Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

Catherine A M Morgan; Jack Jameson; Ian J Craddock; Emma L. Tonkin; George Oikonomou; Hanna Kristiina Isotalus; Farnoosh Heidarivincheh; Ryan McConville; Gregory J. L. Tourte; Kirsi M. Kinnunen; Alan L Whone

doi:10.1016/j.parkreldis.2022.11.007

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

Catherine A M Morgan^*, Jack Jameson, Ian J Craddock, Emma L. Tonkin, George Oikonomou, Hanna Kristiina Isotalus, Farnoosh Heidarivincheh, Ryan McConville, Gregory J. L. Tourte, Kirsi M. Kinnunen, Alan L Whone

^*Corresponding author for this work

Research output: Contribution to journal › Article (Academic Journal) › peer-review

3 Citations (Scopus)

Abstract

Introduction
Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.

Methods
11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.

Results
From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p
Conclusion
This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

Original language	English
Pages (from-to)	114-122
Number of pages	9
Journal	Parkinsonism and Related Disorders
Volume	105
DOIs	https://doi.org/10.1016/j.parkreldis.2022.11.007
Publication status	Published - 10 Nov 2022

Bibliographical note

Funding Information:
From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) “OFF” medications. A positive correlation was seen “ON” medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living.The current gold-standard clinical rating scale used in clinical trials to measure mobility outcomes, the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale motor sub-score (MDS-UPDRS III) [11] has limitations including related to its ‘snapshot’ nature which cannot capture real-world functional turning performance of patients [12], its non-linear and discontinuous scoring system, the inter-rater variability [13] and Hawthorne effect [14] of being observed on how someone mobilizes [15,16].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]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress.Full approval from NHS Wales Research Ethics Committee 6 was granted on 17th of December 2019, and Health Research Authority and Health and Care Research Wales approval confirmed on 14th of January 2020; the research was carried out in accord with the Helsinki Declaration of 1975.

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 ]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z ]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress.

Publisher Copyright:
© 2022 The Authors

Structured keywords

SPHERE
SPHERE2

Keywords

Remote sensing technology
Home environment
Gait analysis
Mobility
Parkinson's Disease

Access to Document

10.1016/j.parkreldis.2022.11.007Licence: CC BY

Handle.net

Persistent link

SPHERE2
Craddock, I. J., Mirmehdi, M., Piechocki, R. J., Flach, P. A., Oikonomou, G., Burghardt, T., Damen, D., Santos-Rodriguez, R., O'Kane, A. A., McConville, R., Masullo, A. & Gooberman-Hill, R.
1/10/18 → 31/01/23
Project: Research, Parent

Cite this

Morgan, C. A. M., Jameson, J., Craddock, I. J., Tonkin, E. L., Oikonomou, G., Isotalus, H. K., Heidarivincheh, F., McConville, R., Tourte, G. J. L., Kinnunen, K. M., & Whone, A. L. (2022). Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset. Parkinsonism and Related Disorders, 105, 114-122. https://doi.org/10.1016/j.parkreldis.2022.11.007

@article{27e60f94bca64521861d36ae3f1c5104,

title = "Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset",

abstract = "IntroductionTurning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.Methods11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.ResultsFrom 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p ConclusionThis study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.",

keywords = "Remote sensing technology, Home environment, Gait analysis, Mobility, Parkinson's Disease",

author = "Morgan, {Catherine A M} and Jack Jameson and Craddock, {Ian J} and Tonkin, {Emma L.} and George Oikonomou and Isotalus, {Hanna Kristiina} and Farnoosh Heidarivincheh and Ryan McConville and Tourte, {Gregory J. L.} and Kinnunen, {Kirsi M.} and Whone, {Alan L}",

note = "Funding Information: From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) “OFF” medications. A positive correlation was seen “ON” medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living.The current gold-standard clinical rating scale used in clinical trials to measure mobility outcomes, the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale motor sub-score (MDS-UPDRS III) [11] has limitations including related to its {\textquoteleft}snapshot{\textquoteright} nature which cannot capture real-world functional turning performance of patients [12], its non-linear and discontinuous scoring system, the inter-rater variability [13] and Hawthorne effect [14] of being observed on how someone mobilizes [15,16].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]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress.Full approval from NHS Wales Research Ethics Committee 6 was granted on 17th of December 2019, and Health Research Authority and Health and Care Research Wales approval confirmed on 14th of January 2020; the research was carried out in accord with the Helsinki Declaration of 1975. 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 ]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z ]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress. Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2022",

month = nov,

day = "10",

doi = "10.1016/j.parkreldis.2022.11.007",

language = "English",

volume = "105",

pages = "114--122",

journal = "Parkinsonism and Related Disorders",

issn = "1353-8020",

publisher = "Elsevier Science",

}

TY - JOUR

T1 - Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

AU - Morgan, Catherine A M

AU - Jameson, Jack

AU - Craddock, Ian J

AU - Tonkin, Emma L.

AU - Oikonomou, George

AU - Isotalus, Hanna Kristiina

AU - Heidarivincheh, Farnoosh

AU - McConville, Ryan

AU - Tourte, Gregory J. L.

AU - Kinnunen, Kirsi M.

AU - Whone, Alan L

N1 - Funding Information: From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) “OFF” medications. A positive correlation was seen “ON” medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living.The current gold-standard clinical rating scale used in clinical trials to measure mobility outcomes, the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale motor sub-score (MDS-UPDRS III) [11] has limitations including related to its ‘snapshot’ nature which cannot capture real-world functional turning performance of patients [12], its non-linear and discontinuous scoring system, the inter-rater variability [13] and Hawthorne effect [14] of being observed on how someone mobilizes [15,16].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]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress.Full approval from NHS Wales Research Ethics Committee 6 was granted on 17th of December 2019, and Health Research Authority and Health and Care Research Wales approval confirmed on 14th of January 2020; the research was carried out in accord with the Helsinki Declaration of 1975. 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 ]; and the Elizabeth Blackwell Institute for Health Research, University of Bristol and the Wellcome Trust Institutional Strategic Support Fund [grant code: 204813/Z/16/Z ]; and 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 to support research into Parkinson's Disease. The funding pays for the salary of CM, the PhD student and Clinical Research Fellow, and she reports to the donors on her progress. Publisher Copyright: © 2022 The Authors

PY - 2022/11/10

Y1 - 2022/11/10

N2 - IntroductionTurning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.Methods11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.ResultsFrom 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p ConclusionThis study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

AB - IntroductionTurning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD.Methods11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters.ResultsFrom 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between “ON” and “OFF” medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p ConclusionThis study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes.

KW - Remote sensing technology

KW - Home environment

KW - Gait analysis

KW - Mobility

KW - Parkinson's Disease

U2 - 10.1016/j.parkreldis.2022.11.007

DO - 10.1016/j.parkreldis.2022.11.007

M3 - Article (Academic Journal)

C2 - 36413901

SN - 1353-8020

VL - 105

SP - 114

EP - 122

JO - Parkinsonism and Related Disorders

JF - Parkinsonism and Related Disorders

ER -

Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset

Abstract

Bibliographical note

Structured keywords

Keywords

Access to Document

Handle.net

Fingerprint

Projects

SPHERE2

Cite this