As digital health technology becomes more pervasive, machine learning (ML) provides a robust way to analyze and interpret the myriad of collected features. The purpose of this preliminary work was to use ML classification to assess the benefits and relevance of neurocognitive features both tablet-based assessments and self-reported metrics, as they relate to Parkinson’s Disease (PD) and its stages [Hoehn and Yahr (H&Y) Stages 1–5]. Further, this work aims to compare perceived versus sensor-based neurocognitive abilities. In this study, 75 participants ( $$n = 50$$n = 50 PD; $$n = 25$$n = 25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multifunction), functional movement assessments (e.g., Berg Balance Scale), and standardized health questionnaires (e.g., PDQ-39). Decision tree classification of sensor-based features allowed for the discrimination of PD from healthy controls with an accuracy of $$92.6\%$$92.6 % , and early and advanced stages of PD with an accuracy of $$73.7\%$$73.7 % ; compared to the current gold standard tools [e.g., standardized health questionnaires ( $$78.3\%$$78.3 % accuracy) and functional movement assessments ( $$70\%$$70 % accuracy)]. Significant features were also identified using decision tree classification. Device magnitude of acceleration was significant in 12 of 14 tests ( $$85.7\%$$85.7 % ), regardless of test type. For classification between diagnosed and control populations, 17 motor (e.g., device magnitude of acceleration), 9 accuracy (e.g., number of correct/incorrect interactions), and 8 timing features (e.g., time to between interactions) were significant. For classification between early (H&Y Stages 1 and 2) and advanced (H&Y Stages 3, 4, and 5) stages of PD, 7 motor, 12 accuracy, and 14 timing features were significant. Finally, this work depicts that perceived functionality of individuals with PD differed from sensor-based functionalities. In early-stage PD was shown to be $$21.6\%$$21.6 % lower than sensor-based scores with notable perceived deficits in memory and executive function. However, individuals in advanced stages had elevated perceptions (1.57x) for executive and behavioral functions compared to early-stage populations. Machine learning in digital health systems allows for a more comprehensive understanding of neurodegenerative diseases and their stages and may also depict new features that influence the ways digital health technology should be configured.

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.

Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech. Deficits in any of these systems can cause changes in speech signal patterns. Increasing efforts are being made to develop speech-based clinical decision support systems. Objective: This systematic scoping review investigated the technological revolution and recent digital clinical speech signal analysis trends to understand the key concepts and research processes from clinical and technical perspectives. Methods: A systematic scoping review was undertaken in 6 databases guided by a set of research questions. Articles that focused on speech signal analysis for clinical decision-making were identified, and the included studies were analyzed quantitatively. A narrower scope of studies investigating neurological diseases were analyzed using qualitative content analysis. Results: A total of 389 articles met the initial eligibility criteria, of which 72 (18.5%) that focused on neurological diseases were included in the qualitative analysis. In the included studies, Parkinson disease, Alzheimer disease, and cognitive disorders were the most frequently investigated conditions. The literature explored the potential of speech feature analysis in diagnosis, differentiating between, assessing the severity and monitoring the treatment of neurological conditions. The common speech tasks used were sustained phonations, diadochokinetic tasks, reading tasks, activity-based tasks, picture descriptions, and prompted speech tasks. From these tasks, conventional speech features (such as fundamental frequency, jitter, and shimmer), advanced digital signal processing–based speech features (such as wavelet transformation–based features), and spectrograms in the form of audio images were analyzed. Traditional machine learning and deep learning approaches were used to build predictive models, whereas statistical analysis assessed variable relationships and reliability of speech features. Model evaluations primarily focused on analytical validations. A significant research gap was identified: the need for a structured research process to guide studies toward potential technological intervention in clinical settings. To address this, a research framework was proposed that adapts a design science research methodology to guide research studies systematically. Conclusions: The findings highlight how data science techniques can enhance speech signal analysis to support clinical decision-making. By combining knowledge from clinical practice, speech science, and data science within a structured research framework, future research may achieve greater clinical relevance.

Due to the growing capabilities of mobile phones and devices, mobile crowd sensing (MCS) is rapidly gaining popularity among researchers in different fields, given its ability to collect data at scale and low cost. MCS is particularly important in the healthcare domain since it provides opportunities to collect health, wellness, and Quality of Life information from a large and diverse population. For example, MCS can be used to detect early signs of emerging health conditions, track the spread of infectious diseases, and assess the effectiveness of interventions, without the need for frequent clinical visits. Consequently, MCS can also reduce healthcare costs and help overcome barriers to healthcare access. This article takes a closer look at MCS systems that have been used to collect data for research in the medical and healthcare domains. We provide a thorough analysis of selected systems based on their different health-related objectives, such as monitoring physical activity, detecting and preventing disorders, and providing medical treatment. We also adopt a three-layered architecture to structure health-centric MCS frameworks, consisting of application, data, and sensing layers. In the application layer, we analyze participant recruitment, incentive mechanisms, and task allocation strategies. In the data layer, we analyze the types of data collected and how they are stored and processed for future use. The sensing layer specifies the sensing methods and explains the fundamental requirements at a lower level. Additionally, we explore the significant challenges faced by existing MCS systems and domains that offer promising avenues for future research, which are user privacy, resource utilization, data quality, and user compliance. This work provides insights into some practical applications of MCS, highlights challenges faced by existing MCS solutions, and how they can be addressed, all of which can help catalyze future research in MCS development.

Due to the complicated nature of Parkinson disease (PD), a number of subjective considerations (eg, staging schemes, clinical assessment tools, or questionnaires) on how best to assess clinical deficits and monitor clinical progression have been published; however, none of these considerations include a comprehensive, objective assessment of all functional areas of neurocognition affected by PD (eg, motor, memory, speech, language, executive function, autonomic function, sensory function, behavior, and sleep). This paper highlights the increasing use of digital health technology (eg, smartphones, tablets, and wearable devices) for the classification, staging, and monitoring of PD. Furthermore, this Viewpoint proposes a foundation for a new staging schema that builds from multiple clinically implemented scales (eg, Hoehn and Yahr Scale and Berg Balance Scale) for ease and homogeneity, while also implementing digital health technology to expand current staging protocols. This proposed staging system foundation aims to provide an objective, symptom-specific assessment of all functional areas of neurocognition via inherent device capabilities (eg, device sensors and human-device interactions). As individuals with PD may manifest different symptoms at different times across the spectrum of neurocognition, the modernization of assessments that include objective, symptom-specific monitoring is imperative for providing personalized medicine and maintaining individual quality of life.