Associations between smartphone keystroke dynamics and cognition in MS

Abstract

Objective: Examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis (MS).

Methods: Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline.

Results: A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001).

Conclusion: Typing speed may be sensitive to cognitive functions subserved by the frontal-subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression.

Keywords: Multiple sclerosis; digital phenotyping; mHealth; passive monitoring; typing.

Figure 1.

Keystroke metrics. Panel A illustrates the frequency distribution of interkey delay (latency between consecutive keypresses) during transitions between alphanumeric characters in a sample typing session, with the blue dotted line denoting the median. Given the skewness of interkey delay, the median value is used to represent typing speed for each typing session. Median absolute deviation is used to represent variability in typing speed. Panel B illustrates rates of autocorrection and backspace in a sample typing session. In multilevel models, rates of autocorrection and backspace are represented with the total number of autocorrection events and backspaces used as the outcomes and offset by a log-transformed character count for each typing session.

Figure 2.

Group differences in keystroke dynamics. Panel A displays differences between MS and HC groups in median interkey delay (typing speed); the MS group typed significantly slower. Panel B displays moderating effect of median interkey delay on number of autocorrection events (offset with number of characters per session); interkey delay is split into lower, median, and upper quartiles for visualization. The HC group had more autocorrection events than the MS group, but only among those who typed faster. Panel C displays the moderating effect of diagnostic group on the relationship between backspace and autocorrection rates; the relationship was steeper among HC than MS. Plots represent predicted values from multilevel models, and error bars/bands represent 95% confidence intervals. MS: multiple sclerosis; HC: healthy control.

Figure 3.

Associations between median interkey delay and neuropsychological performance/symptom rating. Plots represent predicted values from multilevel models, and error bands represent 95% confidence intervals. Faster typing speed was associated with better performance on neuropsychological tests (e.g., SDMT and verbal fluency in Panels A and B and less severe symptom ratings (e.g., MFIS in Panel C). SDMT: Symbol Digit Modalities Test; MFIS: Modified Fatigue Impact Scale.

Figure 4.

Moderating effects of median interkey delay on associations between autocorrection events and neuropsychological performance/symptom rating. To visualize moderating effects, interkey delay is split into lower, median, and upper quartiles. Plots represent predicted values from multilevel models, and error bands represent 95% confidence intervals. Better neuropsychological performance was associated with more autocorrection events among fast typers only (Panels A and B). More severe depressive symptoms were associated with more autocorrection events (Panel C), and more severe anxiety symptoms were associated with fewer autocorrection events (Panel D); these relationships were only found among fast typers. SDMT: Symbol Digit Modalities Test; CMDI: Chicago Multiscale Depression Inventory; STAI: State-Trait Anxiety Inventory.

Figure 5.

Moderating effects of neuropsychological performance/symptom rating on associations between autocorrection events and backspace use. To visualize moderating effects, each neuropsychological test, and symptom inventory is split into the mean and one standard deviation above and below the mean. Plots represent predicted values from multilevel models, and error bands represent 95% confidence intervals. Those with better cognitive functioning (Panels A and B) or less severe symptom ratings (Panels C and D) exhibited a stronger negative correlation between backspace and autocorrection frequencies. SDMT: Symbol Digit Modalities Test; CWI: Color-Word Interference; MFIS: Modified Fatigue Impact Scale; STAI: State-Trait Anxiety Inventory.