An expandable approach for design and personalization of digital, just-in-time adaptive interventions

TLDR

The study presents a framework aimed at enabling the design of theory‑driven, just‑in‑time adaptive digital interventions for chronic illnesses and at generating personalized delivery strategies that tailor intervention components to individuals’ needs, contexts, and psychosocial factors. The framework comprises a template‑based design mechanism with a rule‑definition language for specifying trigger conditions, continuous monitoring of personal data, and reinforcement‑learning‑driven personalization of timing, frequency, and content, validated through a simulation testbed with two personas. Evaluation shows the mechanism can produce intervention definitions aligned with behavior‑change taxonomies and clinical guidelines, including a diabetes care program, and that the personalization algorithm adapts delivery strategies to simulated personal preferences, traits, and lifestyle patterns.

Abstract

Abstract Objective We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people’s individual needs, momentary contexts, and psychosocial variables. Materials and Methods We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions. Results We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns. Conclusion While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.