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Rate of force development and relaxation scaling factors are highly sensitive to detect upper extremity motor impairments in multiple sclerosis
Journal of the Neurological Sciences 408 (2020) 116500
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Rate of force development and relaxation scaling factors are highly sensitive to detect upper extremity motor impairments in multiple sclerosis
T
Mehmet Uygura, , Paulo B. de Freitasb, Donald A. Baronec ⁎
a
Department of Health and Exercise Science, Rowan University, Glassboro, NJ 08028, USA Interdisciplinary Graduate Program in Health Sciences Cruzeiro do Sul University, São Paulo, Brazil c Neurological Institute, Cooper Medical School of Rowan University, Camden, NJ 08084, USA b
ARTICLE INFO
ABSTRACT
Keywords: Rate of force development Rate of force relaxation Spasticity Motor symptom Neurological disease muscle
Objective: Clinical assessment of upper extremity in multiple sclerosis is mainly limited to 9-hole peg test (9HPT), which is commonly criticized due to its limited sensitivity. There is a need for sensitive outcome measures for the assessment of motor symptoms in individuals with multiple sclerosis (iMS). We evaluated our recently developed brief force pulse protocol to simultaneously quantify the motor control of hand function and neuromuscular quickness in iMS. Additionally, we compared the sensitivity of the studied outcome measures with 9HPT in detecting the differences between iMS and controls. Methods: Twelve iMS and 12 controls grasped a grip- (GF; perpendicular force) and load-force (LF; tangential force) measuring handle and produced around 100 isometric LF pulses to various submaximal levels by pushing down on it as quickly as possible, followed by quick relaxation. The GF-LF ratio quantified the motor control of hand function. The slopes of linear regressions between peak forces and corresponding peak rates of force development (rate of force development scaling factor; RFD-SF) and relaxation (rate of force relaxation scaling factor; RFR-SF) quantified the control of neuromuscular quickness. Results: All of the selected variables were different between groups (all p-values < .05), and the effect sizes obtained from RFD-SF (d = 2.87) and RFR-SF (d = 1.93) were larger than the effect sizes obtained from 9-HPT (d = 1.07). Conclusion: Measures of neuromuscular quickness are more sensitive to detect disease related differences than 9HPT and, therefore, can be used as a tool in clinical and rehabilitative settings to objectively evaluate therapeutic interventions and disease progression in iMS.
1. Introduction Multiple sclerosis is a central nervous system (CNS) disease associated with various motor symptoms. While walking impairment is considered a hallmark symptom of the disease [1], the motor control of hand function and the ability to quickly generate and relax muscle forces is highly affected in individuals with multiple sclerosis (iMS) [2,3]. As a consequence, iMS face difficulties while manipulating objects [4,5], performing activities of daily living [6,7], and generating quick muscle forces [8], which could be crucial to produce over an externally fixed object (e.g., cane or a handrail) to avoid a fall. Standard clinical assessments of motor symptoms of the disease are mainly limited to subjective neurological rating scales (e.g., Expanded
Disability Status Scale; EDSS) and performance-based timed tests (e.g., 9 hole peg test; 9-HPT) [9,10]. Although commonly used in rehabilitative settings and clinical trials, these assessment tools are criticized due to their limited sensitivity to detect mild disability, monitor disease progression, and evaluate the effects of the applied treatments [9]. Moreover, most of the standard clinical assessment tools are mainly limited to lower extremity and, therefore, there is a need for the development of objective and sensitive neurobehavioral outcome measures for the evaluation of upper extremity motor symptoms in iMS [3]. The kinetic analysis of object manipulation has been used to evaluate the motor control of hand function in iMS [4,5,11]. Specifically, a certain amount of force should be applied perpendicularly (i.e. grip force; GF) to a handheld object to create frictional forces that enable an
Abbreviations: iMS, individuals with multiple sclerosis; EDSS, Expanded Disability Status Scale; 9-HPT, nine-hole peg test; GF, grip force; LF, load force; GFMAX, grip force maximum; LFMAX, load force maximum; GF/LF, grip- to load-force ratio; BFP, brief force pulse; RFD-SF, rate of force development scaling factor; RFR-SF, rate of force relaxation scaling factor; R2, r-squared ⁎ Corresponding author at: Department of Health and Exercise Science, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, USA. E-mail address: [email protected] (M. Uygur). https://doi.org/10.1016/j.jns.2019.116500 Received 30 May 2019; Received in revised form 21 August 2019; Accepted 17 September 2019 Available online 15 October 2019 0022-510X/ © 2019 Elsevier B.V. All rights reserved.
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individual to generate tangential forces (i.e. load force; LF) needed to manipulate the object [12]. When individuals manipulate an object, they apply higher GF than the minimum GF required to avoid slippage. The difference between the employed and minimum required GF is kept relatively invariant even when LF changes, indicating a high temporal coupling between these forces [13]. This elaborate coordination between GF and LF is commonly affected in iMS, resulting in the generation of excessive GF with respect to the exerted LF [5,11,14]. iMS have consistently demonstrated a higher ratio between GF and LF (GF/ LF) both in static and dynamic object manipulation tasks. Therefore, GF/LF has been suggested as a sensitive variable to evaluate the motor control of hand function in iMS [5,14]. The ability to generate and relax muscle forces quickly across various submaximal levels (i.e. neuromuscular quickness) is highly relevant to the activities of daily life involving both upper and lower extremities [2,15–17]. To evaluate neuromuscular quickness, recent studies introduced an isometric brief force pulse (BFP) protocol that requires individuals to generate and relax discrete force pulses to various submaximal levels under the instruction to produce and relax each force pulse as rapidly as possible [18–21]. There exists a strong linear relationship between the peak values of the force pulses produced and the corresponding peak rates of force development (RFD), demonstrating an invariance in the time required to reach peak forces regardless of its magnitude [22,23]. Recently, using the same BFP protocol, we demonstrated a similar linear relationship between the peak forces and the corresponding peak rates of force relaxation (RFR) during the relaxation phases of quick force pulses [24]. The outcome measures of neuromuscular quickness were extracted from the linear regression relationship between the peak values of the force pulses produced and their corresponding RFD and RFR. The slope of the relationship between peak force and RFD was named as rate of force development scaling factor (RFD-SF). The similar slope obtained from the relationship between peak forces and RFR was named as rate of force relaxation scaling factor (RFR-SF). While scaling factors were considered as the main variables of interest, the R-squared values (R2) obtained from those relationships evaluate the consistency of the scaling of RFD and RFR with the magnitude of the forces produced across submaximal levels [18,21]. Both RFD-SF and RFR-SF were speculated to be a proxy measures of central (e.g., motor unit firing rate, doublet discharge) and peripheral (e.g., muscle fiber type) properties of neuromuscular system underlying the activities requiring submaximal, yet quick, contractions followed by quick relaxations [24–26]. Previous research has shown the clinical utility of RFD-SF in detecting deteriorations in neuromuscular function in clinical populations, such as people with Parkinson's disease [27,28] and stroke [29], and in the elderly [30,31]. However, the aforementioned outcome measures of neuromuscular quickness have never been assessed in iMS and mainly neglected in the upper extremity. Recently, we developed and tested the reliability of a static object manipulation task that simultaneously quantifies motor control of hand function and neuromuscular quickness [21]. This task mimics conditions during which one has to generate quick reaction forces by pushing down against an externally fixed object (e.g., cane or handrail) to avoid falling [18,21]. Individuals were asked to grasp an externally fixed, GF and LF measuring device to generate brief isometric LF pulses to various submaximal amplitudes followed by quick force relaxations. To be able to generate a quick LF pulse over an externally fixed object, one also needs to produce a quick GF pulse to avoid the slippage of the hand off the object. Therefore, the proposed measurement tool provides the outcome measures of neuromuscular quickness of both LF (explicitly) and GF (implicitly) in an ecologically valid task. Moreover, the GF/LF obtained from the recorded GF and LF time series also serves to evaluate the motor control of hand function. In this study, we aimed to evaluate the motor control of hand function and neuromuscular quickness of iMS through an ecologically
