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Toilet-based continuous health monitoring using urine

Nature Reviews Urology volume 19, pages 219–230 (2022)Cite this article

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Abstract

Regular health monitoring can result in early detection of disease, accelerate the delivery of medical care and, therefore, considerably improve patient outcomes for countless medical conditions that affect public health. A substantial unmet need remains for technologies that can transform the status quo of reactive health care to preventive, evidence-based, person-centred care. With this goal in mind, platforms that can be easily integrated into people’s daily lives and identify a range of biomarkers for health and disease are desirable. However, urine — a biological fluid that is produced in large volumes every day and can be obtained with zero pain, without affecting the daily routine of individuals, and has the most biologically rich content — is discarded into sewers on a regular basis without being processed or monitored. Toilet-based health-monitoring tools in the form of smart toilets could offer preventive home-based continuous health monitoring for early diagnosis of diseases while being connected to data servers (using the Internet of Things) to enable collection of the health status of users. In addition, machine learning methods can assist clinicians to classify, quantify and interpret collected data more rapidly and accurately than they were able to previously. Meanwhile, challenges associated with user acceptance, privacy and test frequency optimization should be considered to facilitate the acceptance of smart toilets in society.

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Fig. 1: Health-care spending and insurance coverage in the world and the USA.
Fig. 2: Wearable health monitoring publications highlighting sweat-based analysis.
Fig. 3: Toilet-based continuous health monitoring platforms.
Fig. 4: Future perspective of continuous health monitoring using smart toilets.

References

  1. National Science Foundation. Smart and Connected Health. https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504739 (2016).

  2. U.S. Centers for Medicare & Medicaid Services. National Health Expenditure Fact Sheet. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NHE-Fact-Sheet (2020).

  3. U.S. Centers for Medicare & Medicaid Services. National Health Expenditure Data, Historical. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/NationalHealthAccountsHistorical (2020).

  4. Department of Health and Human Services Tips about the Health Insurance Marketplace. https://www.healthcare.gov/quick-guide/ (2021).

  5. Sandhu, G. S. & Andriole, G. L. Overdiagnosis of prostate cancer. J. Natl Cancer Inst. Monogr. 2012, 146–151 (2012).

    PubMed  PubMed Central  Google Scholar 

  6. Gilbert, N. The pros and cons of screening. Nature 579, S2–S2 (2020).

    CAS  PubMed  Google Scholar 

  7. Batarseh, F. A., Ghassib, I., Chong, D. S. & Su, P.-H. Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics. J. Big Data 7, 1–25 (2020).

    Google Scholar 

  8. Malone, K. M. & Hinman, A. R. Vaccination mandates: the public health imperative and individual rights. Law Public Health Pract. 338, 339–340 (2003).

    Google Scholar 

  9. American Cancer Society. Key statistics for ovarian cancer. cancer.org https://www.cancer.org/cancer/ovarian-cancer/about/key-statistics.html (2021).

  10. National Heart, Lung, and Blood Institute. Blood tests. nhlbi.nih.gov https://www.nhlbi.nih.gov/health-topics/blood-tests#::text=Specifically%2C%20blood%20tests%20can%20help,risk%20factors%20for%20heart%20disease

  11. Dabbagh, S. R. et al. 3D-printed microneedles in biomedical applications. iScience 24, 102012 (2021).

    CAS  PubMed  Google Scholar 

  12. Topkas, E., Keith, P., Dimeski, G., Cooper-White, J. & Punyadeera, C. Evaluation of saliva collection devices for the analysis of proteins. Clin. Chim. Acta 413, 1066–1070 (2012).

    CAS  PubMed  Google Scholar 

  13. Liu, J. & Duan, Y. Saliva: a potential media for disease diagnostics and monitoring. Oral. Oncol. 48, 569–577 (2012).

    PubMed  Google Scholar 

  14. Pfaffe, T., Cooper-White, J., Beyerlein, P., Kostner, K. & Punyadeera, C. Diagnostic potential of saliva: current state and future applications. Clin. Chem. 57, 675–687 (2011).

    CAS  PubMed  Google Scholar 

  15. Papacosta, E. & Nassis, G. P. Saliva as a tool for monitoring steroid, peptide and immune markers in sport and exercise science. J. Sci. Med. Sport. 14, 424–434 (2011).

