Published in the Journal of Pharmaceutical Analysis, the study explores how sweat could be used for real-time assessment of hormones, medication levels, and other biomarkers, as well as for early identification of diseases such as diabetes, cancer, Parkinson's and Alzheimer's.

"Collecting sweat is painless, simple and non-invasive," said co-author Dr. Dayanne Bordin, an analytical chemist at the University of Technology Sydney (UTS). "It's an attractive alternative to blood or urine, especially for continuous monitoring in real-time."

Growing Interest in Wearables That Analyze Sweat

"Anyone who is already interested in tracking their health using wearables such as an Apple watch -- for example their heart rate, step count or blood pressure -- would be interested in the information sweat can provide.

"There are already sweat monitoring devices on the market such as the Gatorade sweat patch, which is a single-use, wearable sticker that pairs with an app to analyse your sweat rate and sodium loss, and provide tailored advice."

Recent progress in areas like microfluidics, stretchable electronics and wireless communication has made it possible to build a new class of wearable sensors. These lightweight, flexible patches rest directly on the skin and continuously collect sweat samples.

When these devices are combined with artificial intelligence, they may be able to identify specific metabolites and interpret complex chemical patterns, which could give users more personalized health information and earlier warning of various medical conditions.

Potential Uses for Athletes and Patients

Athletes could use them to track electrolyte loss during workouts and to demonstrate that they are drug free before competitions. People managing diabetes may eventually rely on sweat based glucose detection rather than blood tests.

"Sweat is an under-used diagnostic fluid," said co-author Dr. Janice McCauley from the UTS Faculty of Science.

"The ability to measure multiple biomarkers simultaneously, and transmit that data wirelessly, provides enormous potential for preventive health care.

"The year 2023 was marked by an evolutionary step in artificial intelligence, opening the door for improved pattern analysis and classification algorithms to improve diagnostic precision and therapeutic accuracy," she said.

Advances in AI and Ultra Sensitive Devices

Modern AI systems can now analyze very large datasets to connect subtle chemical signals in sweat with particular physiological conditions. The authors note that the next major milestone will involve pairing this analytical capability with compact, low power devices that can send data securely.

UTS researchers are currently investigating the basic physiological characteristics of sweat. They are also creating microfluidic tools that can detect extremely small concentrations of biomarkers such as glucose and cortisol.

Although much of this work is still in the prototype phase, interest from industry continues to increase.

"We're not far from a future where your wearable can tell you when you've got high stress hormone levels, and by monitoring this over time, whether you are at risk of chronic health conditions," Dr. Bordin said.

An interdisciplinary research group led by Y. Alicia Hong, a professor in the Department of Health Administration and Policy who specializes in mobile and wearable technology, investigated this question. The team found that the college setting naturally encourages behaviors that increase how much students eat, which can lead to steady weight gain.

"Social and environmental factors are key determinants of eating behavior. College students are affected by the eating environment, especially where they eat and whom they eat with. Our research found that they consume more calories when eating in groups or formal dining settings," said Alicia Hong.

How Group Settings and Campus Locations Influence Eating

During a four-week period, participating students used a mobile app to track their food choices, eating locations, and emotional states such as stress and mood. The app data showed clear patterns: students tended to consume more food when they were with at least one other person and when they ate in places like dining halls or restaurants. When students ate alone or at home, their overall intake was lower.

Misjudging Portions and the Role of Emotion

The findings also suggested that many college students are not fully aware of their actual eating habits. Their self-reported perceptions often did not match the caloric intake recorded through the app. Gender and emotional influences, including stress and mood fluctuations, added additional layers to the complexity of these behaviors.

"College students' eating behaviors are complex, with individual, interpersonal, and environmental factors interacting to influence dietary intake. This research underscores the importance of context in dietary intervention and incorporating digital tools for dietary assessment," said Hong.

Who Conducted the Study and Where It Was Published

The research team included Distinguished University Professor in the Department of Nutrition and Food Studies Larry Cheskin, Associate Professor in the Department of Health Administration and Policy Hong Xue, and MS Health Informatics graduate Jo-Vivian Yu.

The study, titled "The Dynamics of Eating Behaviors and Eating Environment in College Students: Discrepancies Between App-Tracked Dietary Intake and Self-Perceived Food Consumption," was published in mHealth. It was supported by the George Mason University College of Public Health Pilot Grant (PI: YAH).