short-paper

Sleep tracking in the real world: a qualitative study into barriers for improving sleep

Authors:

Bernd PlodererAuthors Info & Claims

OzCHI '16: Proceedings of the 28th Australian Conference on Computer-Human Interaction

Pages 537 - 541

https://doi.org/10.1145/3010915.3010988

Published: 29 November 2016 Publication History

Abstract

Wearable devices like Fitbit and Apple Watch provide convenient access to personal information about sleep habits. However, it is unclear if awareness of one's sleep habits also translates into improved sleep. Hence, we conducted an interview study with 12 people who track their sleep with Fitbit devices to investigate if they have managed to improve their sleep and to examine potential barriers for improving sleep. The participants reported increased awareness of sleep habits, but none of the participants managed to improve their sleep. They faced three barriers in improving their sleep: (1) not knowing what is normal sleep, (2) not being able to diagnose the reasons for a lack of sleep, and (3) not knowing how to act. This paper discusses how to address these barriers, both conceptually as well through design considerations - reference points, connections to lifestyle data, and personalised recommendations - to help users gain improvements in wellbeing from their personal data.

References

[1]

Braun, V. and Clarke, V. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77--101.

[2]

Choe, E.K., Consolvo, S., Watson, N.F. and Kientz, J.A. Opportunities for computing technologies to support healthy sleep behaviors. In Proc. CHI 2011, ACM (2011), 3053--3062.

Digital Library

[3]

Choe, E.K., Lee, B., Kay, M., Pratt, W. and Kientz, J.A. SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors. In Proc. UbiComp 2015, ACM (2015), 121--132.

Digital Library

[4]

Cordeiro, F., Bales, E., Cherry, E. and Fogarty, J. Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture. In Proc. CHI 2015, ACM (2015), 3207--3216.

Digital Library

[5]

Dijk, D.J. and von Schantz, M. Timing and consolidation of human sleep, wakefulness, and performance by a symphony of oscillators. Journal of Biological Rhythms 20, 4 (2005), 279--290.

[6]

Evenson, K.R., Goto, M.M. and Furberg, R.D. Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity 12, 1 (2015), 1--22.

[7]

Fox, S. and Duggan, M. Tracking for Health. Pew Internet and American Life Project, Washington, DC, 2013, http://www.pewinternet.org/Reports/2013/Tracking-for-Health.aspx

[8]

Fritz, T., Huang, E.M., Murphy, G.C. and Zimmermann, T. Persuasive technology in the real world: a study of long-term use of activity sensing devices for fitness. In Proc. CHI 2014, ACM (2014), 487--496.

Digital Library

[9]

Klasnja, P., Consolvo, S. and Pratt, W. How to evaluate technologies for health behavior change in HCI research, In CHI 2011.ACM, (2011), 3063--3072.

Digital Library

[10]

Kolla, B.P., Mansukhani, S. and Mansukhani, M.P. Consumer sleep tracking devices: a review of mechanisms, validity and utility. Expert Review of Medical Devices 13, 5 (2016), 497--506.

[11]

Lawson, S., Jamison-Powell, S., Garbett, A., Linehan, C., Kucharczyk, E., Verbaan, S., Rowland, D.A. and Morgan, K. Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep. In Proc. CHI 2013, ACM (2013), 2497--2506.

Digital Library

[12]

Li, I., Dey, A. and Forlizzi, J. A stage-based model of personal informatics systems. In Proc. CHI 2010, ACM (2010), 557--566.

Digital Library

[13]

Liang, Z., Alberto, M., Chapa Martell, M.A. and Nishimura, T. Mining hidden correlations between sleep and lifestyle factors from quantified-self data. In Adjunct Proc. Ubicomp/ISWC 2016 (2016).

Digital Library

[14]

Liu, W., Ploderer, B. and Hoang, T. In bed with technology: challenges and opportunities for sleep tracking. In Proc. OzCHI 2015, ACM (2015), 142--151.

Digital Library

[15]

Lupton, D. Self-tracking cultures: towards a sociology of personal informatics. In Proc. OzCHI 2014, ACM (2014), 77--86.

Digital Library

[16]

Min, J.-K., Doryab, A., Wiese, J., Amini, S., Zimmerman, J. and Hong, J.I. Toss 'n' turn: smartphone as sleep and sleep quality detector. In Proc. CHI 2014, ACM (2014), 477--486.

Digital Library

[17]

Rooksby, J., Rost, M., Morrison, A. and Chalmers, M.C. Personal Tracking as Lived Informatics. In Proc. CHI 2014 (2014), 1163--1172

Digital Library

[18]

Shirazi, A.S., Clawson, J., Hassanpour, Y., Tourian, M.J., Schmidt, A., Chi, E.H., Borazio, M. and Laerhoven, K. V. Already up? using mobile phones to track & share sleep behavior. Internation Journal Human-Computer Studies 71, 9 (2013), 878--888.

Digital Library

[19]

Smith, W., Wadley, G., Webber, S., Ploderer, B. and Lederman, R. Unbounding the interaction design problem: the contribution of HCI in three interventions for well-being. In Proc. OZCHI 2014, ACM (2014), 392--395.

Digital Library

[20]

Wolf, G. The quantified self. TED (2010), Retrieved from http://www.ted.com/talks/gary_wolf_the_quantified_self.html

[21]

Xiao, M., Yan, H., Song, J., Yang, Y. and Yang, X. Sleep stages classification based on heart rate variability and random forest. Biomedical Signal Processing and Control 8, 6 (2013), 624--633.

