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Your AI assistant can generate a working prototype in minutes — but trusting it without testing is a recipe for disaster. These 10 practical tips for writing code with generative AI come from Automators Anonymous at the University of Michigan, where members build clinical, research, and operational tools using Power Automate, Power BI, Power Apps, JavaScript, SQL, SharePoint, and AI. From prompt engineering to data validation, this is the survival guide we wish we had when we started.

Standardized data flow for research studies that utilize mobile technologies at the University of Michigan. It depicts how data typically moves from a smart watch or wearable device, into University resources behind a firewall, and finally lands on long-term storage for preservation and analytics.

Hiring the right person for any position can be challenging, but this is especially true for research teams involved in mobile health (mHealth) studies. These projects often require a unique skill set to handle data management and technical hurdles that differ significantly from traditional research support roles. In this article, we explore the necessity of hiring the right technical support person for your research team, and what types of questions you should ask.

Sleep estimation is a near ubiquitous feature of smart watches and fitness trackers; therefore, sleep parameters have become desirable to include in research as both predictor variables or outcomes. However, not all sleep parameters provided by wearable sleep trackers are considered reliable for the purpose of research and appropriate selection and calculation of aggregate sleep values are required for hypothesis generation and testing.

Review of best practices and examples of enrolling and consenting participants using mobile or web applications.

This article provides general guidance for sharing mobile data, based on the lessons learned by members of MeTRIC (https://metric.umich.edu), and evolving best practices from government and academic institutions worldwide.