Illner, Vojtěch; Novotný, Michal; Kouba, Tomáš; Tykalová, Tereza; Šimek, Michal; Sovka, Pavel; Švihlík, Jan; Růžička, Evžen; Šonka, Karel; Dušek, Petr; Rusz, Jan (2024). Smartphone Voice Calls Provide Early Biomarkers of Parkinsonism in Rapid Eye Movement Sleep Behavior Disorder. (In Press). Movement disorders Wiley 10.1002/mds.29921
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Movement_Disorders_-_2024_-_Illner_-_Smartphone_Voice_Calls_Provide_Early_Biomarkers_of_Parkinsonism_in_Rapid_Eye_Movement.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial-No Derivative Works (CC-BY-NC-ND). Download (1MB) | Preview |
BACKGROUND
Speech dysfunction represents one of the initial motor manifestations to develop in Parkinson's disease (PD) and is measurable through smartphone.
OBJECTIVE
The aim was to develop a fully automated and noise-resistant smartphone-based system that can unobtrusively screen for prodromal parkinsonian speech disorder in subjects with isolated rapid eye movement sleep behavior disorder (iRBD) in a real-world scenario.
METHODS
This cross-sectional study assessed regular, everyday voice call data from individuals with iRBD compared to early PD patients and healthy controls via a developed smartphone application. The participants also performed an active, regular reading of a short passage on their smartphone. Smartphone data were continuously collected for up to 3 months after the standard in-person assessments at the clinic.
RESULTS
A total of 3525 calls that led to 5990 minutes of preprocessed speech were extracted from 72 participants, comprising 21 iRBD patients, 26 PD patients, and 25 controls. With a high area under the curve of 0.85 between iRBD patients and controls, the combination of passive and active smartphone data provided a comparable or even more sensitive evaluation than laboratory examination using a high-quality microphone. The most sensitive features to induce prodromal neurodegeneration in iRBD included imprecise vowel articulation during phone calls (P = 0.03) and monopitch in reading (P = 0.05). Eighteen minutes of speech corresponding to approximately nine calls was sufficient to obtain the best sensitivity for the screening.
CONCLUSION
We consider the developed tool widely applicable to deep longitudinal digital phenotyping data with future applications in neuroprotective trials, deep brain stimulation optimization, neuropsychiatry, speech therapy, population screening, and beyond. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology |
UniBE Contributor: |
Rusz, Jan |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1531-8257 |
Publisher: |
Wiley |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
15 Jul 2024 14:05 |
Last Modified: |
16 Jul 2024 22:17 |
Publisher DOI: |
10.1002/mds.29921 |
PubMed ID: |
39001636 |
Uncontrolled Keywords: |
Parkinson's disease machine learning prodromal synucleinopathy biomarker speech wearables |
BORIS DOI: |
10.48350/198982 |
URI: |
https://boris.unibe.ch/id/eprint/198982 |