valid brief force pulse protocol. We also aimed to compare the sensitivity (as assessed from effect sizes) of the studied outcome measures to the sensitivity obtained from the currently used standard clinical outcome measure of upper extremity (e.g., 9-HPT) in detecting the differences in motor function between iMS and healthy controls. Finally, to evaluate the concurrent validity, we explored the correlations among the standard outcome measures of the disability (e.g., EDSS and 9-HPT) and the studied outcome measures of this study. Our overall hypothesis is that the proposed tool would provide outcome measures that are more sensitive than the currently used standard outcome measure in detecting the differences in the motor function between iMS and healthy controls. 2. Methods 2.1. Subjects Twelve individuals with multiple sclerosis (iMS) and 12 age- and gender-matched controls participated in this study (see Table 1 for the descriptive information). The gender ratio of this study represented the higher prevalence of multiple sclerosis in females (60–75%) [32]. Inclusion criteria included clinically stable iMS with no relapses for at least 12 months, intact vision, being able to walk without an assistive device, and being able to lift and manipulate an object. Individuals with additional neurological and musculoskeletal disorders were excluded. Except for two control and two iMS, all participants were right handed. Patients were mainly recruited from the School of Osteopathic Medicine of the Rowan University. Written informed consent was obtained from all participants. The institutional review board of Rowan University approved the study protocol and it was conducted in accordance with the Declaration of Helsinki. 2.2. Device The device used in this study is similar to the devices that were used in other static object manipulation studies [33,34]. The device consisted of two parallel grasping metal surfaces connected by a single axis force transducer (WMC-50, Interface Inc., USA) and a multi axis force transducer (Mini40, ATI Industrial Automation, USA) attached Table 1 Descriptive characteristics of individuals with MS (iMS) and age- and gendermatched controls. Participants
Sex
Age (y)
EDSS
9-HPT (s)
MMSE
Time S. diag. (y)
iMS iMS_1 iMS_2 iMS_3 iMS_4 iMS_5 iMS_6 iMS_7 iMS_8 iMS_9 iMS_10 iMS_11 iMS_12 Mean (SD)
F F F F F F M F M M F F 9F, 3M
62 67 65 48 54 54 65 46 76 51 41 60 57.4 (10.2)
2 4 4 3 3 1 6 3.5 6 5 2.5 3 3.6 (1.5)
33.5 19.1 21.6 21.6 25.1 15.7 45.6 22.7 27.6 24.1 21.8 20.5 24.9 (7.9)
30 30 30 30 28 30 27 29 26 28 30 30 29 (1.4)
13 18 21 14 20 5 18 16 7 25 12 19 15.7 (5.8)
Healthy controls Mean (SD) 9F, 3M
57.3 (8.9)
18.8 (1.7)
iMS: Individuals with multiple sclerosis; EDSS: Self Assessed Expanded Disability Status Scale; 9-HPT: Nine Hole Peg Test; MMSE: Mini-Mental State Examination; Time S. Diag: Time since diagnosis.
2
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Fig. 1. (A) The instrumented grip- (GF) and loadforce (LF) measuring device and the formulas used to calculate them. (B) The visual feedback screen displaying the quick pulses generated upon the device by pushing it in down direction. The visual feedback only displays the current LF and the four horizontal lines representing 20, 40, 60, and 80% of the maximum LF.
underneath the grasping surfaces (Fig. 1A). The grasping surfaces were covered with rubber to increase the frictional forces between the fingers and the device. The single axis force transducer recorded the compression force (FC) and, together with the net force recorded by the multi-axis transducer in the same direction (FY), was used to calculate grip force (GF). The tangential forces recorded by the multi-axis transducer (FX and FZ) were used to calculate LF (see Fig. 1A for the equations used in GF and LF calculations). The device was externally fixed to a height adjustable base, which was secured on a table. The height of the base was adjusted so that the elbows of the standing subjects were at 900 of flexion while grasping the device (Fig. 1B).
percent of LFMAX, was displayed as a black line (Fig. 1B). The ranges between 20 and 40%, 40–60%, and 60–80% were referred to as small, medium, and large, which were used as a “go” command and to instruct participants about the expected force magnitude. Participants were instructed ‘to produce each force pulse as fast as possible and to relax immediately without trying to be accurate with its magnitude’. A total of approximately 100 pulses were recorded in four separate trials, each lasting 60 s (Fig. 2A and B). 2.4. Data acquisition and reduction Both data acquisition and analysis were made by custom written LabView routines (National Instruments Corp., Austin, Texas, USA). The signals from the transducers were sampled at 200 Hz and filtered using a fourth-order low-pass Butterworth filter with a cutoff frequency of 10 Hz. The point-by-point derivatives of both GF and LF curves were also calculated and filtered with a similar filter with a cutoff frequency of 5 Hz as this method provided the highest reliability for the calculation of RFD-SF [37]. Four curves representing GF and LF and their time derivatives were plotted for each pulse during data analysis (Fig. 2C and D). For every individual pulse, two cursors were automatically placed on the GF and LF curves depicting the peak forces and two different cursors placed on time derivative curves of each force corresponding to peak magnitudes of RFD and RFR. An experienced researcher then visually inspected each pulse to validate the cursor placement before saving their values for further analysis. Any pulse with a duration greater than the mean plus two standard deviations of the durations of all pulses were excluded from analysis [21,24].
2.3. Procedure All data collection was completed in a single session, lasting approximately one hour. Before the completion of force testing protocol, iMS completed standard clinical tests including self assessed EDSS [35], Mini Mental State Examination [36], and the 9-hole peg test (9-HPT). Two trials were recorded for the 9-HPT (only the dominant hand) and the best performance was used in the analysis. After the completion of the standard clinical assessments, each participant completed the following force testing protocol. During force testing, standing participants were asked to grasp the device using a five-digit grasp [33,34] and only the right hands of the participants were tested. Both groups followed the same testing order during the protocol. The force testing started with the tests of the maximal voluntary isometric contractions of GF (GFMAX) and LF (LFMAX), separately. The order of maximal testing was block randomized within each group. In GFMA, participants were asked to “squeeze the grasping surfaces as hard as” they could. In LFMAX, participants were asked to “push down on the device as hard as” they could without letting the digits slip off the handle. Three maximal trials, separated by sixty seconds of rest, were recorded separately for GFMAX and LFMAX and their largest values were used in analysis. We also tested GFMAX and LFMAX after the completion of the BFP protocol to test whether the protocol was associated with fatigue. After maximal testing, participants completed the BFP protocol. In this protocol, participants were asked to generate rapid LF pulses of varying submaximal magnitudes by pushing down on the device. Four red horizontal lines representing 20, 40, 60, and 80% of the recorded LFMAX were displayed on the feedback monitor. Real time LF, as a
2.5. Data analysis We studied GF/LF to evaluate the motor control of hand function and calculated it as the average value of the ratio between peak GF and peak LF obtained across all of the recorded force pulses [4,5,11,14]. For the assessment of neuromuscular quickness, we obtained the linear regression parameters from the relationships between the peak forces and the corresponding peak RFD and RFR (Fig. 3). Participants were instructed to grasp and generate quick LF pulses over externally fixed device and, therefore, they had to generate quick GF pulses to produce LF. Consequently, we were able to study the neuromuscular quickness of the LF (explicitly) and GF (implicitly) from the same task. 3
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Fig. 2. Sample recordings of LF and GF time curves obtained from (A) a control participant and (B) an iMS. (C) and (D) represent the recorded LF, GF, and their rates obtained from a LF pulse of a similar magnitude in control and iMS, respectively. The LF curves were negated for the visual clarity.
The slope obtained from the relationship between PF and peak RFD was named as rate of force development scaling factor (RFD-SF) while the slope obtained from the relationship between PF and peak RFR was named as RFR-SF. Both scaling factors quantified the magnitude of the scaling of RFD and RFR with the amplitude of force produced while the R2s obtained from the same regressions revealed the consistency of these scaling. Similar to the previous research [18,21,38], we excluded y-intercept of the regressions from our analyses.
within each group, paired t-tests were used to compare GFMAX and LFMAX recorded before and after the protocol. Pearson correlation coefficients were employed to evaluate the concurrent validity of the main outcome measures obtained from the protocol (GF/LF, RFD-SFs, and RFR-SFs of LF and GF) with respect to the standard clinical assessment (i.e. 9-HPT). Significance level was set at p < .05.
