journal article·2019-08-14Open access

Smartphone-Based Point-of-Care Cholesterol Blood Test Performance Evaluation Compared with a Clinical Diagnostic Laboratory Method

Kyongsik Yun, Jaekyu Choi, In-Uk Song, Yong-An Chung

Applied Sciences· Volume 9· Issue 16

Abstract

Managing blood cholesterol levels is important for the treatment and prevention of diabetes, cardiovascular disease, and obesity. An easy-to-use, portable cholesterol blood test could accelerate more frequent testing by patients and at-risk populations. We aim to evaluate the performance of smartphone-based point-of-care cholesterol blood tests compared to that of hospital-grade laboratory tests. We used smartphone systems that are already familiar to many people. Because smartphone systems can be carried around everywhere, blood can be measured easily and frequently. We compared the results of cholesterol tests with those of existing clinical diagnostic laboratory methods. We found that smartphone-based point-of-care lipid blood tests were as accurate as hospital-grade laboratory tests (N = 116, R > 0.97, p < 0.001 for all three cholesterol blood tests, i.e., total cholesterol, high density lipoprotein, and triglyceride). Our system could be useful for those who need to manage blood cholesterol levels to motivate them to track and control their behavior.
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Research context

OpenAlex topics: Mobile Health and mHealth Applications · Biosensors and Analytical Detection · Advanced Biosensing Techniques and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

BBB Inc., Seoul 06194, Korea

Department of Neurology, Incheon St. Mary’s Hospital, The Catholic University of Korea, Incheon 21431, Korea

Department of Radiology, Incheon St. Mary’s Hospital, The Catholic University of Korea, Incheon 21431, Korea

References (18)

Show source-provided references
1. The Significance of Blood Lipid Alterations in Diabetes Mellitus10.2337/diab.12.3.208 ↗
2. Test of effect of lipid lowering by diet on cardiovascular risk. The Minnesota Coronary Survey
3. Infant feeding and adult glucose tolerance, lipid profile, blood pressure, and obesity10.1136/adc.82.3.248 ↗
4. Pai, N.P., Vadnais, C., Denkinger, C., Engel, N., and Pai, M. (2012). Point-of-Care Testing for Infectious Diseases: Diversity, Complexity, and Barriers in Low- And Middle-Income Countries. PLoS Med., 9.10.1371/journal.pmed.1001306 ↗
5. Cholesterol testing on a smartphone10.1039/C3LC51194D ↗
6. Assessment of the diagnostic accuracy of laboratory tests using receiver operating characteristic curves; approved guideline
7. International Organization for Standardization (2003). In Vitro Diagnostic Test Systems: Requirements for Blood-glucose Monitoring Systems for Self-testing in Managing Diabetes Mellitus, ISO.
8. (2018, March 26). 45 CFR 46, Available online: https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html.
9. Performance of some resistant rules for outlier labeling10.1080/01621459.1986.10478363 ↗
10. Tukey and data analysis10.1214/ss/1076102418 ↗
11. Comparative performance of two point-of-care analysers for lipid testing
12. Smartphone technology can be transformative to the deployment of lab-on-chip diagnostics10.1039/C4LC00142G ↗
13. Roda, A., Calabretta, M.M., Calabria, D., Caliceti, C., Cevenini, L., Lopreside, A., and Zangheri, M. (2017). Smartphone-Based Biosensors for Bioanalytics: A Critical Review. Past, Present and Future Challenges of Biosensors and Bioanalytical Tools in Analytical Chemistry: A Tribute to Professor Marco Mascini, Volume 77, Elsevier Science Ltd.. Chapter 8.10.1016/bs.coac.2017.05.007 ↗
14. Cholesterol point-of-care testing for community pharmacies: A review of the current literature10.1177/0897190016645023 ↗
15. Does use of point-of-care testing improve cost-effectiveness of the NHS Health Check programme in the primary care setting? A cost-minimisation analysis10.1136/bmjopen-2016-015494 ↗
16. Classification of suicide attempts through a Machine learning algorithm Based on Multiple systemic Psychiatric scales10.3389/fpsyt.2017.00192 ↗
17. Oh, J., Yun, K., Maoz, U., Kim, T.-S., and Chae, J.-H. (2019). Identifying Depression in the National Health and Nutrition Examination Survey Data using a Deep Learning Algorithm. J. Affect. Disord.10.1016/j.jad.2019.06.034 ↗
18. Differences in lipid and lipoprotein concentrations of capillary and venous blood samples10.1016/0009-8981(79)90426-1 ↗