AI-Powered Wearables Predict Labor Onset and Reduce Stress, Paving New Paths in Preventive Healthcare

The integration of artificial intelligence (AI) and wearable sensors is opening new frontiers in preventive healthcare, offering real-time monitoring and predictive analytics to enhance human health. This innovative approach is particularly promising in areas like predicting labor onset and reducing stress, where traditional methods fall short. Shravan Aras, Ph.D., Assistant Director of Sensor Analysis and Smart Health Platforms at the University of Arizona Health Sciences Center for Biomedical Informatics and Biostatistics, is at the forefront of this research. Aras, who has a background in computer science, sees wearables as a powerful tool to gather and analyze physiological data, driving advancements in health monitoring. Predicting Labor Onset One of the most significant applications of AI and wearables is in predicting when a pregnant woman will go into labor. Currently, due dates are estimated by counting 40 weeks from the last menstrual period, but human gestation lengths can vary from 37 to 42 weeks. Clinical tools lack the precision to predict labor accurately, often leading to high rates of false positives in self-reported signs of labor. This can result in unexpected home births, inadequate time for medical intervention in preterm deliveries, or premature inductions for women living far from hospitals. To address this issue, Aras and a team of researchers conducted a study published in the journal BMC Pregnancy and Childbirth. They collaborated with a wearable sensor company that used a smart ring to collect temperature data every minute, rather than the daily readings common in fertility tracking. Leveraging this continuous, high-frequency data, Aras developed a deep neural network-based AI model to predict labor onset. Deep neural networks mimic the human brain's activity, processing data through multiple layers to perform complex calculations and learn from the data. By applying these techniques, the model accurately predicted the start of labor within a 4.6-day window at seven days before actual labor for 79% of spontaneous cases. At 10 days before true labor, the model narrowed the prediction window to 7.4 days. The researchers aim to validate the model in a larger study and eventually develop it into a software application that can be integrated into existing wearables or medical devices. This could revolutionize prenatal care by providing healthcare providers with early and precise warnings of impending labor, allowing for better planning and intervention. Reducing Stress Through Nature Walks Another area where AI and wearables show promise is in stress reduction. Aras and his colleagues conducted a study published in the International Journal of Environmental Research and Public Health to quantify the stress-reducing effects of nature walks. The study involved participants walking for 20 minutes on two different paths—a nature-filled Green Road and an urban path—at the Walter Reed National Military Medical Center in Bethesda. The researchers used wearable sensors to monitor heart rate variability (HRV) and collected cortisol levels from saliva samples. They also recorded self-reported mood and mindfulness scales. The findings revealed that walking on the Green Road significantly reduced cortisol levels compared to walking on the urban path. However, heart rate variability showed more individual variability, indicating that personal experiences, such as encountering a snake, can dramatically affect stress responses. While the original study did not use AI, Aras is now working on an extended project with Dr. Esther Sternberg and Dr. J. Ray Runyon. This new research employs AI to assess a person’s stress response using digital, sweat-based biomarkers, which could provide more personalized and reliable results. The team hopes to publish their findings this summer. The Potential of AI and Wearables Aras emphasizes that the combination of AI and wearable sensors offers immense potential for healthcare. AI's capability to process vast amounts of data quickly and efficiently allows for more sophisticated and accurate predictive models. These models can identify patterns and make inferences that are not easily discernible through traditional methods. His goal is to use wearables to predict nonsymptomatic conditions proactively, rather than reacting to symptoms after they appear. This proactive approach could lead to better management of chronic conditions, early detection of diseases, and improved overall well-being. Industry Insights Industry experts and healthcare professionals are optimistic about the future of AI and wearables in healthcare. The ability to continuously monitor and analyze physiological data opens avenues for personalized medicine and preventive care. Companies are increasingly investing in wearable technology and AI, recognizing the value in improving health outcomes and reducing healthcare costs. Universities and research institutions are also contributing to this field, collaborating with tech companies to refine and apply these technologies in real-world settings. The University of Arizona Health Sciences Center, where Aras conducts his research, is a hub for interdisciplinary collaborations in health informatics and biostatistics. Their work with wearables and AI underscores the institution's commitment to advancing healthcare through technology and innovation. In conclusion, the synergy between AI and wearable sensors is transforming preventive healthcare, offering new and effective ways to monitor and predict health conditions. As research progresses and models become more refined, these technologies are poised to play a crucial role in improving the quality and efficiency of healthcare services.