A Novel Measurement Concept to Objectively Quantify Severity of Vocal and Speech Related Symptoms Associated With Parkinson's Disease
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 91
- 试验地点
- 1
- 主要终点
- Usability of digital speech assessments
研究概览
简要总结
The aim of this research program is to develop and validate a smartphone app-based digital measurement concept that:
- Objectively quantifies the severity of Parkinson's Disease (PD) related vocal and speech symptoms;
- Accurately and sensitively identifies vocal and speech abnormalities associated with the prodromal stage of PD.
详细描述
Although multiple approaches to this problem have been proposed in addition to commercially available speech analytics platforms, there is currently no established measure which incorporates the disparate aspects of affected speech to fully characterize Parkinson's symptom progression, particularly in the prodromal phase.
The measurement concept being evaluated in the present study utilizes a custom smartphone-based speech assessment tool to extract multiple hypothesis-driven acoustic features from patient speech in a real-life environment. The resultant features will be used to train a pair of supervised machine learning models to predict clinical PD symptom severity scores, and to distinguish prodromal PD patients from both healthy matched controls and PD patients in more advanced phases of disease progression.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 30 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Male or female age 30 years or older at Screening Visit.
- •Diagnosis of PD as defined by MDS PD diagnostic criteria [1]
- •PD severity at Screening Visit of either:
- •PD Hoehn and Yahr Stage 1-2, inclusive (PD Cohort I)
- •PD Hoehn and Yahr Stages 3-4, inclusive (PD Cohort II)
- •Able and willing to complete all aspects of the study, including at home smartphone app and Zoom telehealth assessments.
- •Able to provide informed consent.
- •Prodromal PD:
- •Confirmation that participant is eligible based on clinician determined predictive criteria of known risk of PD including
- •Rapid eye movement sleep behavior disorder (RBD), possible, probable or definite, OR
- •Hyposmia defined as less than 10th percentile on University of Pennsylvania Smell Identification Test (UPSIT), age and gender adjusted, OR
- •Known genetic variants associated with PD risk, AND Confirmed eligible DAT scan.
- •Male or female age 30 or older at Screening Visit.
- •Able and willing to complete all aspects of the study, including at home smartphone app and Zoom telehealth assessments
- •Able to provide informed consent.
- •Age & Sex Matched Healthy Control:
- •Male or female age 30 years or older at Screening visit.
- •Able and willing to complete all aspects of the study, including at home smartphone app and Zoom telehealth assessments
- •Able to provide informed consent.
排除标准
- •Late-stage PD diagnosis (i.e., Hoehn & Yahr Stage 5) at Screening Visit
- •Symptomatic or atypical PD syndromes due to either drugs (e.g., metoclopramide, flunarizine, neuroleptics) or metabolic disorders (e.g., Wilson's disease), encephalitis, or degenerative diseases (e.g., progressive supranuclear palsy).
- •Current or active clinically significant neurological disorder other than PD (in the opinion of the Investigator).
- •Significant cognitive impairment or clinical dementia at Screening that, in the opinion of the Investigator, would interfere with study evaluation.
- •History of drug and/or alcohol abuse within the past year prior to Screening Visit.
- •Inability or unwillingness to complete all aspects of the study - including use of a provisioned smartphone for study assessments; completion of telehealth assessments.
- •Any other medical or psychiatric condition, which in the opinion of the investigator might preclude participation.
- •Prodromal PD:
- •Clinical diagnosis of PD, other parkinsonism, or dementia.
- •Any current or active clinically significant neurological disorder (in the opinion of the Investigator).
- •Previously obtained MRI scan with evidence of clinically significant neurological disorder (in the opinion of the Investigator).
- •Significant cognitive impairment or clinical dementia at Screening that, in the opinion of the Investigator, would interfere with study evaluation.
- •History of drug and/or alcohol abuse within the past year prior to Screening Visit.
- •Inability or unwillingness to complete all aspects of the study - including use of a provisioned smartphone for study assessments; completion of telehealth assessments.
- •Any other medical or psychiatric condition, which in the opinion of the investigator might preclude participation.
- •Age & Sex Matched Healthy Control:
- •First degree relative with PD (i.e., biologic parent, sibling, child).
- •Any current or active clinically significant neurological disorder (in the opinion of the Investigator).
- •Previously obtained MRI scan with evidence of clinically significant neurological disorder (in the opinion of the Investigator).
- •Significant cognitive impairment or clinical dementia at Screening that, in the opinion of the Investigator, would interfere with study evaluation.
- •History of drug and/or alcohol abuse within the past year prior to Screening Visit.
- •Inability or unwillingness to complete all aspects of the study - including use of a provisioned smartphone for study assessments; completion of telehealth assessments.
- •Any other medical or psychiatric condition, which in the opinion of the investigator might preclude participation
结局指标
主要结局
Usability of digital speech assessments
时间窗: 8 weeks
o SUS Usability scores by score, grade and adjective rating
Content validity of digital speech assessments
时间窗: 8 weeks
o Percent of patients that score Excellent or Good for usability ratings
Compliance of digital speech assessment data recorded via smartphone assessments
时间窗: 8 weeks
o % Interpretable minutes of data per patient
Quality of digital speech assessment data recorded via smartphone assessments
时间窗: 8 weeks
o % Interpretable vs. expected number of minutes of data per patient by complete days on study
次要结局
- Predictive performance of ML classification model(8 weeks)
- Characterization and reliability of digital speech assessment features(8 weeks)
- Reliability of digital speech assessment features(8 weeks)
- Predictive performance of machine learning (ML) regression model(8 weeks)
研究者
Tanya Simuni, MD
Professor
Northwestern University