    PubMed  Google Scholar 

  16. de la Torre, R. et al. Clinical pharmacokinetics of amfetamine and related substances. Clin. Pharmacokinet. 43, 157–185 (2004).

    PubMed  Google Scholar 

  17. Caplan, Y. H. & Goldberger, B. A. Alternative specimens for workplace drug testing. J. Anal. Toxicol. 25, 396–399 (2001).

    CAS  PubMed  Google Scholar 

  18. Tai, L.-C. et al. Nicotine monitoring with a wearable sweat band. ACS Sens. 5, 1831–1837 (2020).

    CAS  PubMed  Google Scholar 

  19. Kudo, H., Suzuki, Y., Tojo, Y., Saito, H. & Enomoto, K. in 2019 IEEE 14th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS) 44–47 (IEEE, 2019).

  20. Hourlier-Fargette, A. et al. Skin-interfaced soft microfluidic systems with modular and reusable electronics for in situ capacitive sensing of sweat loss, rate and conductivity. Lab Chip 20, 4391–4403 (2020).

    CAS  PubMed  Google Scholar 

  21. Jadoon, S. et al. Recent developments in sweat analysis and its applications. Int. J. Anal. Chem. 2015, 4391 (2015).

    Google Scholar 

  22. Wang, J., Mercier, P. & Noghera, C. Center for wearable sensors. ucsd.edu https://cws.ucsd.edu/about (2020).

  23. Merriam Webster Online. Definition of urine. https://www.merriam-webster.com/dictionary/urine (2020).

  24. Baig, A. Biochemical composition of normal urine. Nat. Prec. https://doi.org/10.1038/npre.2011.6595.1 (2011).

    Article  Google Scholar 

  25. Dantzler, W. H. in Comparative Physiology of the Vertebrate Kidney 37–80 (Springer, 2016).

  26. Lepowsky, E., Ghaderinezhad, F., Knowlton, S. & Tasoglu, S. Paper-based assays for urine analysis. Biomicrofluidics 11, 051501 (2017).

    PubMed  PubMed Central  Google Scholar 

  27. Beasley-Green, A. Urine proteomics in the era of mass spectrometry. Int. Neurourol. J. 20, S70 (2016).

    PubMed  PubMed Central  Google Scholar 

  28. Antic, T. & DeMay, R. M. The fascinating history of urine examination. J. Am. Soc. Cytopathol. 3, 103–107 (2014).

    PubMed  Google Scholar 

  29. Sharp, V. J., Antes, L. M., Sanders, M. L. & Lockwood, G. M. Urine Tests — A Case-Based Guide to Clinical Evaluation and Application (Springer, 2020).

  30. Decramer, S. et al. Urine in clinical proteomics. Mol. Cell. Proteom. 7, 1850–1862 (2008).

    CAS  Google Scholar 

  31. Barratt, J. & Topham, P. Urine proteomics: the present and future of measuring urinary protein components in disease. CMAJ 177, 361–368 (2007).

    PubMed  PubMed Central  Google Scholar 

  32. Afkarian, M. et al. Optimizing a proteomics platform for urine biomarker discovery. Mol. Cell. Proteom. 9, 2195–2204 (2010).

    CAS  Google Scholar 

  33. Ipe, D. S., Horton, E. & Ulett, G. C. The basics of bacteriuria: strategies of microbes for persistence in urine. Front. Cell. Infect. Microbiol. 6, 14 (2016).

    PubMed  PubMed Central  Google Scholar 

  34. Thomas-White, K. J. et al. Urinary microbes and postoperative urinary tract infection risk in urogynecologic surgical patients. Int. Urogynecol. J. 29, 1797–1805 (2018).

    PubMed  PubMed Central  Google Scholar 

  35. Brubaker, L. & Wolfe, A. J. The female urinary microbiota, urinary health and common urinary disorders. Ann. Transl. Med. 5, 34 (2017).

    PubMed  PubMed Central  Google Scholar 

  36. Bae, J.-H. & Lee, H.-K. User health information analysis with a urine and feces separable smart toilet system. IEEE Access. 6, 78751–78765 (2018).

    Google Scholar 

  37. Ramlakhan, S. L., Burke, D. P. & Goldman, R. S. Dipstick urinalysis for the emergency department evaluation of urinary tract infections in infants aged less than 2 years. Eur. J. Emerg. Med. 18, 221–224 (2011).

