Fig 4.

Description of data and technology segments—the 3As—best practices for digital interventions. Recommendations for digital behavior change interventions include the leverage of the 3As (access, analytics, and artificial intelligence [AI]), which combines data- and technology-driven approaches to improve and sustain health outcomes. Access regards the acquisition of data and consideration of the precision and clarity of data sources, such as their temporality (i.e., frequency of refreshing and/or updating), granularity (e.g., individual- versus population-level data and geographic context), interoperability (i.e., how data sources can be aggregated), and completeness (i.e., gaps in what was expected to be collected vs. actual collection). Additional considerations may include the level of data transparency and consumer-mediated data exchange, which may be related to blockchain and other key technologies. Analytics is a broad classification of applied mathematics that analyzes acquired data and draws conclusions based on that information. Categories of analytics include descriptive (e.g., data summarization of historical trends), predictive (e.g., techniques to infer trends), and prescriptive (e.g., specification of actions that can be taken to meet relevant objectives) analytics. AI includes methods that are designed to emulate various aspects of human intelligence, including the ability to learn and/or reason. AI may be separated into two categories: machine learning and symbolic reasoning. Machine learning is the method of data analysis that makes decisions with limited human intervention after the system learns from the data and identified patterns; it includes the methods of natural language processing and simple or complex (e.g., deep learning) neural network modeling. Implementation of symbolic reasoning methods follow logical rules to produce new knowledge in expert systems, such as rules-based dialogue systems.