» Articles » PMID: 35016250

Digital Phenotyping in Clinical Neurology

Overview
Journal Semin Neurol
Specialty Neurology
Date 2022 Jan 11
PMID 35016250
Authors
Affiliations
Soon will be listed here.
Abstract

Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.

Citing Articles

Multimodal Digital Phenotyping of Behavior in a Neurology Clinic: Development of the Neurobooth Platform and the First Two Years of Data Collection.

Nunes A, Patel S, Oubre B, Jas M, Kulkarni D, Luddy A medRxiv. 2025; .

PMID: 39974013 PMC: 11838688. DOI: 10.1101/2024.12.28.24319527.


Eye Tracking during Passage Reading Supports Precise Oculomotor Assessment in Ataxias.

Oubre B, Yang F, Luddy A, Manohar R, Soja N, Stephen C medRxiv. 2025; .

PMID: 39867398 PMC: 11759587. DOI: 10.1101/2025.01.13.25320487.


The Newborn Screening Programme Revisited: An Expert Opinion on the Challenges of Rett Syndrome.

Singh J, Santosh P Genes (Basel). 2025; 15(12.

PMID: 39766837 PMC: 11675257. DOI: 10.3390/genes15121570.


Sensitive Quantification of Cerebellar Speech Abnormalities Using Deep Learning Models.

Vattis K, Oubre B, Luddy A, Ouillon J, Eklund N, Stephen C IEEE Access. 2024; 12:62328-62340.

PMID: 39606584 PMC: 11601984. DOI: 10.1109/access.2024.3393243.


Pathways to personalized medicine-Embracing heterogeneity for progress in clinical therapeutics research in Alzheimer's disease.

Arnold S, Hyman B, Betensky R, Dodge H Alzheimers Dement. 2024; 20(10):7384-7394.

PMID: 39240044 PMC: 11485305. DOI: 10.1002/alz.14063.


References
1.
Comeau D, Pfeifer N . Diagnosis of Concussion on the Sidelines. Semin Pediatr Neurol. 2019; 30:26-34. DOI: 10.1016/j.spen.2019.03.005. View

2.
Egger H, Dawson G, Hashemi J, Carpenter K, Espinosa S, Campbell K . Automatic emotion and attention analysis of young children at home: a ResearchKit autism feasibility study. NPJ Digit Med. 2019; 1:20. PMC: 6550157. DOI: 10.1038/s41746-018-0024-6. View

3.
Berry J, Paganoni S, Carlson K, Burke K, Weber H, Staples P . Design and results of a smartphone-based digital phenotyping study to quantify ALS progression. Ann Clin Transl Neurol. 2019; 6(5):873-881. PMC: 6529832. DOI: 10.1002/acn3.770. View

4.
Del Din S, Galna B, Godfrey A, Bekkers E, Pelosin E, Nieuwhof F . Analysis of Free-Living Gait in Older Adults With and Without Parkinson's Disease and With and Without a History of Falls: Identifying Generic and Disease-Specific Characteristics. J Gerontol A Biol Sci Med Sci. 2018; 74(4):500-506. PMC: 6417445. DOI: 10.1093/gerona/glx254. View

5.
Griffiths R, Kotschet K, Arfon S, Xu Z, Johnson W, Drago J . Automated assessment of bradykinesia and dyskinesia in Parkinson's disease. J Parkinsons Dis. 2013; 2(1):47-55. DOI: 10.3233/JPD-2012-11071. View