PROJECT SUMMARY
Night or rotating schedules under shiftwork are unavoidable, especially in healthcare work. Shiftwork can disturb
circadian rhythms due to unusual light exposure and has been linked to many long-term negative health
outcomes such as cardiovascular and cardiometabolic disorders. Studying disturbances in circadian rhythms in
shift workers has proven challenging due to limitations in measuring circadian rhythms in humans under real-
world conditions. However, with recent developments in wearable sensors, we can now capture continuous,
remote, and longitudinal measurements of physiological signals (e.g., heart rate, breathing rate, biomechanics),
behavioral patterns (e.g., activity tracking), and other external stimuli (e.g., light exposure), enabling the
characterization of circadian disturbances and supporting identifying actionable strategies for shift workers to
regain and maintain internal synchrony. This proposal seeks to examine the impact of the coordination of central
and peripheral body clocks internally and with the external environment in the real-world. We hypothesize that
internal and external synchrony of the body’s clocks is critical for regaining stability after perturbations such as
shiftwork. In this proposal, we seek to develop a quantitative framework for assessing body clock synchrony in
the real-world using wearable sensors. In the long term, we seek to leverage this work to interrogate the impact
of the coordination of body clocks on health state and develop timely interventions to regain stability. To do this,
in Aim 1, I will improve circadian phase prediction algorithms using real-world light diets to enable the study of
circadian rhythms outside of the laboratory and in real-world settings. In Aim 2, I will assess internal synchrony
leveraging continuous, ambulatory monitoring of physiological signals and estimation of central clock phase to
develop a quantitative framework for measuring the coordination of central and peripheral body clocks. This will
enable deeper understanding of their coordination and contribution to recovery from disturbances. In Aim 3, I
will develop predictive algorithms using machine learning to enable the evaluation of light diets to improve
synchrony in shift workers to translate tools to empower them to maintain synchrony and reduce risk of long-
term negative health outcomes. Completing these aims and working in collaboration with my sponsor, Dr. Jamie
Zeitzer, and co-sponsor, Dr. Todd Coleman, will enable me to develop the skillset necessary to study disease
dynamics in the real-world using wearable sensors and pursue a future career as a robust, independent, and
interdisciplinary researcher. My sponsor and co-sponsor are well equipped to provide training in measuring
complex human physiological signals, developing engineering skills in statistical signal processing and
estimation theory, and communication and leadership skills to facilitate the translation of my work to a wider
audience. The support of my mentors as well as the plentiful resources and collaborative environment of the
Bioengineering PhD program and Stanford University are opportune for the successful completion of this work
and complementary training goals.
