Using passively collected person-generated health data to explore population-specific relationships between social determinants of health, sleep patterns, and cognitive outcomes

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Project Summary Alzheimer’s disease and related dementias (ADRDs) impose disproportionate burden in underserved populations (e.g., African Americans and other minorities, low-income individuals), which experience higher rates and earlier onset of cognitive decline. Social and structural determinants of health--including economic and educational disparities, healthcare access and quality, systemic racism, and lifetime stress--account for ~80% of modifiable risk factors and profoundly contribute to such disparities. However, social determinants are understudied in the context of ADRDs in the underserved, and there is limited understanding of how differential lifetime experience of social determinants across various populations may influence heterogeneity in cognitive outcomes. In addition, the ability to detect subtle cognitive changes in everyday life--often years prior to the onset of discernible ADRD symptoms--is a methodological challenge in gold-standard approaches. Such barriers impede development of effective screening, treatment, and preventive interventions that are appropriately tailored to underserved populations. Consumer digital technologies offer non-invasive tools for measuring cognitive change and experience of social determinants in everyday life. Today, individual lived-experiences and social determinants can be precisely characterized by person-generated health data (PGHD) (spanning environment, geolocation, diet, exercise, social interactions, and other activities) generated from nearly- ubiquitous smartphones and wearable devices. Applied to cognition, our team members at Evidation Health and others have demonstrated that several objective indicators passively collected from digital technologies may be associated with cognitive decline and may precede clinical ADRD manifestation by 10-15 years. In our NLM- funded parent R01LM013237, we propose a sustainable infrastructure to use continuous PGHD to explore the multi-level influence of social determinants on sleep health. Poor sleep health is an emerging risk factor for ADRDs that may be an important and understudied biobehavioral pathway linking individual social determinants with poor cognitive outcomes. Our objective here, in response to NOT-AG-20-034, is to (1) add clinically validated measures of cognition to our in-scope data collection and sleep study to (2) explore the dynamic and multilevel relationships between individual-level social determinants, physical activity, sleep health, cognitive function, and ADRD risk. We will collaborate with Dr. Sliwinski (U2CAG060408), whose group has developed a battery of clinically validated smartphone-based assessments of cognitive function. Collectively, our multidisciplinary approach will overlay (1) monthly self-reported health metrics; (2) periodic clinical measures of cognition; and, (3) continuous PGHD on a 12-month time series from each individual. This work may significantly advance cognitive science and transform public health, enabling future innovation towards reducing ADRD burden across all socio-demographic groups, including the historically underserved.