A digital therapeutic — a prescribed, app-based treatment program for conditions like chronic pain or depression — only produces its clinical benefit if the patient keeps using it consistently for the whole prescribed course. This simulation runs the same underlying patient-engagement pattern through two program designs at once: a static program (identical content every day, no matter what) and an adaptive program (shortens sessions, adds motivational nudges and reminders whenever engagement risk rises).
Run the same simulated patient-behavior pattern through a static, one-size-fits-all digital therapeutic and an adaptive one that fires re-engagement interventions, then compare cumulative treatment effect against a clinically-meaningful threshold.
A fixed-content program's adherence erodes as fatigue accumulates over the course; an adaptive program that shortens sessions and nudges the patient at the right moment keeps adherence — and therefore accumulated clinical benefit — much higher through the same course length.
Set course length, simulation speed, static decay strength and adaptive intervention sensitivity, then watch both weekly adherence and cumulative treatment effect update live for both programs, running on one shared patient seed.
Real-world digital therapeutic trials consistently show adherence as the strongest predictor of clinical outcome — a program that isn't used consistently for its full prescribed course rarely reaches the effect size seen in its clinical trial.