    PubMed  Google Scholar 

  38. Sinawe, H. & Casadesus, D. Urine Culture (StatPearls Publishing LLC, 2021).

  39. Yarbrough, M. L., Wallace, M. A., Marshall, C., Mathias, E. & Burnham, C.-A. D. Culture of urine specimens by use of chromID CPS Elite medium can expedite Escherichia coli identification and reduce hands-on time in the clinical laboratory. J. Clin. Microbiol. 54, 2767–2773 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. Najeeb, S. et al. Comparison of urine dipstick test with conventional urine culture in diagnosis of urinary tract infection. J. Coll. Physicians Surg. Pak. 25, 108–110 (2015).

    PubMed  Google Scholar 

  41. Mejuto, P., Luengo, M. & Díaz-Gigante, J. Automated flow cytometry: an alternative to urine culture in a routine clinical microbiology laboratory? Int. J. Microbiol. 2017, 8532736 (2017).

    PubMed  PubMed Central  Google Scholar 

  42. Monsen, T. & Rydén, P. Flow cytometry analysis using sysmex UF-1000i classifies uropathogens based on bacterial, leukocyte, and erythrocyte counts in urine specimens among patients with urinary tract infections. J. Clin. Microbiol. 53, 539–545 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Broeren, M., Nowacki, R., Halbertsma, F., Arents, N. & Zegers, S. Urine flow cytometry is an adequate screening tool for urinary tract infections in children. Eur. J. Pediatr. 178, 363–368 (2019).

    CAS  PubMed  Google Scholar 

  44. Owens, C. L., VandenBussche, C. J., Burroughs, F. H. & Rosenthal, D. L. A review of reporting systems and terminology for urine cytology. Cancer Cytopathol. 121, 9–14 (2013).

    PubMed  Google Scholar 

  45. Xing, J. & Reynolds, J. P. Diagnostic advances in urine cytology. Surg. Pathol. Clin. 11, 601–610 (2018).

    PubMed  Google Scholar 

  46. Vap, L. M. & Shropshire, S. B. Urine cytology: collection, film preparation, and evaluation. Vet. Clin. North Am. Small Anim. Pract. 47, 135–149 (2017).

    PubMed  Google Scholar 

  47. Becker, G. J., Garigali, G. & Fogazzi, G. B. Advances in urine microscopy. Am. J. Kidney Dis. 67, 954–964 (2016).

    PubMed  Google Scholar 

  48. Goldani, J. C. et al. Urine microscopy as a biomarker of acute kidney injury following cardiac surgery with cardiopulmonary bypass. Braz. J. Nephrol. 42, 18–23 (2020).

    Google Scholar 

  49. Premasiri, W. R., Clarke, R. H. & Womble, M. E. Urine analysis by laser Raman spectroscopy. Lasers Surg. Med. 28, 330–334 (2001).

    CAS  PubMed  Google Scholar 

  50. Žukovskaja, O. et al. Towards Raman spectroscopy of urine as screening tool. J. Biophotonics 13, e201900143 (2020).

    PubMed  Google Scholar 

  51. Miller, I. J. et al. Real-time health monitoring through urine metabolomics. NPJ Digit. Med. 2, 1–9 (2019).

    Google Scholar 

  52. Mambatta, A. K. et al. Reliability of dipstick assay in predicting urinary tract infection. J. Fam. Med. Prim. Care 4, 265 (2015).

    Google Scholar 

  53. Shimoni, Z., Glick, J., Hermush, V. & Froom, P. Sensitivity of the dipstick in detecting bacteremic urinary tract infections in elderly hospitalized patients. PLoS One 12, e0187381 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. Dadzie, I. et al. The effectiveness of dipstick for the detection of urinary tract infection. Can. J. Infect. Dis. Med. Microbiol. 2019, 8642628 (2019).

    PubMed  PubMed Central  Google Scholar 

  55. Herman-Saffar, O. et al. Early non-invasive detection of breast cancer using exhaled breath and urine analysis. Comput. Biol. Med. 96, 227–232 (2018).

    PubMed  Google Scholar 

  56. Xylinas, E. et al. Urine markers for detection and surveillance of bladder cancer. Paper presented at: urologic oncology: seminars and original investigations. Urol. Oncol. 32, 222–229 (2014).

    CAS  PubMed  Google Scholar 

  57. Bax, C. et al. Innovative diagnostic methods for early prostate cancer detection through urine analysis: a review. Cancers 10, 123 (2018).

