Unobtrusive and continuous monitoring of cognitive changes using smartphones

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Project Summary Acquired cognitive impairment associated with aging, neurocognitive disorders, like Alzheimer’s disease, and traumatic brain injury (TBI) pose major challenges to healthcare systems throughout the world. To facilitate successful aging, effective methods are needed for monitoring cognition in order to detect early signs of impaired cognition and dementia. However, the effectiveness of existing neuropsychological assessments is thwarted by their sporadicity, difficulty in accounting for the context-dependent nature of patients’ health (e.g., having a “good” or a “bad” day), and reliance on frequently inaccurate patient and caregiver reports. Thus, new approaches are needed for objective and ecologically valid assessment of cognitive function and for early detection of impaired cognition associated with neurocognitive disorders like Alzheimer’s disease and TBI. Current mHealth and AI approaches enable continuous context inference from smartphone use and location data. Patient reports of Instrumental Activities of Daily Living (IADLs) can be greatly enhanced with continuous, objective context inferences from passively collected smartphone-based data that can contribute to earlier and more accurate diagnoses of neurocognitive disorder including Alzheimer’s disease and related dementia. Cross-sectional studies involving younger and older adults suggest that characteristics of an individual’s mobile application use and typing speed correlate with working memory, attention, and psychomotor function. Therefore, the proposed augmentation of clinical assessments with continuous and objective estimates of cognitive changes derived computationally and unobtrusively from mobile application use characteristics and motor interactions has the potential to inform both the early detection and diagnosis of impaired cognition associated with neurocognitive disorders, such as Alzheimer’s disease and TBI, and potentially treatment selection for their amelioration. The central hypothesis to be tested in this longitudinal study is that changes in cognition, including impaired cognition associated with Alzheimer’s disease and other neurocognitive disorders, can be inferred from smartphone data. In the F99 phase, Mr. Kos will a) augment existing measures derived from IADLs and lifespace mobility with analogous measures inferred from mobile location-finding systems (e.g., GPS) and application use data, and b) propose a digital biomarker for tracking cognitive changes and impaired cognition based on temporal patterns in application use and motor interactions with smartphones. Validation will be conducted on 80 middle-aged individuals; 20 with subjective cognitive impairment, 20 with diagnosed Mild Cognitive Impairment, 20 with TBI, and 20 cognitively intact controls. In the K00 phase, Mr. Kos will select the subset of these measures determined to be most applicable to tracking cognitive changes and impaired cognition and, thus, prime for detecting early signs of Alzheimer’s disease and other types of neurocognitive disorder in older adults. Their refinement and validation in clinical trials using biomarkers and brain imaging data will enable developing a mechanistic understanding of a) the relationships between impaired cognition, Alzheimer’s disease, and neurocognitive changes including TBI and b) how these relationships are reflected in interactions with mobile technologies.