Incorporating mobile health (mHealth) into cancer care holds growing promise for improving quality and access, especially in rural and disadvantaged populations, who continue to face disparities in distress prevalence and healthcare engagement. Regardless, sustained engagement with mHealth solutions remains challenging due to motivational, technical, and usability barriers. We developed and piloted a HIPAA-compliant mHealth app, Assuage, in a 45-day longitudinal study with seven cancer patients, the majority from rural backgrounds. Participants alternated between daily and every-other-day symptom reporting. Our findings reflect adherence trends across reporting frequencies, stable and favorable system usability measures before and after the study, and qualitative insights about participants' willingness to use mHealth for symptom self-reporting.

Automated design of analog and radio-frequency circuits using supervised or reinforcement learning from simulation data has recently been studied as an alternative to manual expert design. It is straightforward for a design agent to learn an inverse function from desired performance metrics to circuit parameters. However, it is more common for a user to have threshold performance criteria rather than an exact target vector of feasible performance measures. In this work, we propose a method for generating from simulation data a dataset on which a system can be trained via supervised learning to design circuits to meet threshold specifications. We moreover perform the to-date most extensive evaluation of automated analog circuit design, including experimenting in a significantly more diverse set of circuits than in prior work, covering linear, nonlinear, and autonomous circuit configurations, and show that our method consistently reaches success rate better than 90% at 5% error margin, while also improving data efficiency by upward of an order of magnitude.