    PubMed Central  Google Scholar 

  58. Perazella, M. A. The urine sediment as a biomarker of kidney disease. Am. J. Kidney Dis. 66, 748–755 (2015).

    CAS  PubMed  Google Scholar 

  59. Bach, S. et al. Detection of colorectal cancer in urine using DNA methylation analysis. Sci. Rep. 11, 1–11 (2021).

    Google Scholar 

  60. Ryan, D., Robards, K., Prenzler, P. D. & Kendall, M. Recent and potential developments in the analysis of urine: a review. Anal. Chim. Acta 684, 17–29 (2011).

    CAS  Google Scholar 

  61. Kemperman, R. F. et al. Comparative urine analysis by liquid chromatography− mass spectrometry and multivariate statistics: method development, evaluation, and application to proteinuria. J. Proteome Res. 6, 194–206 (2007).

    CAS  PubMed  Google Scholar 

  62. Simerville, J. A., Maxted, W. C. & Pahira, J. J. Urinalysis: a comprehensive review. Am. Fam. Physician 71, 1153–1162 (2005).

    PubMed  Google Scholar 

  63. Zamanzad, B. Accuracy of dipstick urinalysis as a screening method for detection of glucose, protein, nitrites and blood. East. Mediterr. Health J. 15, 1323–1328 (2009).

    CAS  PubMed  Google Scholar 

  64. Sarabi, M. R., Ahmadpour, A., Yetisen, A. K. & Tasoglu, S. Finger-actuated microneedle array for sampling body fluids. Appl. Sci. 11, 5329 (2021).

    CAS  Google Scholar 

  65. Leipheimer, J. M. et al. First-in-human evaluation of a hand-held automated venipuncture device for rapid venous blood draws. Technology 7, 98–107 (2019).

    PubMed  Google Scholar 

  66. WHO. WHO best practices for injections and related procedures toolkit. Report No. 9245599256, https://www.who.int/publications/i/item/who-best-practices-for-injections-and-related-procedures-toolkit (2010).

  67. Lapostolle, F. et al. Prospective evaluation of peripheral venous access difficulty in emergency care. Intensive Care Med. 33, 1452–1457 (2007).

    PubMed  Google Scholar 

  68. Hess, H. A. A biomedical device to improve pediatric vascular access success. Pediatr. Nurs. 36, 259–263 (2010).

    PubMed  Google Scholar 

  69. Mishra, A., Greaves, R. & Massie, J. The relevance of sweat testing for the diagnosis of cystic fibrosis in the genomic era. Clin. Biochem. Rev. 26, 135 (2005).

    PubMed  PubMed Central  Google Scholar 

  70. McAdams, E. et al. Wearable sensor systems: the challenges. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 3648–3651 (IEEE, 2011).

  71. Gambhir, S. S., Ge, T. J., Vermesh, O. & Spitler, R. Toward achieving precision health. Sci. Transl. Med. 10 (2018).

  72. Gambhir, S. S., Ge, T. J., Vermesh, O., Spitler, R. & Gold, G. E. Continuous health monitoring: an opportunity for precision health. Sci. Transl. Med. 13, eaao3612 (2021).

    Google Scholar 

  73. Centers for Disease Control and Prevention. Chronic kidney disease in the United States, 2021. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2021 https://www.cdc.gov/kidneydisease/publications-resources/ckd-national-facts.html (CDC, 2021).

  74. Luyckx, V. A., Tonelli, M. & Stanifer, J. W. The global burden of kidney disease and the sustainable development goals. Bull. World Health Organ. 96, 414 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).

    PubMed  Google Scholar 

  76. Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States, 2021. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2021 Chronic Kidney Disease Initiative, https://www.cdc.gov/kidneydisease/basics.html (CDC, 2020).

  77. Park, S.-m et al. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat. Biomed. Eng. 4, 624–635 (2020).

    PubMed  PubMed Central  Google Scholar 

  78. Mohanty, M. D. & Mohanty, M. N. A cognitive approach for design of smart toilet in healthcare units. In: Mallick P., Balas V., Bhoi A., Zobaa A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768 771–780 (Springer, 2019).

  79. Shaikh, F., Shaikh, F., Sayed, K., Mittha, N. & Khan, N. Smart Toilet Based on IoT. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) 248–250 (IEEE, 2019).

