Smartphone-Based Neuropsychological Assessment in Parkinson's Disease: Feasibility, Validity, and Contextually Driven Variability in Cognition

Integration of sex/gender and utilization of ecological Momentary assessment of cognition in clinical populations: A scoping review

Objectives: We aimed to describe the methods of smartphone-based cognitive ecological momentary assessment designs in clinical populations, with an intention to evaluate how the role of sex and/or gender has been considered in the design and analyses, particularly including female-specific physiology. Methods: This scoping review was conducted based on JBI scoping review methodology. On March 2nd, 2023, we searched for literature across four databases. Screening of the results and data extraction were conducted in duplicate according to the a priori methods in the pre-registered protocol. Results: 31 articles were included in this review. Participants ranged in age from 15-85 years old with various clinical disorders. Prompts were given between 1-7 times per day for 7-84 days. Executive function was the most frequently assessed cognitive domain. Over half the studies (n = 17, 55%) did not investigate the effects of sex and/or gender, and only one study considered the impact of hormonal therapy. Many studies (n = 14, 45%) used sex and gender interchangeably or incorrectly. Conclusions: Studies varied in design, with heterogeneity in the reporting of methodological information. The lack of attention to sex/gender on neuropsychological outcomes can lead to confusion and contradiction regarding its potential impact on cognition in clinical populations. This may hinder the identification of effective interventions for those assigned female at birth who have been overlooked or considered indistinguishable from their male counterparts. Given the well-documented impact of sex/gender on cognition, it is essential that future neuropsychological research, especially EMA-based studies, prioritize investigating sex/gender to ensure better outcomes for all.

Mobile Application Applied for Cognitive Rehabilitation: A Systematic Review

The COVID-19 pandemic has increased the demand for home rehabilitation services, leading to the development and rising demand for cognitive rehabilitation apps. However, a comprehensive analysis of the content and effectiveness of these apps is needed. This study systematically reviewed and analyzed the literature on mobile apps for cognitive rehabilitation. One researcher and an external expert conducted the study selection, data extraction, and quality assessment following the PRISMA Checklist 2020 guidelines. Our review of 18 studies identified 8 randomized controlled trials (RCTs) (44.44%) of high quality and 10 non-RCT studies (55.56%) of moderate quality. Overall, 16 of the 18 studies (88.88%) demonstrated positive clinical implications for cognitive function after using cognitive rehabilitation apps. Eight studies showed a correlation between app use and improved cognitive function, and four highlighted the potential for cognitive function evaluation through apps. All studies employed various occupational therapy (OT) intervention approaches, with prevention being the most common (100%), followed by establishment and restoration (83.33%), maintenance (77.77%), and creation and promotion (38.88%). This study clinically validates the effectiveness of cognitive rehabilitation mobile applications from an occupational therapy perspective.

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.

Online cognitive testing in Parkinson's disease: advantages and challenges

Parkinson's disease (PD) is primarily characterized by motor symptoms. Yet, many people with PD experience cognitive decline, which is often unnoticed by clinicians, although it may have a significant impact on quality of life. For over half a century, traditional in-person PD cognitive assessment lacked accessibility, scalability, and specificity due to its inherent limitations. In this review, we propose that novel methods of online cognitive assessment could potentially address these limitations. We first outline the challenges of traditional in-person cognitive testing in PD. We then summarize the existing literature on online cognitive testing in PD. Finally, we explore the advantages, but also the limitations, of three major processes involved in online PD cognitive testing: recruitment and sampling methods, measurement and participation, and disease monitoring and management. Taking the limitations into account, we aim to highlight the potential of online cognitive testing as a more accessible and efficient approach to cognitive testing in PD.

Capturing learning curves with the multiday Boston Remote Assessment of Neurocognitive Health (BRANCH): Feasibility, reliability, and validity

OBJECTIVE

Unsupervised remote digital cognitive assessment makes frequent testing feasible and allows for measurement of learning over repeated evaluations on participants' own devices. This provides the opportunity to derive individual multiday learning curve scores over short intervals. Here, we report feasibility, reliability, and validity, of a 7-day cognitive battery from the Boston Remote Assessment for Neurocognitive Health (Multiday BRANCH), an unsupervised web-based assessment.

