Automated stress detection using mobile application and wearable sensors improves symptoms of mental health disorders in military personnel

Abstract

Leading causes in global health-related burden include stress, depression, anger, fatigue, insomnia, substance abuse, and increased suicidality. While all individuals are at risk, certain career fields such as military service are at an elevated risk. Cognitive behavioral therapy (CBT) is highly effective at treating mental health disorders but suffers from low compliance and high dropout rates in military environments. The current study conducted a randomized controlled trial with military personnel to assess outcomes for an asymptomatic group (n = 10) not receiving mental health treatment, a symptomatic group (n = 10) using a mHealth application capable of monitoring physiological stress via a commercial wearable alerting users to the presence of stress, guiding them through stress reduction techniques, and communicating information to providers, and a symptomatic control group (n = 10) of military personnel undergoing CBT. Fifty percent of symptomatic controls dropped out of CBT early and the group maintained baseline symptoms. In contrast, those who used the mHealth application completed therapy and showed a significant reduction in symptoms of depression, anxiety, stress, and anger. The results from this study demonstrate the feasibility of pairing data-driven mobile applications with CBT in vulnerable populations, leading to an improvement in therapy compliance and a reduction in symptoms compared to CBT treatment alone. Future work is focused on the inclusion of passive sensing modalities and the integration of additional data sources to provide better insights and inform clinical decisions to improve personalized support.