2.6. Statistics
3.1. Standard clinical assessments
Statistical analyses were performed in SPSS (version 24, IBM SPSS Statistics, Armonk, NY). As R2 values are inherently not normally distributed, their Z-transformed values were used in statistical analyses. The results of the Kolmogorov-Smirnov test indicated that none of the studied variables violated the normality assumption. We used independent sample t-tests to compare the selected variables between iMS and control groups. The Cohen's d values were calculated to estimate the effect sizes. To determine whether BFP protocol led to fatigue,
The demographics of participants are provided in Table 1. In iMS, GFMAX and LFMAX were not statistically different before and after the completion of force testing protocol (GFMAX: 103.14 ± 27.81 N vs. 104.82 ± 31.65 N; p = .71 and LFMAX: 80.68 ± 18.39 N vs. 79.91 ± 24.47 N; p = .80). In controls, while the LFMAX was not different before and after the completion of protocol (92.49 ± 22.17 N vs. 92.61 ± 20.39 N; p = .15), GFMAX was higher after the completion of the protocol (136.20 ± 32.29 N vs. 146.34 ± 39.43 N; p = .01).
3. Results
Fig. 3. The peak force-peak rate of force development (upper panel) and peak force - peak rate of force relaxation relationships obtained (lower panel) for LF (A) and GF (B). The rightward shift of the peak GF values in iMS could indicate an excessive exertion of GF during the generation of LF pulses. 4
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correlated with the standard clinical test of upper extremity (i.e., 9HPT) while none of the studied outcome measures were significantly correlated with the EDSS.
Table 2 Comparisons between iMS and control subjects in clinical tests and the outcome measures obtained from the brief force protocol. iMS
Control
P-value
Effect size (95% CI)
Clinical test 9-HPT (s) LFmax (N)
24.9 ± 7.9 80.68 ± 18.39
18.8 ± 1.7 92.49 ± 22.17
0.016 0.169
GFmax (N)
103.14 ± 27.81
136.20 ± 32.92
0.013
1.07 (0.18–1.88) 0.58 (−0.26–1.38) 1.10 (0.20–1.91)
0.98 ± 0.16
0.033
0.93 (0.06–1.74)
10.58 ± 1.42
< 0.001
2.87 (1.65–3.89)
0.94 ± 0.04 −7.84 ± 1.47
< 0.001 < 0.001
2.28 (1.19–3.21) 1.93 (0.90–2.82)
0.87 ± 0.11 9.32 ± 1.59
0.016 < 0.001
1.07 (0.18–1.89) 1.75 (0.76–2.62)
0.93 ± 0.06 −5.84 ± 1.36
< 0.001 0.025
1.84 (0.83–2.72) 0.99 (0.10–1.79)
0.82 ± 0.11
0.022
1.01 (0.12–1.81)
Hand function GF/LF 1.19 ± 0.28 Neuromuscular quickness RFD-SFLF 6.06 ± 1.71 (1/s) 2 0.76 ± 0.13 RLF RFR-SFLF −5.15 ± 1.32 (1/s) 0.76 ± 0.14 R2LF RFD-SFGF 6.23 ± 1.92 (1/s) 0.72 ± 0.18 R2GF RFR-SFGF −4.62 ± 1.11 (1/s) R2GF 0.68 ± 0.17
4. Discussion The aim of this study was to evaluate the motor control of hand function and neuromuscular quickness of iMS through an ecologically valid static object manipulation task. We also aimed to compare the sensitivity (as assessed by the effect sizes) of the studied outcome measures with the sensitivity of the standard clinical test of upper extremity (9-HPT) in detecting differences between iMS and healthy controls. We found differences between iMS and controls in 9-HPT and in all of the studied outcome measures of the motor control of hand function (GF/LF) and neuromuscular quickness (RFD-SF, RFR-SF, and the R2s) of both LF and GF. In general, the effect sizes obtained from the studied outcome measures tended to be higher than the one obtained from 9-HPT (please refer to 95% confidence intervals of effect sizes in Table 2 for interpretation). This may suggest higher sensitivity of the studied outcome measures in detecting the differences in motor function between iMS and control than the same sensitivity obtained from the standard clinical assessment of upper extremity function. We also found significant correlations among 9-HPT and the scaling factors of both RFD and RFR obtained from LF.
Values represent mean ± standard deviation. Effect sizes and p-values of R2s were calculated from the fisher-transformed values of the same variable. The 95% CI refers to the 95% confidence interval.
4.1. Motor control of hand function We used an ecologically valid, isometric force production task, during which one has to grasp an externally fixed object and produce reaction forces by pushing down as quickly as possible. Therefore, we were able to measure both the applied GF and LF during a quick LF production task. Similar to the previous studies that explored the disease related deteriorations in the motor control of hand function [4,5,11], we also studied the ratio between the GF and LF (GF/LF). Although GF/LF assessed in a quick LF production task is novel to our study, our findings are similar to previous ones, indicating iMS exert excessive amount of grip forces regardless of the manipulation task they perform. The elevated GF/LF was speculated to be a safety strategy adopted by iMS to compensate the disease related deficiencies in the sensorimotor integration [4]. Despite the significant difference found between groups with a large effect size (d = 0.93), the sensitivity of GF/LF to detect the differences between groups was not different than the same sensitivity of the 9-HPT (d = 1.03). Although the mechanistic importance of GF/LF in understanding of the effects of the multiple sclerosis on the control of the hand function is undeniable, this outcome measure is not more sensitive than the 9-HPT to detect the impairment of the upper extremity in iMS and should not be used to replace this clinical standard test in clinical studies.
Results revealed significant differences in 9-HPT (p = .016, d = 1.07) and GFMAX (p = .013, d = 1.10) between groups, indicating better performance in the control group. The LFMAX was not statistically different between groups (p = .169, d = 0.58; Table 2). 3.2. Motor control of hand function The GF/LF in iMS was higher than in controls (p = .033, d = 0.93) (Table 2). 3.3. Neuromuscular quickness All of the regression parameters obtained from the relationship between peak force and its rate of force development (i.e. RFD-SF and R2) of both GF and LF were higher in control than in iMS with large effect sizes (all p values < .001, Cohen's d values were between 1.75 and 2.87; Table 2). Regarding the force relaxation, similar regression parameters obtained from the relationship between peak force and its rate of force relaxation (i.e. RFR-SF and R2) of both GF and LF were significantly different between groups with large effect sizes (p values were between 0.016 and < 0.001, d values were between 0.99 and 1.92; Table 2).
4.2. Neuromuscular quickness
3.4. Correlations among studied and standard outcome measures
The RFD-SF obtained in BFP protocol have recently been suggested as a robust and reliable measure of the control of neuromuscular quickness [18,24,38] that could represent a general characteristic of an individual rather than being muscle specific [18,30]. Our results revealed that the RFD-SF of both LF and GF were highly reduced in iMS indicating a reduced ability to produce high rates of force development across submaximal ranges. Previous research has speculated that both the central (e.g., initial motor unit firing frequency and number of doublet discharges) and peripheral (e.g., muscle fiber type) properties of the neuromuscular system contribute to RFD-SF both in healthy young and older adults [26,31,39]. We believe that the central, but not the peripheral, characteristics of neuromuscular system could explain the reductions in the RFD-SF in iMS. In fact, Ng et al. (2004) found that iMS produced similar magnitudes of rate of tetanic force development as compared to healthy controls when the RFD was recorded during
Table 3 displays the correlation coefficients obtained from the studied outcome measures and the standard clinical tests. Both RFD-SF and RFR-SF obtained from LF, but not from GF, were significantly Table 3 Relationship between outcome measures obtained from brief force pulse protocol and the standard clinical tests.
