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Decoding human melancholy using wearables

In this day and age, we cannot avoid stress. The demand we put on our minds and bodies can trigger multiple physiological responses and one such response is anxiety. In this article we will discuss this lifestyle disease and the bio-sensor technology-based approach being used to tackle it.

Decoding human melancholy using wearables

Anxiety Disorders Each one of us experience anxiety in our own way, every day. While traumatic events and dangerous circumstances can simply distress us, public appearances and facing a review committees can lead to regular episodes of anxiety. However, such anxiety bouts happen to almost everyone and they might even act in favor of our brain performance. It is when such uneasy and uncomfortable state of mind, prolongs and makes it difficult for us to perform our daily activities that the emotion ‘anxiety’ turns into a disorder. It not just affects our performance to think, but the trigger of irrational fears housed in our minds affects our otherwise normal physiological activities such as heart rate, breathing patterns, muscle tension, etc.

Global Impact Out of the total number of mental illness-associated patients, the number of people diagnosed with anxiety disorders is the highest [1] . This disorder affects approximately 40 million adults in USA and more than 160 million in the European Union [2] . Anxiety disorder is an umbrella term which includes panic disorders, social anxiety disorders, and post-traumatic stress disorder amongst others. The increasing occurrences of these health conditions are in-turn increasing the burden on the global economy with more than US $1 trillion of healthcare related cost. The Arrival of Healthcare Wearables The information collected from healthcare sensors are improving the existing evaluations on patient symptoms. Wearable devices (such as Fitbit) can provide continuous data via heart rate, sleep and accelerometer sensors. This physiological data can give insights into patient’s symptoms and its variation. During panic attacks, individuals can suffer a variety of physiological symptoms; from increased heartbeat to shortness of breath and even numbness along with body tremors. Under such circumstances, knowledge of heart beats, movement and energy consumption has been found to be indicative of patient’s anxiety symptoms and hence their measurements can be insightful. The wearables worn by patients can monitor these parameters and could be used to predict panic attack symptoms [2] . Anxiety disorders can vary among individuals based on the severity of the symptoms and their thresholds. Hence, wearables can be a good means to capture and label individual changes. Anxiety associated depression can often lead to alcohol consumption. It has been found that Fitbit aided physical activity monitoring and an intense exercise regime reduced the anxiety and the associated depressive symptoms.

Managing Anxiety Wearables can be very effective in self-regulation strategies which can further increase physical activity and mental well being. Plus, the relationship between the two has been well documented in the past as well [3] . Multiple health/wellness apps are available that can be integrated with the wearables to identify a patient’s real time health with respect to their behavior and symptoms [4] . The data received on a continuous basis can be very useful in building powerful anxiety disorder prediction models for individuals. Besides prediction, the usage of variables can also influence a patient’s psyche into taking preventive measures and increasing the overall quality of life. Reference 1. Bandelow, Borwin, and Sophie Michaelis. “Epidemiology of anxiety disorders in the 21st century.”Dialogues in clinical neuroscience 17.3 (2015): 327. 2. Pastor, Núria, et al. “Remote Monitoring Telemedicine (REMOTE) platform for patients with anxiety symptoms and alcohol use disorder: protocol for a case- control study”. JMIR research protocols 9.6 (2020): e16964. 3. Fox, Kenneth R.” The influence of physical activity on mental well-being”. Public health nutrition 2.3a (1999): 411-418. 4. Ng, Ada, et al.” Provider perspectives on integrating sensor-captured patient- generated data in mental health care.” Proceedings of the ACM on human-computer interaction 3.CSCW (2019): 1-25.

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