Preprint · Jun 30, 2026
Off-days have real-world consequences: Using high-frequency cognitive assessment to predict naturalistic driving
By Aschenbrenner AJ, Jackson JJ, Stout SH et al.
It is becoming common to measure cognition repeatedly to capture dynamic fluctuations over time. It is currently unknown the extent to which daily deviations in cognitive performance have implications for real-world behaviors or merely reflect short-term change of limited scope, such as assessment artifacts. Daily driving behavior was captured in 160 older adults using GPS tracking across 19,628 individual “trips”, defined as ignition start to ignition off. Automated processing pipelines quantified various aspects of each trip including distance traveled and frequency of “adverse” events such as speeding or hard braking. Participants completed daily assessments of cognition using a remote testing platform. Multilevel negative binomial models were constructed to relate daily cognitive performance to adverse driving events and adaptive behavior such as avoidance of nighttime driving, controlling for global ability, participant age, and distance travelled. Rigorous multi-model inference using AIC-based model averaging indicated that daily cognitive deviations received substantial model support (79-98%) in predicting adverse events and nighttime driving, relative to models that did not include a daily parameter. In contrast, global cognitive ability did not substantially improve prediction of either outcome. These results show that deviations in cognitive performance are not merely “noise”, as they have measurable implications for real-world outcomes. Importantly, it was not global performance that was important but deviations from typical performance. Thus, single-shot assessments of cognition may not be sufficient to make determinations about driving safety. More generally, our findings highlight the utility of repeated cognitive assessments in understanding how cognitive fluctuations may impact real-world behaviors.