» Articles » PMID: 28431622

Quantitative Biomechanical Assessment of Trunk Control in Huntington's Disease Reveals More Impairment in Static Than Dynamic Tasks

Overview
Journal J Neurol Sci
Publisher Elsevier
Specialty Neurology
Date 2017 Apr 23
PMID 28431622
Citations 12
Authors
Affiliations
Soon will be listed here.
Abstract

Postural instability is common in individuals with Huntington's disease (HD), yet little is known about control of the trunk during static and dynamic activities. We compared the trunk motion of 41 individuals with HD and 36 controls at thoracic and pelvic levels during sitting, standing, and walking using wearable iPod sensors. We also examined the ability of individuals with HD to respond to an auditory cue to modify trunk position when the pelvis moved >8° in sagittal or frontal planes during sitting using custom software. We found that amplitude of thoracic and pelvic trunk movements was significantly greater in participants with HD, and differences were more pronounced during static (i.e. sitting, standing) than dynamic (i.e. walking) tasks. In contrast to the slow, smooth sinusoidal trunk movements of controls, individuals with HD demonstrated rapid movements with varying amplitudes that continuously increased without stabilizing. Ninety-seven percent of participants with HD were able to modify their trunk position in response to auditory cues. Our results demonstrate that wearable iPod sensors are clinically useful for rehabilitation professionals to measure and monitor trunk stability in persons with HD. Additionally, auditory cueing holds potential as a useful training tool to improve trunk stability in HD.

Citing Articles

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review.

Poleur M, Markati T, Servais L Orphanet J Rare Dis. 2023; 18(1):224.

PMID: 37533072 PMC: 10398976. DOI: 10.1186/s13023-023-02813-3.


Accelerometry applications and methods to assess standing balance in older adults and mobility-limited patient populations: a narrative review.

Bohlke K, Redfern M, Rosso A, Sejdic E Aging Clin Exp Res. 2023; 35(10):1991-2007.

PMID: 37526887 PMC: 10881067. DOI: 10.1007/s40520-023-02503-x.


Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT).

Iseki C, Hayasaka T, Yanagawa H, Komoriya Y, Kondo T, Hoshi M Sensors (Basel). 2023; 23(13).

PMID: 37448065 PMC: 10346151. DOI: 10.3390/s23136217.


Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.

Das R, Paul S, Mourya G, Kumar N, Hussain M Front Neurosci. 2022; 16:859298.

PMID: 35495059 PMC: 9051393. DOI: 10.3389/fnins.2022.859298.


Predicting Severity of Huntington's Disease With Wearable Sensors.

Scheid B, Aradi S, Pierson R, Baldassano S, Tivon I, Litt B Front Digit Health. 2022; 4:874208.

PMID: 35445206 PMC: 9013843. DOI: 10.3389/fdgth.2022.874208.