscular Dystrophy(REACH-DMD)

Start date: November 1, 2020
Phase: Phase 3
Study type: Interventional

The purpose of this study is to evaluate the efficacy and safety of TAS-205 in patients with Duchenne muscular dystrophy

NCT ID: NCT04585464 Completed - Healthy Volunteer Clinical Trials

A Study to Assess Safety, Tolerability, and PK of EDG-5506 in Healthy Volunteers and Becker Muscular Dystrophy Adults

Start date: October 12, 2020
Phase: Phase 1
Study type: Interventional

EDG-5506 is an investigational product intended to protect and improve function of dystrophic muscle fibers. This Phase 1 study of EDG-5506 will assess the safety, tolerability, and pharmacokinetics (PK) and of EDG-5506 in adult healthy volunteers and in adults with Becker muscular dystrophy (BMD).

NCT ID: NCT04583917 Recruiting - Clinical trials for Duchenne Muscular Dystrophy

Brain Involvement in Dystrophinopathies Part 1

Start date: March 30, 2021
Phase:
Study type: Observational

The objective of this study is to collect data from a large cohort of individuals with DMD and BMD focusing on the neurobehavioural aspects of these conditions and their correlation to the location of the DMD gene mutation.

NCT ID: NCT04574934 Active, not recruiting - Muscular Dystrophy Clinical Trials

Effect of Aquatic Therapy on Pulmonary Functions in Patients With Muscular Dystrophy

Start date: June 1, 2020
Phase: N/A
Study type: Interventional

This study aimed to assess the efficacy of aquatic therapy on pulmonary functions in patients with muscular dystrophy.

NCT ID: NCT04529707 Recruiting - Clinical trials for Duchenne Muscular Dystrophy

Sleep Intervention in Young Boys With Duchenne Muscular Dystrophy

DMD
Start date: February 17, 2021
Phase: N/A
Study type: Interventional

This project will systematically plan and evaluate the implementation of the Transdiagnostic Sleep and Circadian Intervention for youth (TranS-CY). As an early stage study, investigators will focus on recruitment strategies to reach the target population and collection of preliminary data on primary and secondary effects of the TranS-CY. Weekly remote (video web conferencing) parent training sessions will allow investigators to explore adoption through parent adherence and examine whether the essential elements of the TranS-CY intervention (e.g., motivational interviewing, goal setting, problem solving, sleep routine scheduling, monitoring) can be consistently taught by clinicians and implemented by parents into the home setting.

NCT ID: NCT04525742 Completed - COVID-19 Clinical Trials

COVID-19 Pandemic and Parents of Disabled Children

Start date: July 5, 2020
Phase: N/A
Study type: Interventional

Pandemic period could affect the disabled children's rehabilitation and follow-up negatively because of preventive measures and this could create adverse results on their parents. In this research, it is aimed to determine the positive and negative effects of pandemic on parents and disabled children and to provide an insight for future solutions.

NCT ID: NCT04478981 Completed - Clinical trials for SELENON-related Myopathy

The Natural History of Patients With Mutations in SEPN1 (SELENON) or LAMA2

Start date: August 26, 2020
Phase:
Study type: Observational

SEPN1 (SELENON) is a rare congenital myopathy due to mutations in the SELENON gene. MDC1A is a rare congenital muscle dystrophy due to mutations in the LAMA2 gene. Currently, not much is known about the natural history of these two muscle diseases and no (curative) treatment options exist. The investigators aim to study the natural history of SELENON- and LAMA2-related myopathy/congenital muscular dystrophy patients and prepare for future trials by selection of the most appropriate outcome measures. To this end, a standard medical history, neurological examination, functional measures, questionnaires, cardiac examination, respiratory function tests, radiological examination and accelerometry will be performed over an one and-a-half year period.

NCT ID: NCT04475926 Recruiting - Clinical trials for Limb-girdle Muscular Dystrophy

A Study of the Natural History of Participants With LGMD2E/R4, LGMD2D/R3, LGMD2C/R5, and LGMD2A/R1 ≥ 4 Years of Age, Who Are Managed in Routine Clinical Practice

Start date: April 22, 2021
Phase:
Study type: Observational

This study will follow participants who are screened and confirmed with a genetic diagnosis of Limb-girdle muscular dystrophy type 2E (LGMD2E/R4), Limb-girdle muscular dystrophy type 2D (LGMD2D/R3), Limb-girdle muscular dystrophy type 2C (LGMD2C/R5), or Limb-girdle muscular dystrophy type 2A (LGMD2A/R1). These enrolled participants will be followed to evaluate mobility and pulmonary function for up to 3 years after enrollment. Additional participant data will be collected from the time the individual began experiencing LGMD symptoms to the present.

NCT ID: NCT04468919 Recruiting - Clinical trials for Spinal Cord Injuries

Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit

BCI-FIT
Start date: July 15, 2022
Phase: N/A
Study type: Interventional

This project adds to non-invasive BCIs for communication for adults with severe speech and physical impairments due to neurodegenerative diseases. Researchers will optimize & adapt BCI signal acquisition, signal processing, natural language processing, & clinical implementation. BCI-FIT relies on active inference and transfer learning to customize a completely adaptive intent estimation classifier to each user's multi-modality signals simultaneously. 3 specific aims are: 1. develop & evaluate methods for on-line & robust adaptation of multi-modal signal models to infer user intent; 2. develop & evaluate methods for efficient user intent inference through active querying, and 3. integrate partner & environment-supported language interaction & letter/word supplementation as input modality. The same 4 dependent variables are measured in each SA: typing speed, typing accuracy, information transfer rate (ITR), & user experience (UX) feedback. Four alternating-treatments single case experimental research designs will test hypotheses about optimizing user performance and technology performance for each aim.Tasks include copy-spelling with BCI-FIT to explore the effects of multi-modal access method configurations (SA1.3a), adaptive signal modeling (SA1.3b), & active querying (SA2.2), and story retell to examine the effects of language model enhancements. Five people with SSPI will be recruited for each study. Control participants will be recruited for experiments in SA2.2 and SA3.4. Study hypotheses are: (SA1.3a) A customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to the standard P300 matrix speller. (SA1.3b) Adaptive signal modeling will allow people with SSPI to typing accurately during a copy-spelling task with BCI-FIT without training a new model before each use. (SA2.2) Either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. (SA3.4) Language model enhancements, including a combination of partner and environmental input and word completion during typing, will improve typing performance with BCI-FIT, as measured by ITR during a story-retell task. Optimized recommendations for a multi-modal BCI for each end user will be established, based on an innovative combination of clinical expertise, user feedback, customized multi-modal sensor fusion, and reinforcement learning.