Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Apr 28;9(4):e25249.
doi: 10.2196/25249.

Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives

Ashley Polhemus  1   2 , Jan Novak  3 , Shazmin Majid  3   4 , Sara Simblett  5 , Daniel Morris  5 , Stuart Bruce  6 , Patrick Burke  6 , Marissa F Dockendorf  7 , Gergely Temesi  8 , Til Wykes  5   9
Affiliations
Review

Data Visualization for Chronic Neurological and Mental Health Condition Self-management: Systematic Review of User Perspectives

Ashley Polhemus et al. JMIR Ment Health. .

Abstract

Background: Remote measurement technologies (RMT) such as mobile health devices and apps are increasingly used by those living with chronic neurological and mental health conditions. RMT enables real-world data collection and regular feedback, providing users with insights about their own conditions. Data visualizations are an integral part of RMT, although little is known about visualization design preferences from the perspectives of those living with chronic conditions.

Objective: The aim of this review was to explore the experiences and preferences of individuals with chronic neurological and mental health conditions on data visualizations derived from RMT to manage health.

Methods: In this systematic review, we searched peer-reviewed literature and conference proceedings (PubMed, IEEE Xplore, EMBASE, Web of Science, Association for Computing Machinery Computer-Human Interface proceedings, and the Cochrane Library) for original papers published between January 2007 and September 2021 that reported perspectives on data visualization of people living with chronic neurological and mental health conditions. Two reviewers independently screened each abstract and full-text article, with disagreements resolved through discussion. Studies were critically appraised, and extracted data underwent thematic synthesis.

Results: We identified 35 eligible publications from 31 studies representing 12 conditions. Coded data coalesced into 3 themes: desire for data visualization, impact of visualizations on condition management, and visualization design considerations. Data visualizations were viewed as an integral part of users' experiences with RMT, impacting satisfaction and engagement. However, user preferences were diverse and often conflicting both between and within conditions.

Conclusions: When used effectively, data visualizations are valuable, engaging components of RMT. They can provide structure and insight, allowing individuals to manage their own health more effectively. However, visualizations are not "one-size-fits-all," and it is important to engage with potential users during visualization design to understand when, how, and with whom the visualizations will be used to manage health.

Keywords: data visualization; digital health; mental health; neurology; remote measurement technology; user-centered design.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: AP, JN, SM, MFD, and GT are employees of Merck, Sharp, and Dohme, Inc, and may own stock or stock options.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic review and Meta-Analysis) flow diagram of study screening and selection. RMT: remote measurement technology.
Figure 2
Figure 2
Data visualization as an integral personal component of health management. RMT: remote measurement technology.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Gurupur VP, Wan TTH. Challenges in implementing mHealth interventions: a technical perspective. Mhealth. 2017;3:32. doi: 10.21037/mhealth.2017.07.05. doi: 10.21037/mhealth.2017.07.05.mh-03-2017.07.05 - DOI - DOI - PMC - PubMed
    1. Mohammadzadeh N, Safdari R. Patient monitoring in mobile health: opportunities and challenges. Med Arch. 2014;68(1):57–60. doi: 10.5455/medarh.2014.68.57-60. http://europepmc.org/abstract/MED/24783916 - DOI - PMC - PubMed
    1. Nouri R, R Niakan Kalhori S, Ghazisaeedi M, Marchand G, Yasini M. Criteria for assessing the quality of mHealth apps: a systematic review. J Am Med Inform Assoc. 2018 Aug 01;25(8):1089–1098. doi: 10.1093/jamia/ocy050. http://europepmc.org/abstract/MED/29788283 4996915 - DOI - PMC - PubMed
    1. Orlowski S, Matthews B, Bidargaddi N, Jones G, Lawn S, Venning A, Collin P. Mental Health Technologies: Designing With Consumers. JMIR Hum Factors. 2016 Jan 28;3(1):e4. doi: 10.2196/humanfactors.4336. https://humanfactors.jmir.org/2016/1/e4/ v3i1e4 - DOI - PMC - PubMed
    1. Altman M, Huang TTK, Breland JY. Design Thinking in Health Care. Prev Chronic Dis. 2018 Sep 27;15:E117. doi: 10.5888/pcd15.180128. https://www.cdc.gov/pcd/issues/2018/18_0128.htm E117 - DOI - PMC - PubMed

LinkOut - more resources