Digging For Goals Using Data Science
E-health programs such as the Jellinek Self-Help offer an accessible way of help for (excessive) alcohol or substance use. At the same time, little is known about effective components or mechanisms of these programs. Rich data is available within these programs, such as how often someone logs in, which assignments are made and time spent on assignments. This makes these programs extremely suitable for innovative data analysis methods from the field of data science.
Using data science techniques such as machine learning and growth curve models, patterns will be discovered in the data that can be predictive at an individual level for a) the use of e-health programs, and b) the results of e-health programs. Subsequently, after adjusting the programs based on the findings, it will be investigated whether the adjustments improve the use of these substance use e-health programs.