  80. Mohanty, M. D., Pattnaik, D., Parida, M., Mohanty, S. & Mohanty, M. N. Design of intelligent PID controller for smart toilet of CCU/ICU patients in healthcare systems. In: Bhaskar M., Dash S., Das S., Panigrahi B. (eds) International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 846. (Springer, 2019).

  81. Ikenaga, T., Shigematsu, T., Kusumoto, A., Yamamoto, K. & Yada, M. Toilet device with health examination system. U.S. Patent 4961431 (1990).

  82. Ikenaga, T., Shigematsu, T., Yada, M., Makita, S. & Kitaura. Toilet with urine constituent measuring device. U.S. Patent 4962550 (1990).

  83. Nakayama, C. et al. Toilet-bowl-mounted urinalysis unit. U.S. Patent 5730149 (1998).

  84. Zakaria, F. et al. Evaluation of a smart toilet in an emergency camp. Int. J. Disaster Risk Reduct. 27, 512–523 (2018).

    Google Scholar 

  85. Choden, P., Seesaard, T., Dorji, U., Sriphrapradang, C. & Kerdcharoen, T. Urine odor detection by electronic nose for smart toilet application. In: 2017 14th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) 190–193 (IEEE, 2017).

  86. Syafaah, L., Azizah, D. F., Sofiani, I. R., Lestandy, M. & Faruq, A. Self-monitoring and detection of diabetes with art toilet based on image processing and K-means technique. In 2020 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) 87–91 (IEEE, 2020).

  87. Huang, J.-J., Yu, S.-I. & Syu, H.-Y. Development of the smart toilet equipment with measurements of physiological parameters. In 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing 9–16 (IEEE, 2012).

  88. OutSense. Transforming human waste into lifesaving medical insights https://outsensediagnostics.com/ (2020).

  89. Toi Labs. Effortless health monitoring with every flush https://www.toilabs.com/ (2021).

  90. Szondy, D. Nightclub urinal tells patrons when they’ve had one too many. https://newatlas.com/pee-analyzer/28371/ (2013).

  91. Temirel, M., Yenilmez, B. & Tasoglu, S. Long-term cyclic use of a sample collector for toilet-based urine analysis. Sci. Rep. 11, 2170 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Oyaert, M. & Delanghe, J. Progress in automated urinalysis. Ann. Lab. Med. 39, 15 (2019).

    PubMed  Google Scholar 

  93. Jiang, N., Mück, J. E. & Yetisen, A. K. The regulation of wearable medical devices. Trends Biotechnol. 38, 129–133 (2020).

    CAS  PubMed  Google Scholar 

  94. Yu, Z., Jiang, N., Kazarian, S. G., Tasoglu, S. & Yetisen, A. K. Optical sensors for continuous glucose monitoring. Prog. Biomed. Eng. 3, 022004 (2021).

    Google Scholar 

  95. Kim, S. H., Oh, S. A. & Oh, S. J. Voiding diary might serve as a useful tool to understand differences between bladder pain syndrome/interstitial cystitis and overactive bladder. Int. J. Urol. 21, 179–183 (2014).

    PubMed  Google Scholar 

  96. Nassiff, A. et al. Voiding diary: proposal and assessment of a tool. Int. J. Urol. Nurs. 11, 144–150 (2017).

    Google Scholar 

  97. Smith, G. T. et al. Robust dipstick urinalysis using a low-cost, micro-volume slipping manifold and mobile phone platform. Lab Chip 16, 2069–2078 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Magnay, J. L., O’Brien, S., Gerlinger, C. & Seitz, C. A systematic review of methods to measure menstrual blood loss. BMC Women’s Health 18, 1–13 (2018).

    Google Scholar 

  99. Mukherjee, M., Naqvi, S. A., Verma, A., Sengupta, D. & Parnami, A. MenstruLoss: sensor for menstrual blood loss monitoring. ACM J. 3, 1–21 (2019).

    Google Scholar 

  100. Marcin, A. Black, brown, bright red, and more: what does each period blood color mean? https://www.healthline.com/health/womens-health/period-blood#_noHeaderPrefixedContent (2019).

