Stress prediction using micro-EMA and machine learning during COVID-19 social isolation.

Accurately predicting users' perceived stress is beneficial to aid early intervention and prevent both mental illness and physical disease during the COVID-19 pandemic. However, the existing perceived stress predicting system needs to collect a large amount of previous data for training but has a limited prediction range (i.e., next 1-2 days). Therefore, we propose a perceived stress prediction system based on the history data of micro-EMA for identifying risks 7 days earlier. Specifically, we first select and deliver an optimal set of micro-EMA questions to users every Monday, Wednesday, and Friday for reducing the burden. Then, we extract time-series features from the past micro-EMA responses and apply an Elastic net regularization model to discard redundant features. After that, selected features are fed to an ensemble prediction model for forecasting fine-grained perceived stress in the next 7 days. Experiment results show that our proposed prediction system can achieve around 4.26 (10.65% of the scale) mean absolute error for predicting the next 7 day's PSS scores, and higher than 81% accuracy for predicting the next 7 day's stress labels.

Engaging Patients in Integrated Behavioral Health and Primary Care: A Technology-Based System to Facilitate Behavioral Health Screening for Patients in Rural and Underserved Areas.

Integrating behavioral health into primary care can increase patient access to treatments for behavioral health disorders, especially in rural areas. The first step of integrated care implementations is the need to identify at-risk patients and effectively communicate patient screening results to the care team. The use of technology can facilitate patient screening and incorporate screening data into electronic health records. This paper describes the development of a tablet-based screening system to facilitate behavioral health integration in a rural primary care clinic, discusses the preliminary impact of digital screening on workflows, and provides recommendations for the use of technology for screening. A tablet-based assessment was developed to screen patients for behavioral health concerns and was implemented within a rural primary care clinic. The system was created using the Visual Signature Capture system, which integrates directly with the electronic health record. Following the initial assessment, patients are screened as frequently as every 30 days with a mini-screen assessment, consisting of multiple screening tools for mental health and substance use. The tablet-based screening system had a positive impact on clinician workflows and the overall effectiveness of clinic operations. This system supports providers in addressing the behavioral health needs of patients that often go unrecognized in primary care.