H2Q™ is a proprietary, patent-pending sweat prediction algorithm that runners, walkers, cyclists, swimmers, and soccer athletes can use to translate exercise water losses into actionable, bespoke hydration management data. Proper exercise fluid replacement helps optimize performance and health by minimizing problems related to both under and over drinking. Sports medicine and sports nutrition experts recommend that the amount of fluid planned for consumption should be customized on the basis of individual sweat losses, which must be impractically estimated by acute changes in body weight. Few athletes practice weighing before and after exercise and most do not know their sweating rate. As a result, many practice drinking behaviors that put their performance, and even their health, at risk. In the growing commercial market of wearable technologies designed to provide athletes with the possibility of real-time personalized training feedback, there is no valid and convenient alternative to weighing, until now.
H2Q™ was created by a recognized sports nutrition expert with more than 20 years of hydration research experience. H2Q™ was built from a complex combination of biophysical and physiological models, but reduces to simple, proprietary equations that integrate easily into sports wearables and fitness app platforms with no added user complexity. H2Q™ requires only two inputs (energy expenditure & air temperature) commonly available or easily acquired. A novel, high fidelity hydration management tool like H2Q™ creates a strong point of differentiation in the larger market space of sports wearables, fitness app, sweat patch, smart fluid delivery, and bespoke hydration industries. The published accuracy of H2Q™ also sets it apart from most unproven proprietary algorithms, adding quality assurance for end-users. H2Q™ was originally designed for running exercise, but is now expanded to include other activities and sports, thus broadening the potential user base considerably. A modified version already allows road race directors to use H2Q™ for water planning (Road Race Water Planner©). H2Q™ is primed for adoption by a commercial partner looking for a unique market opportunity with a “best in class” hydration management algorithm.
Disruptive Technology (H2Q™)
• Exercise sweat losses must be replaced for optimal health and performance
• Over-drinking can threaten health (hyponatremia); under-drinking can impair performance (dehydration)
• Experts agree that athletes should know their sweat rate and drink accordingly
• Simple, accurate estimate of individual sweat losses is a holy grail of sports tech
• Growing market for similar-tech
• Mass balance measurements (body weight, fluids ingested, urine excreted) are an impractical gold standard
• Smart bottles and water tracking apps provide no information about body water losses (intakes only)
• Sweat patches cannot yet estimate whole body sweat losses accurately
• Hydration sensors unproven
• Existing sweat prediction equations are not sport-centric
• Easily incorporated into existing sports wearables or fitness app platforms
• Natural complement to sweat patch and smart fluid delivery technologies
• > 90% accuracy
• More precise than scale weights
• Improve performance (reduce dehydration) and sustain health (reduce hyponatremia risk)
• Estimate water losses before, during and after exercise or forecast for different venues
H2Q is designed for API licensing and integration into existing wearable and fitness app technologies. It is also available as a responsive web app for individual stand-alone usage. Sweat prediction activities include walking, running, cycling, swimming, and soccer. Questions? Please email: email@example.com.
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Sollanek KJ, Liu M, Carballo A, Caldwell AR, Cheuvront SN. The accurate prediction of sweat rate from energy expenditure and air temperature: A proof-of-concept study. Appl Physiol Nutr Metab (In Press)
Additional Supporting Science
Ravanelli N, Imbeault P, Jay O. Steady-state sweating during exercise is determined by the evaporative requirement for heat balance independently of absolute core and skin temperatures. J Physiol. 2020 Apr 9. doi: 10.1113/JP279447. Epub ahead of print. PMID: 32271468.
Ravanelli N, Cramer M, Imbeault P, Jay O. The optimal exercise intensity for the unbiased comparison of thermoregulatory responses between groups unmatched for body size during uncompensable heat stress. Physiol Rep. 2017 Mar;5(5):e13099. doi: 10.14814/phy2.13099. PMID: 28270588; PMCID: PMC5350162.
Dervis S, Coombs GB, Chaseling GK, Filingeri D, Smoljanic J, Jay O. A comparison of thermoregulatory responses to exercise between mass-matched groups with large differences in body fat. J Appl Physiol (1985). 2016 Mar 15;120(6):615-23. doi: 10.1152/japplphysiol.00906.2015. Epub 2015 Dec 23. PMID: 26702025; PMCID: PMC4796181.
Cramer MN, Jay O. Explained variance in the thermoregulatory responses to exercise: the independent roles of biophysical and fitness/fatness-related factors. J Appl Physiol (1985). 2015 Nov 1;119(9):982-9. doi:10.1152/japplphysiol.00281.2015. Epub 2015 Aug 27. PMID: 26316511; PMCID: PMC4628991.
Smoljanić J, Morris NB, Dervis S, Jay O. Running economy, not aerobic fitness, independently alters thermoregulatory responses during treadmill running. J Appl Physiol (1985). 2014 Dec 15;117(12):1451-9. doi:10.1152/japplphysiol.00665.2014. Epub 2014 Oct 9. PMID: 25301893; PMCID: PMC4269685.
Jay O. Unravelling the true influences of fitness and sex on sweating during exercise. Exp Physiol. 2014 Oct;99(10):1265-6. doi: 10.1113/expphysiol.2014.080994. PMID: 25274337.
Cramer MN, Jay O. Selecting the correct exercise intensity for unbiased comparisons of thermoregulatory responses between groups of different mass and surface area. J Appl Physiol (1985). 2014 May 1;116(9):1123-32. doi: 10.1152/japplphysiol.01312.2013. Epub 2014 Feb 6. PMID: 24505102.
Gagnon D, Jay O, Kenny GP. The evaporative requirement for heat balance determines whole-body sweat rate during exercise under conditions permitting full evaporation. J Physiol. 2013 Jun 1;591(11):2925-35. doi: 10.1113/jphysiol.2012.248823. Epub 2013 Mar 4. PMID: 23459754; PMCID: PMC3690695.
Jay O, Bain AR, Deren TM, Sacheli M, Cramer MN. Large differences in peak oxygen uptake do not independently alter changes in core temperature and sweating during exercise. Am J Physiol Regul Integr Comp Physiol. 2011 Sep;301(3):R832-41. doi: 10.1152/ajpregu.00257.2011. Epub 2011 Jun 22. PMID: 21697517.
Gagnon D, Jay O, Lemire B, Kenny GP. Sex-related differences in evaporative heat loss: the importance of metabolic heat production. Eur J Appl Physiol. 2008 Nov;104(5):821-9. doi: 10.1007/s00421-008-0837-0. Epub 2008 Aug 2. PMID: 18677506.