Study Purpose:
A disease called Amyotrophic lateral sclerosis (or ALS), which leads to difficulty swallowing, breathing, and movement, has been found to be higher for those serving in the military than in the general population. There are approximately 4,200 Veterans with ALS and roughly 1,000 new cases each year. When doctors attempt to determine the degree to which an ALS patient is suffering from the disease, they apply tests that are "graded" by experts. However, this approach to testing patients may not be very accurate. Researchers aim to use a system called DESIPHER to "listen" to ALS patients and find speech mistakes related to their condition. Researchers believe that, by detecting different types of errors, DESIPHER serves as a new kind of indicator of medical problems such as difficulty breathing or swallowing, without human "grading". This may also lead to a better system for automatically understanding ALS patients' speech.Study Status:
Not recruiting
Disease:
Amyotrophic Lateral Sclerosis
Study Type:
Observational
Type of Intervention:
N/A
Intervention Name:
N/A
Placebo:
N/A
Phase:
N/A
Study Chair(s)/Principal Investigator(s):
Samuel L. Phillips, PhD, James A. Haley Veterans' Hospital, Tampa, FL
Clinicaltrials.gov ID:
Neals Affiliated?
No
Coordinating Center Contact Information
James A. Haley Veterans' Hospital
Tampa, Florida, 33612 United States
Full Study Summary:
In 2008, Amyotrophic lateral sclerosis (ALS) or Lou Gehrig's Disease became a presumptively compensable (service connected) disease as the Institute of Medicine (IOM) Committee stated an association between the development of ALS and military service. According to the IOM report, military service increases life risk of ALS by 1.5 fold. There are approximately 4,200 Veterans with ALS and roughly 1,000 new cases each year. At the Tampa VA, since 2007, there has been a consistent rise in the number of Veterans diagnosed and treated with ALS.
Most physiological assessments that are commonly used to determine the functional status of patients with ALS require trained clinical personnel to administer and interpret the results. The investigators propose to use automatic speech understanding and machine learning software (DESIPHER) to: identify speech pathologies and use them to predict other aspects of physiological degeneration associated with ALS (e.g., respiratory difficulty or inability to swallow), and ultimately improve speech recognition for those with speech impairments. The investigators expect this to improve the ability to appropriately identify and intervene when Veterans with ALS are at risk of serious adverse medical issues such as respiratory failure and aspiration. The investigators postulate that analyzing the overall divergence of (impaired) speech, from a "normal" baseline, will prove to be more robust and a better marker for involvement than others that have been proposed.
Specific research questions to be addressed by this study are: (1) Is it possible to train a speech recognition system to adapt to increasingly more frequent language/speech errors of particular types, to produce an accurate textual transcript that would be readable by an ALS patient's caregiver or physician? (2) Are specific changes in physiological functioning: Forced Vital Capacity, tongue strength, speech velocity, weight (loss), aspiration risk, or psychological distress, reflected in different types of language/speech errors associated with ALS?
By understanding how speech functioning correlates with the degree to which other biophysical functioning has degraded, it is possible to apply a new, non-invasive measure for assessing the functionality of an ALS patient. In addition, the features associated with speech degradation it is possible to adapt existing speech recognition software to a patient's speech as it evolves over time, so that the quality of life for patients may be improved through conversation with a computer.
Respiratory failure is the main cause of morbidity and mortality in ALS patients. The investigators expect that the method of analyzing speech will present an excellent biomarker for respiratory function, as there is an expected increase in pauses during speech due to the necessity of increased frequency of respirations, a decrease in loudness, and decreased overall velocity of speech. A second major cause of death is aspiration. As the articular muscles decline, the investigators expect to note a decrease in the clarity of speech. Speech involvement often precedes swallowing involvement in ALS; thus, the investigators expect that increasing "speech divergence" will indicate potential aspiration risk.
Most physiological assessments that are commonly used to determine the functional status of patients with ALS require trained clinical personnel to administer and interpret the results. The investigators propose to use automatic speech understanding and machine learning software (DESIPHER) to: identify speech pathologies and use them to predict other aspects of physiological degeneration associated with ALS (e.g., respiratory difficulty or inability to swallow), and ultimately improve speech recognition for those with speech impairments. The investigators expect this to improve the ability to appropriately identify and intervene when Veterans with ALS are at risk of serious adverse medical issues such as respiratory failure and aspiration. The investigators postulate that analyzing the overall divergence of (impaired) speech, from a "normal" baseline, will prove to be more robust and a better marker for involvement than others that have been proposed.
Specific research questions to be addressed by this study are: (1) Is it possible to train a speech recognition system to adapt to increasingly more frequent language/speech errors of particular types, to produce an accurate textual transcript that would be readable by an ALS patient's caregiver or physician? (2) Are specific changes in physiological functioning: Forced Vital Capacity, tongue strength, speech velocity, weight (loss), aspiration risk, or psychological distress, reflected in different types of language/speech errors associated with ALS?
By understanding how speech functioning correlates with the degree to which other biophysical functioning has degraded, it is possible to apply a new, non-invasive measure for assessing the functionality of an ALS patient. In addition, the features associated with speech degradation it is possible to adapt existing speech recognition software to a patient's speech as it evolves over time, so that the quality of life for patients may be improved through conversation with a computer.
Respiratory failure is the main cause of morbidity and mortality in ALS patients. The investigators expect that the method of analyzing speech will present an excellent biomarker for respiratory function, as there is an expected increase in pauses during speech due to the necessity of increased frequency of respirations, a decrease in loudness, and decreased overall velocity of speech. A second major cause of death is aspiration. As the articular muscles decline, the investigators expect to note a decrease in the clarity of speech. Speech involvement often precedes swallowing involvement in ALS; thus, the investigators expect that increasing "speech divergence" will indicate potential aspiration risk.
Study Sponsor:
VA Office of Research and Development
Estimated Enrollment:
34
Estimated Study Start Date:
01 / 01 / 2016
Estimated Study Completion Date:
12 / 31 / 2017
Posting Last Modified Date:
08 / 18 / 2020
Date Study Added to neals.org:
02 / 05 / 2016
Minimum Age:
18 Years
Maximum Age:
N/A
Inclusion Criteria:- Veterans will be diagnosed with ALS
- Native speakers of U.S. English
- Will have bulbar involvement identified during initial ALS inpatient evaluation
- Forced vital capacity (FVC) of greater than 50% of the expected value for age
- An Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) Score of 40 or greater
Exclusion Criteria:
- A diagnosis of dementia
- FVC less than 50%
- Inability to speak
- Or inability to follow directions
James A. Haley Veterans' Hospital, Tampa, FL
Tampa, Florida
33612
United States