General HealthMed Tech

How does Digital Health help in predicting psychological disorders?

The past few years has cemented the use of digital tools and technologies in the healthcare system. From 2019 to 2020, the percentage of U.S. patients who used telehealth increased from 11 percent to 46 percent, with the COVID-19 pandemic being a key driving factor. 

Digital health refers to the use of data captured via digital technology to measure individuals’ health behavior in daily life and to provide digital therapeutic tools accessible anytime and anywhere. Smartphones, wearable devices and sensors, Internet of Things, enable remote monitoring of behavioral, and physiological features, such as sleep, physical activity, social interactions, electrodermal activity, and cardiac activity. Advances in digital technologies and data analytics have created opportunities to assess various health and behavioral changes and thus contribute to improved health outcomes.

Human behavior is one of the biggest drivers of health and wellness as well as mortality and morbidity. It is linked to many mental health and psychiatric disorders such anxiety and depression. Substance use and addictive behaviours also increases risk of breast, esophageal, and upper digestive, liver and lung cancers.

The current process for identifying diagnosable mental disorders heavily relies on measuring the number and type of symptoms that a person may be experiencing as well as associated distress or impairment. Although this current diagnostic process provides a useful common language of mental disorders for clinicians, the process is largely based on consensus from expert panels and may oversimplify the understanding of human behavior. 

Mental health professionals usually interact with, and provide diagnoses to, patients at a specific moment in patients’ lives. However, recent evidence shows that people with psychological disorders may experience different kinds of disorders over their lifespan. 

Digital health data helps to capture the intricacies of one’s behavior which includes the various factors that influence the short term and long term behavioral changes. Digital health technologies may contribute to discovery science by revealing digital markers of health and risk behavior. They may help to develop better diagnostic classifications of aberrant/dysfunctional behavior and the clinical trajectories of diagnosable disorders over time. 

Digitally derived data have been used to understand behavior and context in the field of Computer Science for over 15 years. This is done through a process called Digital Phenotyping where passively sensed data is used and allows for a moment-by-moment monitoring of behavior. These data can include data derived from smartphone or smartwatch sensors (e.g., an individual’s activity, location), features of voice and speech data collected by mobile devices (e.g. sentiment), and data that captures a person’s interaction with their mobile device (e.g., patterns of typing or scrolling). 

Digital phenotyping collects passive data (to reduce burden to participants in data collection). However, digital measurement also utilises data from other sources where the participants are actively involved including social media data, EMA (ecological momentary assessment) data, and online search engine activity. These “digital exhaust” data or “digital footprints” enable the continuous measurement of individuals’ behavior and physiology in naturalistic settings.

Digital assessment also provides insights into psychological and psychiatric disorders. High-frequency assessment of cognition and mood via wearable devices among persons with major depressive disorder has been shown to be feasible and valid over an extended period. Behavioral indicators passively collected through a mobile sensing platform have been able to predict symptoms of depression and PTSD. 

Movement data from actigraphs (a single measure of gross motor activity from a wrist worn sensor), were able to identify the diagnostic status of individuals with major depression or bipolar vs. healthy controls 89% of the time. Emotion dynamics captured over time have also helped to predict bipolar and depressive symptoms. And, EMA data captured on smartphones has been shown to predict future mood among persons with bipolar disorder. 

The promise of digital health is particularly compelling when applied to the field of psychiatry. Digital assessment allows for the continuous quantification of clinically useful digital biomarkers that can be useful in identifying and refining diagnostic processes over time. These data may help to generate predictive models that reflect the confluence of factors, and their relations over time, that may inform when an individual may be at risk for a clinically significant event (such as a relapse or psychotic event). 

Digital health and data analytics are transforming the healthcare sector. And, the real-world precision assessment that digital health methods enable are providing unprecedented insights into human behavior and psychiatric disorders and can inform interventions that are personalizable and adaptive to individuals’ changing needs and preferences over time. 

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