Our Science and Technology
SleepSpace's background and ethos
SleepSpace believes in open science. The sleep revolution begins when we take sleep detection algorithms out of black boxes and share them with the research and clinical world. That is why we teamed up with the National Institute of Aging, Penn State, and the University of Arizona to create new sleep measurement technologies and publish our findings in peer-reviewed journals. Our primary interest is using wearable and nearable sleep technology to accurately measure and enhance sleep. Once we detect sleep stage, our proprietary technology is designed to deliver sound, light, and vibration stimulation to enhance sleep. We combine this technology with classic cognitive behavioral therapy for insomnia for a new type of sleep improvement therapy. We call this new type of therapy, SleepSpace Cognitive Behavioral Therapy.
Our focus
1. Accurate sleep tracking through wearable devices, the SleepSpace Smart Bed, and subjective reporting
2. Sound, light, and vibration to improve sleep. In a study with Google we showed that red lights can help with sleep perceptions.
3. Connecting the smart bedroom
4. Providing a platform for sleep experts to deliver custom sleep programs
Are you a researcher interested in partnering with SleepSpace?
Recent Publications in Peer Reviewed Journals
Sleep Detection
These publications investigates the accuracy of consumer-grade wearables (Apple Watch, Oura Ring), like fitness trackers and smartwatches, in detecting sleep using heart rate and motion data. Our team compared the data from these devices to the data from two standard methods of measuring sleep, wrist actigraphy (Spectrum Pro, GT3X), and polysomnography. The results showed that the consumer-grade wearables were generally accurate in detecting sleep, but not as accurate as the standard methods. Therefore, the authors suggest that these devices may be useful for tracking sleep patterns, but not for precise measurement of sleep stages or diagnosing sleep disorders.
Roberts, D. M., Schade, M. M., Mathew, G. M., Gartenberg, D. I., & Buxton, O. M. (2020). Detecting Sleep Using Heart Rate and Motion Data from Multisensor Consumer-Grade Wearables, Relative to Wrist Actigraphy and Polysomnography. Sleep. PDF
Roberts, D.M., Schade, M. M., Master, L., Honavar, V., Nahmod, N.G., Chang, A-M., Gartenberg, D., & Buxton, O. M. (2023). Performance of an open machine learning model to classify sleep/wake from actigraphy across ~24-hour intervals without knowledge of rest timing. Sleep Health. PDF
Deep Sleep Stimulation with Audio
This publication discusses a study on how using a technique called "deep sleep stimulation" can improve the quality of sleep by increasing the amount of deep sleep a person gets. The study found that when individuals were exposed to specific sounds during sleep, they experienced more slow-wave activity, which is associated with deep sleep. This, in turn, led to an increase in the proportion of N3 sleep, which is the deepest stage of non-REM sleep. Our team found that this technique could be used as a non-pharmacological method to improve sleep quality in individuals with sleep disorders.
Schade, M. M., Mathew, G. M., Roberts, D. M., Gartenberg, D. & Buxton, O. M. Enhancing Slow Oscillations and Increasing N3 Sleep Proportion with Supervised, Non-Phase-Locked Pink Noise and Other Non-Standard Auditory Stimulation During NREM Sleep (2020). Nature and Science of Sleep. PDF
New Treatment for Insomnia
We also recently conducted a Randomized Clinical Trial (RCT) with our Penn State and University of Arizona colleagues to demonstrate that the SleepSpace technology can be used to more effectively treat insomnia than standard cognitive behavioral therapy for insomnia. In this methods paper, we describe the trial that was completed where we recruited 60+ year olds who had insomnia and compared three different treatments 1) A sleep hygiene control, 2) 6-Weeks of CBTi as usual with weekly therapist consults, and 3) CBTi as usual + the SleepSpace platform. We measured sleep quality both subjectively and objectively, in addition to cognitive assessments and blood-based biomarkers associated with Alzheimer's disease.