EDSS 9-HPT ⁎ ⁎⁎
GF/LF
RFD-SFLF
RFR-SFLF
RFD-SFGF
RFR-SFGF
−0.05 −0.05
−0.55 −0.58⁎
0.30 0.72⁎⁎
−0.14 −0.38
0.15 0.47
p < .05. p < .01. 5
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artificially stimulated contractions, suggesting proper functioning of the peripheral properties of the neuromuscular system. Therefore, the observed reductions in voluntary RFD in iMS as compared to the controls indicate that the quick force generation was impaired mainly due to deficiencies in the CNS (e.g., reduced neural drive and maximal motor unit discharge rates) [40,41]. The ability obtain high rates of force relaxation is arguably as important as quick force generation during the activities of daily life during which consecutive agonist and antagonist contractions are required (e.g., propelling a wheelchair, using an external support for postural correction in a turbulent ride, or cutting with a knife) [41]. Our results revealed a reduction of RFR-SF in iMS, which indicates a reduced ability to quickly relax muscle forces across submaximal ranges. Similarly, previous research has reported reductions in RFR after a tetanic contraction in iMS [40–43]. Unlike RFD, which is determined by the properties of the CNS, this reduction in RFR was attributed to the disease related deficiencies in the peripheral properties of the neuromuscular system (e.g., abnormal calcium kinetics in a muscle) [42]. Taken all together, the outcome measures obtained from the BFP protocol could be used as a probe in clinical settings to simultaneously evaluate disease related deficiencies in both central and peripheral properties of the neuromuscular system. Of importance for the findings of this study are the effect sizes obtained from the outcome measures of neuromuscular quickness in detecting the differences between groups. The RFD-SF obtained from LF (the instructed force component) provided the highest effect size (d = 2.87), approximately 3 times higher than the effect size of the 9HPT. Similarly, RFR-SF obtained from LF also presented approximately 2 times larger effect size than the effect size of the 9-HPT. Although the participants were never instructed about their GF production, our results revealed similar large effect sizes for both RFD-SF and RFR-SF in GF. Based upon these effect sizes, while keeping the partly overlapping 95% confidence intervals in mind, one could suggest that the outcome measures obtained from BFP protocol might be superior than the standard outcome measure of upper extremity in the detection of neurological impairment in iMS and should be considered as primary or secondary outcomes in clinical studies involving iMS. While both RFD-SF and RFR-SF were the main variables of interest, we also explored the effects of disease on the R2s as they represent the amount of variability in rapid force development and relaxation across submaximal force levels [19,21,24]. Our results revealed that iMS not only have reduced scaling in both rates of force development and relaxation across submaximal force levels, but also have reduced consistency in adjusting those rates. Similar reductions were observed in the Rs obtained from RFD-SF in the older adults, which were attributed to the variations in the neural input to muscle [30]. We also speculate that the reductions in the consistency in quick force generation could be due to the prominent variability in the neural input to the muscle in iMS [44]. To improve the magnitudes and the consistency of the rate of force development and relaxation across submaximal ranges in iMS, future studies should consider developing rehabilitation strategies that require consecutive high levels of neural activation followed by quick relaxations (e.g., high speed cycling or arm cranking against a minimum resistance [19]).
standard upper extremity clinical test. This finding provides further support for the assumption that the outcome measures obtained from BFP protocol have clinical and functional relevance. While correlations with a functional task indicate a high concurrent validity of the studied outcome measures of BFP, the high sensitivity obtained from them in detecting the differences between groups makes BFP a viable test protocol for the assessment of neuromuscular functioning in iMS. Future studies should investigate the relationship between the upper extremity neuromuscular quickness and other functional and clinical tests of upper extremity to further signify the importance of the clinical testing of BFP. Regarding the relationships with EDSS, the studied outcome measures obtained in upper extremity muscle groups showed no correlations. This finding was rather expected considering that EDSS uses a non-linear ordinal scale and is heavily weighted toward walking performance [9,46]. Moreover, there might be a higher variability in the self-assessed EDSS used in this study as compared to the same assessment done by an experienced neurologist. Future studies should investigate the possible relationships among the outcome measure obtained in BFP protocol assessed in lower extremity muscle group (e.g., knee extensors and flexors) and standard clinical and functional test of lower extremity (e.g., EDSS, timed 25-ft walk test). 4.4. Studied BFP protocol All subjects completed the experimental protocol without any adverse effects. Including the familiarization, the BFP protocol lasted < 20 min for each subject, which might be longer than the time required to complete 9-HPT (around 5 min). Our results revealed that BFP protocol was not associated with fatigue, which is an important aspect of the testing protocol considering the potential detrimental effects of fatigue on motor function in iMS. Overall, considering the similar effect size observed in the outcome measure of motor control of hand function and higher effect sizes observed in the outcome measures of neuromuscular quickness than those observed from the standard clinical test (i.e., 9-HPT), we recommend future studies to use BFP protocol in both upper and lower extremity muscle groups to evaluate motor functioning in iMS. Future studies could also simplify the testing protocol by using a simpler testing device (e.g., single force transducer or standard isokinetic device). 4.5. Study limitations The main limitation of this study is the small sample size. However, even with a small number of participants, the observed statistical significances in the studied variables along with the observed high effect sizes indicate that the number of participants was enough to test the aims of the present study. However, future studies with larger sample sizes are required to validate the observed correlations between the standard clinical test and the studied outcome measures of BFP. The second limitation was that the patients of this study were not recruited to represent a specific form of multiple sclerosis (10 relapsing-remitting, 1 secondary-progressive, and 1 primary-progressive) and represented mildly affected patients. Therefore, the findings of this study can be generalized neither to a certain form of the disease nor to moderately or highly involved iMS. Future studies should compare different types of multiple sclerosis and iMS with different levels of motor impairment to explore the sensitivity of the outcome variables of this protocol in detecting the effects of the disease progression. The third limitation of this study is that we did not collect data on the physical activity levels of our participants, which is commonly reduced in iMS [47] and could be a factor contributing to reductions in the outcome measures pertaining to neuromuscular quickness [48].
4.3. Correlations with a clinical test We found that both RFD-SF and RFR-SF obtained from LF are correlated to the performance obtained from the standard functional test of upper extremity (i.e., 9-HPT). Previous studies documented positive relationships between isometric RFD obtained from knee extensor and flexors with the lower extremity functional capacity as measured from walking speed and distance [2], sit to stand [45], and foot-tap speed [41]. To our knowledge, this study is the first one showing that both the ability to quickly generate and relax muscle forces in the upper extremity can moderately predict the performance of iMS during a
5. Conclusion Overall, the findings of this study suggest that BFP protocol provides 6
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highly sensitive and objective outcome measures for the clinical assessment of motor function in iMS. These outcome measures may be part of future proof of concept studies that aim to evaluate the effects of therapeutic interventions, detect the differences between the episodes of relapsing and remitting periods, and monitor disease progression in iMS. Finally, the outcome measures obtained from BFP protocol can also be used in different populations with similar motor deficiencies (e.g., Parkinson's disease, stroke, and traumatic brain injuries).
