However, the underlying mechanisms mediating the adverse effects of poor sleep remain unknown. Chronic sleep deprivation also poses economical burdens to society, contributing to premature mortality, loss of working time, and suboptimal education outcomes that cost the US $280.6-411 billion annually. Although the body has remarkable compensatory mechanisms for acute sleep deprivation, chronic poor sleep quality and suboptimal sleep duration are linked to many adverse health outcomes, including increased risk of diabetes, metabolic abnormalities, cardiovascular disease, hypertension, obesity, and anxiety and depression. A 2014 study from the Centers for Disease Control and Prevention found that over one third of Americans (34.8%) regularly sleep less than the recommended 7 hours per night. These findings can hopefully be used to guide future sensor-based sleep research.īetween 50 and 70 million Americans currently suffer from poor sleep. Additionally, Oura Ring’s total sleep duration and efficiency in relation to the PSQI measure was statistically significant at p = 0.004 and p = 0.033, respectively. We did not find any significant associations between SRSMs and PSQI or cognitive scores. For cognitive scores, we found the Withings latency was statistically significant for afternoon and evening timepoints at p = 0.016 and p = 0.013. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant ( p < 0.05). Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions.
We analyzed correlation between multiple device-related sleep metrics. In this pilot study of 21 participants, we investigated whether sleep metric outputs from self-reported sleep metrics (SRSMs) and four sensors, specifically Fitbit Surge (a smart watch), Withings Aura (a sensor pad that is placed under a mattress), Hexoskin (a smart shirt), and Oura Ring (a smart ring), were related to known cognitive and psychological metrics, including the n-back test and Pittsburgh Sleep Quality Index (PSQI). Each device company makes different claims of accuracy and measures different features of sleep quality, and it is still unknown how well these devices correlate with one another and perform in a research setting. The sleep-related output metrics from these devices include sleep staging and total sleep duration and are derived via proprietary algorithms that utilize a variety of these physiological recordings. Recently, numerous commercial digital devices have been developed that record physiological data, such as movement, heart rate, and respiratory rate, which can act as a proxy for sleep quality in lieu of standard electroencephalogram recording equipment. Standard methods for assessing sleep involve overnight studies in hospital settings, which are uncomfortable, expensive, not representative of real sleep, and difficult to conduct on a large scale. Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains.
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