Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic.
Michael HsuDavid K AhernJoji SuzukiPublished in: JMIR mental health (2020)
Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.
Keyphrases
- machine learning
- randomized controlled trial
- end stage renal disease
- big data
- acute myeloid leukemia
- depressive symptoms
- palliative care
- drinking water
- chronic kidney disease
- artificial intelligence
- blood pressure
- public health
- free survival
- peritoneal dialysis
- diffuse large b cell lymphoma
- heart rate
- replacement therapy
- electronic health record
- smoking cessation
- multiple myeloma
- pain management
- loop mediated isothermal amplification