Passive Sensing to Prevent Mental Health Issues during the COVID-19 Pandemic BY STEPHEN ADJEI '25 Cover Image: Passive data sensing tools can measure behavior in individuals through the real-time collection of personal data Image Source: Pixabay
Introduction Smart devices are now helping our healthcare system address one of the most significant challenges currently plaguing psychiatry: the difficulty of accurately labeling disease phenotypes (Torous et al., 2016). Recent developments in smart devices and wearable technologies such as smartphones allow for realtime data collection, effectively transforming smart devices into health records. Today, the healthcare system can utilize data collected from these devices to provide personalized diagnostics and proper preventive treatment. (Torous et al., 2016). Critically, this technology can enable health professionals to aid children and adolescents struggling with mental health issues during the pandemic by identifying early indicators of worsening mental health (de Figueredo et al., 2021). This report will address both the benefits and issues associated with passive data sensing and defend an argument for its implementation. This article will outline how this technology has helped youth during the pandemic and discuss studies that demonstrate its effectiveness. Lastly, this article will address ethical issues and discuss
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measures the government and the healthcare industry can take to address concerns. Passive Data Sensing Passive Data Sensing (PDS) is the indirect acquisition of data about a person's or group's activity at a specific location in real-time. Passive data sensing tools can measure behavior in individuals through the real-time collection of personal data, which is referred to as digital phenotyping (Martinez-Martin et al., 2021). Digital phenotyping is essential to healthcare. The analysis of sleep patterns, movement, social media activity, and more can predict relapses in schizophrenia, depression, and mania, among many other mental illnesses. The concept originated from research in the computer science field regarding 'contextaware' computing. Context-aware devices monitor changes within the environment, such as location changes, lighting, network connectivity, and noise level (Schilit et al., 1994). These devices interact with their primary users and other context-aware computers within a specified range and vicinity (Schilit et al., 1994). DARTMOUTH UNDERGRADUATE JOURNAL OF SCIENCE
