Abstract Objective To determine the potential for improving amyotrophic lateral sclerosis (ALS) clinical trials by having patients or caregivers perform frequent self‐assessments at home. Methods and Participants We enrolled ALS into a nonblinded, longitudinal 9‐month study in which obtained daily data using several different instruments, including slow‐vital capacity device, hand grip dynamometer, an electrical impedance myography‐based fitness activity tracker, speech app, functional...

Abstract Bulbar deterioration in amyotrophic lateral sclerosis (ALS) is a devastating characteristic that impairs patients’ ability to communicate, and linked shorter survival. The existing clinical instruments for assessing bulbar function lack sensitivity early changes. In this paper, using cohort of N = 65 ALS patients who provided regular speech samples 3–9 months, we demonstrated it possible remotely detect changes track progression via automated algorithmic assessment collected digitally.

<b><i>Introduction:</i></b> Changes in speech have the potential to provide important information on diagnosis and progression of various neurological diseases. Many researchers relied open-source features develop algorithms for measuring changes clinical populations as they are convenient easy use. However, repeatability context diseases has not been studied. <b><i>Methods:</i></b> We used a longitudinal sample healthy controls, individuals...

Abstract Background and Hypothesis Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional features to develop diagnostic prognostic models, but less been done use linguistic output assess downstream functional outcomes, which critically important for care. In this work, we study the relationship between automated composites variables that...

Objective: This study's objective was to develop and test a smartphone app that supports learning using coping skills for managing tinnitus. Design: The app's content based on are taught as part of progressive tinnitus management (PTM). study involved three phases: (1) prototype conduct usability testing; (2) two focus groups obtain initial feedback from individuals representing potential users; (3) field evaluate the app, with successive participants. Study Sample: Participants were adults...

In this study, we present and provide validation data for a tool that predicts forced vital capacity (FVC) from speech acoustics collected remotely via mobile app without the need any additional equipment (e.g. spirometer). We trained machine learning model on sample of healthy participants with amyotrophic lateral sclerosis (ALS) to learn mapping FVC used predict values in new different study ALS. further evaluated cross-sectional accuracy its sensitivity within-subject change FVC. found...

Oral diadochokinesis is a useful task in assessment of speech motor function the context neurological disease. Remote collection tasks provides convenient alternative to in-clinic visits, but scoring these assessments can be laborious process for clinicians. This work describes Wav2DDK, an automated algorithm estimating diadochokinetic (DDK) rate on remotely collected audio from healthy participants and with amyotrophic lateral sclerosis (ALS).

We developed and evaluated an automatically extracted measure of cognition (semantic relevance) using automated manual transcripts audio recordings from healthy cognitively impaired participants describing the Cookie Theft picture Boston Diagnostic Aphasia Examination. describe rationale metric validation. on one dataset it a large database (>2000 samples) by comparing accuracy against manually calculated evaluating its clinical relevance. The fully was accurate (r = .84), had moderate to...

Detecting early signs of neurodegeneration is vital for planning treatments neurological diseases. Speech plays an important role in this context because it has been shown to be a promising indicator decline, and can acquired remotely without the need specialized hardware. Typically, symptoms are characterized by clinicians using subjective discrete scales. The poor resolution subjectivity these scales make earliest speech changes hard detect. In paper, we propose algorithm objective...

Background and Hypothesis:Automated language analysis is becoming an increasingly popular tool in clinical research involving individuals with mental health disorders. Previous work has largely focused on using high-dimensional features to develop diagnostic prognostic models, but less been done use linguistic output assess downstream functional outcomes, which critically important for care. In this work, we study the relationship between automated composites variables that characterize...

Remote and digital assessment of neurological health has the potential for earlier detection decline more sensitive tracking health. The implications this are considerable. More outcome measures can lead to tools early diagnosis or help power clinical trials with fewer samples. Speech is an important modality in context because it easy collect taxes both cognitive motor systems. Aural Analytics (A2) a platform monitoring cognitive-linguistic outcomes at-home in-clinic using automated speech...

Remote and digital assessment of neurological health has the potential for earlier detection decline more sensitive tracking health. The implications this are considerable. More outcome measures can lead to tools early diagnosis or help power clinical trials with fewer samples. Speech is an important modality in context because it easy collect taxes both cognitive motor systems. Aural Analytics (A2) a platform monitoring cognitive-linguistic outcomes at-home in-clinic using automated speech...

Cognitive decline is associated with deficits in attention to tasks and relevant details. We developed a metric, semantic relevance (SemR), which algorithmically extracted from speech measures overlap between picture's content the words used describe picture. In this study, we validate it sample that was not when developing it. automatically extract SemR transcripts of Cookie Theft (BDAE) evaluate its cross-sectional longitudinal clinical validity using four groups: Normal Cognition (NC),...

Neuropsychological testing requires an in-person visit with a trained administrator using standard/fixed materials. Speech-based cognitive on mobile devices enables more frequent and timely test administration, but head-to-head comparisons of remote versions tests are rare. We compare responses to well-validated task conducted under supervised (in-person) unsupervised (remote) conditions.We used two data sets containing Cookie Theft picture descriptions (BDAE), one collected in-person,...

Abstract Background Measures of language have demonstrated value in the detection cognitive decline. Here we automatically extracted speech (audio) and (transcripts) features from Cookie Theft picture descriptions (BDAE) to develop two classification models separating healthy participants those with mild impairment (MCI) dementia. Method We used Dementia Bank Wisconsin Registry for Alzheimer’s Prevention (WRAP) datasets evaluated first observation 1011 participants: 51 MCI (based on MMSE...