Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke

 You do realize how fucking useless 'measurements' are to getting survivors recovered?  Or do you not understand what survivors want? They want recovery; not some useless measurement you therapists have to do to get paid.

Measurement properties of movement smoothness metrics for upper limb reaching movements in people with moderate to severe subacute stroke

• Gwenaël Cornec,
• Mathieu Lempereur,
• Johanne Mensah-Gourmel,
• Johanna Robertson,
• Ludovic Miramand,
• Beatrice Medee,
• Soline Bellaiche,
• Raphael Gross,
• Jean-Michel Gracies,
• Olivier Remy-Neris &
• Nicolas Bayle 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 90 (2024) Cite this article

• Metrics details

Abstract

Background

Movement smoothness is a potential kinematic biomarker of upper extremity (UE) movement quality and recovery after stroke; however, the measurement properties of available smoothness metrics have been poorly assessed in this group. We aimed to measure the reliability, responsiveness and construct validity of several smoothness metrics.

Methods

This ancillary study of the REM-AVC trial included 31 participants with hemiparesis in the subacute phase of stroke (median time since stroke: 38 days). Assessments performed at inclusion (Day 0, D0) and at the end of a rehabilitation program (Day 30, D30) included the UE Fugl Meyer Assessment (UE-FMA), the Action Research Arm Test (ARAT), and 3D motion analysis of the UE during three reach-to-point movements at a self-selected speed to a target located in front at shoulder height and at 90% of arm length. Four smoothness metrics were computed: a frequency domain smoothness metric, spectral arc length metric (SPARC); and three temporal domain smoothness metrics (TDSM): log dimensionless jerk (LDLJ); number of submovements (nSUB); and normalized average rectified jerk (NARJ).

Results

At D30, large clinical and kinematic improvements were observed. Only SPARC and LDLJ had an excellent reliability (intra-class correlation > 0.9) and a low measurement error (coefficient of variation < 10%). SPARC was responsive to changes in movement straightness (r_Spearman=0.64) and to a lesser extent to changes in movement duration (r_Spearman=0.51) while TDSM were very responsive to changes in movement duration (r_Spearman>0.8) and not to changes in movement straightness (non-significant correlations). Most construct validity hypotheses tested were verified except for TDSM with low correlations with clinical metrics at D0 (r_Spearman<0.5), ensuing low predictive validity with clinical metrics at D30 (non-significant correlations).

Conclusions

Responsiveness and construct validity of TDSM were hindered by movement duration and/or noise-sensitivity. Based on the present results and concordant literature, we recommend using SPARC rather than TDSM in reaching movements of uncontrolled duration in individuals with spastic paresis after stroke.

Trial Registration

NCT01383512, https://clinicaltrials.gov/, June 27, 2011.

Highlights

Reliability, responsiveness and construct validity of SPARC were satisfactory.

Responsiveness and construct validity of LDLJ, NARJ and nSUB were highly related to movement duration.

LDLJ had an excellent reliability and a low measurement error, but not NARJ and nSUB.

Introduction

Spastic paresis of the upper extremity (UE) was reported in 48% of survivors at 1 week after stroke in a community-based population (n = 421), with full UE function achieved at discharge by 79% of those with mild paresis but only 18% of those with severe paresis [1]. Three main symptoms are well described in spastic paresis syndrome [2]: structural alterations relating to immobility (spastic myopathy, leading to muscle contractures) [3, 4], impaired motor control (stretch-sensitive paresis) of the agonist muscles [5, 6], and overactivity of antagonist muscles [7, 8], (including spasticity [8,9,10], spastic dystonia [11] and spastic cocontractions [12,13,14,15]).

Spastic paresis directly alters the movement trajectories and velocity with spatial (poor movement control, less efficient trajectories) and temporal (longer movement duration) discontinuities, resulting in a lack of smoothness [16,17,18]. Changes in the smoothness of the hand trajectory after stroke have been studied during reaching, grasping, and pointing movements [19], and the evaluation of smoothness has been suggested as a valid indicator of the quality of spontaneous motor recovery [20,21,22,23] and rehabilitation-induced recovery [18, 24,25,26].

The assessment of measurement properties of smoothness metrics is needed for the evaluation of changes in the poststroke spastic paretic UE. To date, many metrics have been used to explore movement recovery after stroke [27]. Research involving robotic rehabilitation systems in the last fifteen years has particularly contributed to the development of kinematic metrics, including smoothness, as potential biomarkers for movement recovery [24, 25, 27,28,29]. However, the use of smoothness metrics in clinical research remains limited, as those metrics require particular instrumentation and expertise that might be an obstacle for multicentric studies, are often insufficiently defined mathematically (some are even robot-specific metrics) and validated, and are often non-reproducible, non-dimensionless (i.e. highly relying on movement time), poorly robust against measurement noise, or are not related to the intermittency of movement [19, 27, 30].

New smoothness metrics that attempt to avoid those limitations have been developed and used to assess point-to-reach and point-to-grasp movement in healthy subjects and individuals after stroke [23, 31,32,33], namely the log dimensionless jerk (LDLJ), a smoothness metric conceived in the temporal domain and the spectral arc length metric (SPARC). The SPARC was conceived in the frequency domain by Balasubramanian and colleagues, notably to overcome the bias of movement duration and noise-sensitivity in previously developed smoothness metrics, who tested its content validity and described it as a robust to noise, sensitive, reliable, and practical metric after tests on mathematical models [30, 34].

In an earlier study, we compared the properties of four smoothness metrics currently used in the literature (SPARC, and three temporal domain smoothness metrics (TDSM): LDLJ, number of zero-crossings in the acceleration profile also called number of submovements (nSUB) and normalized average rectified jerk (NARJ)) during UE reaching movements in 32 middle-aged healthy participants [33]. In this setting, the SPARC had the lowest measurement error, and seemed independent of movement duration whereas the TDSM were highly time-dependent. A better understanding of the measurement properties of these metrics is still needed for patients with poststroke UE impairment. An international consensus was reached on the taxonomy, terminology and definitions of measurement properties within the COSMIN initiative (COnsensus-based Standards for the selection of health Measurement INstruments) setting a framework for the present study [35].

This study aimed to assess the measurement properties (reliability, responsiveness and construct validity) of the SPARC and three TDSM (NARJ, LDLJ and nSUB) for point-to-reach movements in people with moderate to severe impairment in the subacute phase of stroke, before and after a rehabilitation program.

Based on our previous work in healthy subjects [33] and literature, we hypothesized that the three TDSM would be more associated with movement duration while the SPARC would be more associated with movement straightness in the present context.