Microfluidic wearables move microliter biofluids across soft, low-impedance interfaces and into stable transducers on skin, enabling time-stamped chemistry without pumps. In this review (2015-2025), we take a system view: how specific fluidic choices (e.g., capillary-burst gating, chronological reservoirs, bubble control) preserve temporal fidelity; how materials and transduction (PEDOT: PSS hydrogels vs. MXene films; electrochemical vs. colorimetry) set bias and signal-to-noise; and how radios/power must follow use-case cadence. Two case studies ground the discussion: a battery-free NFC (near-field communication) sweat patch that couples passive microfluidics with imaging readout (field-tested colorimetric panels) via field-tested colorimetric panels and a large-cohort chloride/sweat-rate program (n ≈ 312 athletes) linking local measurements to whole-body estimates. We argue that agreement-centric validation (Bland-Altman limits, mean absolute relative difference (MARD), concordance) should be stratified by flow, site, and temperature, and we use energy per insight as a pragmatic yardstick to compare architectures by the energy needed for a minute of trusted trend or a defensible threshold call. We close with falsifiable targets for low-flow operation and sequence-sampled hormones and list open practices to make on-body chemistry more reproducible.
Page, M.J., et al. (2021) PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ , 372, n71.
Whiting, P.F., Rutjes, A.W.S., Westwood, M.E., Mallett, S., Deeks, J.J., Reitsma, J.B., et al. (2011) QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Annals of Internal Medicine , 155, 529-536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Bland, J.M. and Altman, D.G. (1986) Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement. The Lancet , 1, 307-310.
Kovatchev, B.P., Patek, S.D., Ortiz, E.A. and Breton, M.D. (2015) Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring. Diabetes Technology & Therapeutics , 17, 177-186. https://doi.org/10.1089/dia.2014.0272
Xu, Z., Song, J., Liu, B., Lv, S., Gao, F., Luo, X., et al. (2021) A Conducting Polymer PEDOT:PSS Hydrogel Based Wearable Sensor for Accurate Uric Acid Detection in Human Sweat. Sensors and Actuators B : Chemical , 348, 130674. https://doi.org/10.1016/j.snb.2021.130674
Peng, H., Zhang, Y., Liu, H. and Gao, C. (2024) Flexible Wearable Electrochemical Sensors Based on AuNR/PEDOT:PSS for Simultaneous Monitoring of Levodopa and Uric Acid in Sweat. ACS Sensors , 9, 3296-3306. https://doi.org/10.1021/acssensors.4c00649
Otgonbayar, Z. and Oh, W. (2023) Comprehensive and Multi-Functional MXene Based Sensors: An Updated Review. FlatChem , 40, Article ID: 100524. https://doi.org/10.1016/j.flatc.2023.100524
Mathew, M. and Rout, C.S. (2021) Electrochemical Biosensors Based on Ti 3 C 2 T x MXene: Future Perspectives for On-Site Analysis. Current Opinion in Electrochemistry , 30, Article ID: 100782. https://doi.org/10.1016/j.coelec.2021.100782
Ganesan, S., Ramajayam, K., Kokulnathan, T. and Palaniappan, A. (2023) Recent Advances in Two-Dimensional MXene-Based Electrochemical Biosensors for Sweat Analysis. Molecules , 28, Article 4617. https://doi.org/10.3390/molecules28124617
Guo, J., Wang, Y., Xu, D. and Zhao, Y. (2023) Conductive Microfibers from Microfluidics for Flexible Electronics. Chinese Science Bulletin , 68, 1653-1665. https://doi.org/10.1360/tb-2022-1267
Bandodkar, A.J., Gutruf, P., Choi, J., Lee, K., Sekine, Y., Reeder, J.T., et al. (2019) Battery-free, Skin-Interfaced Microfluidic/electronic Systems for Simultaneous Electrochemical, Colorimetric, and Volumetric Analysis of Sweat. Science Advances , 5, eaav3294. https://doi.org/10.1126/sciadv.aav3294
Koh, A., Kang, D., Xue, Y., Lee, S., Pielak, R.M., Kim, J., et al. (2016) A Soft, Wearable Microfluidic Device for the Capture, Storage, and Colorimetric Sensing of Sweat. Science Translational Medicine , 8, 366ra165. https://doi.org/10.1126/scitranslmed.aaf2593
Nyein, H.Y.Y., Tai, L., Ngo, Q.P., Chao, M., Zhang, G.B., Gao, W., et al. (2018) A Wearable Microfluidic Sensing Patch for Dynamic Sweat Secretion Analysis. ACS Sensors , 3, 944-952. https://doi.org/10.1021/acssensors.7b00961
Shitanda, I., Ozone, Y., Morishita, Y., Matsui, H., Loew, N., Motosuke, M., et al. (2023) Air-Bubble-Insensitive Microfluidic Lactate Biosensor for Continuous Monitoring of Lactate in Sweat. ACS Sensors , 8, 2368-2374. https://doi.org/10.1021/acssensors.3c00490
Baker, L.B., Model, J.B., Barnes, K.A., Anderson, M.L., Lee, S.P., Lee, K.A., et al. (2020) Skin-Interfaced Microfluidic System with Personalized Sweating Rate and Sweat Chloride Analytics for Sports Science Applications. Science Advances , 6, eabe3929. https://doi.org/10.1126/sciadv.abe3929
Baker, L.B., et al. (2022) Skin-Interfaced Microfluidic System with Machine Learning-Enabled Image Processing of Sweat Biomarkers in Remote Settings. Advanced Materials Technologies , 7, Article ID: 2200249.
