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  1. null (Ed.)
    Digital health technology is becoming more ubiquitous in monitoring individuals’ health as both device functionality and overall prevalence increase. However, as individuals age, challenges arise with using this technology particularly when it involves neurodegenerative issues (e.g., for individuals with Parkinson’s disease, Alzheimer’s disease, and ALS). Traditionally, neurodegenerative diseases have been assessed in clinical settings using pen-and-paper style assessments; however, digital health systems allow for the collection of far more data than we ever could achieve using traditional methods. The objective of this work is the formation and implementation of a neurocognitive digital health system designed to go beyond what pen-and-paper based solutions can do through the collection of (a) objective, (b) longitudinal, and (c) symptom-specific data, for use in (d) personalized intervention protocols. This system supports the monitoring of all neurocognitive functions (e.g., motor, memory, speech, executive function, sensory, language, behavioral and psychological function, sleep, and autonomic function), while also providing methodologies for personalized intervention protocols. The use of specifically designed tablet-based assessments and wearable devices allows for the collection of objective digital biomarkers that aid in accurate diagnosis and longitudinal monitoring, while patient reported outcomes (e.g., by the diagnosed individual and caregivers) give additional insights for use in the formation of personalized interventions. As many interventions are a one-size-fits-all concept, digital health systems should be used to provide a far more comprehensive understanding of neurodegenerative conditions, to objectively evaluate patients, and form personalized intervention protocols to create a higher quality of life for individuals diagnosed with neurodegenerative diseases. 
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  2. null (Ed.)
    Mobile devices contain an increasing number of sensors, many of which can be used for disease diagnosis and monitoring. Thus along with the ease of access and use of mobile devices there is a trend towards developing neurological tests onto mobile devices. Speech-based approaches have shown particular promise in detection of neurological conditions. However, designing such tools carries a number of challenges, such as how to manage noise, delivering the instructions for the speech based tasks, handling user error, and how to adapt the design to be accessible to specific populations with Parkinson’s Disease and Amyotrophic Lateral Sclerosis. This report discusses our experiences in the design of a mobile-based application that assesses and monitors disease progression using speech changes as a biomarker. 
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  3. null (Ed.)
    Mobile devices are becoming more pervasive in the monitoring of individuals’ health as device functionalities increase as does overall device prevalence in daily life. Therefore, it is necessary that these devices and their interactions are usable by individuals with diverse abilities and conditions. This paper assesses the usability of a neurocognitive assessment application by individuals with Parkinson’s Disease (PD) and proposes a design that focuses on the user interface, specifically on testing instructions, layouts, and subsequent user interactions. Further, we investigate potential benefits of cognitive interference (e.g., the addition of outside stimuli that intrude on task-related activity) on a user’s task performance. Understanding the population’s usability requirements and their performance on configured tasks allows for the formation of usable and objective neurocognitive assessments. 
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  4. Background Comprehensive exams such as the Dean-Woodcock Neuropsychological Assessment System, the Global Deterioration Scale, and the Boston Diagnostic Aphasia Examination are the gold standard for doctors and clinicians in the preliminary assessment and monitoring of neurocognitive function in conditions such as neurodegenerative diseases and acquired brain injuries (ABIs). In recent years, there has been an increased focus on implementing these exams on mobile devices to benefit from their configurable built-in sensors, in addition to scoring, interpretation, and storage capabilities. As smartphones become more accepted in health care among both users and clinicians, the ability to use device information (eg, device position, screen interactions, and app usage) for subject monitoring also increases. Sensor-based assessments (eg, functional gait using a mobile device’s accelerometer and/or gyroscope or collection of speech samples using recordings from the device’s microphone) include the potential for enhanced information for diagnoses of neurological conditions; mapping the development of these conditions over time; and monitoring efficient, evidence-based rehabilitation programs. Objective This paper provides an overview of neurocognitive conditions and relevant functions of interest, analysis of recent results using smartphone and/or tablet built-in sensor information for the assessment of these different neurocognitive conditions, and how human-device interactions and the assessment and monitoring of these neurocognitive functions can be enhanced for both the patient and health care provider. Methods This survey presents a review of current mobile technological capabilities to enhance the assessment of various neurocognitive conditions, including both neurodegenerative diseases and ABIs. It explores how device features can be configured for assessments as well as the enhanced capability and data monitoring that will arise due to the addition of these features. It also recognizes the challenges that will be apparent with the transfer of these current assessments to mobile devices. Results Built-in sensor information on mobile devices is found to provide information that can enhance neurocognitive assessment and monitoring across all functional categories. Configurations of positional sensors (eg, accelerometer, gyroscope, and GPS), media sensors (eg, microphone and camera), inherent sensors (eg, device timer), and participatory user-device interactions (eg, screen interactions, metadata input, app usage, and device lock and unlock) are all helpful for assessing these functions for the purposes of training, monitoring, diagnosis, or rehabilitation. Conclusions This survey discusses some of the many opportunities and challenges of implementing configured built-in sensors on mobile devices to enhance assessments and monitoring of neurocognitive functions as well as disease progression across neurodegenerative and acquired neurological conditions. 
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