Emert, S. E., Taylor, D. J., Gartenberg, D., Schade, M. M., Roberts, D. M., Nagy, S. M., Huskey, A., Wardle-Pinkston, S., Gamaldo, A., & Buxton, O. M. (2023), A non-pharmacological multi-modal therapy to improve sleep and cognition and reduce mild cognitive impairment risk: Design and methodology of a randomized clinical trial. Contemporary Clinical Trials. PDF
Open Data is Our Ethos
We believe in open data and science at SleepSpace. This poster summarizes our sleep detection work, presented at the Behavioral Sleep Medicine first annual conference.
We have received grants from the National Science Foundation and the National Institute of Health to validate our innovative way of accurately measuring sleep and actually improving your sleep quality. We don’t believe in taking a cookie cutter approach to sleep improvement. Instead we give personalized feedback based on each person’s unique sleep situation. Whether that means figuring out their best schedule, helping them feel more alert throughout the day, or optimizing their sleep for intense physical performance. A good night’s sleep sets each person up for success the next day. Our app includes a new technique of making your sleep deeper with a special sound that we play at the right stage of your sleep. This helps you feel more rested and rejuvenated the next day.
Sleep is the Foundation
SleepSpace is changing sleep culture in our society and training people in how to apply the methods we’ve developed to improve their sleep. We aim to establish a new generation of analytically-minded sleep coaches and spread the idea of sleep being thought of as one of the three parts of the foundation of good health, along with diet and exercise.
Our innovative technology, scientific method, and holistic approach to sleep are what enable us to stand apart from the rest.
Custom Sleep Tools
With the constant stresses and hustle of life today, humans don’t know what sleep is anymore. More people have sleep problems today than ever before and many people try a lot of gimmicks to try to improve their sleep, which results in little improvement and more stress. SleepSpace helps these people understand their unique sleep needs and what they can do to improve their sleep health and well-being, while keeping each individual’s daily schedule demand and lifestyle in mind. We provide customized sleep improvement plans that fit each individual’s life.
Our Journal Articles
Roberts, D. M., Schade, M. M., Mathew, G. M., Gartenberg, D. I., & Buxton, O. M. (2020). Detecting Sleep Using Heart Rate and Motion Data from Multisensor Consumer-Grade Wearables, Relative to Wrist Actigraphy and Polysomnography. Sleep. PDF
Schade, M. M., Mathew, G. M., Roberts, D. M., Gartenberg, D. & Buxton, O. M. Enhancing Slow Oscillations and Increasing N3 Sleep Proportion with Supervised, Non-Phase-Locked Pink Noise and Other Non-Standard Auditory Stimulation During NREM Sleep. Nature and Science of Sleep ((accepted)).
Gartenberg, D., Gunzelmann, G., Hassanzadeh-Behbahani, S., & Trafton, J. G. (accepted). Examining the role of task requirements in the magnitude of the vigilance decrement, Frontiers in Psychology (doi: 10.3389/fpsyg.2018.01504) PDF
Brunet, J. F., Dagenais, D., Therrien, M., Gartenberg, D., & Forest, G. (2017). Validation of sleep-2-Peak: A smartphone application that can detect fatigue-related changes in reaction times during sleep deprivation. Behavior Research Methods, 49, 4, 1460-1469. PDF
Youmans, R. J., Smith, M. A., Gartenberg, D., Sarbone, B., Liu, S., Higgins, J., Lee, P., Penaranda, N., & Liu, S. (2014). FleetTM: A distributed information gathering and processing system for the alleviation of commercial air travel anxiety. The Journal of Air Traffic Control, 56, 1, 32-38. PDF
Gartenberg, D., & Thornton, R., Mortazavi, M., Pfannenstiel, D., Taylor, D., & Parasuraman, R. (2013). Collecting health-related data on the smart phone: mental models, cost of collection, and perceived benefit of feedback. Pers Ubiquit Comput, 17, 3, 561-570. PDF
Dissertation
