Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis

Abstract

The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson’s disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82–0.90, I2 = 79.49%) and 0.83 (CI 0.79–0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65–1.00, I2 = 79.10%) and 0.87 (95% CI 0.80–0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74–0.96, I2 = 50.39%) and 0.82 (95% CI 0.70–0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a “co-creation” approach that stems from mechanistic explanations of patients’ characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.

Associations between smartphone keystroke dynamics and cognition in MS

“… 6 , 7 Keystroke dynamics (i.e., how individuals type on smartphone keyboards) is a promising digital phenotyping approach and may be used to infer real-life cognitive functioning continuously and unobtrusively. Keystroke dynamics (e.g., slower typing speed, typing inconsistency) have been associated with motor functioning among individuals with Parkinson's 8 , 9 and Huntington's 10 diseases (see recent meta-analysis 11 on the associations between keystroke dynamics and fine motor functions) and depressive and manic symptoms among individuals with bipolar disorder. 12 , 13 In MS, keystroke dynamics have been linked with performances on motor and cognitive tests, brain lesions, and neurological disability.…”

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

“…The motor symptoms characteristic of the disease affect simple daily activities performed by patients, such as typing on a computer’s keyboard [ 14 , 15 ]. In view of that, and aligned to the advances in AI-based solutions and current computational power, researchers [ 16 , 17 , 18 ] are aiming to find new approaches capable of detecting the motor impairments characteristic of PD [ 19 ].…”

Detection of Mental Fatigue in the General Population: Feasibility Study of Keystroke Dynamics as a Real-world Biomarker

“…Finger kinematics during typing are fine motor skills ruled by the neuromotor cortex and have also been presented as a powerful biomarker in the diagnosis and monitoring of different neurodegenerative diseases, including Parkinson disease [16][17][18], multiple sclerosis [19], and Alzheimer disease [20]. A recent meta-analysis carried out by Alfalahi et al [21] demonstrates the promising performance of keystroke dynamics-based models for the diagnosis of fine motor impairment in Parkinson disease and mild cognitive impairment diseases. However, the authors show caution in the transition from a controlled assessment in the clinic to the unsupervised remote diagnosis and monitoring, owing to the sparsity and unpredictable nature of typing activity in the real-world context.…”