Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health

Sleep is an important aspect of our health, but it is difficult for people to track manually because it is an unconscious activity. The ability to sense sleep has aimed to lower the barriers of tracking sleep. Although sleep sensors are widely available, their usefulness and potential to promote healthy sleep behaviors has not been fully realized. To understand people's perspectives on sleep sensing devices and their potential for promoting sleep health, we surveyed 87 and interviewed 12 people who currently use or have previously used sleep sensors, interviewed 5 sleep medical experts, and conducted an in-depth qualitative analysis of 6986 reviews of the most popular commercial sleep sensing technologies. We found that the feedback provided by current sleep sensing technologies affects users' perceptions of their sleep and encourages goals that are in tension with evidence-based methods for promoting good sleep health. Our research provides design recommendations for improving the feedback of sleep sensing technologies by bridging the gap between expert and user goals.

[1]  Sunny Consolvo,et al.  ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display , 2012, CHI.

[2]  Eric C. Larson,et al.  The design and evaluation of prototype eco-feedback displays for fixture-level water usage data , 2012, CHI.

[3]  E. Stepanski,et al.  Use of sleep hygiene in the treatment of insomnia. , 2003, Sleep medicine reviews.

[4]  Sunny Consolvo,et al.  Lullaby: a capture & access system for understanding the sleep environment , 2012, UbiComp.

[5]  K. Adam,et al.  Sleep as a restorative process and a theory to explain why. , 1980, Progress in brain research.

[6]  Dan Morris,et al.  There's no such thing as gaining a pound: reconsidering the bathroom scale user interface , 2013, UbiComp.

[7]  M. Kryger,et al.  Principles and Practice of Sleep Medicine: Fifth Edition , 2010 .

[8]  Jodi Forlizzi,et al.  A stage-based model of personal informatics systems , 2010, CHI.

[9]  Sean A. Munson,et al.  A lived informatics model of personal informatics , 2015, UbiComp.

[10]  Mark W. Newman,et al.  Learning from a learning thermostat: lessons for intelligent systems for the home , 2013, UbiComp.

[11]  Karen Holtzblatt,et al.  Contextual design , 1997, INTR.

[12]  A. Sadeh,et al.  The role of actigraphy in sleep medicine. , 2002, Sleep medicine reviews.

[13]  Daniel J Buysse,et al.  The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research , 1989, Psychiatry Research.

[14]  Daniel J Buysse.  Sleep health: can we define it? Does it matter? , 2014, Sleep.

[15]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[16]  Wanda Pratt,et al.  SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors , 2015, UbiComp.

[17]  Sean A. Munson,et al.  A framework for self-experimentation in personalized health , 2016, J. Am. Medical Informatics Assoc..

[18]  Daniel J Buysse,et al.  The role of sleep hygiene in promoting public health: A review of empirical evidence. , 2015, Sleep medicine reviews.

[19]  S. Quan,et al.  AASM Scoring Manual Updates for 2017 (Version 2.4). , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[20]  Gregory D. Abowd,et al.  Barriers and Negative Nudges: Exploring Challenges in Food Journaling , 2015, CHI.

[21]  Marijn C. W. Kroes,et al.  Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes? , 2014, Trends in Neurosciences.

[22]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[23]  Sean A. Munson,et al.  When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems , 2016, CHI.

[24]  Shwetak N. Patel,et al.  DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar , 2015, UbiComp.

[25]  Thuong N. Hoang,et al.  In Bed with Technology: Challenges and Opportunities for Sleep Tracking , 2015, OZCHI.

[26]  Anind K. Dey,et al.  Why and why not explanations improve the intelligibility of context-aware intelligent systems , 2009, CHI.

[27]  John Zimmerman,et al.  Toss 'n' turn: smartphone as sleep and sleep quality detector , 2014, CHI.

[28]  H. Montgomery-Downs,et al.  Movement toward a novel activity monitoring device , 2012, Sleep and Breathing.

[29]  Sunny Consolvo,et al.  Opportunities for computing technologies to support healthy sleep behaviors , 2011, CHI.

[30]  M. Hirshkowitz,et al.  Monitoring and Staging Human Sleep , 2013 .

[31]  Jeff Huang,et al.  SleepCoacher: A Personalized Automated Self-Experimentation System for Sleep Recommendations , 2016, UIST.

[32]  Mark W. Newman,et al.  When fitness trackers don't 'fit': end-user difficulties in the assessment of personal tracking device accuracy , 2015, UbiComp.

[33]  Lauren Hale,et al.  Inadequate sleep duration as a public health and social justice problem: can we truly trade off our daily activities for more sleep? , 2014, Sleep.