Since Parkinson's disease (PD) is a heterogeneous disorder with symptoms, such as tremors, gait and speech disturbances, or memory loss, individualized diagnostics are needed to optimize treatment. In their current form, the typical paper-pencil methods traditionally used to track disease progression are too coarse to capture the subtleties of clinical phenomena. For this reason, digital biomarkers that capture, for example, motor function, cognition, and behavior using apps, wearables, and tracking systems are becoming increasingly established. However, given the high prevalence of cognitive impairment in PD, digital cognitive biomarkers to predict mental progression are important in clinical practice. This pilot study aimed to identify those components of our digital version of the TMT (dTMT) that allow discrimination between PD patients with and without cognitive deficits. A total of 30 healthy control (age 66.3 ± 8.61) and 30 participants with PD (age 68.3 ± 9.66) performed the dTMT using a touch-sensitive tablet to capture enhanced performance metrics, such as the speed between and inside circles. The decomposition of cognitive abilities based on integrating additional variables in the dTMT revealed that the Parkinson's disease group was significantly more sensitive to parameters of inhibitory control. In contrast, the mild cognitive impairment group was sensitive to parameters of cognitive flexibility and working memory. The dTMT allows objective, ecologically valid, and long-term cognitive and fine-motor performance tracking, suggesting its potential as a digital biomarker in neurodegenerative disorders.
Keywords: Computerized cognitive assessment; digital biomarkers; executive functions; neuropsychological tests.