Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity
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 automat...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Karger Publishers
2023-08-01
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| Series: | Digital Biomarkers |
| Subjects: | |
| Online Access: | https://beta.karger.com/Article/FullText/530953 |
| _version_ | 1797690751168020480 |
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| author | Catherine Morgan Alessandro Masullo Majid Mirmehdi Hanna Kristiina Isotalus Ferdian Jovan Ryan McConville Emma L. Tonkin Alan Whone Ian Craddock |
| author_facet | Catherine Morgan Alessandro Masullo Majid Mirmehdi Hanna Kristiina Isotalus Ferdian Jovan Ryan McConville Emma L. Tonkin Alan Whone Ian Craddock |
| author_sort | Catherine Morgan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T02:03:39Z |
| format | Article |
| id | doaj.art-d5c5bf7978494f0b8f0d6fc6e4608757 |
| institution | Directory Open Access Journal |
| issn | 2504-110X |
| language | English |
| last_indexed | 2024-03-12T02:03:39Z |
| publishDate | 2023-08-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Digital Biomarkers |
| spelling | doaj.art-d5c5bf7978494f0b8f0d6fc6e46087572023-09-07T07:56:39ZengKarger PublishersDigital Biomarkers2504-110X2023-08-01719210310.1159/000530953530953Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease SeverityCatherine Morgan0https://orcid.org/0000-0003-0333-2417Alessandro Masullo1https://orcid.org/0000-0002-6510-835XMajid Mirmehdi2Hanna Kristiina Isotalus3https://orcid.org/0000-0002-3393-9263Ferdian Jovan4https://orcid.org/0000-0003-4911-540XRyan McConville5Emma L. Tonkin6Alan Whone7Ian Craddock8Translational Health Sciences, University of Bristol, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKTranslational Health Sciences, University of Bristol, Bristol, UKFaculty of Engineering, University of Bristol, Digital Health Offices, 1 Cathedral Square, Bristol, UKIntroduction: 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.https://beta.karger.com/Article/FullText/530953parkinson’s disease-related motor symptomsinfluence and/or predict health-related outcomesobjective datahome environmentmobilityvideo recording |
| spellingShingle | Catherine Morgan Alessandro Masullo Majid Mirmehdi Hanna Kristiina Isotalus Ferdian Jovan Ryan McConville Emma L. Tonkin Alan Whone Ian Craddock Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity Digital Biomarkers parkinson’s disease-related motor symptoms influence and/or predict health-related outcomes objective data home environment mobility video recording |
| title | Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity |
| title_full | Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity |
| title_fullStr | Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity |
| title_full_unstemmed | Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity |
| title_short | Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity |
| title_sort | automated real world video analysis of sit to stand transitions predicts parkinson s disease severity |
| topic | parkinson’s disease-related motor symptoms influence and/or predict health-related outcomes objective data home environment mobility video recording |
| url | https://beta.karger.com/Article/FullText/530953 |
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