Investigator:
Erik Page, Blue Iris.
MassAITC Cohort: Year 2 (AD/ADRD)
Light is the primary regulator of human circadian rhythms. Through our prior NIH-funded research, we have developed and commercialized the first wearable light exposure devices that gather all physical properties of light thought to impact circadian health. Sleep quality is a critical measure of circadian health. Sleep trackers are in mass commercial use and are improving in accuracy and precision. Here, we seek to identify correlations between what are arguably the most important environmental inputs (light exposure) and health outcomes (sleep) in circadian science.
We propose gathering data using commercially available devices (i.e., our light exposure tracker and leading commercial sleep trackers) and machine learning techniques to identify the light exposure parameters and patterns that correlate most strongly with measured sleep metrics. We propose to collect light exposure and sleep metric data from at least 15 (non-AD/ADRD) individuals for at least 50 days per individual using methods that could scale commercially.
This pilot test is primarily intended to validate the methodology related to correlating light exposure and sleep metric data rather than to directly estimating the nature or magnitude of these correlations. These data will be analyzed for correlations between light exposure inputs and sleep quality outputs. Specifically, we propose to identify how the intensity, spectrum, and timing of light exposure of individuals correlate with their sleep duration, sleep phases, and sleep scores, as reported by various commercial sleep trackers.
We further propose to investigate how these correlations vary between individuals, seeking to identify potential parameters that influence interpersonal variations, such as demographic differences and differences in personal light sensitivity. If the methods proposed in this pilot are validated, the study results are expected to inform the design of a larger clinical trial that includes participants who have Alzheimer’s disease (AD) or related dementia (ADRD), with the aim of identifying specific light exposure patterns associated with specific, quantifiable improvements in sleep metrics in these populations.