Real-world smartphone data can trace the behavioural impact of epilepsy: A Case study

Abstract

Neurobehavioural comorbidities have a detrimental effect on the quality of life of people with epilepsy, yet tracking their impact is challenging as behaviour may vary with seizures and anti-seizure medication side effects. Smartphones have the potential to monitor day-to-day neurobehavioural patterns objectively. We present the case of a man in his late twenties with refractory focal epilepsy in whom we ascertained the effects of ASMs withdrawal and a focal-to-bilateral tonic-clonic convulsion on his touchscreen interactions. Using a dedicated app, we recorded over 185 days, the timestamps of 718,357 interactions. We divided the various smartphone behaviours according to the next-interval dynamics of the interactions by using a joint interval distribution (JID). We analysed JIDs during two ASM load transitions: before versus during tapering and restarting medication versus tapering. Cluster-based permutation testing revealed significant differences, with accelerated next-interval dynamics during tapering and a reversal upon medication restart. We also compared the JID of the fbTCS day to the average of the three days before, showing markedly slower next-interval dynamics on the day of the convulsion. This suggests that smartphone interactions’ temporal dynamics may help monitor neurobehavioural comorbidities in epilepsy.