METHOD

Multiday BRANCH was administered remotely to 181 cognitively unimpaired older adults using their own electronic devices. For 7 consecutive days, participants completed three tests with associative memory components (Face-Name, Groceries-Prices, Digit Signs), using the same stimuli, to capture multiday learning curves for each test. We assessed the feasibility of capturing learning curves across the 7 days. Additionally, we examined the reliability and associations of learning curves with demographics, and traditional cognitive and subjective report measures.

RESULTS

Multiday BRANCH was feasible with 96% of participants completing all study assessments; there were no differences dependent on type of device used (t = 0.71, p = .48) or time of day completed (t = -0.08, p = .94). Psychometric properties of the learning curves were sound including good test-retest reliability of individuals' curves (intraclass correlation = 0.94). Learning curves were positively correlated with in-person cognitive tests and subjective report of cognitive complaints.

CONCLUSIONS

Multiday BRANCH is a feasible, reliable, and valid cognitive measure that may be useful for identifying subtle changes in learning and memory processes in older adults. In the future, we will determine whether Multiday BRANCH is predictive of the presence of preclinical Alzheimer's disease. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Improving naturalistic neuroscience with patient engagement strategies

Introduction

The clinical implementation of chronic electrophysiology-driven adaptive deep brain stimulation (DBS) algorithms in movement disorders requires reliable representation of motor and non-motor symptoms in electrophysiological biomarkers, throughout normal life (naturalistic). To achieve this, there is the need for high-resolution and -quality chronic objective and subjective symptom monitoring in parallel to biomarker recordings. To realize these recordings, an active participation and engagement of the investigated patients is necessary. To date, there has been little research into patient engagement strategies for DBS patients or chronic electrophysiological recordings.

Concepts and results

We here present our concept and the first results of a patient engagement strategy for a chronic DBS study. After discussing the current state of literature, we present objectives, methodology and consequences of the patient engagement regarding study design, data acquisition, and study infrastructure. Nine patients with Parkinson's disease and their caregivers participated in the meeting, and their input led to changes to our study design. Especially, the patient input helped us designing study-set-up meetings and support structures.

Conclusion

We believe that patient engagement increases compliance and study motivation through scientific empowerment of patients. While considering patient opinion on sensors or questionnaire questions may lead to more precise and reliable data acquisition, there was also a high demand for study support and engagement structures. Hence, we recommend the implementation of patient engagement in planning of chronic studies with complex designs, long recording durations or high demand for individual active study participation.

Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward

Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.

Side of motor symptom onset predicts sustained attention deficits and motor improvements after attention training in Parkinson's disease

OBJECTIVE

Parkinson's disease (PD) side of motor symptom onset has been associated with distinct cognitive deficits; individuals with left-side onset (LPD) show more visuospatial impairments, whereas those with right-side onset (RPD) show more verbal impairments. Non-spatial attention is a critical cognitive ability associated with motor functioning that is right hemisphere lateralized but has not been characterized with regard to PD side of onset. We compared individuals with LPD and RPD on non-spatial attention tasks and examined differential responses to a 4-week sustained attention training program.

METHOD

Participants included 9 with LPD and 12 with RPD, who performed both brief and extended go/no-go continuous performance tasks and an attentional blink task. Participants also engaged in an at-home sustained attention training program, Tonic and Phasic Alertness Training (TAPAT), 5 days/week for 4 weeks. We assessed cognitive and motor symptoms before and after training, and after a 4-week no-contact period.

RESULTS

At baseline, participants with LPD exhibited worse performance than those with RPD on the extended continuous performance task, indicating specific deficits in sustaining attention. Poorer attention was associated with worse clinical motor scores. Notably, side of onset had a significant effect on clinical motor changes after sustained attention training, with only LPD participants improving after training, and 4/9 showing clinically meaningful improvements.

CONCLUSIONS

Compared to RPD, participants with LPD had poorer sustained attention pre-training and were more likely to improve on clinical motor functioning after sustained attention training. These findings support mechanistic differences between LPD and RPD and suggest potential differential treatment approaches.

Web-based cognitive assessment in older adults: Where do we stand?

PURPOSE OF REVIEW

The use of digital tools for remote cognitive measurement of older adults is generating increasing interest due to the numerous advantages offered for accessibility and scalability. However, these tools also pose distinctive challenges, necessitating a thorough analysis of their psychometric properties, feasibility and acceptability.

RECENT FINDINGS

In this narrative review, we present the recent literature on the use of web-based cognitive assessment to characterize cognition in older adults and to contribute to the diagnosis of age-related neurodegenerative diseases. We present and discuss three types of web-based cognitive assessments: conventional cognitive tests administered through videoconferencing; unsupervised web-based assessments conducted on a computer; and unsupervised web-based assessments performed on smartphones.

