Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.

Machine Learning and Digital Biomarkers Can Detect Early Stages of Neurodegenerative Diseases

Neurodegenerative diseases (NDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) are devastating conditions that can develop without noticeable symptoms, causing irreversible damage to neurons before any signs become clinically evident. NDs are a major cause of disability and mortality worldwide. Currently, there are no cures or treatments to halt their progression. Therefore, the development of early detection methods is urgently needed to delay neuronal loss as soon as possible. Despite advancements in Medtech, the early diagnosis of NDs remains a challenge at the intersection of medical, IT, and regulatory fields. Thus, this review explores "digital biomarkers" (tools designed for remote neurocognitive data collection and AI analysis) as a potential solution. The review summarizes that recent studies combining AI with digital biomarkers suggest the possibility of identifying pre-symptomatic indicators of NDs. For instance, research utilizing convolutional neural networks for eye tracking has achieved significant diagnostic accuracies. ROC-AUC scores reached up to 0.88, indicating high model performance in differentiating between PD patients and healthy controls. Similarly, advancements in facial expression analysis through tools have demonstrated significant potential in detecting emotional changes in ND patients, with some models reaching an accuracy of 0.89 and a precision of 0.85. This review follows a structured approach to article selection, starting with a comprehensive database search and culminating in a rigorous quality assessment and meaning for NDs of the different methods. The process is visualized in 10 tables with 54 parameters describing different approaches and their consequences for understanding various mechanisms in ND changes. However, these methods also face challenges related to data accuracy and privacy concerns. To address these issues, this review proposes strategies that emphasize the need for rigorous validation and rapid integration into clinical practice. Such integration could transform ND diagnostics, making early detection tools more cost-effective and globally accessible. In conclusion, this review underscores the urgent need to incorporate validated digital health tools into mainstream medical practice. This integration could indicate a new era in the early diagnosis of neurodegenerative diseases, potentially altering the trajectory of these conditions for millions worldwide. Thus, by highlighting specific and statistically significant findings, this review demonstrates the current progress in this field and the potential impact of these advancements on the global management of NDs.

Diagnosing homo digitalis: towards a standardized assessment for digital tool competencies

Introduction

In the 21st century, digital devices have become integral to our daily lives. Still, practical assessments designed to evaluate an individual's digital tool competencies are absent. The present study introduces the "Digital Tools Test" ("DIGI"), specifically designed for the evaluation of one's proficiency in handling common applications and functions of smartphones and tablets. The DIGI assessment has been primarily tailored for prospective use among older adults and neurological patients with the latter frequently suffering from so-called apraxia, which potentially also affects the handling of digital tools. Similar to traditional tool use tests that assess tool-selection and tool-action processes, the DIGI assessment evaluates an individual's ability to select an appropriate application for a given task (e.g., creating a new contact), their capacity to navigate within the chosen application and their competence in executing precise and accurate movements, such as swiping.

Methods

We tested the implementation of the DIGI in a group of 16 healthy adults aged 18 to 28 years and 16 healthy adults aged 60 to 74 years. All participants were able to withstand the assessment and reported good acceptance.

Results

The results revealed a significant performance disparity, with older adults displaying notably lower proficiency in the DIGI. The DIGI performance of older adults exhibited a correlation with their ability to employ a set of novel mechanical tools, but not with their ability to handle a set of familiar common tools. There was no such correlation for the younger group.

Conclusion

In conclusion, this study introduces an innovative assessment tool aimed at evaluating common digital tool competencies. Our preliminary results demonstrate good acceptance and reveal expected group differences. For current cohorts of older adults, the results seem to indicate that the ability to use novel tools may aid digital tool use. In the next step, the psychometric properties of the DIGI assessment should be evaluated in larger and more diverse samples. The advancement of digital tool competency assessments and rehabilitation strategies is essential when we aim at facilitating societal inclusion and participation for individuals in affected populations.

Digital Health Technologies for Alzheimer's Disease and Related Dementias: Initial Results from a Landscape Analysis and Community Collaborative Effort

Digital health technologies offer valuable advantages to dementia researchers and clinicians as screening tools, diagnostic aids, and monitoring instruments. To support the use and advancement of these resources, a comprehensive overview of the current technological landscape is essential. A multi-stakeholder working group, convened by the Digital Medicine Society (DiMe), conducted a landscape review to identify digital health technologies for Alzheimer's disease and related dementia populations. We searched studies indexed in PubMed, Embase, and APA PsycInfo to identify manuscripts published between May 2003 to May 2023 reporting analytical validation, clinical validation, or usability/feasibility results for relevant digital health technologies. Additional technologies were identified through community outreach. We collated peer-reviewed manuscripts, poster presentations, or regulatory documents for 106 different technologies for Alzheimer's disease and related dementia assessment covering diverse populations such as Lewy Body, vascular dementias, frontotemporal dementias, and all severities of Alzheimer's disease. Wearable sensors represent 32% of included technologies, non-wearables 61%, and technologies with components of both account for the remaining 7%. Neurocognition is the most prevalent concept of interest, followed by physical activity and sleep. Clinical validation is reported in 69% of evidence, analytical validation in 34%, and usability/feasibility in 20% (not mutually exclusive). These findings provide clinicians and researchers a landscape overview describing the range of technologies for assessing Alzheimer's disease and related dementias. A living library of technologies is presented for the clinical and research communities which will keep findings up-to-date as the field develops.