Research surrounding brain health and neurological disease is increasing rapidly, yet many research studies are still utilizing old-school pencil and paper legacy tests or digitized versions of such assessments. Two major areas of research requiring robust methods for measuring and monitoring brain health include:
Many of these studies require highly granular data, diverse data streams, and/or large quantities of data to confirm hypotheses or detect new traits or patterns associated with a particular neurological disease. Meeting these intensive requirements for data acquisition is particularly challenging when utilizing traditional neurocognitive assessment tools to quantify brain health for research advancements. Research shows that portable technologies for monitoring brain health—such as sensors, wearables, and mobile devices—may provide new and valuable insights to research studies and clinical trials due to the rich, large quantities of data that can be collected.
Below, we detail the limitations of traditional neurocognitive assessment tools for research applications and explore requirements for quantifying brain health for research advancements within each of the two research categories listed above.
Conventional neurocognitive assessment tools, such as the Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MoCA), and Mini-Cog, are commonly used for neurocognitive function screening and are often used for research purposes. While these assessments are good indicators of overall cognitive function, the very nature of these assessments poses challenges to research and clinical trial applications.
Limitations include the following:
Many studies and clinical trials have moved away from pencil and paper tests and towards digitized versions, such as BrainCheck, Savonix, Cogstate, and Cantab Mobile. While digitized versions provide a means for remote brain health monitoring, they retain many of the above limitations.
Every research study and clinical trial will have unique requirements for measuring and analyzing brain health. However, there are common characteristics to look for in a brain health assessment tool for specific research areas, such as those assessing brain health as a dependent variable and those tracking brain health within a population to inform drug development.
When performing studies that analyze brain health as a dependent variable while altering independent variables, such as sleep, diet, and exercise, tools that produce extremely granular data are crucial. For such studies, tools must be sensitive to intra-individual changes in brain health, as these small changes in neurocognitive function may be indicative of the efficacy of the given independent variable. As these types of studies typically take place over a significant period of time, they may require a brain health assessment tool that allows subjects to be monitored remotely. Finally, it is important to produce easily reportable data that researchers can view, digest, and report on in an organized and systematic manner.
Research studies that aim to identify new traits, patterns, correlations, and biomarkers associated with a particular neurological disease or disorder to inform treatment options and drug development have unique challenges. Data granularity is key, but so is data quantity. Such areas of research require diverse data streams that assess a wide range of cognitive and functional abilities across many neurocognitive domains.
Altoida’s mission is to accelerate and improve drug development, neurological disease research, and patient care. To learn more about our precision-neurology platform and app-based medical device, contact us!