Tu, J., Yeom, J., Ulloa, J.C., Solomon, S.A., Min, J., Heng, W., et al. (2025) Stressomic: A Wearable Microfluidic Biosensor for Dynamic Profiling of Multiple Stress Hormones in Sweat. Science Advances , 11, eadx6491. https://doi.org/10.1126/sciadv.adx6491
Gao, W., Emaminejad, S., Nyein, H.Y.Y., Challa, S., Chen, K., Peck, A., et al. (2016) Fully Integrated Wearable Sensor Arrays for Multiplexed in Situ Perspiration Analysis. Nature , 529, 509-514. https://doi.org/10.1038/nature16521
Hu, R., Liu, Y., Shin, S., Huang, S., Ren, X., Shu, W., et al. (2020) Emerging Materials and Strategies for Personal Thermal Management. Advanced Energy Materials , 10, Article ID: 1903921. https://doi.org/10.1002/aenm.201903921
Anastasova, S., Crewther, B., Bembnowicz, P., Curto, V., Ip, H.M., Rosa, B., et al. (2017) A Wearable Multisensing Patch for Continuous Sweat Monitoring. Biosensors and Bioelectronics , 93, 139-145. https://doi.org/10.1016/j.bios.2016.09.038
Nyein, H.Y.Y., Bariya, M., Kivimäki, L., Uusitalo, S., Liaw, T.S., Jansson, E., et al. (2019) Regional and Correlative Sweat Analysis Using High-Throughput Microfluidic Sensing Patches toward Decoding Sweat. Science Advances , 5, eaaw9906. https://doi.org/10.1126/sciadv.aaw9906
Park, W., Seo, H., Kim, J., Hong, Y., Song, H., Joo, B.J., et al. (2024) In-Depth Correlation Analysis between Tear Glucose and Blood Glucose Using a Wireless Smart Contact Lens. Nature Communications , 15, Article No. 2828. https://doi.org/10.1038/s41467-024-47123-9
Parrilla, M., et al. (2019) Wearable Potentiometric Ion Patch for On-Body Electrolyte Monitoring in Sweat: Toward a Validation Strategy to Ensure Physiological Relevance. Analytical Chemistry , 91, 8644-8651.
Katsumata, Y., et al. (2024) Sweat Lactate Sensor for Detecting Anaerobic Threshold in Heart Failure: A Prospective Clinical Trial (LacS-001). Scientific Reports , 14, Article No. 18985.
Yang, Y., Song, Y., Bo, X., Min, J., Pak, O.S., Zhu, L., et al. (2019) A Laser-Engraved Wearable Sensor for Sensitive Detection of Uric Acid and Tyrosine in Sweat. Nature Biotechnology , 38, 217-224. https://doi.org/10.1038/s41587-019-0321-x
Torrente-Rodríguez, R.M., Tu, J., Yang, Y., Min, J., Wang, M., Song, Y., et al. (2020) Investigation of Cortisol Dynamics in Human Sweat Using a Graphene-Based Wireless mHealth System. Matter , 2, 921-937. https://doi.org/10.1016/j.matt.2020.01.021
Kim, J., Jeerapan, I., Imani, S., Cho, T.N., Bandodkar, A., Cinti, S., et al. (2016) Noninvasive Alcohol Monitoring Using a Wearable Tattoo-Based Iontophoretic-Biosensing System. ACS Sensors , 1, 1011-1019. https://doi.org/10.1021/acssensors.6b00356
He, W., Wang, C., Wang, H., Jian, M., Lu, W., Liang, X., et al. (2019) Integrated Textile Sensor Patch for Real-Time and Multiplex Sweat Analysis. Science Advances , 5, eaax0649. https://doi.org/10.1126/sciadv.aax0649
Lin, P., Sheu, S., Chen, C., Huang, S. and Li, B. (2022) Wearable Hydrogel Patch with Noninvasive, Electrochemical Glucose Sensor for Natural Sweat Detection. Talanta , 241, Article ID: 123187. https://doi.org/10.1016/j.talanta.2021.123187
Currano, L.J., et al. (2018) Wearable Sensor System for Detection of Lactate in Sweat. Scientific Reports , 8, Article No. 15890.
