Since this was done in healthy individuals there will never be a followup in stroke survivors. We have fucking failures of stroke associations that DO ABSOLUTELY NOTHING FOR STROKE. I have never gotten an iota of useful information from them, my doctors or my stroke hospital. My only saving grace was my occupational therapist.
Robotic tests for position sense and movement discrimination in the upper limb reveal that they each are highly reproducible but not correlated in healthy individuals
Abstract
Background
Robotic technologies for neurological assessment provide sensitive, objective measures of behavioural impairments (Where the fuck are these objective measurements so we can tell our doctors about them?)associated with injuries or disease such as stroke. Previous robotic tasks to assess proprioception typically involve single limbs or in some cases both limbs. The challenge with these approaches is that they often rely on intact motor function and/or working memory to remember/reproduce limb position, both of which can be impaired following stroke. Here, we examine the feasibility of a single-arm Movement Discrimination Threshold (MDT) task to assess proprioception by quantifying thresholds for sensing passive limb movement without vision. We use a staircase method to adjust movement magnitude based on subject performance throughout the task in order to reduce assessment time. We compare MDT task performance to our previously-designed Arm Position Matching (APM) task. Critically, we determine test-retest reliability of each task in the same population of healthy controls.Method
Healthy participants (N = 21, age = 18–22 years) completed both tasks in the End-Point Kinarm robot. In the MDT task the robot moved the dominant arm left or right and participants indicated the direction moved. Movement displacement was systematically adjusted (decreased after correct answers, increased after incorrect) until the Discrimination Threshold was found. In the APM task, the robot moved the dominant arm and participants “mirror-matched” with the non-dominant arm.Results
Discrimination Threshold for direction of arm displacement in the MDT task ranged from 0.1–1.3 cm. Displacement Variability ranged from 0.11–0.71 cm. Test-retest reliability of Discrimination Threshold based on ICC confidence intervals was moderate to excellent (range, ICC = 0.78 [0.52–0.90]). Interestingly, ICC values for Discrimination Threshold increased to 0.90 [0.77–0.96] (good to excellent) when the number of trials was reduced to the first 50. Most APM parameters had ICC’s above 0.80, (range, ICC = [0.86–0.88]) with the exception of variability (ICC = 0.30). Importantly, no parameters were significantly correlated across tasks as Spearman rank correlations across parameter-pairings ranged from − 0.27 to 0.30.Conclusions
The MDT task is a feasible and reliable task, assessing movement discrimination threshold in ~ 17 min. Lack of correlation between the MDT and a position-matching task (APM) indicates that these tasks assess unique aspects of proprioception that are not strongly related in young, healthy individuals.Background
Proprioception can be divided into three distinct percepts: position sense, kinesthesia (sense of motion) and sense of effort [1, 2].
The two former percepts are predominantly provided by primary and
secondary muscle afferents, although cutaneous afferents also play a
role, particularly related to the hand [3, 4].
These sources of sensory information travel to the primary
somatosensory cortex via the dorsal column-medial lemniscus pathway and
through the ventral posterior lateral nucleus of the thalamus [5].
Beyond primary somatosensory cortex, there are several other cortical
regions that have been implicated in proprioceptive function [6,7,8,9].
When quantifying impairments following stroke, the assessment of motor function has been the dominant focus due to the common impact that stroke has on one’s ability to move and interact in the world. Far less attention has been placed on tools to quantify impairments in sensory function, even though sensory function is commonly impacted following stroke [10, 11] and linked to poor functional recovery [12]. Some clinical tools have been developed to assess somatosensation, such as the Nottingham Sensory Assessment protocol [13]. However, proprioceptive function is commonly assessed simply by having an individual close their eyes while passively moving their finger up or down, and asking them if they can identify the direction of movement [14]. For the proximal arm, a Thumb Localizer Task has been developed where the individual shuts their eyes and an examiner moves their hand to a randomly chosen location and then the individual must attempt to grasp their thumb with their opposite hand [15]. Unfortunately, these types of scales lack sensitivity, can show poor inter-rater reliability and lack specificity [16, 17] (however, see [13]).
Robotic technologies are emerging as a new approach for neurological assessment. They offer advantages over existing clinical tools as they can provide objective and precise measures of many sensory, motor and cognitive behaviours [18,19,20]. In recent years, there have been several new tasks developed using robotic technology for evaluating different aspects of proprioception including position sense [10, 20,21,22,23,24,25,26,27], sense of effort [28] and kinesthesia [29]. Many of these studies highlight that impairments in proprioception are common following stroke with highly variable patterns of recovery [11]. Importantly, such impairments slow functional recovery [12].
There are two common approaches for quantifying proprioception. The first approach involves passively moving the limb to a specified location and then the subject mirror matches this position with the other arm without vision [10]. The potential caveat with this approach is the assumption that proprioceptive and motor function of the other arm is not impaired. However, ipsilesional impairments can be observed in some individuals (~ 30%) following stroke [30,31,32,33,34]. Further, bilateral impairments are common in other diseases such as ALS [35,36,37]. The second approach is to passively move the subject’s arm to a specified target position and then back to the ‘start’ position. The subject is then asked to actively move their limb to the same location. This removes the influence of the other arm, but requires the use of working memory [38, 39] and sufficient motor function to move the arm to the specified location, both of which may be impaired following stroke [40, 41].
An alternate approach is to assess proprioception by quantifying the threshold for sensing limb movement, an aspect of kinesthesia. In this approach, proprioceptive acuity is typically measured as the threshold for detecting a difference between two movements [42, 43] or the detection of the onset of passive movement of the limb [44, 45]. Advantages to this approach are the fact that it does not rely on the use of the contralateral limb or as much on working memory, both of which could confound results. However, a major challenge with these tasks is that they commonly can take a long time to complete. Typically, a large set of different positions and/or speeds must be assessed for the algorithms used to calculate detection thresholds, commonly resulting in total assessment times of up to 45 min [22, 43, 46].
