Real-time cognitive performance metrics derived from a digital therapeutic for inattention predict ADHD-related clinical outcomes: Replication across three independent trials of AKL-T01

AKL-T01 is a digital therapeutic (DTx) that targets attention by generating conflict at dynamically updated difficulty levels during a multitasking game. Clinical trials support AKL-T01’s efficacy in attention-deficit/hyperactivity disorder (ADHD), but there is a need to understand how in-game data can be used to monitor patient changes in cognition. We aimed to derive a real-time measure of attention from AKL-T01 gameplay data and validate it against clinical outcomes. Trials of AKL-T01 included: STARS-ADHD-Adult (NCT05183919), a 6-week trial in adults 18 and older ( n = 221; M age = 39.9; 70% female); STARS-ADHD-Adolescent (NCT04897074), a 4-week trial in adolescents ages 13–17 ( n = 162; M age = 14.4; 41% female); and STARS-ADHD (NCT02674633), a 4-week trial in children ages 8–12 ( n = 180; M age = 9.7; 31% female). A cognitive metric was derived from targeting response speed, targeting sensitivity (d-prime), and navigation skill level. Using multiple linear regression models, we analyzed relationships between cognitive metric change and changes in the Test of Variables of Attention (TOVA)-Attention Comparison Score (ACS), controlling for TOVA-ACS baseline, cognitive metric baseline, age, and sex. We explored associations with ADHD symptoms and quality of life. Increases in the cognitive metric significantly predicted increases in TOVA-ACS in the adult ( β = 0.16, p