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.

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.