Here we develop a movement discrimination task to quantify the threshold for discriminating movement of the arm. In order to reduce the amount of time to complete the task, we implement a staircase method to adjust movement magnitude based on the subject’s performance. We present the performance of young, healthy controls to determine the feasibility of the technique to determine a proprioceptive threshold. We also examine the test-retest reliability of the task.
Finally, we compare performance on this movement discrimination threshold task to a standard arm position matching (APM) task in which the robot moves one arm and the subject must mirror match with the other arm. Our working hypothesis is that performance in these two tasks should be correlated. Intuitively, it seems plausible for movement discrimination to be highly related to accuracy in a position matching task: the better you are able to detect small differences in positions or movements, the more accurate you would be able to match the position or motion of your arm. Further, similar cortical regions are involved in position sense and kinesthesia [9]. However, previous work, primarily in the lower limb, has found that position sense in healthy populations is not correlated with measures such as the threshold for detecting passive movement [47, 48] or the “just noticeable difference” between two positions [42]. The problem with many of these previous studies is that they do not report the test-retest reliability of the proprioceptive testing methods for the same population of subjects. In order to make conclusions about the relationship between the performances on two proprioceptive tests, we perform a test-retest on both tasks so that we can determine whether variability in performance across tasks reflects differences due to natural performance variability or due to the differences between the two proprioceptive tasks.
When quantifying impairments following stroke, the assessment of motor function has been the dominant focus due to the common impact that stroke has on one’s ability to move and interact in the world. Far less attention has been placed on tools to quantify impairments in sensory function, even though sensory function is commonly impacted following stroke [10, 11] and linked to poor functional recovery [12]. Some clinical tools have been developed to assess somatosensation, such as the Nottingham Sensory Assessment protocol [13]. However, proprioceptive function is commonly assessed simply by having an individual close their eyes while passively moving their finger up or down, and asking them if they can identify the direction of movement [14]. For the proximal arm, a Thumb Localizer Task has been developed where the individual shuts their eyes and an examiner moves their hand to a randomly chosen location and then the individual must attempt to grasp their thumb with their opposite hand [15]. Unfortunately, these types of scales lack sensitivity, can show poor inter-rater reliability and lack specificity [16, 17] (however, see [13]).
Robotic technologies are emerging as a new approach for neurological assessment. They offer advantages over existing clinical tools as they can provide objective and precise measures of many sensory, motor and cognitive behaviours [18,19,20]. In recent years, there have been several new tasks developed using robotic technology for evaluating different aspects of proprioception including position sense [10, 20,21,22,23,24,25,26,27], sense of effort [28] and kinesthesia [29]. Many of these studies highlight that impairments in proprioception are common following stroke with highly variable patterns of recovery [11]. Importantly, such impairments slow functional recovery [12].
There are two common approaches for quantifying proprioception. The first approach involves passively moving the limb to a specified location and then the subject mirror matches this position with the other arm without vision [10]. The potential caveat with this approach is the assumption that proprioceptive and motor function of the other arm is not impaired. However, ipsilesional impairments can be observed in some individuals (~ 30%) following stroke [30,31,32,33,34]. Further, bilateral impairments are common in other diseases such as ALS [35,36,37]. The second approach is to passively move the subject’s arm to a specified target position and then back to the ‘start’ position. The subject is then asked to actively move their limb to the same location. This removes the influence of the other arm, but requires the use of working memory [38, 39] and sufficient motor function to move the arm to the specified location, both of which may be impaired following stroke [40, 41].
An alternate approach is to assess proprioception by quantifying the threshold for sensing limb movement, an aspect of kinesthesia. In this approach, proprioceptive acuity is typically measured as the threshold for detecting a difference between two movements [42, 43] or the detection of the onset of passive movement of the limb [44, 45]. Advantages to this approach are the fact that it does not rely on the use of the contralateral limb or as much on working memory, both of which could confound results. However, a major challenge with these tasks is that they commonly can take a long time to complete. Typically, a large set of different positions and/or speeds must be assessed for the algorithms used to calculate detection thresholds, commonly resulting in total assessment times of up to 45 min [22, 43, 46].
Here we develop a movement discrimination task to quantify the threshold for discriminating movement of the arm. In order to reduce the amount of time to complete the task, we implement a staircase method to adjust movement magnitude based on the subject’s performance. We present the performance of young, healthy controls to determine the feasibility of the technique to determine a proprioceptive threshold. We also examine the test-retest reliability of the task.
Finally, we compare performance on this movement discrimination threshold task to a standard arm position matching (APM) task in which the robot moves one arm and the subject must mirror match with the other arm. Our working hypothesis is that performance in these two tasks should be correlated. Intuitively, it seems plausible for movement discrimination to be highly related to accuracy in a position matching task: the better you are able to detect small differences in positions or movements, the more accurate you would be able to match the position or motion of your arm. Further, similar cortical regions are involved in position sense and kinesthesia [9]. However, previous work, primarily in the lower limb, has found that position sense in healthy populations is not correlated with measures such as the threshold for detecting passive movement [47, 48] or the “just noticeable difference” between two positions [42]. The problem with many of these previous studies is that they do not report the test-retest reliability of the proprioceptive testing methods for the same population of subjects. In order to make conclusions about the relationship between the performances on two proprioceptive tests, we perform a test-retest on both tasks so that we can determine whether variability in performance across tasks reflects differences due to natural performance variability or due to the differences between the two proprioceptive tasks.
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