Application of precision medicine to phototherapy: a stepped care approach to consolidate sleep and slow cognitive decline in older adults - PROJECT SUMMARY/ABSTRACT While consolidated sleep is crucial for healthy cognition and mood in older individuals, many suffer from sleep- wake fragmentation, a risk factor for developing Alzheimer's disease. One countermeasure of sleep-wake fragmentation is exposure to bright light ( phototherapy ). Studies using morning phototherapy, which targets circadian phase, to restore sleep-wake fragmentation have reported mixed results. However, both mathematical models of the circadian pacemaker and data from our lab suggest that afternoon light exposure, targeting circadian amplitude, will have greater effects on sleep-wake consolidation. Since phototherapy can be administered without significant adverse effects, it is a promising tool to reverse sleep-wake fragmentation and slow cognitive decline. Therefore, the overarching goal of the proposed studies is to slow cognitive deterioration in older individuals with mild cognitive impairment (MCI) by investigating the utility of afternoon phototherapy. The Research Training Plan will leverage state-of-the-art artificial intelligence techniques on big datasets (specific aims 1 and 3) and an intervention clinical trial (specific aim 2). In aim 1 (K99), the applicant, Dr. Lok, will train with Dr. Kochenderfer as she applies state-of-the-art machine learning techniques to determine underlying factors contributing to sleep-wake fragmentation and cognitive decline. During this time, Dr. Lok will also learn to conduct neurocognitive testing in individuals with mild cognitive impairment. Dr. Lok will conduct a clinical trial (R00), investigating the utility of afternoon phototherapy in a stepped care approach to reduce sleep- wake fragmentation and improve cognition. Finally, Dr. Lok will use machine-learning techniques to develop a personalized phototherapy model to create a prediction score calculator. These endeavors ensure that these projects' outcomes benefit the scientific and medical communities. Dr. Lok has the requisite training in machine learning and clinical trials to undertake the proposed projects. The career development plan is intricately designed to empower Dr. Lok with enhanced machine-learning skills and to facilitate a deeper understanding of gerontology and the social determinants of aging. Mentor Dr. Zeitzer is a leading expert in human translational chronobiology. Co-mentors Drs. Kochenderfer (machine learning), Fairchild (neuropsychology), and advisors Drs. Jo (statistician) and Yesavage (Alzheimer's disease) offer complementary expertise. Dr. Lok proposes to pursue these development goals and begin the proposed research with the support of the Department of Psychiatry and Behavioral Science at Stanford University, which provides an ideal environment of research support and resources to attain her training and research goals. In summary, the solid mentoring team, environment, and proposed training plan anticipate fully launching Dr. Lok's independent career. The proposed study will increase knowledge about contributory factors to sleep-wake fragmentation and cognition, as well as a scalable intervention with the potential to ameliorate cognitive decline and other concomitants of fragmented sleep, prevent Alzheimer's disease onset, delay institutionalization, and improve quality of life in older individuals.
