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The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its interaction with various diseases and their therapeutic responses, both in clinical trials and routine care. In this article, we develop an advanced machine learning algorithm for passively monitoring sleep and nocturnal breathing from radio waves reflected off people while asleep. Validation results in comparison with the gold standard (i.e., polysomnography) (n=849) demonstrate that the model captures the sleep hypnogram (with an accuracy of 81% for 30-second epochs categorized into Wake, Light Sleep, Deep Sleep, or REM), detects sleep apnea (AUROC = 0.88), and measures the patient's Apnea-Hypopnea Index (ICC=0.95; 95% CI = [0.93, 0.97]). Notably, the model exhibits equitable performance across race, sex, and age. Moreover, the model uncovers informative interactions between sleep stages and a range of diseases including neurological, psychiatric, cardiovascular, and immunological disorders. These findings not only hold promise for clinical practice and interventional trials but also underscore the significance of sleep as a fundamental component in understanding and managing various diseases. 

The COVID-19 pandemic has gifted us a pivot point, an opportunity, in which we can consider ways to do things differently than we have "always" done them. Traditionally, students view statistics as an obstacle to overcome, rather than an opportunity to pursue their own interests and passions. The Passion-Driven Statistics curriculum challenges this viewpoint by exposing students to a meaningful and powerful data analysis experience during a 3-day "boot camp" or as a short project over a few weeks. This provides major student outcomes (e.g., an empirical poster presentation) with minor faculty investment (e.g., time, technology). Our model can be quickly personalized to meet the needs of you and your students, which is especially important during moments of an unexpected pivot. In addition to face-to-face, the outcomes can be met in a fully online, remote, or hybrid environment, making this model suitable for use in a variety of contexts. The "boot camp" model could serve as a way for your student lab members to gain research experience, skill-building workshop for your psychology club students, or project for a content-based course. This NSF-funded (DUE #1820766) model is a multidisciplinary, project-based curriculum that supports students in conducting original research, asking original questions, and communicating methods and results using the language of statistics. The course attracts higher rates of under-represented minority (URM) students compared to a traditional math statistics course (Dierker et al., 2015) and higher rates of female and URM students compared to an introductory programming course (Cooper & Dierker, 2017). Students reported the course more rewarding, were more likely to accomplish more than expected, found the course more useful than other courses, increased confidence in working with data, increased interest in pursuing advanced statistics courses, and received more individualized support than other courses (Dierker et al., 2018). 

Passion-Driven Statistics is a project-based, introductory curriculum implemented as a course in statistics, research methods, data science, a capstone experience, and a summer research boot camp with students from a wide variety of academic settings. Funded by the National Science Foundation, the curriculum engages students in authentic projects with large, real-world data sets. Passion-Driven Statistics students were more likely to report increased confidence in working with data and increased interest in pursuing advanced statistics course work compared to students from the traditional statistics course (Dierker et al., 2018a). This presentation draws on pre/post data from 67 instructors attending a Passion-Driven Statistics faculty development workshop. Analyses evaluate instructor characteristics, attitudes, and experiences that predict its implementation. Findings show that nearly half of the instructors who reported being likely to implement passion-driven statistics and a quarter of the overall sample employed the project-based curriculum by the end of the first full academic year following the workshop. Those showing this fast uptake were more likely to be female than those who had not yet implemented (87.5% vs. 55.3%), were more likely to hold a Ph.D. (94.1% vs. 59.2%), and were more likely to be employed by a private rather than public institution (76.5% vs. 46.0%). Those showing fast uptake were also more likely to have been previously using statistical software (i.e., SAS, JMP, R, Stata, Python, or SPSS) in their target course (70.6% vs. 40.0%) and to have greater prior experience with project-based skills and fewer post workshop concerns about their likely success. Results from these analyses will guide recommendations for 1) engaging instructors ready to implement innovation in learning and teaching and 2) supporting those instructors requiring additional time, training and skills. Instructors are the most important resource for promoting innovations and all materials are open educational resources