A Systematic Review on Visualizations for Self-Generated Health Data for Daily Activities
Open accessAbstract
Due to the development of sensing technology people can easily track their health in various ways, and the interest in personal healthcare data is increasing. Individuals are interested in controlling their wellness, which requires self-awareness and an understanding of various health conditions. Self-generated health data are easily accessed through mobile devices, and data visualization is commonly used in applications. A systematic literature review was conducted to better understand the role of visualizations and learn how to develop effective ones. Thirteen papers were analyzed for types of data, characteristics of visualizations, and effectiveness for healthcare management. The papers were selected because they represented research on personal health data and visualization in a non-clinical setting, and included health data tracked in everyday life. This paper suggests six levels for categorizing the efficacy of visualizations that take into account cognitive and physical changes in users. Recommendations for future work on conducting evaluations are also identified. This work provides a foundation for personal healthcare data as more applications are developed for mobile and wearable devices.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Greenwood, D. A., Gee, P. M., Fatkin, K. J., & Peeples, M. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817713506 · Cited by 526 · see citing works
Qudah, B., & Luetsch, K. (2019) · Patient Education and Counseling · doi:10.1016/j.pec.2019.01.021 · Cited by 200 · see citing works
Flagged first, then by citations
- Authors
- Kim, S.-H.
- Affiliations
- Dong-Eui University (KR)
- Corresponding author
- Kim, Sung-Hee
- ORCID
- Kim, Sung-Hee
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2022-09-06
- Volume
- 19
- Issue
- 18
- Pages
- 11166
- ISSN
- 1660-4601 (ISSN-L)
- PMC
- PMC9517532
- OpenAlex
- W4295065153
- Cited by
- 22
- References
- 2
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 9 · 2023: 4
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Environmental Research and Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- MSIT (Ministry of Science and ICT) (IITP-2022-2020-0-01791, 2022R1F1A1066060116)
- IITP (Institute for Information & communications Technology Planning&Evaluation) (IITP-2022-2020-0-01791, 2022R1F1A1066060116)
- Basic Science Research Program (IITP-2022-2020-0-01791, 2022R1F1A1066060116)
- National Research Foundation of Korea (NRF) (IITP-2022-2020-0-01791, 2022R1F1A1066060116)
- Ministry if Education (IITP-2022-2020-0-01791, 2022R1F1A1066060116)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- health data visualization, personal health data, mobile health, wearable devices, visualization effectiveness, health management
- MeSH terms
- Delivery of Health Care, Humans, Computers, Handheld
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
