research-article

Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis

Authors:

Simon L. Jones,

Rosemarie Barnett,

Violet Henderson,

Raj SenguptaAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 16, Pages 1 - 24

https://doi.org/10.1145/3448093

Published: 30 March 2021 Publication History

Abstract

The use of interactive mobile and wearable technologies for understanding and managing health conditions is a growing area of interest for patients, health professionals and researchers. Self-tracking technologies such as smartphone apps and wearable devices for measuring symptoms and behaviours generate a wealth of patient-centric data with the potential to support clinical decision making. However, the utility of self-tracking technologies for providing insight into patients' conditions is impacted by poor adherence with data logging. This paper explores factors associated with adherence in smartphone-based tracking, drawing on two studies of patients living with axial spondyloarthritis (axSpA), a chronic rheumatological condition. In Study 1, 184 axSpA patients used the uMotif health tracking smartphone app for a period of up to 593 days. In Study 2, 108 axSpA patients completed a survey about their experience of using self-tracking technologies. We identify six significant correlates of self-tracking adherence, providing insight into the determinants of tracking behaviour. Specifically, our data provides evidence that adherence correlates with the age of the user, the types of tracking devices that are being used (smartphone OS and physical activity tracker), preferences for types of data to record, the timing of interactions with a self-tracking app, and the reported symptom severity of the user. We discuss how these factors may have implications for those designing, deploying or using mobile and wearable tracking technologies to support monitoring and management of chronic diseases.

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Cited By

Moorthy PWeinert LSchüttler CSvensson LSedlmayr BMüller JNagel T(2024)Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping ReviewJMIR mHealth and uHealth10.2196/5217912(e52179)Online publication date: 5-Apr-2024
https://doi.org/10.2196/52179
Xia KLi WShao YLu SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Vi2ACT:Video-enhanced Cross-modal Co-learning with Representation Conditional Discriminator for Few-shot Human Activity RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681664(1848-1856)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681664
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
https://dl.acm.org/doi/10.1145/3645091
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Index Terms

Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence from Axial Spondyloarthritis
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing

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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 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

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Copyright © 2021 ACM.

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Published: 30 March 2021

Published in IMWUT Volume 5, Issue 1

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Cited By

Moorthy PWeinert LSchüttler CSvensson LSedlmayr BMüller JNagel T(2024)Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping ReviewJMIR mHealth and uHealth10.2196/5217912(e52179)Online publication date: 5-Apr-2024
https://doi.org/10.2196/52179
Xia KLi WShao YLu SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Vi2ACT:Video-enhanced Cross-modal Co-learning with Representation Conditional Discriminator for Few-shot Human Activity RecognitionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681664(1848-1856)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681664
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
https://dl.acm.org/doi/10.1145/3645091
Bian SLiu MZhou BLukowicz PMagno M(2024)Body-Area Capacitive or Electric Field Sensing for Human Activity Recognition and Human-Computer InteractionProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435558:1(1-49)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643555
Xia KLi WGan SLu S(2024)TS2ACTProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314457:4(1-22)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631445
Stefanidi EWassmann JWoźniak PSpellmeyer GRogers YNiess J(2024)MoodGems: Designing for the Well-being of Children with ADHD and their Families at HomeProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655795(480-494)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655795
Brozena JBlair JRichardson TMatthews MMukherjee DSaunders EAbdullah S(2024)Supportive Fintech for Individuals with Bipolar Disorder: Financial Data Sharing Preferences for Longitudinal Care ManagementProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642645(1-15)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642645
Velykoivanenko LSalehzadeh Niksirat KTeofanovic SChapuis BMazurek MHuguenin K(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
https://dl.acm.org/doi/10.1145/3613904.3642572
Beierle FChada WAizawa APryss R(2024)Predicting adherence to ecological momentary assessmentsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124738255:PCOnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124738
Thomassen EBerg IKristianslund ETveter ABakland GGossec LHakim SMacfarlane Gde Thurah AØsterås N(2024)Patients with axial spondyloarthritis reported willingness to use remote care and showed high adherence to electronic patient-reported outcome measures: an 18-month observational studyRheumatology International10.1007/s00296-024-05673-744:10(2089-2098)Online publication date: 21-Aug-2024
https://doi.org/10.1007/s00296-024-05673-7
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