We recently posted about why digital health researchers should consider using Python to work with their data. Python offers so much power and flexibility, such as automating cleaning and processing data and sharing reproducible notebooks. If you are getting started with Python and feel overwhelmed, know that a large and supportive Python community is waiting …
Top Python Resources for Digital Health Researchers Read More »
As a digital health researcher, you are likely familiar with using statistical packages such as SPSS, SAS, or Stata for your data analysis and cleaning needs. While these programs have a relatively low initial learning curve, they may not provide the versatility, scalability, and ease of use you need to tackle complex and large data …
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The promise of digital health interventions is partly rooted in their flexibility and convenience. While traditional, face-to-face modalities (e.g., group sessions, individual counseling) often have a set, prescribed exposure (e.g., 12 weekly sessions), mHealth interventions (e.g., apps, websites, chatbots) are accessible 24/7 and are used within the context of participants’ daily lives. User engagement with …
Exploring the Challenges of Evaluating mHealth Engagement Read More »
