We aid in neurocognitive monitoring outside the hospital environment by enabling app-based measurements of visual reaction time (saccade latency) and error rate in a cohort of subjects spanning the adult age spectrum. Methods: We developed an iOS app to record subjects with the frontal camera during pro- and anti-saccade tasks. We further developed automated algorithms for measuring saccade latency and error rate that take into account the possibility that it might not always be possible to determine the eye movement from app-based recordings. Results: To measure saccade latency on a tablet, we ensured that the absolute timing error between on-screen task presentation and the camera recording is within 5 ms. We collected over 235,000 eye movements in 80 subjects ranging in age from 20 to 92 years, with 96% of recorded eye movements either declared good or directional errors. Our error detection code achieved a sensitivity of 0.97 and a specificity of 0.97. Confirming prior reports, we observed a positive correlation between saccade latency and age while the relationship between error rate and age was not significant. Finally, we observed significant intra- and inter-subject variations in saccade latency and error rate distributions, which highlights the importance of individualized tracking of these visual digital biomarkers. Conclusion and Significance: Our system and algorithms allow ubiquitous tracking of saccade latency and error rate, which opens up the possibility of quantifying patient state on a finer timescale in a broader population than previously possible.