14 August 2018
What is the optimal approach for using digital cognitive biomarkers in clinical trials?
Digital cognitive biomarkers are typically active or passive: but which approach enhances clinical trial success? Here we will define how active and passive cognitive biomarkers function in clinical trials, to conclude their optimal application.
In our last post, we defined what digital cognitive biomarkers are, and touched upon its potential in developing personalised psychiatric treatments. Here we will delve further into the different types (active and passive) of digital cognitive biomarker, and conclude how they can be best used to optimize clinical trials
What is an active cognitive biomarker?
Active cognitive biomarkers are those which require participants to specifically engage in a task targeting different domains of cognitive function, such as the CANTAB battery of tasks which have been shown to be more engaging and better tolerated than traditional neuropsychological assessments.
In contrast, passive cognitive biomarkers are data collected from multiple sources (behind the scenes) from regular activities that are non-specific to regular cognitive assessment – such as prosody of speech, smartphone usage or activity levels – to gain insight into cognitive function 1.
One of the most established cognitive biomarkers is the CANTAB Paired Associates (PAL) task of visual episodic memory that has been used in more than 30,000 patients in the NHS for memory impairment screening and is regularly used to enrich participant recruitment samples in Alzheimer’s disease trials.
More recently, we’ve been working on clinical trials to bring these active cognitive biomarkers onto participants’ own smartphone and wearable devices as a method of monitoring cognitive function in real-world environments to enhance patient compliance – even in hard-to-reach groups such as major depressive disorder (MDD).
The ‘bring your own device’ (BYOD) method maximizes data validity by presenting active assessments on devices that consumers are already using and will respond to notifications on.
An example of an active digital cognitive biomarker is our short 30 second 2-Back variant of the N-Back paradigm, which we implemented on an Apple Watch in conjunction with Takeda and a major depressive population. This project ascertained participants’ cognitive and mood function multiple times per day, longitudinally, with adherence rates in excess of 95%.
What is a passive cognitive biomarker?
Passive biomarkers are a hot topic at the moment with the rise of digital phenotyping and the prospect of being able to monitor a person’s health status non-invasively without the participant having to engage in specific tasks. To facilitate these passive biomarkers, large datasets are required with extensive validation in healthy and clinical populations.
Passive data sources can be combined to create complex phenotypic signatures which relate to a person’s disease status. These can be enhanced by coupling passive data sources with active cognitive assessments to determine relationships with health.
Active cognitive biomarkers serve as both the immediate gold-standard endpoints for cognitive function, but also as the benchmark for training and validating novel passive measurements against.
Figure 1. Overview of the validation process for novel passive biomarkers using gold standard active cognitive biomarkers.
The optimal cognitive biomarker approach combines active and passive
Through studying the relationships between passive behavioral data such as heart rate, fitness activity and smartphone usage with the active tasks of cognitive assessment we can better understand which passive sources are the best candidates for biomarkers moving forward.
The combination of active and passive data sources also provides greater granularity into the current state of an individual. For example, data highlighting an improvement in cognitive function over the course of a treatment regimen may demonstrate pro-cognitive effects. However, these pro-cognitive effects may largely be driven by positive drug effects on lifestyle aspects such as the quality and duration of sleep.
Aggregating both active and passive cognitive sources together yields a better understanding of drug characterization, thus improving the signal-to-noise ratio when evaluating trial outcomes.
The integration of multiple data sources is the bedrock of digital phenotyping. Below is an illustration of the combination of data sources which hold valuable insight into a person’s cognitive function, and wider mental and physical wellbeing.
Figure 2. Overview of the active and passive data sources captured and integrated when creating a phenotypic signature of an individual or cohort.
Digital cognitive biomarkers have disrupted traditional neuroscience to enable personalized clinical trials
Psychiatric and neurological disorders are highly heterogeneous and complex, with each patient potentially presenting with a different phenotypic signature; making precision medicine an important new avenue for the field. Digital cognitive biomarkers offer a unique opportunity to employ precision psychiatry and revolutionize clinical trials: but are active, passive, or combined approaches likely to yield the best results?
Cognitive tests are scientifically-validated, objective and sensitive measures for monitoring the progression of many CNS disorders, and are beginning to be recognized as robust biomarkers for clinical trial label claims 2.
Coupling active cognitive tests with novel passive data from consumer devices such as smartphones and wearables offers the opportunity to: (i) validate potentially useful passive methods and (ii) use interrelationships between the measures (passive and active) to better predict and monitor complex disease progression.
Want to discuss the optimal digital cognitive biomarker design for your next clinical trial?
- Dagum P. Digital biomarkers of cognitive function. npj Digit Med. 2018;1(1):10. doi:10.1038/s41746-018-0018-4.
- McIntyre RS, Harrison J, Loft H, Jacobson W, Olsen CK. The Effects of Vortioxetine on Cognitive Function in Patients with Major Depressive Disorder: A Meta-Analysis of Three Randomized Controlled Trials. Int J Neuropsychopharmacol. 2016;19(10). doi:10.1093/ijnp/pyw055.
Written by Matt Hobbs and Sally Jennings