Association Between Finger-to-Nose Kinematics and Upper Extremity Motor Function in Subacute Stroke: A Principal Component Analysis

Abstract

BackgroundKinematic analysis facilitates interpreting the extent and mechanisms of motor restoration after stroke. This study was aimed to explore the kinematic components of finger-to-nose test obtained from principal component analysis (PCA) and the associations with upper extremity (UE) motor function in subacute stroke survivors.MethodsThirty-seven individuals with subacute stroke and twenty healthy adults participated in the study. Six kinematic metrics during finger-to-nose task (FNT) were utilized to perform PCA. Clinical assessments for stroke participants included the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Modified Barthel Index (MBI).ResultsThree principal components (PC) accounting for 91.3% variance were included in multivariable regression models. PC1 (48.8%) was dominated by mean velocity, peak velocity, number of movement units (NMU) and normalized integrated jerk (NIJ). PC2 (31.1%) described percentage of time to peak velocity and movement time. PC3 (11.4%) profiled percentage of time to peak velocity. The variance explained by principal component regression in FMA-UE (R2 = 0.71) were higher than ARAT (R2 = 0.59) and MBI (R2 = 0.29) for stroke individuals.ConclusionKinematic components during finger-to-nose test identified by PCA are associated with UE motor function in subacute stroke. PCA reveals the intrinsic association among kinematic metrics, which may add value to UE assessment and future intervention targeted for kinematic components for stroke individuals.Clinical Trial RegistrationChinese Clinical Trial Registry (http://www.chictr.org.cn/) on 17 October 2019, identifier: ChiCTR1900026656.

Assessing Smoothness of Arm Movements With Jerk: A Comparison of Laterality, Contraction Mode and Plane of Elevation. A Pilot Study

“…Considering the novelty of the comparisons of jerk metrics between different conditions of active analytic and functional arm movements, we were not able to perform a power analysis. Based on previous studies of jerk metrics the sample size of 30 participants appears to be appropriate (Pan et al, 2020;Chen et al, 2021). This sample size is also suitable for a reliability study (Fermanian, 1984).…”

“…In asymptomatic individuals, studies assessed lifting performance (Tammana et al, 2018) and specific sports movement (baseball overhead pitching) (Lapinski et al, 2019). With patients, measuring movement smoothness using jerk helped assess arm function in multiple sclerosis (Carpinella et al, 2014) and in children with hemiparesis (Newman et al, 2017) as well as spontaneous upper-limb motion in acute stroke patients (Datta et al, 2021) and the finger-to-nose test in subacute stroke patients (Chen et al, 2021) and characterize bilateral manual arm use among stroke survivors (de Lucena et al, 2017) and children with unilateral cerebral palsy (Pan et al, 2020). The studies using jerk metrics focused on the association between upper extremity motor function and jerk.…”

Change in upper limb function in people with multiple sclerosis treated with nabiximols: a quantitative kinematic pilot study

“…Such value is representative of repeated accelerations and decelerations during movement performance and reflects efficiency and smoothness of movement [ 35 ]. An ideal perfectly smooth movement would have only one movement unit and reference values for healthy individuals approximately lie in the range 1.1–1.2 [ 36 , 37 ]. Frequency of changes in direction (Hz): this is another measure of smoothness which characterizes the finger displacements associated to the possible presence of tremor [ 30 , 38 ].…”

“…Such value is representative of repeated accelerations and decelerations during movement performance and reflects efficiency and smoothness of movement [ 35 ]. An ideal perfectly smooth movement would have only one movement unit and reference values for healthy individuals approximately lie in the range 1.1–1.2 [ 36 , 37 ].…”

Using immersive virtual reality to remotely examine performance differences between dominant and non-dominant hands

“…In contrast to conventional measures, kinematics were able to identify ‘impaired’ movements even in survivors who are classified as ‘normal’ or ‘near normal’ on the Chedoke-McMaster Stroke Assessment Scale. Furthermore, kinematics have been useful for tracking compensatory movements during reaching tasks (van Kordelaar et al 2012 ; Alt Murphy et al 2011 ; Subramanian et al 2010 ) and can provide information on internal processes such as motor planning (Chen et al 2021 ; Alt Murphy et al 2017 ; Tan et al 2012 ; Zollo et al 2011 ). In addition to point-to-point and reaching tasks, circle drawing provides a potentially powerful insight into arm function as this task is simple to administer while yielding a wide range of quantitative metrics.…”