  101. American College of Obstetricians and Gynecologists Committee. American College of Obstetricians and Gynecologists Committee on adolescent health care menstruation in girls and adolescents: using the menstrual cycle as a vital sign. https://www.acog.org/clinical/clinical-guidance/committee-opinion/articles/2015/12/menstruation-in-girls-and-adolescents-using-the-menstrual-cycle-as-a-vital-sign?utm_source=redirect&utm_medium=web&utm_campaign=int (2015).

  102. Rose, C., Parker, A., Jefferson, B. & Cartmell, E. The characterization of feces and urine: a review of the literature to inform advanced treatment technology. Crit. Rev. Environ. Sci. Technol. 45, 1827–1879 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. Carabotti, M., Scirocco, A., Maselli, M. A. & Severi, C. The gut-brain axis: interactions between enteric microbiota, central and enteric nervous systems. Ann. Gastroenterol. 28, 203 (2015).

    PubMed  PubMed Central  Google Scholar 

  104. Cryan, J. F. et al. The microbiota-gut-brain axis. Physiol. Rev. 99, 1877–2013 (2019).

    CAS  PubMed  Google Scholar 

  105. Mück, J. E., Ünal, B., Butt, H. & Yetisen, A. K. Market and patent analyses of wearables in medicine. Trends Biotechnol. 37, 563–566 (2019).

    PubMed  Google Scholar 

  106. Grand View Research. Smart bathroom market size, share & trends analysis report by product (toilet, soap dispenser, faucet, shower), by application (residential, commercial), by region, and segment forecasts, 2020–2027, https://www.grandviewresearch.com/industry-analysis/smart-bathrooms-market (2020).

  107. Sahu, M. L., Atulkar, M. & Ahirwal, M. K. IOT-based smart healthcare system: a review on constituent technologies. J. Circuits Syst. Comput. 30, 2130008 (2021).

    Google Scholar 

  108. Dabbagh, S. R., Rabbi, F., Doğan, Z., Yetisen, A. K. & Tasoglu, S. Machine learning-enabled multiplexed microfluidic sensors. Biomicrofluidics 14, 061506 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Paramita, S., Bebartta, H. N. D. & Pattanayak, P. IoT based healthcare monitoring system using 5 G communication and machine learning models in Health Informatics: A Computational Perspective in Healthcare. 159–182 (Springer, 2021).

  110. Dumka, A. Smart information technology for universal healthcare in Healthcare Data Analytics and Management 211–226 (Elsevier, 2019).

  111. Cheng, H. T. & Zhuang, W. Bluetooth-enabled in-home patient monitoring system: early detection of Alzheimer’s disease. IEEE Wirel. Commun. 17, 74–79 (2010).

    Google Scholar 

  112. Lavric, A., Petrariu, A. I. & Popa, V. Long range sigfox communication protocol scalability analysis under large-scale, high-density conditions. IEEE Access. 7, 35816–35825 (2019).

    Google Scholar 

  113. Tengshe, R. R. & Sahoo, A. NB-IoT for Healthcare in Principles and Applications of Narrowband Internet of Things (NBIoT) 127–152 (IGI Global, 2021).

  114. Malik, H., Alam, M. M., Le Moullec, Y. & Kuusik, A. NarrowBand-IoT performance analysis for healthcare applications. Procedia Comput. Sci. 130, 1077–1083 (2018).

    Google Scholar 

  115. Valach, A. & Macko, D. Exploration of the LoRa technology utilization possibilities in healthcare IoT devices. in 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA) 623–628 (IEEE, 2018).

  116. Valach, A. & Macko, D. Optimization of LoRa devices communication for applications in healthcare. in 2020 43rd International Conference on Telecommunications and Signal Processing (TSP) 511–514 (IEEE, 2021).

  117. Chang, S.-H., Chiang, R.-D., Wu, S.-J. & Chang, W.-T. A context-aware, interactive M-health system for diabetics. IT Professional 18, 14–22 (2016).

    Google Scholar 

  118. Doukas, C. & Maglogiannis, I. Bringing IoT and cloud computing towards pervasive healthcare. in 2012 Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing 922–926 (IEEE, 2012).

  119. Shickel, B., Tighe, P. J., Bihorac, A. & Rashidi, P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J. Biomed. Health Inform. 22, 1589–1604 (2017).

    PubMed  PubMed Central  Google Scholar 

  120. Yang, G. et al. A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans. Ind. Inform. 10, 2180–2191 (2014).

    Google Scholar 

  121. Sulmasy, L. S., López, A. M. & Horwitch, C. A. Ethical implications of the electronic health record: in the service of the patient. J. Gen. Intern. Med. 32, 935–939 (2017).

