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

Published: 12 September 2016 Publication History

Abstract

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.

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Cited By

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  • (2024)Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using WearablesDiagnostics10.3390/diagnostics1405050114:5(501)Online publication date: 26-Feb-2024
  • (2024)Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of fociSocial Networks10.1016/j.socnet.2022.02.00477(151-165)Online publication date: May-2024
  • (2023)Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble LearningProceedings of the 2023 8th International Conference on Machine Learning Technologies10.1145/3589883.3589900(116-120)Online publication date: 10-Mar-2023
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  1. Experiences measuring sleep and physical activity patterns across a large college cohort with fitbits

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    cover image ACM Conferences
    ISWC '16: Proceedings of the 2016 ACM International Symposium on Wearable Computers
    September 2016
    207 pages
    ISBN:9781450344609
    DOI:10.1145/2971763
    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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    Publication History

    Published: 12 September 2016

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    Author Tags

    1. health
    2. mobile sensing
    3. social aspects
    4. user studies

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    UbiComp '16

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    ISWC '16 Paper Acceptance Rate 18 of 95 submissions, 19%;
    Overall Acceptance Rate 38 of 196 submissions, 19%

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    Cited By

    View all
    • (2024)Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using WearablesDiagnostics10.3390/diagnostics1405050114:5(501)Online publication date: 26-Feb-2024
    • (2024)Generators or diffusers? Examining differences in the dynamic coupling of context and social ties across multiple types of fociSocial Networks10.1016/j.socnet.2022.02.00477(151-165)Online publication date: May-2024
    • (2023)Sleep Quality Prediction from Wearables using Convolution Neural Networks and Ensemble LearningProceedings of the 2023 8th International Conference on Machine Learning Technologies10.1145/3589883.3589900(116-120)Online publication date: 10-Mar-2023
    • (2023)A Study on Mobile Crowd Sensing Systems for Healthcare ScenariosIEEE Access10.1109/ACCESS.2023.334215811(140325-140347)Online publication date: 2023
    • (2023)Predicting Relationship Labels and Individual Personality Traits From Telecommunication History in Social Networks Using Hawkes ProcessesIEEE Access10.1109/ACCESS.2023.323897011(8492-8503)Online publication date: 2023
    • (2023)Nightly sleep duration predicts grade point average in the first year of collegeProceedings of the National Academy of Sciences10.1073/pnas.2209123120120:8Online publication date: 13-Feb-2023
    • (2022)Predicting Homophily and Social Network Connectivity From Dyadic Behavioral Similarity Trajectory ClustersSocial Science Computer Review10.1177/089443932092312340:1(195-211)Online publication date: 22-Jun-2022
    • (2022)Sleep Patterns and Sleep Alignment in Remote Teams during COVID-19Proceedings of the ACM on Human-Computer Interaction10.1145/35552176:CSCW2(1-31)Online publication date: 11-Nov-2022
    • (2022)Feasibility of Longitudinal Eye-Gaze Tracking in the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/35308896:ETRA(1-21)Online publication date: 13-May-2022
    • (2022)Semantic Gap in Predicting Mental Wellbeing through Passive SensingProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502037(1-16)Online publication date: 29-Apr-2022
    • Show More Cited By

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