SleepCoacher: A Personalized Automated Self-Experimentation System for Sleep Recommendations

ABSTRACT

We present SleepCoacher, an integrated system implementing a framework for effective self-experiments. SleepCoacher automates the cycle of single-case experiments by collecting raw mobile sensor data and generating personalized, data-driven sleep recommendations based on a collection of template recommendations created with input from clinicians. The system guides users through iterative short experiments to test the effect of recommendations on their sleep. We evaluate SleepCoacher in two studies, measuring the effect of recommendations on the frequency of awakenings, self-reported restfulness, and sleep onset latency, concluding that it is effective: participant sleep improves as adherence with SleepCoacher's recommendations and experiment schedule increases. This approach presents computationally-enhanced interventions leveraging the capacity of a closed feedback loop system, offering a method for scaling guided single-case experiments in real time.

References

1. 2014. Brain Basics: Understanding Sleep. (2014). Retrieved September 24, 2015 from http://ninds.nih.gov/disorders/brain_ basics/understanding_sleep.htm.
2. 2014. Healthy Sleep Tips. (2014). Retrieved November 23, 2014 from http://sleepfoundation. org/sleep-tools-tips/healthy-sleep-tips.
3. 2015. Sleep as Android. (2015). Retrieved September 24, 2015 from https: //sites.google.com/site/sleepasandroid/.
4. S. Ancoli-Israel, R. Cole, C. Alessi, M. Chambers, W. Moorcroft, and C. Pollak. 2003. The role of actigraphy in the study of sleep and circadian rhythms. American Academy of Sleep Medicine Review Paper. Sleep 26, 3 (2003), 342--392.
5. J.S. Bauer, S. Consolvo, B. Greenstein, J. Schooler, E. Wu, N.F. Watson, and J. Kientz. 2012. ShutEye: Encouraging Awareness of Healthy Sleep Recommendations with a Mobile, Peripheral Display. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12). 1401--1410. DOI: http://dx.doi.org/10.1145/2207676.2208600
6. F. Bentley, K. Tollmar, P. Stephenson, L. Levy, B. Jones, S. Robertson, E. Price, R. Catrambone, and J. Wilson. 2013. Health Mashups: Presenting Statistical Patterns Between Wellbeing Data and Context in Natural Language to Promote Behavior Change. ACM Trans. Comput.-Hum. Interact. 20, 5 (2013), 30:1--30:27. DOI: http://dx.doi.org/10.1145/2503823
7. A. Blaivas. 2014. Polysomnography. (2014). Retrieved April 13, 2015 from http://www.nlm.nih. gov/medlineplus/ency/article/003932.htm.
8. Z. Chen, M. Lin, F. Chen, N.D. Lane, G. Cardone, R. Wang, T. Li, Y. Chen, T. Choudhury, and A.T. Campbell. 2013. Unobtrusive sleep monitoring using smartphones. In Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2013 7th International Conference on. IEEE, 145--152.
9. E. K. Choe, S. Consolvo, N. F. Watson, and J. A. Kientz. 2011. Opportunities for Computing Technologies to Support Healthy Sleep Behaviors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). 3053--3062. DOI: http://dx.doi.org/10.1145/1978942.1979395
10. N. Daskalova, N. Ford, A. Hu, Moorehead K., B. Wagnon, and J. Davis. 2014. Informing Design of Suggestion and Self-Monitoring Tools through Participatory Experience Prototypes. In Proc. Persuasive Tech. DOI:http: //dx.doi.org/10.1007/978-3-319-07127-5_7
11. J. A. Durlak. 2009. How to select, calculate, and interpret effect sizes. Journal of pediatric psychology (2009), jsp004.
12. B. S. Fjeldsoe, A. L. Marshall, and Y. D. Miller. 2009. Behavior change interventions delivered by mobile telephone short-message service. American journal of preventive medicine 36, 2 (2009), 165--173.
13. B. J. Fogg. 2002. Persuasive Technology: Using Computers to Change What We Think and Do. Ubiquity 2002, December, Article 5 (2002). DOI: http://dx.doi.org/10.1145/764008.763957
