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

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.

Digital Biomarkers in Parkinson's Disease: Missing the Forest for the Trees?

“…A second problem of the single digital biomarker hypothesis is the vulnerability of motor‐centric digital measures to confounding variables (Fig. 1C) and other sources of variation 16 . Here, systematic changes in a digital measurement are caused by unmeasured confounds rather than the neural dysfunction of Parkinson's disease.…”

“…Walking carefully on an icy pavement could be misinterpreted as bradykinesia. Many aspects of human life unrelated to the neural dysfunction of Parkinson's disease will interact in unknown ways with digital measurement 16 . Active task‐based measures—where the patient is performing a particular task such as tapping a smartphone screen —provide some level of control against unmeasured environmental confounds, but are still susceptible to another kind.…”

“…1C) and other sources of variation. 16 Here, systematic changes in a digital measurement are caused by unmeasured confounds rather than the neural dysfunction of Parkinson's disease. Unlike expert-rated assessments such as the MDS-UPDRS, digital measures are typically taken at-home without supervision.…”

“…We must understand what features are meaningful for patients, 17 be concrete in exactly what is being measured, 1 and make sure digital tools are safe, robust and reliable. 2,16,18 We must be aware that some aspects such as the number of falls in the last year are countable and easily measured, whereas other aspects such as the loss of confidence following a fall are not and so harder to quantify. Perhaps many important aspects are immeasurable.…”

Wearables for Parkinson's: Fast‐Paced toward Novel Outcome Measures

“…Second, integration of features is likely necessary to enhance performance in longitudinal measurements. Combining multiple feature modalities allows for better separation of true effects from noise, improves individual‐level specificity, and mitigates bias and confounding 4 . A recent PD trial demonstrated a tendency toward effects on a longitudinal digital composite score consisting of multiple active and passive features, but not on gold standard clinical assessment 5 .…”