Situation-Based Assess Tree for User Behavior Assessment in Persuasive Telehealth

Existing telehealth systems do not perform as effectively as would be expected due to their asymmetric focus on sensing and monitoring with little support or assurance to affect or alter behaviors. We developed Action-based Behavior Model (ABM) that supports persuasive telehealth. However, ABM requires an ongoing assessment of user behavior response and compliance to cyber influence. Method: To measure compliance, we developed Situation-based Assess Tree (SAT) as a methodology and an algorithm for domain-specific behavior assessment under ABM. We followed a proof-of-concept validation approach based on a trace-driven simulation. Results: Preliminary results demonstrate that SAT is sentient to the full spectrum of compliance clearly discerning between compliant and noncompliant user responses. Results also demonstrate SAT´s ability to learn different user personas through the assessment process.