    PubMed  PubMed Central  Google Scholar 

  122. Wang, Y., Zhang, A., Zhang, P. & Wang, H. Cloud-assisted EHR sharing with security and privacy preservation via consortium blockchain. IEEE Access. 7, 136704–136719 (2019).

    Google Scholar 

  123. Esposito, C., De Santis, A., Tortora, G., Chang, H. & Choo, K.-K. R. Blockchain: a panacea for healthcare cloud-based data security and privacy? IEEE Cloud Comput. 5, 31–37 (2018).

    Google Scholar 

  124. Xiao, Z. & Xiao, Y. Security and privacy in cloud computing. IEEE Commun. Surv. Tutor. 15, 843–859 (2012).

    Google Scholar 

  125. Karthiban, K. & Smys, S. Privacy preserving approaches in cloud computing. in 2018 2nd International Conference on Inventive Systems and Control (ICISC) 462–467 (IEEE, 2018).

  126. Schönberger, D. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Int. J. Law Inf. Technol. 27, 171–203 (2019).

    Google Scholar 

  127. Singh, N. & Singh, A. K. Data privacy protection mechanisms in cloud. Data Sci. Eng. 3, 24–39 (2018).

    Google Scholar 

  128. Azaria, A., Ekblaw, A., Vieira, T. & Lippman, A. MedRec: Using Blockchain for Medical Data Access and Permission Management in 2016 2nd International Conference on Open and Big Data (OBD) 25–30 (IEEE, 2016).

  129. Zhang, J., Xue, N. & Huang, X. A secure system for pervasive social network-based healthcare. IEEE Access 4, 9239–9250 (2016).

    Google Scholar 

  130. Maher, N. A. et al. Passive data collection and use in healthcare: a systematic review of ethical issues. Int. J. Med. Inform. 129, 242–247 (2019).

    PubMed  Google Scholar 

  131. Shah, P., Thornton, I., Turrin, D. & Hipskind, J. E. Informed Consent, https://www.ncbi.nlm.nih.gov/books/NBK430827/ (2020).

  132. Zakaria, F. et al. User acceptance of the eSOS® Smart Toilet in a temporary settlement in the Philippines. Water Pract. Technol. 12, 832–847 (2017).

    Google Scholar 

  133. Bettiga, D., Lamberti, L. & Lettieri, E. Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach. Health Care Manag. Sci. 23, 1–12 (2019).

    Google Scholar 

  134. Marangunić, N. & Granić, A. Technology acceptance model: a literature review from 1986 to 2013. Univers. Access. Inf. Soc. 14, 81–95 (2015).

    Google Scholar 

  135. Lanter, D. & Essinger, R. User-Centered Design. in International Encyclopedia of Geography: People, the Earth, Environment and Technology: People, the Earth, Environment and Technology 1–4 (Wiley-Blackwell, 2016).

  136. Dabbagh, S. R. et al. Increasing the packing density of assays in paper-based microfluidic devices. Biomicrofluidics 15, 011502 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Lei, R., Huo, R. & Mohan, C. Current and emerging trends in point-of-care urinalysis tests. Expert. Rev. Mol. Diagn. 20, 69–84 (2020).

    CAS  PubMed  Google Scholar 

  138. Mahoney, E., Kun, J., Smieja, M. & Fang, Q. Point-of-care urinalysis with emerging sensing and imaging technologies. J. Electrochem. Soc. 167, 037518 (2019).

    Google Scholar 

  139. Lin, C.-C., Tseng, C.-C., Chuang, T.-K., Lee, D.-S. & Lee, G.-B. Urine analysis in microfluidic devices. Analyst 136, 2669–2688 (2011).

    CAS  PubMed  Google Scholar 

  140. Yang, Y., Chen, Y., Tang, H., Zong, N. & Jiang, X. Microfluidics for biomedical analysis. Small Methods 4, 1900451 (2020).

    CAS  Google Scholar 

  141. Ozdalgic, B. et al. Microfluidics for microalgal biotechnology. Biotechnol. Bioeng. 118, 1716–1734 (2021).

    Google Scholar 

  142. Pandey, C. M. et al. Microfluidics based point-of-care diagnostics. Biotechnol. J. 13, 1700047 (2018).

    Google Scholar 

  143. Cui, P. & Wang, S. Application of microfluidic chip technology in pharmaceutical analysis: a review. J. Pharm. Anal. 9, 238–247 (2019).