14. T. Hao, G. Xing, and G. Zhou. 2013. iSleep: Unobtrusive Sleep Quality Monitoring Using Smartphones. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). Article 4, 4:1--4:14 pages. DOI: http://dx.doi.org/10.1145/2517351.2517359
15. M. Heyvaert and P. Onghena. 2014. Randomization tests for single-case experiments: State of the art, state of the science, and state of the application. Journal of Contextual Behavioral Science 3, 1 (2014), 51--64.
16. G. Jean-Louis, D.F. Kripke, R.J. Cole, J.D. Assmus, and R.D. Langer. 2001. Sleep detection with an accelerometer actigraph: comparisons with polysomnography. Physiology & Behavior 72, 1 (2001), 21--28.
17. M. Kay, E. K. Choe, J. Shepherd, B. Greenstein, N. Watson, S. Consolvo, and J. A. Kientz. 2012. Lullaby: A Capture & Access System for Understanding the Sleep Environment. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (UbiComp '12). 226--234. DOI: http://dx.doi.org/10.1145/2370216.2370253
18. M. A. Killingsworth and D. T. Gilbert. 2010. A wandering mind is an unhappy mind. Science 330, 6006 (2010), 932--932.
19. T. R. Kratochwill, J. H. Hitchcock, R. H. Horner, J. R. Levin, S. L. Odom, D. M. Rindskopf, and W. R. Shadish. 2012. Single-case intervention research design standards. Remedial and Special Education (2012), 0741932512452794.
20. R. Manolov and A. Solanas. 2008. Comparing N= 1 effect size indices in presence of autocorrelation. Behavior Modification 32, 6 (2008), 860--875.
21. J. Min, A. Doryab, J. Wiese, S. Amini, J. Zimmerman, and J. I. Hong. 2014. Toss "N" Turn: Smartphone As Sleep and Sleep Quality Detector. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '14). 477--486. DOI: http://dx.doi.org/10.1145/2556288.2557220
22. V. Natale, M. Drejak, A. Erbacci, L. Tonetti, M. Fabbri, and M. Martoni. 2012. Monitoring sleep with a smartphone accelerometer. Sleep and Biological Rhythms 10, 4 (2012), 287--292. DOI:http://dx. doi.org/10.1111/j.1479--8425.2012.00575.x
23. Northcube. 2015. SleepCycle. (2015). Retrieved September 24, 2015 from http://www.sleepcycle.com/.
24. W. Ooteman, M. Koeter, R. Vserheul, G. M. Schippers, and W. Brink. 2006. Measuring craving: an attempt to connect subjective craving with cue reactivity. Alcoholism: Clinical and Experimental Research 30, 1 (2006), 57--69.
25. J. Paquet, A. Kawinska, and J. Carrier. 2007. Wake detection capacity of actigraphy during sleep. Sleep 30, 10 (2007), 1362.
26. C. Pollak, W. Tryon, H. Nagaraja, and R. Dzwonczyk. 2001. How Accurately Does Wrist Actigraphy Identify the States of Sleep and Wakefulness' 24, 8 (2001), 957--965.
27. A. Sadeh, P.J. Hauri, D.F. Kripke, and P. Lavie. 1995. The role of actigraphy in the evaluation of sleep disorders. Sleep 18, 4 (1995), 288--302.
28. A. Sadeh, K. M. Sharkey, and M. A. Carskadon. 1994. Activity-Based SleepWake Identification: An Empirical Test of Methodological Issues. Sleep 17, 3 (1994), 201--207.
29. H. Shin, B. Choi, D. Kim, and J. Cho. 2014. Robust Sleep Quality Quantification Method for a Personal Handheld Device. Telemedicine and e-Health 20, 6 (2014), 522--30.
30. L. L. Sievert, K. Begum, R. Sharmeen, O. Chowdhury, S. Muttukrishna, and G. Bentley. 2008. Patterns of occurrence and concordance between subjective and objective hot flashes among Muslim and Hindu women in Sylhet, Bangladesh. American Journal of Human Biology 20, 5 (2008), 598--604.
31. J. D. Smith. 2012. Single-case experimental designs: A systematic review of published research and current standards. Psychological Methods 17, 4 (2012), 510.
32. Mayo Clinical Staff. 2014. Polysomnography (sleep study). (2014). Retrieved April 13, 2015 from http://www.mayoclinic.org/tests-procedures/ polysomnography/basics/definition/ prc-20013229.
33. G. M. Sullivan and R. Feinn. 2012. Using effect size-or why the P value is not enough. Journal of graduate medical education 4, 3 (2012), 279--282.

Index Terms

• Human-centered computing