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Knight, Age-related decline in the rate of force development scaling factor, Mot. Control. 17 (4) (2013) 370–381. [31] M. Klass, S. Baudry, J. Duchateau, Age-related decline in rate of torque development is accompanied by lower maximal motor unit discharge frequency during fast contractions, J. Appl. Physiol. (1985) 104 (3) (2008) 739–746, https://doi.org/10. 1152/japplphysiol.00550.2007. [32] C.C. Whitacre, Sex differences in autoimmune disease, Nat. Immunol. 2 (9) (2001) 777–780, https://doi.org/10.1038/ni0901-777. [33] P.B. de Freitas, K.C. Lima, Grip force control during simple manipulation tasks in non-neuropathic diabetic individuals, Clin. Neurophysiol. 124 (9) (2013) 1904–1910, https://doi.org/10.1016/j.clinph.2013.04.002. [34] M. Uygur, X. Jin, O. Knezevic, S. Jaric, Two-dimensional static manipulation tasks: does force coordination depend on change of the tangential force direction? Exp. Brain Res. 222 (4) (2012) 365–375, https://doi.org/10.1007/s00221-012-3221-6. [35] G. Kobelt, J. Berg, P. Lindgren, B. Jonsson, Costs and quality of life in multiple sclerosis in Europe: method of assessment and analysis, Eur. J. Health Econ. 7 (Suppl. 2) (2006) S5–13, https://doi.org/10.1007/s10198-006-0365-y. [36] M.F. Folstein, S.E. Folstein, P.R. McHugh, “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician, J. Psychiatr. Res. 12 (3) (1975) 189–198. [37] D. Djordjevic, M. Uygur, Methodological considerations in the calculation of the rate of force development scaling factor, Physiol. Meas. 39 (1) (2017) 015001, , https://doi.org/10.1088/1361-6579/aa9f51. [38] N.C. Casartelli, R. Lepers, N.A. Maffiuletti, Assessment of the rate of force development scaling factor for the hip muscles, Muscle Nerve 50 (6) (2014) 932–938, https://doi.org/10.1002/mus.24229. [39] M. Van Cutsem, J. Duchateau, K. 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Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contribution MU designed the study. DB recruited participants. MU collected and analyzed data. MU, PF, and DB interpreted the results of the experiment. MU wrote the first draft of the manuscript. PF contributed to editing of the text. Declaration of Competing Interest None. The authors declare that there is no conflict of interest. Acknowledgements We are thankful for the volunteers of this study. We appreciated the comments by Dr. Erin Pletcher on this manuscript. References [1] C. Heesen, J. Bohm, C. Reich, J. Kasper, M. Goebel, S.M. Gold, Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable, Mult. Scler. 14 (7) (2008) 988–991, https://doi.org/10.1177/1352458508088916. [2] T. Kjolhede, K. Vissing, D. Langeskov-Christensen, E. Stenager, T. Petersen, U. Dalgas, Relationship between muscle strength parameters and functional capacity in persons with mild to moderate degree multiple sclerosis, Mult. Scler. Relat. Disord. 4 (2) (2015) 151–158, https://doi.org/10.1016/j.msard.2015.01.002. [3] G.H. Kraft, D. Amtmann, S.E. Bennett, M. Finlayson, M.H. Sutliff, M. Tullman, et al., Assessment of upper extremity function in multiple sclerosis: review and opinion, Postgrad. Med. 126 (5) (2014) 102–108, https://doi.org/10.3810/pgm.2014.09. 2803. [4] V. Iyengar, M.J. Santos, M. Ko, A.S. Aruin, Grip force control in individuals with multiple sclerosis, Neurorehabil. Neural Repair 23 (8) (2009) 855–861, https://doi. org/10.1177/1545968309338194. [5] V. Krishnan, S. Jaric, Hand function in multiple sclerosis: force coordination in manipulation tasks, Clin. Neurophysiol. 119 (10) (2008) 2274–2281, https://doi. org/10.1016/j.clinph.2008.06.011. [6] A. Guclu-Gunduz, S. Citaker, B. Nazliel, C. Irkec, Upper extremity function and its relation with hand sensation and upper extremity strength in patients with multiple sclerosis, Neurorehabilitation 30 (4) (2012) (369–74 6p). [7] N. Yozbatiran, F. Baskurt, Z. Baskurt, S. Ozakbas, E. Idiman, Motor assessment of upper extremity function and its relation with fatigue, cognitive function and quality of life in Multiple Sclerosis patients, J. Neurol. Sci. 246 (1–2) (2006) 117–122, https://doi.org/10.1016/j.jns.2006.02.018. [8] M. Jorgensen, U. Dalgas, I. Wens, L.G. Hvid, Muscle strength and power in persons with multiple sclerosis - a systematic review and meta-analysis, J. Neurol. Sci. 376 (2017) 225–241, https://doi.org/10.1016/j.jns.2017.03.022. [9] J.A. Cohen, S.C. Reingold, C.H. Polman, J.S. Wolinsky, International advisory committee on clinical trials in multiple, Disability outcome measures in multiple sclerosis clinical trials: current status and future prospects, Lancet Neurol. 11 (5) (2012) 467–476, https://doi.org/10.1016/S1474-4422(12)70059-5. [10] M.D. Goldman, R.W. Motl, R.A. Rudick, Possible clinical outcome measures for clinical trials in patients with multiple sclerosis, Ther. Adv. Neurol. Disord. 3 (4) (2010) 229–239, https://doi.org/10.1177/1756285610374117. [11] K. Allgower, C. Kern, J. Hermsdorfer, Predictive and reactive grip force responses to rapid load increases in people with multiple sclerosis, Arch. Phys. Med. Rehabil. 98 (3) (2017) 525–533, https://doi.org/10.1016/j.apmr.2016.08.465. [12] R.S. Johansson, G. Westling, Roles of Glabrous skin receptors and sensorimotor memory in automatic-control of precision grip when lifting rougher or more slippery objects, Exp. Brain Res. 56 (3) (1984) 550–564. [13] G. Westling, R.S. Johansson, Factors influencing the force control during precision grip, Exp. Brain Res. 53 (2) (1984) 277–284.
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M. Uygur, et al. [41] A.V. Ng, R.G. Miller, D. Gelinas, J.A. Kent-Braun, Functional relationships of central and peripheral muscle alterations in multiple sclerosis, Muscle Nerve 29 (6) (2004) 843–852, https://doi.org/10.1002/mus.20038. [42] K.R. Sharma, J. Kent-Braun, M.A. Mynhier, M.W. Weiner, R.G. Miller, Evidence of an abnormal intramuscular component of fatigue in multiple sclerosis, Muscle Nerve 18 (12) (1995) 1403–1411, https://doi.org/10.1002/mus.880181210. [43] J.A. Kent-Braun, K.R. Sharma, M.W. Weiner, R.G. Miller, Effects of exercise on muscle activation and metabolism in multiple sclerosis, Muscle Nerve 17 (10) (1994) 1162–1169, https://doi.org/10.1002/mus.880171006. [44] L.J. Dorfman, J.E. Howard, K.C. McGill, Motor unit firing rates and firing rate variability in the detection of neuromuscular disorders, Electroencephalogr. Clin. Neurophysiol. 73 (3) (1989) 215–224. [45] H.B. Jensen, J.L. Nielsen, M. Ravnborg, U. Dalgas, P. Aagaard, E. Stenager, Effect of
slow release-Fampridine on muscle strength, rate of force development, functional capacity and cognitive function in an enriched population of MS patients. A randomized, double blind, placebo controlled study, Mult. Scler. Relat. Disord. 10 (2016) 137–144, https://doi.org/10.1016/j.msard.2016.07.019. [46] M.P. Amato, E. Portaccio, Clinical outcome measures in multiple sclerosis, J. Neurol. Sci. 259 (1–2) (2007) 118–122, https://doi.org/10.1016/j.jns.2006.06.031. [47] B. Casey, S. Coote, R. Galvin, A. Donnelly, Objective physical activity levels in people with multiple sclerosis: meta-analysis, Scand. J. Med. Sci. Sports 28 (9) (2018) 1960–1969, https://doi.org/10.1111/sms.13214. [48] D.P. Laroche, C.A. Knight, J.L. Dickie, M. Lussier, S.J. Roy, Explosive force and fractionated reaction time in elderly low- and high-active women, Med. Sci. Sports Exerc. 39 (9) (2007) 1659–1665, https://doi.org/10.1249/mss. 0b013e318074ccd9.
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Rate of force development and relaxation scaling factors are highly sensitive to detect upper extremity motor impairments in multiple sclerosis
T
Mehmet Uygura, , Paulo B. de Freitasb, Donald A. Baronec ⁎
a
Department of Health and Exercise Science, Rowan University, Glassboro, NJ 08028, USA Interdisciplinary Graduate Program in Health Sciences Cruzeiro do Sul University, São Paulo, Brazil c Neurological Institute, Cooper Medical School of Rowan University, Camden, NJ 08084, USA b
ARTICLE INFO
ABSTRACT
Keywords: Rate of force development Rate of force relaxation Spasticity Motor symptom Neurological disease muscle
Objective: Clinical assessment of upper extremity in multiple sclerosis is mainly limited to 9-hole peg test (9HPT), which is commonly criticized due to its limited sensitivity. There is a need for sensitive outcome measures for the assessment of motor symptoms in individuals with multiple sclerosis (iMS). We evaluated our recently developed brief force pulse protocol to simultaneously quantify the motor control of hand function and neuromuscular quickness in iMS. Additionally, we compared the sensitivity of the studied outcome measures with 9HPT in detecting the differences between iMS and controls. Methods: Twelve iMS and 12 controls grasped a grip- (GF; perpendicular force) and load-force (LF; tangential force) measuring handle and produced around 100 isometric LF pulses to various submaximal levels by pushing down on it as quickly as possible, followed by quick relaxation. The GF-LF ratio quantified the motor control of hand function. The slopes of linear regressions between peak forces and corresponding peak rates of force development (rate of force development scaling factor; RFD-SF) and relaxation (rate of force relaxation scaling factor; RFR-SF) quantified the control of neuromuscular quickness. Results: All of the selected variables were different between groups (all p-values < .05), and the effect sizes obtained from RFD-SF (d = 2.87) and RFR-SF (d = 1.93) were larger than the effect sizes obtained from 9-HPT (d = 1.07). Conclusion: Measures of neuromuscular quickness are more sensitive to detect disease related differences than 9HPT and, therefore, can be used as a tool in clinical and rehabilitative settings to objectively evaluate therapeutic interventions and disease progression in iMS.
