Morgan, CatherineMasullo, AlessandroMirmehdi, MajidIsotalus, Hanna KristiinaJovan, FerdianMcConville, RyanTonkin, Emma L.Whone, AlanCraddock, Ian2023-08-282023-08-282023-08-01Morgan, C, Masullo, A, Mirmehdi, M, Isotalus, H K, Jovan, F, McConville, R, Tonkin, E L, Whone, A & Craddock, I 2023, 'Automated Real-World Video Analysis of Sit-to-Stand Transitions Predicts Parkinson’s Disease Severity', Digital Biomarkers, vol. 7, no. 1, pp. 92-103. https://doi.org/10.1159/0005309532504-110XBibtex: 10.1159/000530953ORCID: /0000-0003-4911-540X/work/141300873https://hdl.handle.net/2164/21564Acknowledgments We gratefully acknowledge the study participants for their time and efforts in participating in this research. We also acknowledge the local Parkinson’s and Other Movement Disorders Health Integration Team (Patient and Public Involvement Group) for their assistance at each step of study design. Funding Sources 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.121184958engParkinson's disease-related motor symtomsInfluence and/or predict health-related outcomesObjective dataHome environmentMobilityVideo RecordingR MedicineWellcome Trust204813/Z/16/ZEngineering and Physical Sciences Research Council (EPSRC)EP/R005273/1RJournal article10.1159/000530953https://doi.org/10.1159/00053095371