Virtual coaching has rapidly evolved into a foundational component of modern clinical practice. At a time when healthcare professionals are in short supply and the demand for low-cost treatments is everincreasing, virtual health coaches (VHCs) offer intervention-ondemand for those limited by finances or geographic access to care. More recently, AIpowered virtual coaches have become a viable complement to human coaches. However, the push for AIpowered coaching systems raises several important issues for researchers, designers, clinicians, and patients. In this paper, we present a novel Figure 1: The figure shows four main domains of a successful framework to guide the design and development of virtual coaching virtual health coach throughout a data science pipeline.

Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.