Nyein, H.Y.Y., Bariya, M., Tran, B., Ahn, C.H., Brown, B.J., Ji, W., et al. (2021) A Wearable Patch for Continuous Analysis of Thermoregulatory Sweat at Rest. Nature Communications , 12, Article No. 1823. https://doi.org/10.1038/s41467-021-22109-z
Davis, B.C., Lin, K., Shahub, S., Ramasubramanya, A., Fagan, A., Muthukumar, S., et al. (2024) A Novel Sweat Sensor Detects Inflammatory Differential Rhythmicity Patterns in Inpatients and Outpatients with Cirrhosis. npj Digital Medicine , 7, Article No. 382. https://doi.org/10.1038/s41746-024-01404-1
Hossain, N.I., Noushin, T. and Tabassum, S. (2024) StressFit: A Hybrid Wearable Physicochemical Sensor Suite for Simultaneously Measuring Electromyogram and Sweat Cortisol. Scientific Reports , 14, Article No. 29667. https://doi.org/10.1038/s41598-024-81042-5
Wang, M., Yang, Y., Min, J., Song, Y., Tu, J., Mukasa, D., et al. (2022) A Wearable Electrochemical Biosensor for the Monitoring of Metabolites and Nutrients. Nature Biomedical Engineering , 6, 1225-1235. https://doi.org/10.1038/s41551-022-00916-z
Xuan, X., Pérez-Ràfols, C., Chen, C., Cuartero, M. and Crespo, G.A. (2021) Lactate Biosensing for Reliable On-Body Sweat Analysis. ACS Sensors , 6, 2763-2771. https://doi.org/10.1021/acssensors.1c01009
Jagannath, B., Lin, K., Pali, M., Sankhala, D., Muthukumar, S. and Prasad, S. (2021) Temporal Profiling of Cytokines in Passively Expressed Sweat for Detection of Infection Using Wearable Device. Bioengineering & Translational Medicine , 6, e10220. https://doi.org/10.1002/btm2.10220
Vivaldi, F., Dallinger, A., Poma, N., Bonini, A., Biagini, D., Salvo, P., et al. (2022) Sweat Analysis with a Wearable Sensing Platform Based on Laser-Induced Graphene. APL Bioengineering , 6, Article ID: 036104. https://doi.org/10.1063/5.0093301
Kwon, K., Kim, J.U., Deng, Y., Krishnan, S.R., Choi, J., Jang, H., et al. (2021) An On-Skin Platform for Wireless Monitoring of Flow Rate, Cumulative Loss and Temperature of Sweat in Real Time. Nature Electronics , 4, 302-312. https://doi.org/10.1038/s41928-021-00556-2
Choi, J., Ghaffari, R., Baker, L.B. and Rogers, J.A. (2018) Skin-Interfaced Systems for Sweat Collection and Analytics. Science Advances , 4, eaar3921. https://doi.org/10.1126/sciadv.aar3921
Brueck, A., Iftekhar, T., Stannard, A., Yelamarthi, K. and Kaya, T. (2018) A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring. Sensors , 18, Article 533. https://doi.org/10.3390/s18020533
Xuan, X., Rojas, D., Lozano, I.M.D., Cuartero, M. and Crespo, G.A. (2024) Demonstration of a Validated Direct Current Wearable Device for Monitoring Sweat Rate in Sports. Sensors , 24, Article 7243. https://doi.org/10.3390/s24227243
Tabasum, H., Gill, N., Mishra, R. and Lone, S. (2022) Wearable Microfluidic-Based E-Skin Sweat Sensors. RSC Advances , 12, 8691-8707.
