Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits

In the past few years, a wide variety of highly capable and inexpensive wearable health sensors have emerged. One of the interesting aspects of such sensors is the capability for researchers to longitudinally and automatically quantify important health behaviors, such as physical activity and sleep, with little intervention required by the participant. While the accuracy of these devices has been evaluated in laboratory settings, there exists little public data with respect to user compliance and the consistency of the resulting measurements at a large scale. The focus of this paper is to share our experience in distributing five hundred Fitbit Charge HR devices across a group of college freshmen and to introduce the resulting dataset from our study, the NetHealth Study. We find that when users are compliant, they tend to be exceptionally so, having an average compliance of 86%. User non-compliance does play a role, however, reducing the overall average compliance rate to 67%. We discuss various reasons for non-compliance and also briefly highlight preliminary monitored characteristics of physical activity and sleep in our student population.

[1]  Nadia Bianchi-Berthouze,et al.  Tracking physical activity: problems related to running longitudinal studies with commercial devices , 2014, UbiComp Adjunct.

[2]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[3]  Akane Sano,et al.  Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).

[4]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[5]  H. G. Lund,et al.  Sleep patterns and predictors of disturbed sleep in a large population of college students. , 2010, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[6]  Piotr Sapiezynski,et al.  Measuring Large-Scale Social Networks with High Resolution , 2014, PloS one.

[7]  Jianmin Guan,et al.  A Meta-Analysis of College Students' Physical Activity Behaviors , 2005, Journal of American college health : J of ACH.

[8]  Ling-Ling Tsai,et al.  Sleep patterns in college students: gender and grade differences. , 2004, Journal of psychosomatic research.

[9]  Gregory J Welk,et al.  Accuracy of armband monitors for measuring daily energy expenditure in healthy adults. , 2010, Medicine and science in sports and exercise.

[10]  Christian Poellabauer,et al.  Lessons learned from the netsense smartphone study , 2013, HotPlanet '13.

[11]  Alex Pentland,et al.  Social fMRI: Investigating and shaping social mechanisms in the real world , 2011, Pervasive Mob. Comput..

[12]  C. Tudor-Locke,et al.  How Many Steps/Day Are Enough? , 2004, Sports medicine.