At present, one in 10 Americans age 65 and older has Alzheimer's dementia. Further, the global prevalence of dementia is expected to triple from 50 million to over 150 million by 2050. Given this trend, there is a need for increasingly efficient, objective, and sensitive methods to characterize cognition, assess risk, and discriminate individuals at various stages of the disease spectrum. Traditional neuropsychological assessments have been extensively validated for these purposes, yet present several methodological drawbacks such as limited ecological validity, practice effects, and burdensome testing and scoring procedures that are prone to human error. On the other hand, smartphones are ubiquitous, are owned by older adults at increasing rates, and present a unique method to capture subtle cognitive and behavioral profiles in everyday life, outside of the laboratory or clinic. In the proposed observational study, we aim to explore the validity of a digital phenotyping protocol as a novel method for characterizing cognition and function among a heterogeneous group of older adults. Specifically, we will investigate the relations between passively captured smartphone-based digital features and gold-standard neuropsychological measures. We also will explore optimal sampling rates for collection of digital data along with clinically-useful digital phenotypes to inform future studies. A total of 90 participants age 65 and older with normal cognition, mild cognitive impairment, and mild Alzheimer's dementia will be recruited from the Philadelphia region and from a pool of eligible participants who have completed recent aging studies. Participants will use their own personal smartphones naturally during a four-week study period while a secure software application unobtrusively and continuously obtains de-identified raw sensor- based data spanning domains including device activity and usage, spatial trajectories and mobility, and social interactions. Daily surveys delivered via the study software will be used to complement the passively collected data. Participants also will complete traditional neuropsychological measures at a baseline visit to examine construct validity. This study will explore whether digital phenotyping may provide a valid, highly efficient, and naturalistic method for tracking cognition, function and disease burden both cross-sectionally and, eventually, over time. If successful, this tool can be applied in several clinical and research contexts to yield improved assessment efficiency, enhanced diagnostic accuracy, and personalized treatment interventions, which together will generate tremendous cost savings and improved health outcomes. A training plan has been designed in consultation with experts in the fields of digital phenotyping, everyday cognition and function in aging populations, and secure computer systems to develop the applicant's expertise in designing and adapting digital phenotyping platforms for use in aging populations, longitudinal data analysis for continuous multivariate data, and security protections related to personal digital data.
As the global burden of dementia continues to plague our healthcare system, efficient, objective, and sensitive tools to characterize cognition and detect underlying neurodegenerative disease increasingly are needed. Digital phenotyping relies on passive, continuous collection of smartphone sensor data during everyday life to measure activities, behaviors, and mood. This project will investigate the validity of a digital phenotyping protocol to measure cognitive and functional ability in older adults with potential application in a wide range of contexts to detect early cognitive decline, inform diagnosis, personalize interventions, and track treatment effectiveness in a cost-effective manner.