research-article

Defining Adherence: Making Sense of Physical Activity Tracker Data

Authors:

Daniel A. Epstein,

Judy KayAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 2, Issue 1

Article No.: 37, Pages 1 - 22

https://doi.org/10.1145/3191769

Published: 26 March 2018 Publication History

Abstract

Increasingly, people are collecting detailed personal activity data from commercial trackers. Such data should be able to give important insights about their activity levels. However, people do not wear or carry tracking devices all day, every day and this means that tracker data is typically incomplete. This paper aims to provide a systematic way to take account of this incompleteness, by defining adherence, a measure of data completeness, based on how much people wore their tracker. We show the impact of different adherence definitions on 12 diverse datasets, for 753 users, with over 77,000 days with data, interspersed with over 73,000 days without data. For example, in one data set, one adherence measure gives an average step count of 6,952 where another gives 9,423. Our results show the importance of adherence when analysing and reporting activity tracker data. We provide guidelines for defining adherence, analysing its impact and reporting it along with the results of the tracker data analysis. Our key contribution is the foundation for analysis of physical activity data, to take account of data incompleteness.

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Index Terms

Defining Adherence: Making Sense of Physical Activity Tracker Data
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 2, Issue 1

March 2018

1370 pages

EISSN:2474-9567

DOI:10.1145/3200905

Issue’s Table of Contents

Copyright © 2018 ACM.

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Publication History

Published: 26 March 2018

Accepted: 01 January 2018

Revised: 01 January 2018

Received: 01 August 2017

Published in IMWUT Volume 2, Issue 1

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https://doi.org/10.1515/icom-2024-0007
Salehzadeh Niksirat KVelykoivanenko LZufferey NCherubini MHuguenin KHumbert M(2024)Wearable Activity Trackers: A Survey on Utility, Privacy, and SecurityACM Computing Surveys10.1145/364509156:7(1-40)Online publication date: 9-Apr-2024
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Kouaho WEpstein D(2024)Socioeconomic Class in Physical Activity Wearables Research and DesignProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642789(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642789
Strömel KHenry SJohansson TNiess JWoźniak P(2024)Narrating Fitness: Leveraging Large Language Models for Reflective Fitness Tracker Data InterpretationProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642032(1-16)Online publication date: 11-May-2024
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