A5048840881- Digital Mental Health Interventions
- Mobile Health and mHealth Applications
- Substance Abuse Treatment and Outcomes
- Behavioral Health and Interventions
- Mental Health via Writing
- Mental Health Research Topics
- Data Stream Mining Techniques
Stevens Institute of Technology
2022-2023
Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 men per occasion) in young adults but need to be optimized timing content. Delivering support messages the hours prior BDEs could improve intervention impact.We aimed determine feasibility of developing a machine learning (ML) model accurately predict future, that is, same-day 1 6 BDEs, using smartphone sensor data identify most informative phone features associated with...
<sec> <title>BACKGROUND</title> Acute marijuana intoxication can impair motor skills and cognitive functions (e.g., attention, information processing). However, existing tools blood, urine, saliva tests) do not accurately reflect ‘real-time’ acute intoxication. </sec> <title>OBJECTIVE</title> Considering the absence of screening to detect impairment-related harms, our objective is examine whether integration smartphone-based sensors with a wearable activity tracker (Fitbit), as more...
<sec> <title>BACKGROUND</title> Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 men per occasion) in young adults but need to be optimized timing content. Delivering support messages the hours prior BDEs could improve intervention impact. </sec> <title>OBJECTIVE</title> We aimed determine feasibility of developing a machine learning (ML) model accurately predict <i>future</i>, that is, <i>same-day 1 6...