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

Abstract

AbstractBackgroundNeurobehavioural 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.MethodsUsing 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.ResultsThe 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.ConclusionOur findings suggest that the temporal dynamics of smartphone touchscreen interactions may help monitor neurobehavioural comorbidities in neurological care.