    PubMed  Google Scholar 

  144. Zhu, H., Fohlerová, Z., Pekárek, J., Basova, E. & Neužil, P. Recent advances in lab-on-a-chip technologies for viral diagnosis. Biosens. Bioelectron. 153, 112041 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Bhatia, S. N. & Ingber, D. E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014).

    CAS  PubMed  Google Scholar 

  146. Tian, C., Tu, Q., Liu, W. & Wang, J. Recent advances in microfluidic technologies for organ-on-a-chip. TrAC Trends Anal. Chem. 117, 146–156 (2019).

    CAS  Google Scholar 

  147. Ustun, M., Rahmani Dabbagh, S., Ilci, I. S., Bagci-Onder, T. & Tasoglu, S. Glioma-on-a-chip models. Micromachines 12, 490 (2021).

    PubMed  PubMed Central  Google Scholar 

  148. Wu, Q. et al. Organ-on-a-chip: recent breakthroughs and future prospects. Biomed. Eng. Online 19, 9 (2020).

    PubMed  PubMed Central  Google Scholar 

  149. Sununta, S., Rattanarat, P., Chailapakul, O. & Praphairaksit, N. Microfluidic paper-based analytical devices for determination of creatinine in urine samples. Anal. Sci. 34, 109–113 (2018).

    CAS  PubMed  Google Scholar 

  150. Berthier, J., Brakke, K., Gosselin, D., Berthier, E. & Navarro, F. Thread-based microfluidics: flow patterns in homogeneous and heterogeneous microfiber bundles. Med. Eng. Phys. 48, 55–61 (2017).

    CAS  PubMed  Google Scholar 

  151. Nilghaz, A. et al. Flexible microfluidic cloth-based analytical devices using a low-cost wax patterning technique. Lab Chip 12, 209–218 (2012).

    CAS  PubMed  Google Scholar 

  152. Guan, W., Liu, M. & Zhang, C. Electrochemiluminescence detection in microfluidic cloth-based analytical devices. Biosens. Bioelectron. 75, 247–253 (2016).

    CAS  PubMed  Google Scholar 

  153. Temirel, M., Dabbagh, S. R. & Tasoglu, S. Hemp-based microfluidics. Micromachines 12, 182 (2021).

    PubMed  PubMed Central  Google Scholar 

  154. Vrijburg, K. & Hernández-Peña, P. Global spending on health: weathering the storm 2020 (World Health Organization, 2020).

  155. Ward, B. W., Clarke, T. C., Nugent, C. N. & Schiller, J. S. Early release of selected estimates based on data from the 2015 National Health Interview Survey. Vol. 46 (National Center for Health Statistics, 2016).

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Acknowledgements

S.T. acknowledges Alexander von Humboldt Research Fellowship for Experienced Researchers, Marie Skłodowska-Curie Individual Fellowship (101003361), and Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership award (120N019) for financial support of this research. We acknowledge Prof. Gary Curhan for giving feedback on this manuscript.

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  1. Department of Mechanical Engineering, Koc University, Istanbul, Turkey

    Savas Tasoglu

  2. Koç University Translational Medicine Research Center (KUTTAM), Koç University, Sarıyer, Istanbul, Turkey

    Savas Tasoglu

  3. Boğaziçi Institute of Biomedical Engineering, Boğaziçi University, Çengelköy, Istanbul, Turkey

    Savas Tasoglu

  4. Physical Intelligence Department, Max Planck Institute for Intelligent Systems, Stuttgart, Germany

    Savas Tasoglu

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S.T. researched data for the article, decided on the content, wrote the manuscript and reviewed and edited the manuscript before submission.

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Correspondence to Savas Tasoglu.

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S.T. is a co-founder of ZetaMatrix, Inc., focusing on novel bioinks for 3D bioprinting technologies.

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Nature Reviews Urology thanks Teerakiat Kerdcharoen and other anonymous peer reviewer(s) for their contribution to the peer review of this work.

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Tasoglu, S. Toilet-based continuous health monitoring using urine. Nat Rev Urol 19, 219–230 (2022). https://doi.org/10.1038/s41585-021-00558-x

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