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

Abstract

Background: 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 (ASM) 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 drug-resistant focal epilepsy in whom we ascertained the effects of ASM withdrawal and a convulsive seizure on his touchscreen interactions.

Methods: 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). During two ASM load transitions, namely before versus during tapering and tapering versus restarting medication, we used cluster-based permutation tests to compare the JIDs. We also compared the JID of the seizure day to the average of the previous 3 days.

Results: The cluster-based permutation tests revealed significant differences, with accelerated next-interval dynamics during tapering and a reversal upon medication restart. The day of the convulsion exhibited a marked slowing of next-interval dynamics compared to the preceding 3 days.

Conclusion: Our findings suggest that the temporal dynamics of smartphone touchscreen interactions may help monitor neurobehavioural comorbidities in neurological care.