Cited by 326Mobile Health and mHealth ApplicationsContext-Aware Activity Recognition SystemsInnovative Human-Technology Interaction

The DOI matches its Crossref record; 176 of the 182 references it cites were found in a registry or an index.

Wearable activity trackers, accuracy, adoption, acceptance and health impact: A systematic literature review

Open access
Shin, Grace, Jarrahi, Mohammad Hossein, Fei, Yu, Karami, Amir, Gafinowitz, Nicci, Byun, Ahjung, Lu, Xiaopeng·2019·Journal of Biomedical Informatics

Abstract

Wearable activity trackers (WAT) are electronic monitoring devices that enable users to track and monitor their health-related physical fitness metrics including steps taken, level of activity, walking distance, heart rate, and sleep patterns. Despite the proliferation of these devices in various contexts of use and rising research interests, there is limited understanding of the broad research landscape. The purpose of this systematic review is therefore to synthesize the existing wealth of research on WAT, and to provide a comprehensive summary based on common themes and approaches. This article includes academic work published between 2013 and 2017 in PubMed, Embase, Scopus, Web of Science, ACM Digital Library, and Google Scholar. A final list of 463 articles was analyzed for this review. Topic modeling methods were used to identify six key themes (topics) of WAT research, namely: (1) Technology Focus, (2) Patient Treatment and Medical Settings, (3) Behavior Change, (4) Acceptance and Adoption (Abandonment), (5) Self-monitoring Data Centered, and (6) Privacy. We take an interdisciplinary approach to wearable activity trackers to propose several new research questions. The most important research gap we identify is to attempt to understand the rich human-information interaction that is enabled by WAT adoption.

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6 of the 182 references couldn’t be matched to a known paper.

Research works printed but not linked: 5

  1. Cyr, 2014, Security analysis of wearable fitness devices (fitbit), Massachusetts Inst. Technol., 1
  2. Sena, 2015, Validity and usability testing of the fitbit pedometer in patients with COPD, European Respiratory J.: Official J. European Soc. Clin. Respiratory Physiol., 46, PA2067
  3. Ledger, 2014, Inside wearables: how the science of human behavior change offers the secret to long-term engagement, Endeavour Partners, 200, 1
  4. Motti, 2014, Human factors considerations in the design, Human Factors, 927–931
  5. IDC. Worldwide Wearables Market Grows 7.3% in Q3 2017 as Smart Wearables Rise and Basic Wearables Decline, Says IDC. Press release from IDC on November 30, 2017. https://www.idc.com/getdoc.jsp?containerId=prUS43260217. Accessed March 25, 2019.

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Metadata

Authors
Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X.
Affiliations
  • University of North Carolina at Chapel Hill (US)
  • University of South Carolina (US)
  • Seoul National University (KR)
Corresponding author
Shin, Grace
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2019-05
Volume
93
Pages
103153
ISSN
1532-0464 (ISSN-L)
OpenAlex
W2927358919
Cited by
326
References
182
Citations by year
2026: 23 · 2025: 45 · 2024: 45 · 2023: 47 · 2022: 52 · 2021: 63 · 2020: 42 · 2019: 9
FWCI
7.61
Type
journal article
Language
en
Open access
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
  • Scholar Commons (University of South Carolina) (submittedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
Keywords
wearable activity trackers, physical activity monitoring, device accuracy, technology adoption, user acceptance, abandonment, behavior change, self-monitoring, privacy, human-computer interaction, health effects, wearable sensors
MeSH terms
Fitness Trackers, Adult, Diffusion of Innovation, Humans, Patient Acceptance of Health Care

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