1. Introduction Multiple sclerosis is a central nervous system (CNS) disease associated with various motor symptoms. While walking impairment is considered a hallmark symptom of the disease [1], the motor control of hand function and the ability to quickly generate and relax muscle forces is highly affected in individuals with multiple sclerosis (iMS) [2,3]. As a consequence, iMS face difficulties while manipulating objects [4,5], performing activities of daily living [6,7], and generating quick muscle forces [8], which could be crucial to produce over an externally fixed object (e.g., cane or a handrail) to avoid a fall. Standard clinical assessments of motor symptoms of the disease are mainly limited to subjective neurological rating scales (e.g., Expanded
Disability Status Scale; EDSS) and performance-based timed tests (e.g., 9 hole peg test; 9-HPT) [9,10]. Although commonly used in rehabilitative settings and clinical trials, these assessment tools are criticized due to their limited sensitivity to detect mild disability, monitor disease progression, and evaluate the effects of the applied treatments [9]. Moreover, most of the standard clinical assessment tools are mainly limited to lower extremity and, therefore, there is a need for the development of objective and sensitive neurobehavioral outcome measures for the evaluation of upper extremity motor symptoms in iMS [3]. The kinetic analysis of object manipulation has been used to evaluate the motor control of hand function in iMS [4,5,11]. Specifically, a certain amount of force should be applied perpendicularly (i.e. grip force; GF) to a handheld object to create frictional forces that enable an
Abbreviations: iMS, individuals with multiple sclerosis; EDSS, Expanded Disability Status Scale; 9-HPT, nine-hole peg test; GF, grip force; LF, load force; GFMAX, grip force maximum; LFMAX, load force maximum; GF/LF, grip- to load-force ratio; BFP, brief force pulse; RFD-SF, rate of force development scaling factor; RFR-SF, rate of force relaxation scaling factor; R2, r-squared ⁎ Corresponding author at: Department of Health and Exercise Science, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, USA. E-mail address: [email protected] (M. Uygur). https://doi.org/10.1016/j.jns.2019.116500 Received 30 May 2019; Received in revised form 21 August 2019; Accepted 17 September 2019 Available online 15 October 2019 0022-510X/ © 2019 Elsevier B.V. All rights reserved.
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individual to generate tangential forces (i.e. load force; LF) needed to manipulate the object [12]. When individuals manipulate an object, they apply higher GF than the minimum GF required to avoid slippage. The difference between the employed and minimum required GF is kept relatively invariant even when LF changes, indicating a high temporal coupling between these forces [13]. This elaborate coordination between GF and LF is commonly affected in iMS, resulting in the generation of excessive GF with respect to the exerted LF [5,11,14]. iMS have consistently demonstrated a higher ratio between GF and LF (GF/ LF) both in static and dynamic object manipulation tasks. Therefore, GF/LF has been suggested as a sensitive variable to evaluate the motor control of hand function in iMS [5,14]. The ability to generate and relax muscle forces quickly across various submaximal levels (i.e. neuromuscular quickness) is highly relevant to the activities of daily life involving both upper and lower extremities [2,15–17]. To evaluate neuromuscular quickness, recent studies introduced an isometric brief force pulse (BFP) protocol that requires individuals to generate and relax discrete force pulses to various submaximal levels under the instruction to produce and relax each force pulse as rapidly as possible [18–21]. There exists a strong linear relationship between the peak values of the force pulses produced and the corresponding peak rates of force development (RFD), demonstrating an invariance in the time required to reach peak forces regardless of its magnitude [22,23]. Recently, using the same BFP protocol, we demonstrated a similar linear relationship between the peak forces and the corresponding peak rates of force relaxation (RFR) during the relaxation phases of quick force pulses [24]. The outcome measures of neuromuscular quickness were extracted from the linear regression relationship between the peak values of the force pulses produced and their corresponding RFD and RFR. The slope of the relationship between peak force and RFD was named as rate of force development scaling factor (RFD-SF). The similar slope obtained from the relationship between peak forces and RFR was named as rate of force relaxation scaling factor (RFR-SF). While scaling factors were considered as the main variables of interest, the R-squared values (R2) obtained from those relationships evaluate the consistency of the scaling of RFD and RFR with the magnitude of the forces produced across submaximal levels [18,21]. Both RFD-SF and RFR-SF were speculated to be a proxy measures of central (e.g., motor unit firing rate, doublet discharge) and peripheral (e.g., muscle fiber type) properties of neuromuscular system underlying the activities requiring submaximal, yet quick, contractions followed by quick relaxations [24–26]. Previous research has shown the clinical utility of RFD-SF in detecting deteriorations in neuromuscular function in clinical populations, such as people with Parkinson's disease [27,28] and stroke [29], and in the elderly [30,31]. However, the aforementioned outcome measures of neuromuscular quickness have never been assessed in iMS and mainly neglected in the upper extremity. Recently, we developed and tested the reliability of a static object manipulation task that simultaneously quantifies motor control of hand function and neuromuscular quickness [21]. This task mimics conditions during which one has to generate quick reaction forces by pushing down against an externally fixed object (e.g., cane or handrail) to avoid falling [18,21]. Individuals were asked to grasp an externally fixed, GF and LF measuring device to generate brief isometric LF pulses to various submaximal amplitudes followed by quick force relaxations. To be able to generate a quick LF pulse over an externally fixed object, one also needs to produce a quick GF pulse to avoid the slippage of the hand off the object. Therefore, the proposed measurement tool provides the outcome measures of neuromuscular quickness of both LF (explicitly) and GF (implicitly) in an ecologically valid task. Moreover, the GF/LF obtained from the recorded GF and LF time series also serves to evaluate the motor control of hand function. In this study, we aimed to evaluate the motor control of hand function and neuromuscular quickness of iMS through an ecologically
valid brief force pulse protocol. We also aimed to compare the sensitivity (as assessed from effect sizes) of the studied outcome measures to the sensitivity obtained from the currently used standard clinical outcome measure of upper extremity (e.g., 9-HPT) in detecting the differences in motor function between iMS and healthy controls. Finally, to evaluate the concurrent validity, we explored the correlations among the standard outcome measures of the disability (e.g., EDSS and 9-HPT) and the studied outcome measures of this study. Our overall hypothesis is that the proposed tool would provide outcome measures that are more sensitive than the currently used standard outcome measure in detecting the differences in the motor function between iMS and healthy controls. 2. Methods 2.1. Subjects Twelve individuals with multiple sclerosis (iMS) and 12 age- and gender-matched controls participated in this study (see Table 1 for the descriptive information). The gender ratio of this study represented the higher prevalence of multiple sclerosis in females (60–75%) [32]. Inclusion criteria included clinically stable iMS with no relapses for at least 12 months, intact vision, being able to walk without an assistive device, and being able to lift and manipulate an object. Individuals with additional neurological and musculoskeletal disorders were excluded. Except for two control and two iMS, all participants were right handed. Patients were mainly recruited from the School of Osteopathic Medicine of the Rowan University. Written informed consent was obtained from all participants. The institutional review board of Rowan University approved the study protocol and it was conducted in accordance with the Declaration of Helsinki. 2.2. Device The device used in this study is similar to the devices that were used in other static object manipulation studies [33,34]. The device consisted of two parallel grasping metal surfaces connected by a single axis force transducer (WMC-50, Interface Inc., USA) and a multi axis force transducer (Mini40, ATI Industrial Automation, USA) attached Table 1 Descriptive characteristics of individuals with MS (iMS) and age- and gendermatched controls. Participants
Sex
Age (y)
EDSS
9-HPT (s)
MMSE
Time S. diag. (y)
iMS iMS_1 iMS_2 iMS_3 iMS_4 iMS_5 iMS_6 iMS_7 iMS_8 iMS_9 iMS_10 iMS_11 iMS_12 Mean (SD)
F F F F F F M F M M F F 9F, 3M
62 67 65 48 54 54 65 46 76 51 41 60 57.4 (10.2)
2 4 4 3 3 1 6 3.5 6 5 2.5 3 3.6 (1.5)
33.5 19.1 21.6 21.6 25.1 15.7 45.6 22.7 27.6 24.1 21.8 20.5 24.9 (7.9)
30 30 30 30 28 30 27 29 26 28 30 30 29 (1.4)
13 18 21 14 20 5 18 16 7 25 12 19 15.7 (5.8)
Healthy controls Mean (SD) 9F, 3M
57.3 (8.9)
18.8 (1.7)
iMS: Individuals with multiple sclerosis; EDSS: Self Assessed Expanded Disability Status Scale; 9-HPT: Nine Hole Peg Test; MMSE: Mini-Mental State Examination; Time S. Diag: Time since diagnosis.
