Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease

George Roussos; Teresa Ruiz Herrero; Derek L Hill; Ariel V Dowling; Martijn L T M Müller; Luc J W Evers; Jackson Burton; Adrian Derungs; Katherine Fisher; Krishna Praneeth Kilambi; Nitin Mehrotra; Roopal Bhatnagar; Sakshi Sardar; Diane Stephenson; Jamie L Adams; E Ray Dorsey; Josh Cosman

doi:10.1038/s41746-022-00643-4

Identifying and characterising sources of variability in digital outcome measures in Parkinson's disease

NPJ Digit Med. 2022 Jul 15;5(1):93. doi: 10.1038/s41746-022-00643-4.

Authors

George Roussos¹, Teresa Ruiz Herrero², Derek L Hill³, Ariel V Dowling⁴, Martijn L T M Müller⁵, Luc J W Evers⁶, Jackson Burton⁷, Adrian Derungs⁸, Katherine Fisher⁷, Krishna Praneeth Kilambi⁷, Nitin Mehrotra⁹, Roopal Bhatnagar⁵, Sakshi Sardar⁵, Diane Stephenson⁵, Jamie L Adams¹⁰, E Ray Dorsey¹⁰, Josh Cosman¹¹

Affiliations

¹ Birkbeck College, University of London, London, UK. g.roussos@bbk.ac.uk.
² Bill and Melinda Gates Foundation, Seattle, WA, USA.
³ Panoramic Digital Health, Grenoble, France.
⁴ Takeda, Deerfield, IL, USA.
⁵ Critical Path Institute, Tucson, AZ, USA.
⁶ Radboud University Medical Center and Radboud University, Nijmegen, The Netherlands.
⁷ Biogen, Cambridge, MA, USA.
⁸ Roche, Basel, Switzerland.
⁹ Alnylam Pharmaceuticals, Cambridge, MA, USA.
¹⁰ University of Rochester, Rochester, NY, USA.
¹¹ Abbvie, North Chicago, IL, USA.

Abstract

Smartphones and wearables are widely recognised as the foundation for novel Digital Health Technologies (DHTs) for the clinical assessment of Parkinson's disease. Yet, only limited progress has been made towards their regulatory acceptability as effective drug development tools. A key barrier in achieving this goal relates to the influence of a wide range of sources of variability (SoVs) introduced by measurement processes incorporating DHTs, on their ability to detect relevant changes to PD. This paper introduces a conceptual framework to assist clinical research teams investigating a specific Concept of Interest within a particular Context of Use, to identify, characterise, and when possible, mitigate the influence of SoVs. We illustrate how this conceptual framework can be applied in practice through specific examples, including two data-driven case studies.