The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data

Lack of sleep can erode mental and physical well-being, often exacerbating health problems such as obesity. Wearable devices that capture and analyze sleep quality through predictive methodologies can help patients and medical practitioners make behavioral health decisions that can lead to better sleep and improved health. In the web extra at https://youtu.be/_zL-t4gk210, guest editor Katarzyna Wac interviews lead author Aarti Sathyanarayana, a PhD student in the University of Minnesota's Department of Computer Science.

[1]  A. Sadeh.  The role and validity of actigraphy in sleep medicine: an update. , 2011, Sleep medicine reviews.

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

[3]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[4]  Meir Kryger,et al.  Reducing motor-vehicle collisions, costs, and fatalities by treating obstructive sleep apnea syndrome. , 2004, Sleep.

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

[6]  Shahrad Taheri,et al.  Sleep Optimization and Diabetes Control: A Review of the Literature , 2015, Diabetes Therapy.

[7]  Jaideep Srivastava,et al.  Robust Automated Human Activity Recognition and Its Application to Sleep Research , 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

[8]  Masato Matsuura,et al.  Newly developed waist actigraphy and its sleep/wake scoring algorithm , 2009 .

[9]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[10]  Harsh Chawla,et al.  Physical activity as a predictor of thirty-day hospital readmission after a discharge for a clinical exacerbation of chronic obstructive pulmonary disease. , 2014, Annals of the American Thoracic Society.

[11]  David S. Matteson,et al.  ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data , 2013, 1309.3295.

[12]  Catherine P. Jayapandian,et al.  Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource. , 2016, Sleep.

[13]  Shafiq R. Joty,et al.  Impact of Physical Activity on Sleep: A Deep Learning Based Exploration , 2016, ArXiv.

[14]  Yul Ha Min,et al.  Daily Collection of Self-Reporting Sleep Disturbance Data via a Smartphone App in Breast Cancer Patients Receiving Chemotherapy: A Feasibility Study , 2014, Journal of medical Internet research.

[15]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[16]  David Geerts,et al.  Sleep monitoring tools at home and in the hospital: Bridging quantified self and clinical sleep research , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[17]  E. Bixler,et al.  Sleep and society: an epidemiological perspective. , 2009, Sleep medicine.

[18]  Geehyuk Lee,et al.  Accelerometer Signal Processing for User Activity Detection , 2004, KES.

[19]  W. Sacco,et al.  Measuring Sleep Efficiency: What Should the Denominator Be? , 2016, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[20]  Georgios C. Anagnostopoulos,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2003, Lecture Notes in Computer Science.

[21]  D. J. Mullaney,et al.  Automatic sleep/wake identification from wrist activity. , 1992, Sleep.

[22]  Tanzeem Choudhury,et al.  Towards circadian computing: "early to bed and early to rise" makes some of us unhealthy and sleep deprived , 2014, UbiComp.

[23]  Max Hirshkowitz,et al.  The History of Polysomnography: Tool of Scientific Discovery , 2015 .

[24]  S. Redline,et al.  The Role of Big Data in the Management of Sleep-Disordered Breathing. , 2016, Sleep medicine clinics.

[25]  Shafiq R. Joty,et al.  Sleep Quality Prediction From Wearable Data Using Deep Learning , 2016, JMIR mHealth and uHealth.