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INTRODUCTION Alzheimer’s Disease (AD) represents a major and rapidly growing burden to the healthcare ecosystem. In the USA alone, some 5 million people suffer from the disease that costs the managed healthcare system in excess of $250 billion. Currently the sixth leading cause of death, AD prevalence has increased by 89% since 2000, underscoring the need for interventive and preventative measures. Despite enormous capital investments, drug development has been problematic. It is generally accepted that the likelihood of reversing anatomic and physiologic changes (e.g., neuronal death) decreases dramatically as the disease advances, placing increased attention on early cohort discovery and patient stratification for any future clinical studies. Accordingly, there is an acute need to detect the disease at prodromal stages. In this quest for monitoring biomarkers for AD assessment, there is growing interest in the identification of readily accessible digital biomarkers, which leverage widely available mobile and wearable technologies, and it is these that are the subject of this review article.

A growing body of evidence indicates that cognitive, sensory and motor changes may precede clinical manifestations of AD by several years.1 In particular, sensory and motor (non-cognitive) changes can help detect a neurological or neurodegenerative disease 10 or 15 years prior their effective diagnosis. This said, existing validated neuropsychological/cognitive tests designed to diagnose neurodegenerative diseases are often less effective in detecting deviations from normal cognitive trajectory in the earliest stages of the disease. Furthermore, cognitive tests can suffer from intrinsic cultural bias, take a relatively long time to administer, provide only episodic information, show “practice effect” or “ceiling effect,” and are rater dependent.2 Explorations into the inclusion of genetic testing, structural MRI imaging and PET molecular imaging of beta-amyloid and tau protein promise earlier detection of disease, though these tests are currently limited to research applications due to their cost and invasive nature. These limitations preclude repeated and frequent use to test an individual and specifically in the early pre-symptomatic stage. Mobile and wearable digital consumer technology has the potential to overcome these limitations, and their application in AD detection has become an area of increased interest.

 

This review article considers disease-relevant aspects of sensor and device design, data collection modalities, and a path to clinical grade digital phenotyping. A non-exhaustive summary of available sensors or digital senses on each wearable/mobile device is presented in Fig. 1.

 

DISCUSSION Given the staggering current and escalating projected costs for providing care to AD patients, consumer grade technologies able to detect and monitor, once diagnosed, AD progression represent an urgent need. When developed to full potential, one can envision digital phenotyping of AD becoming a digitally embedded routine practice, triggering a series of interventional measures. One of the main questions that emerges with such forecasting systems is what to do when a signal is detected. While the debate on the preferred course of action is still on and it involves among others, regulatory, ethical, legal, data privacy and clinical considerations, some options involve: (1) Notifying the user that there is something out of the normal with his/her longitudinal rate of progression of neurological health, so he/she can seek further clinical assessment. (2) Providing longitudinal disease-related digital biomarkers to a healthcare practitioner, to allow for objective and continuous clinical evaluation of a user.

In parallel, such technologies can be used to establish objective, personalized baseline reference standards to design innovative clinical trials that assess the effectiveness of onset delaying or disease modifying treatment, once available. An overwhelming amount of work lies ahead before we can claim forecasting and detection of Alzheimer’s disease especially in the preclinical phase, using consumer grade devices, passive data monitoring and analytics. It will require longitudinal, very large population observational studies, to account for inter and intra subject variability. It will also require new ways of securely managing and processing this vast amount of information. Underscoring the potential for such consumer digital devices to impact healthcare, the FDA recently issued guidelines98 to provide a clear path and encourage technology developers in their quest for efficient digital phenotyping.

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