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Fig. 1. (A) The instrumented grip- (GF) and loadforce (LF) measuring device and the formulas used to calculate them. (B) The visual feedback screen displaying the quick pulses generated upon the device by pushing it in down direction. The visual feedback only displays the current LF and the four horizontal lines representing 20, 40, 60, and 80% of the maximum LF.
underneath the grasping surfaces (Fig. 1A). The grasping surfaces were covered with rubber to increase the frictional forces between the fingers and the device. The single axis force transducer recorded the compression force (FC) and, together with the net force recorded by the multi-axis transducer in the same direction (FY), was used to calculate grip force (GF). The tangential forces recorded by the multi-axis transducer (FX and FZ) were used to calculate LF (see Fig. 1A for the equations used in GF and LF calculations). The device was externally fixed to a height adjustable base, which was secured on a table. The height of the base was adjusted so that the elbows of the standing subjects were at 900 of flexion while grasping the device (Fig. 1B).
percent of LFMAX, was displayed as a black line (Fig. 1B). The ranges between 20 and 40%, 40–60%, and 60–80% were referred to as small, medium, and large, which were used as a “go” command and to instruct participants about the expected force magnitude. Participants were instructed ‘to produce each force pulse as fast as possible and to relax immediately without trying to be accurate with its magnitude’. A total of approximately 100 pulses were recorded in four separate trials, each lasting 60 s (Fig. 2A and B). 2.4. Data acquisition and reduction Both data acquisition and analysis were made by custom written LabView routines (National Instruments Corp., Austin, Texas, USA). The signals from the transducers were sampled at 200 Hz and filtered using a fourth-order low-pass Butterworth filter with a cutoff frequency of 10 Hz. The point-by-point derivatives of both GF and LF curves were also calculated and filtered with a similar filter with a cutoff frequency of 5 Hz as this method provided the highest reliability for the calculation of RFD-SF [37]. Four curves representing GF and LF and their time derivatives were plotted for each pulse during data analysis (Fig. 2C and D). For every individual pulse, two cursors were automatically placed on the GF and LF curves depicting the peak forces and two different cursors placed on time derivative curves of each force corresponding to peak magnitudes of RFD and RFR. An experienced researcher then visually inspected each pulse to validate the cursor placement before saving their values for further analysis. Any pulse with a duration greater than the mean plus two standard deviations of the durations of all pulses were excluded from analysis [21,24].
2.3. Procedure All data collection was completed in a single session, lasting approximately one hour. Before the completion of force testing protocol, iMS completed standard clinical tests including self assessed EDSS [35], Mini Mental State Examination [36], and the 9-hole peg test (9-HPT). Two trials were recorded for the 9-HPT (only the dominant hand) and the best performance was used in the analysis. After the completion of the standard clinical assessments, each participant completed the following force testing protocol. During force testing, standing participants were asked to grasp the device using a five-digit grasp [33,34] and only the right hands of the participants were tested. Both groups followed the same testing order during the protocol. The force testing started with the tests of the maximal voluntary isometric contractions of GF (GFMAX) and LF (LFMAX), separately. The order of maximal testing was block randomized within each group. In GFMA, participants were asked to “squeeze the grasping surfaces as hard as” they could. In LFMAX, participants were asked to “push down on the device as hard as” they could without letting the digits slip off the handle. Three maximal trials, separated by sixty seconds of rest, were recorded separately for GFMAX and LFMAX and their largest values were used in analysis. We also tested GFMAX and LFMAX after the completion of the BFP protocol to test whether the protocol was associated with fatigue. After maximal testing, participants completed the BFP protocol. In this protocol, participants were asked to generate rapid LF pulses of varying submaximal magnitudes by pushing down on the device. Four red horizontal lines representing 20, 40, 60, and 80% of the recorded LFMAX were displayed on the feedback monitor. Real time LF, as a
2.5. Data analysis We studied GF/LF to evaluate the motor control of hand function and calculated it as the average value of the ratio between peak GF and peak LF obtained across all of the recorded force pulses [4,5,11,14]. For the assessment of neuromuscular quickness, we obtained the linear regression parameters from the relationships between the peak forces and the corresponding peak RFD and RFR (Fig. 3). Participants were instructed to grasp and generate quick LF pulses over externally fixed device and, therefore, they had to generate quick GF pulses to produce LF. Consequently, we were able to study the neuromuscular quickness of the LF (explicitly) and GF (implicitly) from the same task. 3
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Fig. 2. Sample recordings of LF and GF time curves obtained from (A) a control participant and (B) an iMS. (C) and (D) represent the recorded LF, GF, and their rates obtained from a LF pulse of a similar magnitude in control and iMS, respectively. The LF curves were negated for the visual clarity.
The slope obtained from the relationship between PF and peak RFD was named as rate of force development scaling factor (RFD-SF) while the slope obtained from the relationship between PF and peak RFR was named as RFR-SF. Both scaling factors quantified the magnitude of the scaling of RFD and RFR with the amplitude of force produced while the R2s obtained from the same regressions revealed the consistency of these scaling. Similar to the previous research [18,21,38], we excluded y-intercept of the regressions from our analyses.
within each group, paired t-tests were used to compare GFMAX and LFMAX recorded before and after the protocol. Pearson correlation coefficients were employed to evaluate the concurrent validity of the main outcome measures obtained from the protocol (GF/LF, RFD-SFs, and RFR-SFs of LF and GF) with respect to the standard clinical assessment (i.e. 9-HPT). Significance level was set at p < .05.
2.6. Statistics
3.1. Standard clinical assessments
Statistical analyses were performed in SPSS (version 24, IBM SPSS Statistics, Armonk, NY). As R2 values are inherently not normally distributed, their Z-transformed values were used in statistical analyses. The results of the Kolmogorov-Smirnov test indicated that none of the studied variables violated the normality assumption. We used independent sample t-tests to compare the selected variables between iMS and control groups. The Cohen's d values were calculated to estimate the effect sizes. To determine whether BFP protocol led to fatigue,
The demographics of participants are provided in Table 1. In iMS, GFMAX and LFMAX were not statistically different before and after the completion of force testing protocol (GFMAX: 103.14 ± 27.81 N vs. 104.82 ± 31.65 N; p = .71 and LFMAX: 80.68 ± 18.39 N vs. 79.91 ± 24.47 N; p = .80). In controls, while the LFMAX was not different before and after the completion of protocol (92.49 ± 22.17 N vs. 92.61 ± 20.39 N; p = .15), GFMAX was higher after the completion of the protocol (136.20 ± 32.29 N vs. 146.34 ± 39.43 N; p = .01).
3. Results
Fig. 3. The peak force-peak rate of force development (upper panel) and peak force - peak rate of force relaxation relationships obtained (lower panel) for LF (A) and GF (B). The rightward shift of the peak GF values in iMS could indicate an excessive exertion of GF during the generation of LF pulses. 4
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correlated with the standard clinical test of upper extremity (i.e., 9HPT) while none of the studied outcome measures were significantly correlated with the EDSS.
