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The increasing miniaturization and affordability of sensors and circuitry has led to the current level of innovation in the area of wearable and microsensor solutions for health monitoring. This facilitates the development of solutions that can be used to measure complex health outcomes in nonspecialist and remote settings. In this article, we review a number of innovations related to brain monitoring including portable and wearable solutions to directly measure brain electrical activity, and solutions measuring aspects related to brain function such as sleep patterns, gait, cognition, voice acoustics, and gaze analysis. Despite the need for more scientific validation work, we conclude that there is enough understanding of how to implement these approaches as exploratory tools that may provide additional valuable insights due to the rich and frequent data they produce, to justify their inclusion in clinical study protocols.

The tetracyclic ergoline derivative cabergoline was investigated in three studies to test its efficacy in treating the motor symptoms of Parkinson’s disease. In two studies, it was used as an add-on agent to the previous medication regimen that included other parkinsonian drugs, including levodopa. In the third study, cabergoline was switched from another dopamine agonist. All studies proved this drug’s effectiveness in treating such motor symptoms as akinesia, dyskinesia, and nocturnal akinesia. Quality of life and disability in activities of daily living assessments were measured by PDQ 39 or UPDRS VI. Treatment with cabergoline showed higher efficacy and greater safety than other parkinsonian drugs.

The report explores the potential digital technology has to generate novel endpoints and digital biomarkers for Alzheimer’s disease drug development studies. Drawing from literature and novel pilots, we explore the value of innovative digital technology to digitize physiological behaviours such as sleep disturbance and gait changes. Technology now exists to monitor and quantify our use and interaction with electronics in the home, the use of social platforms and smart-phones, geolocation, sleep and activity patterns. These multimodal digital data are a feasible alternative to capturing the more complex activities of daily living that require higher cognitive processes and are a sensitive predictor of disease. The combination of biosensors and the internet of things (IoT), offers the potential to collect highly relevant, objective data in a continuous, passive and low burden manner. Digital endpoints and biomarkers could have value in the diagnosis, monitoring and development of therapies for patients living with Alzheimer’s disease.

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