*Article* **Sleep in the Natural Environment: A Pilot Study**

**Fayzan F. Chaudhry 1,2,**†**, Matteo Danieletto 1,2,3,**†**, Eddye Golden 1,2,3, Jerome Scelza 2,3, Greg Botwin 2,3, Mark Shervey 2,3, Jessica K. De Freitas 1,2,3, Ishan Paranjpe 1, Girish N. Nadkarni 1,4,5, Riccardo Miotto 1,2,3, Patricia Glowe 1,2,3, Greg Stock 3, Bethany Percha 2,3, Noah Zimmerman 2,3, Joel T. Dudley 2,3,\* and Benjamin S. Glicksberg 1,2,3,\***


Received: 2 February 2020; Accepted: 29 February 2020; Published: 3 March 2020

**Abstract:** Sleep quality has been directly linked to cognitive function, quality of life, and a variety of serious diseases across many clinical domains. 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. 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. 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. 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. 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). We analyzed correlation between multiple device-related sleep metrics. Furthermore, we investigated relationships between these sleep metrics and cognitive scores across different timepoints and SRSM through univariate linear regressions. We found that correlations for sleep metrics between the devices across the sleep cycle were almost uniformly low, but still significant (*p* < 0.05). 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 did not find any significant associations between SRSMs and PSQI or cognitive scores. 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. These findings can hopefully be used to guide future sensor-based sleep research.

**Keywords:** wearables; biosensors; sleep; Fitbit; Oura; Hexoskin; Withings; cognition

#### **1. Introduction**

Between 50 and 70 million Americans currently suffer from poor sleep [1]. 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 [2]. 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 [3], metabolic abnormalities [4], cardiovascular disease [5], hypertension [6], obesity [7], and anxiety and depression [8]. 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 [9]. However, the underlying mechanisms mediating the adverse effects of poor sleep remain unknown. Diverse factors and complex interactions govern the relationship between health and sleep, and there is likely substantial inter-individual variability. Pronounced gender [10], race [11], and ethnicity differences in sleep-related behaviors are well-established [2].

It is clear that broad, population-level studies of sleep are necessary to understand how lifestyle and environmental factors contribute to poor sleep and to link sleep abnormalities to their attendant negative health effects [12]. It is particularly important to capture individuals' sleep patterns in natural sleep settings (i.e., at home). However, traditional approaches to studying sleep do not permit these types of studies. Polysomnography (PSG), where brain waves, oxygen levels, and eye and leg movements are recorded, is the current "gold standard" approach to studying sleep. A PSG study typically requires the participant to sleep in a hospital or clinic setting with uncomfortable sensors placed on the scalp, face, and legs. These studies, which remove the participant from his/her natural sleep environment, are not well suited to longitudinal assessments of sleep. They also create issues such as the first night effect, which limit the translatability of laboratory sleep studies to real-life environments [13]. The recent development of clinical grade, at-home PSG tools has enabled quantification of the laboratory environment's effect on sleep [14]. Such studies have generally confirmed that participants sleep better at home than they do in a lab, although these findings are not universal [15].

Even with the availability of the at-home PSG, however, it is unlikely that the use of expensive, cumbersome, single-purpose equipment will promote the kinds of large-scale population studies that can quantify the diverse factors affecting sleep and its relationship to health outcomes. More user-friendly, lightweight, and unobtrusive sleep sensors are needed; ideally these would be embedded in devices that study participants already own. Recently, several companies have developed sub-clinical grade "wearable" technologies for the consumer market that passively collect high frequency data on physiological, environmental, activity, and sleep variables [16]. The Food and Drug Administration classifies these as general wellness products and they are not approved for clinical sleep studies. Due to their passivity, low risk, and growing ubiquity amongst consumers, it is clear that these devices present an intriguing new avenue for large-scale sleep data collection [17]. Combined with mobile application (app) software to monitor cognitive outcomes such as reaction time, executive function, and working memory, these devices could feasibly be used for large-scale, fully remote sleep studies.

This study aimed to determine the feasibility of monitoring sleep in a participant's natural environment with surveys completed electronically. Specifically, we performed a week-long pilot comparative study of four commercially available wearable technologies that have sleep monitoring capabilities. For the entire week, 21 participants were instrumented with all four devices, 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). To assess the feasibility of a fully remote study relating sleep features to cognition, we also assessed participants' daily cognitive function via a series of

assessments on a custom-built mobile app. None of the four devices we compared in this study had been previously compared head-to-head for sleep and cognition research. Our results highlight some of the key difficulties involved in designing and executing large-scale sleep studies with consumer-grade wearable devices.

The rest of the paper is organized as follows. In Section 2, we describe the literature of related work including state-of-the-art research. In Section 3, we detail the materials and methods employed in this work, including the participant recruitment process, all metrics collected (e.g., device output), and the statistical tests performed. We detail the results from all assessments in Section 4. We discuss the implications of our work as well as limitations in Section 5 and finally conclude the paper in Section 6.
