Researchers from the University of California – Berkeley, the Lawrence Berkeley National Laboratory, and Stanford have developed a wristband that can continuously monitor electrolytes like sodium and potassium as well as biomarkers like lactate through sweat analysis.
Described in a recent research letter in Nature, this smart wristband makes pretty much every smartwatch or wearables like Fitbit look massive. Everything from the sensor array to the data processing circuitry is embedded into a flexible plastic that can be fashioned into a wristband (or headband). It even comes with Bluetooth, facilitating transfer of the collected data to a smartphone. As described by the researchers,
Our solution bridges the existing technological gap between signal transduction (electrical signal generation by sensors), conditioning (here, amplification and filtering), processing (here, calibration and compensation) and wireless transmission in wearable biosensors by merging commercially available integrated-circuit technologies, consolidated on a flexible printed circuit board (FPCB), with flexible and conforming sensor technologies fabricated on plastic substrates.
They tested their device first against solutions with known concentrations of each of the solutes they aim to measure. After finding reasonable accuracy there, they moved on to studies with volunteers on stationary bikes; there they compared the measurements from their device to sweat samples during exercise. Additionally, they recruited healthy volunteers to wear the device while running, varying participants water consumption to see how dehydration may impact measurements.
The technology is impressive and intriguing. One early potential application, as described by the authors, would be for exercise monitoring in athletes. More intriguing though are ways in which this technology could be used more broadly, especially for people with chronic health conditions. They note that sensors to measure other sweat solutes could be embedded in the band, which can really be applied anywhere.
That’s a big leap though and will require a lot more data to understand what indicators we can tie to specific health conditions & outcomes. As the authors note, that’s a place where the growing number of digital cohort studies like Health ePeople or those using ResearchKit could have a role.
