Digital health technology (DHT), including wearable and environmental sensors, video cameras and other electronic tools, has provided new opportunities for the measurement of movement and functionality in Parkinson’s disease. Compared to current standards for evaluation of the disease (MDS-UPDRS), DHT may offer new possibilities for more frequent objective measurements of the duration, severity and frequency of disease manifestations over time, that may provide more information than periodic clinic visits. However, DHT measurement are only scientifically and medically useful if they are accurate, reliable and clinically meaningful. Verification and validation, also known as analytical validation and clinical validation, of DHT performance is important to ensure the accuracy and precision of measurements, and the specificity of findings. Given the wide range of clinical manifestations associated with Parkinson’s disease and the many tools and metrics to assess them, the challenge is to identify those that may represent a standard for use in clinical trials, and to confirm when digital measurements succeed or fall short of capturing meaningful benefits during drug development.

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UNSTRUCTURED The proliferation and increasing maturity of biometric monitoring technologies allows clinical investigators to measure the health status of trial participants in a more holistic manner, especially outside of clinical settings. This includes capturing meaningful aspects of health in daily living and in a more granular and objective manner compared to traditional tools in clinical settings. Within multidisciplinary teams, statisticians and data scientists are increasingly involved in clinical trials that incorporate digital clinical measures and are called upon to provide input into study planning, generation of evidence on the clinical validity of novel clinical measures, and evaluation of the adequacy of existing evidence. Analysis objectives related to demonstrating clinical validity of novel clinical measures differ from typical objectives related to demonstrating safety and efficacy of therapeutic interventions using established measures which statisticians are most familiar with. This paper discusses statistical considerations for generating evidence for clinical validity through the lens of the type and intended use of a clinical measure. This paper also briefly discusses the regulatory pathways through which clinical validity evidence may be reviewed and highlights challenges that statisticians and data scientists may encounter while dealing with data from biometric monitoring technologies.