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

Published: 02 May 2017 Publication History

Abstract

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.

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

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    cover image ACM Conferences
    CHI '17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems
    May 2017
    7138 pages
    ISBN:9781450346559
    DOI:10.1145/3025453
    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 ACM 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]

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    Published: 02 May 2017

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    Author Tags

    1. behavior change
    2. health monitoring
    3. personal informatics
    4. quantified self
    5. sleep
    6. sleep sensing
    7. sleep tracking

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    CHI '17 Paper Acceptance Rate 600 of 2,400 submissions, 25%;
    Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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    Cited By

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    • (2025)Perspectives Regarding Consumer Sleep Technology and Barriers to its Use or Adoption Among Adults in the United StatesSleep Medicine10.1016/j.sleep.2025.02.004Online publication date: Feb-2025
    • (2024)Classifying Sleep Health and Lifestyle PatternsRevolutionizing Healthcare Systems Through Cloud Computing and IoT10.4018/979-8-3693-7225-8.ch007(151-178)Online publication date: 1-Nov-2024
    • (2024)Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric CareSensors10.3390/s2421680324:21(6803)Online publication date: 23-Oct-2024
    • (2024)Autoethnography of Living with a Sleep RobotMultimodal Technologies and Interaction10.3390/mti80600538:6(53)Online publication date: 18-Jun-2024
    • (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
    • (2024)Testing a consumer wearables program to promote use of positive airway pressure therapy in patients with obstructive sleep apnea: protocol for a pilot randomized controlled trial (Preprint)JMIR Research Protocols10.2196/60769Online publication date: 20-May-2024
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    • (2024)Say You, Say Me: Investigating the Personal insights Generated from One's Own data and Other's dataProceedings of the 13th Nordic Conference on Human-Computer Interaction10.1145/3679318.3685345(1-14)Online publication date: 13-Oct-2024
    • (2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 8-Feb-2024
    • (2024)Designing a Data-Driven Survey System: Leveraging Participants' Online Data to Personalize SurveysProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642572(1-22)Online publication date: 11-May-2024
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