SUMMARY

There have been considerable progress documenting the properties, strengths and limitations of web-based cognitive assessments. For the three types of assessments reported here, the findings support their promising potential for older adults. However, certain aspects, such as the construct validity of these tools and the development of robust norms, remain less well documented. Nonetheless, the beneficial potential of these tools, and their current validation and feasibility data, justify their application [see Supplementary Digital Content (SDC), http://links.lww.com/CONR/A69 ].

A Review on Smartphone Keystroke Dynamics as a Digital Biomarker for Understanding Neurocognitive Functioning

Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Innovations in Neuropsychology: Future Applications in Neurosurgical Patient Care

Over the last century, collaboration between clinical neuropsychologists and neurosurgeons has advanced the state of the science in both disciplines. These advances have provided the field of neuropsychology with many opportunities for innovation in the care of patients prior to, during, and following neurosurgical intervention. Beyond giving a general overview of how present-day advances in technology are being applied in the practice of neuropsychology within a neurological surgery department, this article outlines new developments that are currently unfolding. Improvements in remote platform, computer interface, "real-time" analytics, mobile devices, and immersive virtual reality have the capacity to increase the customization, precision, and accessibility of neuropsychological services. In doing so, such innovations have the potential to improve outcomes and ameliorate health care disparities.

Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment

Introduction

Multiple sclerosis (MS) is characterized by a wide range of disabling symptoms, including cognitive dysfunction, fatigue, depression, anxiety, pain, and sleep difficulties. The current study aimed to examine real-time associations between non-cognitive and cognitive symptoms (latter measured both objectively and subjectively in real-time) using smartphone-administered ecological momentary assessment (EMA).

Methods

Forty-five persons with MS completed EMA four times per day for 3 weeks. For each EMA, participants completed mobile versions of the Trail-Making Test part B (mTMT-B) and a finger tapping task, as well as surveys about symptom severity. Multilevel models were conducted to account for within-person and within-day clustering.

Results

A total of 3,174 EMA sessions were collected; compliance rate was 84%. There was significant intra-day variability in mTMT-B performance (p < 0.001) and levels of self-reported fatigue (p < 0.001). When participants reported depressive symptoms that were worse than their usual levels, they also performed worse on the mTMT-B (p < 0.001), independent of upper extremity motor functioning. Other self-reported non-cognitive symptoms were not associated with real-time performance on the mTMT-B [p > 0.009 (Bonferroni-corrected)]. In contrast, when self-reported fatigue (p < 0.001), depression (p < 0.001), anxiety (p < 0.001), and pain (p < 0.001) were worse than the individual's typical levels, they also reported more severe cognitive dysfunction at the same time. Further, there was a statistical trend that self-reported cognitive dysfunction (not mTMT-B performance) predicted one's self-reported sense of accomplishment in real-time.

Discussion

The current study was the first to identify divergent factors that influence subjectively and objectively measured cognitive functioning in real time among persons with MS. Notably, it is when symptom severity was worse than the individual's usual levels (and not absolute levels) that led to cognitive fluctuations, which supports the use of EMA in MS symptom monitoring.

Exploring the Potential of Apple SensorKit and Digital Phenotyping Data as New Digital Biomarkers for Mental Health Research

The use of digital phenotyping continues to expand across all fields of health. By collecting quantitative data in real-time using devices such as smartphones or smartwatches, researchers and clinicians can develop a profile of a wide range of conditions. Smartphones contain sensors that collect data, such as GPS or accelerometer data, which can inform secondary metrics such as time spent at home, location entropy, or even sleep duration. These metrics, when used as digital biomarkers, are not only used to investigate the relationship between behavior and health symptoms but can also be used to support personalized and preventative care. Successful phenotyping requires consistent long-term collection of relevant and high-quality data. In this paper, we present the potential of newly available, for approved research, opt-in SensorKit sensors on iOS devices in improving the accuracy of digital phenotyping. We collected opt-in sensor data over 1 week from a single person with depression using the open-source mindLAMP app developed by the Division of Digital Psychiatry at Beth Israel Deaconess Medical Center. Five sensors from SensorKit were included. The names of the sensors, as listed in official documentation, include the following: phone usage, messages usage, visits, device usage, and ambient light. We compared data from these five new sensors from SensorKit to our current digital phenotyping data collection sensors to assess similarity and differences in both raw and processed data. We present sample data from all five of these new sensors. We also present sample data from current digital phenotyping sources and compare these data to SensorKit sensors when applicable. SensorKit offers great potential for health research. Many SensorKit sensors improve upon previously accessible features and produce data that appears clinically relevant. SensorKit sensors will likely play a substantial role in digital phenotyping. However, using these data requires advanced health app infrastructure and the ability to securely store high-frequency data.

Feasibility and validity of ecological momentary cognitive testing among older adults with mild cognitive impairment

It is critical to intervene early in the mild cognitive impairment (MCI) stage of the Alzheimer's disease trajectory, but traditional cognitive testing methods are costly, burdensome, and difficult to access. We examined adherence and validity data to a 30-day self-administered ecological momentary cognitive testing protocol among a sample of older adults with MCI and cognitively normal controls to evaluate feasibility, tolerability, and initial validity in comparison to standard neuropsychological tests. Participants included 48 participants with MCI (Mean age = 72 years, SD = 7 years) and 46 demographically-matched cognitively normal (NC) control participants (Mean age = 70 years, SD = 7 years). Participants completed traditional neuropsychological testing to determine MCI status, followed by 30 days of remote ecological momentary cognitive testing. Ecological momentary assessment (EMA) surveys were administered 3 times per day for 30 days (possible total = 90), and mobile cognitive tests were administered every other day (for a total of 15 administrations). Mobile cognitive tests included the Variable Difficulty List Memory Test (VLMT; measure of learning and memory), Memory Matrix (measure of visual working memory), and the Color Trick Test (measure of executive function). EMA and mobile cognitive test adherence, fatigue effects, mobile cognitive test performance and group differences, and psychometrics (reliability, convergent validity, ceiling effects, and practice effects) were examined. Overall mean-level adherence to the mobile cognitive tests was 85% and did not differ by MCI status. The reliability of stable between-person individual differences for the VLMT and Memory Matrix were very high. Moreover, although the reliability of within-person change for Memory Matrix was adequate, the corresponding reliability for VLMT was somewhat low. Averaged performance on the mobile cognitive tests was correlated with lab-based tests measuring the same construct. Participants with MCI performed worse than NCs on the VLMT and Color Trick Test, and there was no evidence of fatigue effects for these two tests. These findings support the feasibility and potential for ecological momentary cognitive testing to support clinical trials and for measuring cognitive changes over time in persons with increased risk for Alzheimer's disease such as those with MCI.

Applying machine learning to smartphone based cognitive and sleep assessments in schizophrenia

Background

Methods

Results

Discussion

Survey Findings About the Experiences, Challenges, and Practical Advice/Solutions Regarding Teleneuropsychological Assessment in Adults

OBJECTIVE

In the wake of the coronavirus pandemic, teleneuropsychology utilization has increased. There is a need to characterize the first-hand experiences of individuals using teleneuropsychology, identify the common teleneuropsychology challenges, and devise practical strategies for mitigating/resolving these challenges.

METHOD

Survey data were collected from U.S. based neuropsychologists and other individuals (e.g., graduate students and research assistants) who were engaged in remote cognitive assessment with adults (n = 87). Frequency analyses were conducted to examine: how respondents used teleneuropsychology (e.g., duration of use, types of measures and devices/technology platforms used); challenges that were encountered with different technology platforms and teleneuropsychology use; and advice for navigating these challenges.

RESULTS

Most respondents began using teleneuropsychology relatively recently in the context of the coronavirus pandemic, with home-to-home or clinic-to-home settings being the most frequently reported teleneuropsychology settings. Zoom®, Doxy.Me®, and MyChart® were the most frequently used platforms, largely due to workplace mandates and/or Health Insurance Portability and Accountability Act-compliant features. Common challenges with teleneuropsychology included internet connection issues and environmental distractions in examinees' homes, and examinees having limited-to-no access to technologies requisite for teleneuropsychology. Providing clear instructions to the examinee prior to the teleneuropsychology visit was the most common advice for ensuring a successful teleneuropsychology evaluation. Similar response patterns were generally noted for those who used TeleNP for clinical or research purposes.

CONCLUSIONS

These survey results reflect common experiences and challenges with teleneuropsychology and identify priority targets for increasing its feasibility, reliability, and validity. Findings provide context for the development of formal teleneuropsychology competencies.