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

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.

Figure 1.

Example instantiation of the JITAI design constructs (ie, rule definition language elements). Overall, the figure shows the instantiation of rule definition language elements leading to several alternatives of motivation interventions. Decision points are the links connecting each intervention type to action plans. Considering the examples, all the intervention types are linked to the action plan slots classified as motivation.

Figure 2.

Analogy between a traditional RL setup and intervention delivery optimization problem. While the left part shows the elements of an RL setup along with the information flow between them, the right part includes the corresponding elements and information flow concerning the optimization of intervention delivery.

Figure 3.

Overall JITAI personalization algorithm. The flow at the top of the figure shows the main steps of the algorithm executed sequentially. First, the set of eligible interventions is identified; then the algorithm selects 1 of the eligible interventions considering current context and past experiences. The placeholders are populated, if there are any. Next is the identification of the best moment to deliver the intervention. Finally, the learning models are updated based on persons’ engagement with interventions.

Figure 4.

Episode vs. intervention count/habit strength plot. This plot shows the inversely proportional relation between the habit strength and number of interventions delivered.

Figure 5.

Person vs. intervention type ratio plot. The plot shows the ratio of the number of a specific intervention type to the total number of interventions delivered for each intervention type for each persona.

Figure 6.

Difference between intervention delivery and behavior performance times. Each bar represents the amount of time difference and the ratio of interventions delivered in that frame to the total interventions.