دورية أكاديمية
أعرض في EDS

Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity.

التفاصيل البيبلوغرافية
العنوان: Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson's Disease Severity.
المؤلفون: Morgan C; Translational Health Sciences, University of Bristol, Bristol, UK.; Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Bristol, UK., Masullo A; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., Mirmehdi M; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., Isotalus HK; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., Jovan F; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., McConville R; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., Tonkin EL; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK., Whone A; Translational Health Sciences, University of Bristol, Bristol, UK.; Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Southmead Hospital, Bristol, UK., Craddock I; Faculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UK.
المصدر: Digital biomarkers [Digit Biomark] 2023 Aug 14; Vol. 7 (1), pp. 92-103. Date of Electronic Publication: 2023 Aug 14 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: S. Karger AG Country of Publication: Switzerland NLM ID: 101707633 Publication Model: eCollection Cited Medium: Internet ISSN: 2504-110X (Electronic) Linking ISSN: 2504110X NLM ISO Abbreviation: Digit Biomark Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel : S. Karger AG, [2017]-
مستخلص: Introduction: Technology holds the potential to track disease progression and response to neuroprotective therapies in Parkinson's disease (PD). The sit-to-stand (STS) transition is a frequently occurring event which is important to people with PD. The aim of this study was to demonstrate an automatic approach to quantify STS duration and speed using a real-world free-living dataset and look at clinical correlations of the outcomes, including whether STS parameters change when someone withholds PD medications.
Methods: Eighty-five hours of video data were collected from 24 participants staying in pairs for 5-day periods in a naturalistic setting. Skeleton joints were extracted from the video data; the head trajectory was estimated and used to estimate the STS parameters of duration and speed.
Results: 3.14 STS transitions were seen per hour per person on average. Significant correlations were seen between automatic and manual STS duration (Pearson rho - 0.419, p = 0.042) and between automatic STS speed and manual STS duration (Pearson rho - 0.780, p < 0.001). Significant and strong correlations were seen between the gold-standard clinical rating scale scores and both STS duration and STS speed; these correlations were not seen in the STS transitions when the participants were carrying something in their hand(s). Significant differences were seen at the cohort level between control and PD participants' ON medications' STS duration (U = 6,263, p = 0.018) and speed (U = 9,965, p < 0.001). At an individual level, only two participants with PD became significantly slower to STS when they were OFF medications; withholding medications did not significantly change STS duration at an individual level in any participant.
Conclusion: We demonstrate a novel approach to automatically quantify and ecologically validate two STS parameters which correlate with gold-standard clinical tools measuring disease severity in PD.
Competing Interests: The authors declare no competing interests.
(© 2023 The Author(s). Published by S. Karger AG, Basel.)
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معلومات مُعتمدة: United Kingdom WT_ Wellcome Trust
فهرسة مساهمة: Keywords: Home environment; Influence and/or predict health-related outcomes; Mobility; Objective data; Parkinson’s disease-related motor symptoms; Video recording
تواريخ الأحداث: Date Created: 20230817 Latest Revision: 20231223
رمز التحديث: 20231223
مُعرف محوري في PubMed: PMC10425718
DOI: 10.1159/000530953
PMID: 37588481
قاعدة البيانات: MEDLINE
الوصف
تدمد:2504-110X
DOI:10.1159/000530953