Cited By

Nagele AHough J(2024)“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-trackingFrontiers in Computer Science10.3389/fcomp.2024.12582896Online publication date: 21-Feb-2024
https://doi.org/10.3389/fcomp.2024.1258289
Manning JBlandford AEdbrooke-Childs J(2024)Facilitators of and Barriers to Teachers’ Engagement With Consumer Technologies for Stress Management: Qualitative StudyJournal of Medical Internet Research10.2196/5045726(e50457)Online publication date: 22-Oct-2024
https://doi.org/10.2196/50457
Dritsa DHouben S(2024)How Design Researchers Make Sense of Data Visualizations in Data-Driven Design: An Uncertainty-Aware Sensemaking ModelACM Transactions on Computer-Human Interaction10.1145/368526831:6(1-53)Online publication date: 31-Jul-2024
https://dl.acm.org/doi/10.1145/3685268
Show More Cited By

Index Terms

Sleep tracking in the real world: a qualitative study into barriers for improving sleep
1. Human-centered computing

Recommendations

SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors
UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Manual tracking of health behaviors affords many benefits, including increased awareness and engagement. However, the capture burden makes long-term manual tracking challenging. In this study on sleep tracking, we examine ways to reduce the capture ...
Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health
CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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,...
Looking for the unusual: how older adults utilize self-tracking techniques for health management
PervasiveHealth '16: Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare

Self-tracking applications for health management have become popular both in industry and in academia. Half of older adults in the U.S. track health indicators, but they rarely use technology for that purpose. We conduct a qualitative study to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

OzCHI '16: Proceedings of the 28th Australian Conference on Computer-Human Interaction

November 2016

706 pages

ISBN:9781450346184

DOI:10.1145/3010915

Conference Chairs:
Henry Duh
University of Tasmania Christopher Lueg, University of Tasmania
,
Christopher Lueg
University of Tasmania
,
Program Chairs:
Mark Billinghust
University of South Australia
,
Weidong Huang
University of Tasmania

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

IEEE-SMCS: Systems, Man & Cybernetics Society
Australian Comp Soc: Australian Computer Society
Data61: Data61, CSIRO
ICACHI: International Chinese Association of Computer Human Interaction
Infoxchange: Infoxchange
HITLab AU: Human Interface Technology Laboratory Australia
James Boag: James Boag
Tourism Tasmania: Tourism Tasmania
HFESA: Human Factors and Ergonomics Society of Australia Inc.
IEEEVIC: IEEE Victorian Section
UTAS: University of Tasmania, Australia

In-Cooperation

ACM: Association for Computing Machinery

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 November 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

OzCHI '16

Sponsor:

IEEE-SMCS
Australian Comp Soc
Data61
ICACHI
Infoxchange
HITLab AU
James Boag
Tourism Tasmania
HFESA
IEEEVIC
UTAS

OzCHI '16: The 28th Australian Conference on Human-Computer Interaction

November 29 - December 2, 2016

Tasmania, Launceston, Australia

Acceptance Rates

Overall Acceptance Rate 362 of 729 submissions, 50%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

46
Total Citations
View Citations
1,752
Total Downloads

Downloads (Last 12 months)207
Downloads (Last 6 weeks)23

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Nagele AHough J(2024)“The sleep data looks way better than I feel.” An autoethnographic account and diffractive reading of sleep-trackingFrontiers in Computer Science10.3389/fcomp.2024.12582896Online publication date: 21-Feb-2024
https://doi.org/10.3389/fcomp.2024.1258289
Manning JBlandford AEdbrooke-Childs J(2024)Facilitators of and Barriers to Teachers’ Engagement With Consumer Technologies for Stress Management: Qualitative StudyJournal of Medical Internet Research10.2196/5045726(e50457)Online publication date: 22-Oct-2024
https://doi.org/10.2196/50457
Dritsa DHouben S(2024)How Design Researchers Make Sense of Data Visualizations in Data-Driven Design: An Uncertainty-Aware Sensemaking ModelACM Transactions on Computer-Human Interaction10.1145/368526831:6(1-53)Online publication date: 31-Jul-2024
https://dl.acm.org/doi/10.1145/3685268
Li HXu BSun ZYan RWu YZhang HZhou KXu L(2024)The role of comfort, personality, and intention in smartwatch usage during sleepHumanities and Social Sciences Communications10.1057/s41599-024-03214-y11:1Online publication date: 30-May-2024
https://doi.org/10.1057/s41599-024-03214-y
Feng SMäntymäki M(2024)Stressors of Sleep Tracking: Instrument Development and ValidationDisruptive Innovation in a Digitally Connected Healthy World10.1007/978-3-031-72234-9_29(344-357)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-3-031-72234-9_29
Liang Z(2023)Effect of Decision Boundary for Logistic Regression Classifiers on Sleep Apnea Screening Accuracy with Wearable SpO2 Data2023 Fourteenth International Conference on Mobile Computing and Ubiquitous Network (ICMU)10.23919/ICMU58504.2023.10412217(1-4)Online publication date: 29-Nov-2023
https://doi.org/10.23919/ICMU58504.2023.10412217
Hoang NLiang Z(2023)Knowledge Discovery in Ubiquitous and Personal Sleep Tracking: Scoping ReviewJMIR mHealth and uHealth10.2196/4275011(e42750)Online publication date: 28-Jun-2023
https://doi.org/10.2196/42750
Kabir KWiese J(2023)A Meta-Synthesis of the Barriers and Facilitators for Personal Informatics SystemsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108937:3(1-35)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3610893
Liang Z(2023)Multiscale Attention Entropy (MSAE) of Overnight Pulse Oximetry for Assessing Sleep ApneaProceedings of the 2023 7th International Conference on Medical and Health Informatics10.1145/3608298.3608314(77-80)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1145/3608298.3608314
Semertzidis NLi Pin Hiung AVranic-Peters MMueller F(2023)Dozer: Towards understanding the design of closed-loop wearables for sleepProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581044(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581044
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten