The current study aimed to examine real-time associations between non-cognitive symptoms and cognitive dysfunction (latter measured both objectively and subjectively in real-time) using ecological momentary assessment (EMA).

Forty-five persons with MS completed EMA four times per day for three weeks. For each EMA, participants completed mobile versions of the Trail-Making Test part B (mTMT-B) and a finger tapping task, as well as surveys about symptom severity. Trait (usual levels of a symptom) and state (when symptom level was higher or lower than the individual's usual levels) aspects of each symptom's severity were calculated. Multilevel models were conducted to account for within-person clustering, with performance on the mTMT-B and self-reported rating of cognitive dysfunction as primary outcomes.

A total of 3,174 EMA sessions were collected; compliance rate was 84%. There was significant intra-day variability in mTMT-B performance, anxiety, fatigue, and pain. More severe state depressive symptoms predicted lower performance on the mTMT-B in real-time. Self-reported difficulties with sleeping the night before predicted mTMT-B performance the following day. In contrast, state (but not trait) fatigue, depression, anxiety, and pain all predicted self-reported cognitive dysfunction in real time. Further, state self-reported cognitive dysfunction (but not mTMT-B performance) was associated with a higher perceived sense of accomplishment.

Self-reported cognitive dysfunction was more susceptible to influences of other MS symptoms (especially when the symptom is more severe than the individual's usual levels) and better predicted perceived sense of accomplishment than objectively measured executive functioning in real-time. Objective executive functioning performance was sensitive to effects of depressive symptoms and sleep difficulties. The current study demonstrated the feasibility of assessing real-time associations among MS symptoms using smartphone-administered EMA.