Table 1:

Definitions of Key Concepts

Epistemology:	How we know what we know
Ontology:	Inquiry into the nature of things; how things are
Pragmatism:	The nature of the phenomenon determines the method; practical needs to inform the development of research frameworks
[Author] Method:	A pragmatic method that generates data, guides analysis and knowledge contextualization to explain and predict for machine learning
Contextualization:	Participants share their own experience of health behavior change, and how the message was understood is provided by clinicians through feedback
Mixed Methodology:	Integrated quantitative and qualitative participatory design
Clinician-Based Machine Learning:	A process whereby expert nurse clinicians assist in training a health-assistive AI agent by contextualizing sensor data with situational health-event information
Artificial Intelligence:	A computer system (machine made of hardware and software) capable of performing tasks that normally require human intelligence; such as speech or activity recognition, or decision-making, and more
Ground Truth:	Data patterns that are labeled by human experts to provide meaningful context; these data are classified by computer scientists and fed to the AI machine to train it