Ursem, R.F.R., Steijlen, A., Parrilla, M., Bastemeijer, J., Bossche, A. and De Wael, K. (2025) Worth Your Sweat: Wearable Microfluidic Flow Rate Sensors for Meaningful Sweat Analytics. Lab on a Chip , 25, 1296-1315. https://doi.org/10.1039/d4lc00927d
Kulkarni, M.B., Rajagopal, S., Prieto-Simón, B. and Pogue, B.W. (2024) Recent Advances in Smart Wearable Sensors for Continuous Human Health Monitoring. Talanta , 272, Article ID: 125817. https://doi.org/10.1016/j.talanta.2024.125817
Barba, A.B., Bianco, G.M., Fiore, L., Arduini, F., Marrocco, G. and Occhiuzzi, C. (2022) Design and Manufacture of Flexible Epidermal NFC Device for Electrochemical Sensing of Sweat. 2022 IEEE International Conference on Flexible and Printable Sensors and Systems ( FLEPS ), Vienna, 10-13 July 2022, 1-4. https://doi.org/10.1109/fleps53764.2022.9781563
Chung, M., Fortunato, G. and Radacsi, N. (2019) Wearable Flexible Sweat Sensors for Healthcare Monitoring: A Review. Journal of the Royal Society Interface , 16, Article ID: 20190217. https://doi.org/10.1098/rsif.2019.0217
Song, Y., Min, J., Yu, Y., Wang, H., Yang, Y., Zhang, H., et al. (2020) Wireless Battery-Free Wearable Sweat Sensor Powered by Human Motion. Science Advances , 6, eaay9842. https://doi.org/10.1126/sciadv.aay9842
Mirzajani, H., Abbasiasl, T., Mirlou, F., Istif, E., Bathaei, M.J., Dağ, Ç., et al. (2022) An Ultra-Compact and Wireless Tag for Battery-Free Sweat Glucose Monitoring. Biosensors and Bioelectronics , 213, Article ID: 114450. https://doi.org/10.1016/j.bios.2022.114450
Cheng, C., Li, X., Xu, G., Lu, Y., Low, S.S., Liu, G., et al. (2021) Battery-Free, Wireless, and Flexible Electrochemical Patch for in Situ Analysis of Sweat Cortisol via near Field Communication. Biosensors and Bioelectronics , 172, Article ID: 112782. https://doi.org/10.1016/j.bios.2020.112782
Gabler, L., Patton, D., Begonia, M., Daniel, R., Rezaei, A., Huber, C., et al. (2022) Consensus Head Acceleration Measurement Practices (CHAMP): Laboratory Validation of Wearable Head Kinematic Devices. Annals of Biomedical Engineering , 50, 1356-1371. https://doi.org/10.1007/s10439-022-03066-0
Rowson, S., Mihalik, J., Urban, J., Schmidt, J., Marshall, S., Harezlak, J., et al. (2022) Consensus Head Acceleration Measurement Practices (CHAMP): Study Design and Statistical Analysis. Annals of Biomedical Engineering , 50, 1346-1355. https://doi.org/10.1007/s10439-022-03101-0
CLSI EP09-A3 (2013) Measurement Procedure Comparison and Bias Estimation Using Patient Samples, 3rd ed.
Garg, S.K., et al. (2022) Accuracy and Safety of Dexcom G7 Continuous Glucose Monitoring in Adults with Diabetes. Diabetes Technology & Therapeutics , 24, 373-380.
Alva, S., et al. (2023) Accuracy of the Third Generation of a 14-Day Continuous Glucose Monitoring System. Diabetes Therapy , 14, 767-776. https://doi.org/10.1007/s13300-023-01385-6
CMS LCD (L38657): Background on CGM Accuracy Metrics and MARD Considerations, 2021-2024.
Martin Bland, J. and Altman, D. (1986) Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement. The Lancet , 327, 307-310. https://doi.org/10.1016/s0140-6736(86)90837-8
Lin, L.I. (1989) A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics , 45, 255-268. https://doi.org/10.2307/2532051
Beniczky, S. and Ryvlin, P. (2018) Standards for Testing and Clinical Validation of Seizure Detection Devices. Epilepsia , 59, 9-13. https://doi.org/10.1111/epi.14049
Lyzwinski, L., Elgendi, M., Shokurov, A.V., Cuthbert, T.J., Ahmadizadeh, C. and Menon, C. (2023) Opportunities and Challenges for Sweat-Based Monitoring of Metabolic Syndrome via Wearable Technologies. Communications Engineering , 2, Article No. 48. https://doi.org/10.1038/s44172-023-00097-w
Welk, G.J., Bai, Y., Lee, J., Godino, J., Saint-Maurice, P.F. and Carr, L. (2019) Standardizing Analytic Methods and Reporting in Activity Monitor Validation Studies. Medicine & Science in Sports & Exercise , 51, 1767-1780. https://doi.org/10.1249/mss.0000000000001966
Vandenberk, T., Stans, J., Mortelmans, C., Van Haelst, R., Van Schelvergem, G., Pelckmans, C., et al. (2017) Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study. JMIR mHealth and uHealth , 5, e129. https://doi.org/10.2196/mhealth.7254