Combating Stress with the Help of Machine Learning and Artificial Intelligence
The number of individuals in the modern world experiencing elevated stress levels is on the rise and it's taking a significant toll on their health, productivity at work, relationships, and many other aspects of their lives. One of the most common sources of stress is work. In fact, most employees today are actively disengaged and stressed at work; 87% of employees worldwide are disengaged at work according to Gallup.
Employees who are actively disengaged at work are more likely to experience higher levels of stress and health problems than engaged employees. According to a December 2015 Gallup poll, of the U.S. employees reporting to be "actively disengaged," 56% said they experienced stress yesterday, 23% said they experienced physical pain yesterday, and 19% said they've been diagnosed with high blood pressure. According to the American Psychological Association (APA), 20% of Americans are experiencing stress levels that are extreme (8-10 on a 10-point scale).
Many individuals are not aware they are under stress, let alone the precise level of their stress, the triggers of their stress, and the potential health problems caused by prolonged stress. In order to effectively combat stress and its ill effects on health, stress triggers and responses to stress must be recognised and managed in real time. Numerous consumer wearables today include sensors and other features for monitoring heart rate (HR), heart rate variability (HRV), physical activity level, and galvanic skin response (GSR). Consumer wearables capable of monitoring all four of these physiological signals can provide an accurate stress status profile in real time.
With the help of artificial intelligence (AI) and machine learning, wearable applications not only make it possible for individuals to monitor and manage stress themselves, but also provide analyses far beyond the reach of traditional medical devices. Most modern consumer wearables (and quite a few smartphones) include capabilities such as physiological sensors, accelerometers, and GPS that applications can utilise to provide detailed health and stress information; information that provides an additional layer of understanding to biometric data. The ability to monitor activity then cross correlate it to biometrics allows for a clearer understanding of an individual's health. A 2010 Carnegie Mellon University study details this methodology explaining how continuous stress monitoring can help individuals better understand their own stress patterns and allow targeted interventions to take place.
Machine learning and AI can also be used to build wearable and smartphone applications that provide highly personalised stress management tools and behavior-changing interventions. For example, the BioBeats Hear and Now platform uses machine learning and AI to provide highly personalised stress monitoring and management tools for individuals as well as corporate wellness programs. The platform collects quantified, continuous biometrics such as HR, HRV, GSR as well as activity data from users. The data is analysed in order to evaluate and understand the user's mental and physiological state. Once Hear and Now determines the mental and physiological state of the user, it can provide highly personalised interventions such as biofeedback, focused breathing, and daily reminders.
Personalised stress monitoring and management platforms like BioBeats Hear and Now provide a number of health benefits for users. They provide an accurate assessment of real-time stress levels allowing users to not only become aware of the severity of their stress, but also have the opportunity to change behaviors by following the suggested interventions. Users can learn how to avoid acute stress related medical events, improve their productivity and efficiency at work, and reduce their healthcare costs.
Wearables, smartphones, and other technologies are now available to consumers at affordable prices and this availability has allowed a wide range of consumers access to healthcare and stress reduction tools, tools that were neither accessible nor affordable before. In the past, individuals could only obtain measurements and insights regarding their stress levels by means of traditional medical devices, devices only available at physician offices and hospitals.
Today, consumers have access to modern wearables and smartphones as well as AI and machine learning-powered platforms like BioBeats Hear and Now. Consumers can take it upon themselves to reduce their stress levels and improve their overall quality of life. To be clear, stress management and other types of healthcare platforms/applications are not intended to be replacements for regular visits to a primary care physician (PCP). There are often cases where high blood pressure, obesity, heart disease, and other stress-related health issues not only require regular visits to a PCP, but also specialized treatments and medications. With that said, consumers can use AI and machine learning-powered stress management platforms to be proactive about stress; changing behaviors and reducing stress levels on their own using focused techniques such as clinically validated breathing exercises, biometric feedback, mindfulness exercises, and meditation.