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Remote Assessments of Hand Function in Neurological Disorders: Systematic Review.

作者信息

Gopal Arpita, Hsu Wan-Yu, Allen Diane D, Bove Riley

机构信息

Weill Institute of Neurosciences, University of California San Francisco, San Francisco, CA, United States.

Department of Physical Therapy and Rehabilitation Science, University of California San Francisco/San Francisco State University, San Francisco, CA, United States.

出版信息

JMIR Rehabil Assist Technol. 2022 Mar 9;9(1):e33157. doi: 10.2196/33157.


DOI:10.2196/33157
PMID:35262502
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8943610/
Abstract

BACKGROUND: Loss of fine motor skills is observed in many neurological diseases, and remote monitoring assessments can aid in early diagnosis and intervention. Hand function can be regularly assessed to monitor loss of fine motor skills in people with central nervous system disorders; however, there are challenges to in-clinic assessments. Remotely assessing hand function could facilitate monitoring and supporting of early diagnosis and intervention when warranted. OBJECTIVE: Remote assessments can facilitate the tracking of limitations, aiding in early diagnosis and intervention. This study aims to systematically review existing evidence regarding the remote assessment of hand function in populations with chronic neurological dysfunction. METHODS: PubMed and MEDLINE, CINAHL, Web of Science, and Embase were searched for studies that reported remote assessment of hand function (ie, outside of traditional in-person clinical settings) in adults with chronic central nervous system disorders. We excluded studies that included participants with orthopedic upper limb dysfunction or used tools for intervention and treatment. We extracted data on the evaluated hand function domains, validity and reliability, feasibility, and stage of development. RESULTS: In total, 74 studies met the inclusion criteria for Parkinson disease (n=57, 77% studies), stroke (n=9, 12%), multiple sclerosis (n=6, 8%), spinal cord injury (n=1, 1%), and amyotrophic lateral sclerosis (n=1, 1%). Three assessment modalities were identified: external device (eg, wrist-worn accelerometer), smartphone or tablet, and telerehabilitation. The feasibility and overall participant acceptability were high. The most common hand function domains assessed included finger tapping speed (fine motor control and rigidity), hand tremor (pharmacological and rehabilitation efficacy), and finger dexterity (manipulation of small objects required for daily tasks) and handwriting (coordination). Although validity and reliability data were heterogeneous across studies, statistically significant correlations with traditional in-clinic metrics were most commonly reported for telerehabilitation and smartphone or tablet apps. The most readily implementable assessments were smartphone or tablet-based. CONCLUSIONS: The findings show that remote assessment of hand function is feasible in neurological disorders. Although varied, the assessments allow clinicians to objectively record performance in multiple hand function domains, improving the reliability of traditional in-clinic assessments. Remote assessments, particularly via telerehabilitation and smartphone- or tablet-based apps that align with in-clinic metrics, facilitate clinic to home transitions, have few barriers to implementation, and prompt remote identification and treatment of hand function impairments.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/effe/8943610/fc6e841333b0/rehab_v9i1e33157_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/effe/8943610/fc6e841333b0/rehab_v9i1e33157_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/effe/8943610/fc6e841333b0/rehab_v9i1e33157_fig1.jpg

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本文引用的文献

[1]
Developing and assessing a new web-based tapping test for measuring distal movement in Parkinson's disease: a Distal Finger Tapping test.

Sci Rep. 2022-1-10

[2]
Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson's Disease and Idiopathic Anosmia.

J Parkinsons Dis. 2021

[3]
Prediction of motor Unified Parkinson's Disease Rating Scale scores in patients with Parkinson's disease using surface electromyography.

Clin Neurophysiol. 2021-7

[4]
A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients.

Sensors (Basel). 2021-2-2

[5]
Smartwatch inertial sensors continuously monitor real-world motor fluctuations in Parkinson's disease.

Sci Transl Med. 2021-2-3

[6]
Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks.

Sensors (Basel). 2021-1-4

[7]
The CloudUPDRS smartphone software in Parkinson's study: cross-validation against blinded human raters.

NPJ Parkinsons Dis. 2020-12-8

[8]
Unobtrusive detection of Parkinson's disease from multi-modal and in-the-wild sensor data using deep learning techniques.

Sci Rep. 2020-12-7

[9]
A validated measure of rigidity in Parkinson's disease using alternating finger tapping on an engineered keyboard.

Parkinsonism Relat Disord. 2020-12

[10]
Real-world keystroke dynamics are a potentially valid biomarker for clinical disability in multiple sclerosis.

Mult Scler. 2021-8

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