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.
Original languageEnglish
Pages (from-to)92-103
Number of pages12
JournalDigital Biomarkers
Issue number1
Early online date15 Aug 2023
Publication statusPublished - 1 Dec 2023

Bibliographical note

Funding Information:
This work was supported by the SPHERE Next Steps Project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) [Grant EP/R005273/1], the Elizabeth Blackwell Institute for Health Research, and the Wellcome Trust Institutional Strategic Support Fund [Grant code: 204813/Z/16/Z]; by Cure Parkinson’s [Grant code AW021]; and by IXICO [Grant code R101507-101]. Dr. Jonathan de Pass and Mrs. Georgina de Pass made a charitable donation to the University of Bristol through the Development and Alumni Relations Office; the funding pays for the salary of CM, but they have no input into her work.

Publisher Copyright:
© 2023 S. Karger AG. All rights reserved.


  • Remote sensing technology
  • Home environment
  • Gait analysis
  • Mobility
  • Parkinson's disease


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