Gartenberg, D. (2016). A Comprehensive Computational Model of Sustained Attention (Doctoral dissertation). Retrieved from ProQuest Dissertations and Theses. Advisor: Greg Trafton, Committee: Glenn Gunzelmann, Tyler Shaw, & Patrick McKnight.PDF
Conferences, Chapters, and Posters
Roberts, D. M., Schade, M. M., Mathew, G. M., Gartenberg, D, & Buxton, O. H (2019). Development of a momentary sleep versus wake classification algorithm using balanced data from two multisensor consumer wearable devices. Birmingham, AL: Society of Behavioral Sleep Medicine. PDF
Schade, M. M., Roberts, D. M., Gartenberg, D, Mathew, G. M., & Buxton, O. H (2018). Auditory stimulation during sleep transiently increases delta power and all-night proportion of NREM stage 3 sleep while preserving total sleep time and continuity. San Diego, CA: Society for Neuroscience. PDF
Brunet, J., Therrien, M., Hebert, K., Tzivanapoulos, N., Gartenberg, D., & Forest, G (2015). Greater decline in reaction time performance on a smartphone application during sleep deprivation is linked to extraversion.In Proceedings of the 29th annual meeting of the Associated Professional Sleep Societies. PDF
Gartenberg, D., Gunzelmann, G., Veksler, B., & Trafton, J. G. (2015). Improving vigilance analysis methodology: Questioning the successive versus simultaneous distinction. In Proceedings of 59th annual meeting of the Human Factors and Ergonomics Society. PDF
Brunet, J., Therrien, M., Gartenberg, D., & Forest, G (2014). Validation of a smartphone psychomotor vigilance application: Preliminary Data. In Proceedings of the 28th annual meeting of the Associated Professional Sleep Societies. PDF
Gartenberg, D., Veksler, B., Gunzelmann, G., & Trafton, J. G. (2014). An ACT-R process model of the signal duration phenomenon of vigilance. In Proceedings of 58th annual meeting of the Human Factors and Ergonomics Society. PDF
Youmans, R. J., Smith, M. A., Gartenberg, D., Liu, S., Higgins, J., Penaranda, N., & Sarbone, B. (2013). FleetTM: A Distributed Information Gathering and Processing System for the Alleviation of Commercial Air Travel Anxiety. Proceedings of the 58th Air Traffic Control Association’s Annual Conference & Exposition, Washington, DC. PDF
Therrien, M., Hebert, M., Gartenberg, D., De Koninck, J., & Forest, G (2013). Awareness of reaction time variations with sleep schedule measured on a smartphone can improve sleep habits and performance. In Proceedings of the 27th annual meeting of the Associated Professional Sleep Societies. PDF
Hebert, M., Gartenberg, D., De Koninck, J., & Forest, G, Therrien, M. (2013). Alertness measured by average reaction time on a smartphone predicts physical performance. In Proceedings of the 27th annual meeting of the Associated Professional Sleep Societies. PDF
Gartenberg, D., Forest, G., & Therrien, M. (2012). A smartphone PVT application is successfully used to identify one’s sleep schedule associated with better daytime alertness. Associated Professional Sleep Societies. PDF
Therrien, M., Hebert, M., Gartenberg, D., De Koninck, J., & Forest, G (2012). High correlation and predictive value between alertness measured by reaction time and physical performance. Associated Professional Sleep Societies. PDF
Eisert, J., Gartenberg, D., Thornton, R., & Youmans, R. (2012). Optimal interface location and limits of gesture proficiency in an automobile. In Proceedings of 56th annual meeting of the Human Factors and Ergonomics Society. PDF
Gartenberg, D. McGarry, R., Phannenstiel, D., Cisler, D., Shaw, T., & Parasuraman, R. (2012). Development and evaluation of a neuroergonomic smart phone application to assess vigilance and arousal. 4th International Conference on Applied Human Factors and Ergonomics. PDF
Gartenberg, D. & Parasuraman, R. (2010). Understanding Brain Arousal and Sleep Quality Using a Neuroergonomic Smart Phone Application. In Marek, T., Karwowski, W., & Rice, V. (Eds.), Advances in Understanding Human Performance, 3rd International Conference on Applied Human Factors and Ergonomics (pp. 210-220). PDF