The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.
Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring
Open accessAbstract
With the increasing prevalence of growing population, aging and chronic diseases continuously rising healthcare costs, the healthcare system is undergoing a vital transformation from the traditional hospital-centered system to an individual-centered system. Since the 20th century, wearable sensors are becoming widespread in healthcare and biomedical monitoring systems, empowering continuous measurement of critical biomarkers for monitoring of the diseased condition and health, medical diagnostics and evaluation in biological fluids like saliva, blood, and sweat. Over the past few decades, the developments have been focused on electrochemical and optical biosensors, along with advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have evolved gradually with a mix of multiplexed biosensing, microfluidic sampling and transport systems integrated with flexible materials and body attachments for improved wearability and simplicity. These wearables hold promise and are capable of a higher understanding of the correlations between analyte concentrations within the blood or non-invasive biofluids and feedback to the patient, which is significantly important in timely diagnosis, treatment, and control of medical conditions. However, cohort validation studies and performance evaluation of wearable biosensors are needed to underpin their clinical acceptance. In the present review, we discuss the importance, features, types of wearables, challenges and applications of wearable devices for biological fluids for the prevention of diseased conditions and real-time monitoring of human health. Herein, we summarize the various wearable devices that are developed for healthcare monitoring and their future potential has been discussed in detail.
Overview
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
Verification
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.
Citations
Cites
Kim, D.-H., Lu, N., Ma, R., Kim, Y.-S., Kim, R.-H., Wang, S., Wu, J., Won, S. M., Tao, H., Islam, A., et al. (2011) · Science · doi:10.1126/science.1206157 · Cited by 4,836
Wearable biosensors for healthcare monitoring
Kim, J., Campbell, A. S., de Ávila, B. E.-F., & Wang, J. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0045-y · Cited by 3,373 · cited 6×
“Recent advancements in wearable contact lens biosensors indicate the use of smartphones for the detection of the analyte of interest in tear fluids [10,193].”
BackgroundInfluentialA review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396
Yuk, H., Lu, B., & Zhao, X. (2019) · Chemical Society Reviews · doi:10.1039/c8cs00595h · Cited by 2,108
Wearable Electronics and Smart Textiles: A Critical Review
Stoppa, M., & Chiolerio, A. (2014) · Sensors · doi:10.3390/s140711957 · Cited by 2,107
Yang, J. C., Mun, J., Kwon, S. Y., Park, S., Bao, Z., & Park, S. (2019) · Advanced Materials · doi:10.1002/adma.201904765 · Cited by 1,641
A highly stretchable autonomous self-healing elastomer
Li, C.-H., Wang, C., Keplinger, C., Zuo, J.-L., Jin, L., Sun, Y., Zheng, P., Cao, Y., Lissel, F., Linder, C., et al. (2016) · Nature Chemistry · doi:10.1038/nchem.2492 · Cited by 1,496
Wearable sensors: modalities, challenges, and prospects
Heikenfeld, J., Jajack, A., Rogers, J., Gutruf, P., Tian, L., Pan, T., Li, R., Khine, M., Kim, J., Wang, J., et al. (2018) · Lab on a Chip · doi:10.1039/c7lc00914c · Cited by 1,434
Song, S., Wang, L., Li, J., Fan, C., & Zhao, J. (2008) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2007.12.004 · Cited by 1,434
Tee, B. C.-K., Wang, C., Allen, R., & Bao, Z. (2012) · Nature Nanotechnology · doi:10.1038/nnano.2012.192 · Cited by 1,419
Cited by
End-to-end design of wearable sensors
Ates, H. C., Nguyen, P. Q., Gonzalez-Macia, L., Morales-Narváez, E., Güder, F., Collins, J. J., & Dincer, C. (2022) · Nature Reviews Materials · doi:10.1038/s41578-022-00460-x · Cited by 1,306
The Influence of Wearables on Health Care Outcomes in Chronic Disease: Systematic Review
Mattison, G., Canfell, O., Forrester, D., Dobbins, C., Smith, D., Töyräs, J., & Sullivan, C. (2022) · Journal of Medical Internet Research · doi:10.2196/36690 · Cited by 171
Co-design of human-centered, explainable AI for clinical decision support
Panigutti, C., Beretta, A., Fadda, D., Giannotti, F., Pedreschi, D., Perotti, A., & Rinzivillo, S. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3587271 · Cited by 127
User Experience of Older People While Using Digital Health Technologies: A Systematic Review
Takano, E., Maruyama, H., Takahashi, T., Mori, K., Nishiyori, K., Morita, Y., Fukuda, T., Kondo, I., & Ishibashi, Y. (2023) · Applied Sciences · doi:10.3390/app132312815 · Cited by 29
Medication management initiatives using wearable devices: scoping review
Iino, H., Kizaki, H., Imai, S., & Hori, S. (2024) · JMIR Human Factors · doi:10.2196/57652 · Cited by 12
Metadata
- Authors
- Sharma, A., Badea, M., Tiwari, S., & Marty, J. L.
- Venue
- Molecules
- Publisher
- MDPI AG
- Published
- 2021-02-01
- Volume
- 26
- Issue
- 3
- Pages
- 748
- ISSN
- 1420-3049 (ISSN-L)
- OpenAlex
- W3129131719
- Cited by
- 357
- References
- 209
- FWCI
- 14.77
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. https://app.cochat.ai/papers/10.3390/molecules26030748
- The paper
- Sharma, A., Badea, M., Tiwari, S., & Marty, J. L. (2021). Wearable Biosensors: An Alternative and Practical Approach in Healthcare and Disease Monitoring. Molecules. https://doi.org/10.3390/molecules26030748
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
