Parkinson's disease (PD) is a progressive nervous system disorder that affects movement. PD has a severely negative impact on the quality of life of patients and their caregivers. The timing of treatment depends, amongst others, on the quantification of patients' motor performance. To date, the resolution used in scaling motor performance is too low to detect subtle behavioral changes over time. This paper investigates if 'longitudinal' data-sets of motor performance data obtained from tracking tasks can detect behavioural changes in motor performance data representative for PD symptoms. Such longitudinal data were approximated using a combined data-set based on 50 trials of collected experiment data from 25 healthy participants (age range 55-75 years), augmented with 25 bootstrapped samples scaled to represent 'Mild' or 'Severe' motor performance degradation. An approach based on general linear regression models was tested for its capacity to detect the adverse trends in typical tracking task metrics (K_p, τ, ζ_nms, ω_nms, RMSe, and RMSu). Overall, it was found that with this approach in at least 50% of all participants, a simulated change in motor behaviour was successfully detected, a number that may increase to 97% for the most sensitive metric (ζ_nms) and consistent participant data. This indicates that the developed approach is promising towards the development of more objective and detailed monitoring of disease progression and treatments in PD patients.