Real-time associations among MS symptoms and cognitive dysfunction using ecological momentary assessment

Abstract

Introduction: Multiple sclerosis (MS) is characterized by a wide range of disabling symptoms, including cognitive dysfunction, fatigue, depression, anxiety, pain, and sleep difficulties. The current study aimed to examine real-time associations between non-cognitive and cognitive symptoms (latter measured both objectively and subjectively in real-time) using smartphone-administered ecological momentary assessment (EMA).

Methods: Forty-five persons with MS completed EMA four times per day for 3 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. Multilevel models were conducted to account for within-person and within-day clustering.

Results: A total of 3,174 EMA sessions were collected; compliance rate was 84%. There was significant intra-day variability in mTMT-B performance (p < 0.001) and levels of self-reported fatigue (p < 0.001). When participants reported depressive symptoms that were worse than their usual levels, they also performed worse on the mTMT-B (p < 0.001), independent of upper extremity motor functioning. Other self-reported non-cognitive symptoms were not associated with real-time performance on the mTMT-B [p > 0.009 (Bonferroni-corrected)]. In contrast, when self-reported fatigue (p < 0.001), depression (p < 0.001), anxiety (p < 0.001), and pain (p < 0.001) were worse than the individual's typical levels, they also reported more severe cognitive dysfunction at the same time. Further, there was a statistical trend that self-reported cognitive dysfunction (not mTMT-B performance) predicted one's self-reported sense of accomplishment in real-time.

Discussion: The current study was the first to identify divergent factors that influence subjectively and objectively measured cognitive functioning in real time among persons with MS. Notably, it is when symptom severity was worse than the individual's usual levels (and not absolute levels) that led to cognitive fluctuations, which supports the use of EMA in MS symptom monitoring.

Keywords: anxiety; cognitive impairment; depression; experience sampling; fatigue; multiple sclerosis (MS); pain; sleep.

FIGURE 1

Screenshots of mTMT-B and finger tapping tasks. Panel (A) is screenshot of the mobile Trail-Making Test part B (mTMT-B) task. Panel (B) is a screenshot of the mobile finger tapping task. Users performed one trial with their right hand, followed by a second with their left hand.

FIGURE 2

Intra-day fluctuations of symptom severity. Both objectively and subjectively measured cognition was worse in the morning and end of day compared to the middle of the day. Anxiety ratings showed the opposite trend and peaked at mid-day. Fatigue ratings increased steadily throughout the day. There were no significant intra-day variations in ratings of pain and depressive symptoms. Each plot represents predicted values from multilevel models for the session fixed effect. Error bars represent 95% confidence intervals. mTMT-B, mobile Trail-Making Test part B.

FIGURE 3

Real-time associations between non-cognitive symptom ratings and mTMT-B performance. Among all non-cognitive symptom ratings, only more severe state depressive symptoms was associated with slower mTMT-B completion time. State and trait aspects of each symptom was tested together in the same model; plots show the marginal effects of the state variables. All models included subject and concatenated subject and day variable as random intercepts; and age, mean bilateral finger tapping performance, state and trait upper extremity weakness rating, and measurement number as fixed effects. mTMT-B, mobile Trail-Making Test part B. Error bands represent 95% confidence intervals.

FIGURE 4

Real-time associations between non-cognitive symptom ratings and self-reported cognitive dysfunction. State fatigue, depressive symptoms, anxiety, and pain were all significant predictors of self-reported cognitive dysfunction. State and trait aspects of each symptom was tested together in the same model; plots show the marginal effects of the state variables. All models included subject and day number as random intercepts and state and trait depressive symptoms as fixed effects. Error bands represent 95% confidence intervals.