Table 2 Comparisons between iMS and control subjects in clinical tests and the outcome measures obtained from the brief force protocol. iMS
Control
P-value
Effect size (95% CI)
Clinical test 9-HPT (s) LFmax (N)
24.9 ± 7.9 80.68 ± 18.39
18.8 ± 1.7 92.49 ± 22.17
0.016 0.169
GFmax (N)
103.14 ± 27.81
136.20 ± 32.92
0.013
1.07 (0.18–1.88) 0.58 (−0.26–1.38) 1.10 (0.20–1.91)
0.98 ± 0.16
0.033
0.93 (0.06–1.74)
10.58 ± 1.42
< 0.001
2.87 (1.65–3.89)
0.94 ± 0.04 −7.84 ± 1.47
< 0.001 < 0.001
2.28 (1.19–3.21) 1.93 (0.90–2.82)
0.87 ± 0.11 9.32 ± 1.59
0.016 < 0.001
1.07 (0.18–1.89) 1.75 (0.76–2.62)
0.93 ± 0.06 −5.84 ± 1.36
< 0.001 0.025
1.84 (0.83–2.72) 0.99 (0.10–1.79)
0.82 ± 0.11
0.022
1.01 (0.12–1.81)
Hand function GF/LF 1.19 ± 0.28 Neuromuscular quickness RFD-SFLF 6.06 ± 1.71 (1/s) 2 0.76 ± 0.13 RLF RFR-SFLF −5.15 ± 1.32 (1/s) 0.76 ± 0.14 R2LF RFD-SFGF 6.23 ± 1.92 (1/s) 0.72 ± 0.18 R2GF RFR-SFGF −4.62 ± 1.11 (1/s) R2GF 0.68 ± 0.17
4. Discussion The aim of this study was to evaluate the motor control of hand function and neuromuscular quickness of iMS through an ecologically valid static object manipulation task. We also aimed to compare the sensitivity (as assessed by the effect sizes) of the studied outcome measures with the sensitivity of the standard clinical test of upper extremity (9-HPT) in detecting differences between iMS and healthy controls. We found differences between iMS and controls in 9-HPT and in all of the studied outcome measures of the motor control of hand function (GF/LF) and neuromuscular quickness (RFD-SF, RFR-SF, and the R2s) of both LF and GF. In general, the effect sizes obtained from the studied outcome measures tended to be higher than the one obtained from 9-HPT (please refer to 95% confidence intervals of effect sizes in Table 2 for interpretation). This may suggest higher sensitivity of the studied outcome measures in detecting the differences in motor function between iMS and control than the same sensitivity obtained from the standard clinical assessment of upper extremity function. We also found significant correlations among 9-HPT and the scaling factors of both RFD and RFR obtained from LF.
Values represent mean ± standard deviation. Effect sizes and p-values of R2s were calculated from the fisher-transformed values of the same variable. The 95% CI refers to the 95% confidence interval.
4.1. Motor control of hand function We used an ecologically valid, isometric force production task, during which one has to grasp an externally fixed object and produce reaction forces by pushing down as quickly as possible. Therefore, we were able to measure both the applied GF and LF during a quick LF production task. Similar to the previous studies that explored the disease related deteriorations in the motor control of hand function [4,5,11], we also studied the ratio between the GF and LF (GF/LF). Although GF/LF assessed in a quick LF production task is novel to our study, our findings are similar to previous ones, indicating iMS exert excessive amount of grip forces regardless of the manipulation task they perform. The elevated GF/LF was speculated to be a safety strategy adopted by iMS to compensate the disease related deficiencies in the sensorimotor integration [4]. Despite the significant difference found between groups with a large effect size (d = 0.93), the sensitivity of GF/LF to detect the differences between groups was not different than the same sensitivity of the 9-HPT (d = 1.03). Although the mechanistic importance of GF/LF in understanding of the effects of the multiple sclerosis on the control of the hand function is undeniable, this outcome measure is not more sensitive than the 9-HPT to detect the impairment of the upper extremity in iMS and should not be used to replace this clinical standard test in clinical studies.
Results revealed significant differences in 9-HPT (p = .016, d = 1.07) and GFMAX (p = .013, d = 1.10) between groups, indicating better performance in the control group. The LFMAX was not statistically different between groups (p = .169, d = 0.58; Table 2). 3.2. Motor control of hand function The GF/LF in iMS was higher than in controls (p = .033, d = 0.93) (Table 2). 3.3. Neuromuscular quickness All of the regression parameters obtained from the relationship between peak force and its rate of force development (i.e. RFD-SF and R2) of both GF and LF were higher in control than in iMS with large effect sizes (all p values < .001, Cohen's d values were between 1.75 and 2.87; Table 2). Regarding the force relaxation, similar regression parameters obtained from the relationship between peak force and its rate of force relaxation (i.e. RFR-SF and R2) of both GF and LF were significantly different between groups with large effect sizes (p values were between 0.016 and < 0.001, d values were between 0.99 and 1.92; Table 2).
4.2. Neuromuscular quickness
3.4. Correlations among studied and standard outcome measures
The RFD-SF obtained in BFP protocol have recently been suggested as a robust and reliable measure of the control of neuromuscular quickness [18,24,38] that could represent a general characteristic of an individual rather than being muscle specific [18,30]. Our results revealed that the RFD-SF of both LF and GF were highly reduced in iMS indicating a reduced ability to produce high rates of force development across submaximal ranges. Previous research has speculated that both the central (e.g., initial motor unit firing frequency and number of doublet discharges) and peripheral (e.g., muscle fiber type) properties of the neuromuscular system contribute to RFD-SF both in healthy young and older adults [26,31,39]. We believe that the central, but not the peripheral, characteristics of neuromuscular system could explain the reductions in the RFD-SF in iMS. In fact, Ng et al. (2004) found that iMS produced similar magnitudes of rate of tetanic force development as compared to healthy controls when the RFD was recorded during
Table 3 displays the correlation coefficients obtained from the studied outcome measures and the standard clinical tests. Both RFD-SF and RFR-SF obtained from LF, but not from GF, were significantly Table 3 Relationship between outcome measures obtained from brief force pulse protocol and the standard clinical tests.
EDSS 9-HPT ⁎ ⁎⁎
GF/LF
RFD-SFLF
RFR-SFLF
RFD-SFGF
RFR-SFGF
−0.05 −0.05
−0.55 −0.58⁎
0.30 0.72⁎⁎
−0.14 −0.38
0.15 0.47
p < .05. p < .01. 5
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artificially stimulated contractions, suggesting proper functioning of the peripheral properties of the neuromuscular system. Therefore, the observed reductions in voluntary RFD in iMS as compared to the controls indicate that the quick force generation was impaired mainly due to deficiencies in the CNS (e.g., reduced neural drive and maximal motor unit discharge rates) [40,41]. The ability obtain high rates of force relaxation is arguably as important as quick force generation during the activities of daily life during which consecutive agonist and antagonist contractions are required (e.g., propelling a wheelchair, using an external support for postural correction in a turbulent ride, or cutting with a knife) [41]. Our results revealed a reduction of RFR-SF in iMS, which indicates a reduced ability to quickly relax muscle forces across submaximal ranges. Similarly, previous research has reported reductions in RFR after a tetanic contraction in iMS [40–43]. Unlike RFD, which is determined by the properties of the CNS, this reduction in RFR was attributed to the disease related deficiencies in the peripheral properties of the neuromuscular system (e.g., abnormal calcium kinetics in a muscle) [42]. Taken all together, the outcome measures obtained from the BFP protocol could be used as a probe in clinical settings to simultaneously evaluate disease related deficiencies in both central and peripheral properties of the neuromuscular system. Of importance for the findings of this study are the effect sizes obtained from the outcome measures of neuromuscular quickness in detecting the differences between groups. The RFD-SF obtained from LF (the instructed force component) provided the highest effect size (d = 2.87), approximately 3 times higher than the effect size of the 9HPT. Similarly, RFR-SF obtained from LF also presented approximately 2 times larger effect size than the effect size of the 9-HPT. Although the participants were never instructed about their GF production, our results revealed similar large effect sizes for both RFD-SF and RFR-SF in GF. Based upon these effect sizes, while keeping the partly overlapping 95% confidence intervals in mind, one could suggest that the outcome measures obtained from BFP protocol might be superior than the standard outcome measure of upper extremity in the detection of neurological impairment in iMS and should be considered as primary or secondary outcomes in clinical studies involving iMS. While both RFD-SF and RFR-SF were the main variables of interest, we also explored the effects of disease on the R2s as they represent the amount of variability in rapid force development and relaxation across submaximal force levels [19,21,24]. Our results revealed that iMS not only have reduced scaling in both rates of force development and relaxation across submaximal force levels, but also have reduced consistency in adjusting those rates. Similar reductions were observed in the Rs obtained from RFD-SF in the older adults, which were attributed to the variations in the neural input to muscle [30]. We also speculate that the reductions in the consistency in quick force generation could be due to the prominent variability in the neural input to the muscle in iMS [44]. To improve the magnitudes and the consistency of the rate of force development and relaxation across submaximal ranges in iMS, future studies should consider developing rehabilitation strategies that require consecutive high levels of neural activation followed by quick relaxations (e.g., high speed cycling or arm cranking against a minimum resistance [19]).
standard upper extremity clinical test. This finding provides further support for the assumption that the outcome measures obtained from BFP protocol have clinical and functional relevance. While correlations with a functional task indicate a high concurrent validity of the studied outcome measures of BFP, the high sensitivity obtained from them in detecting the differences between groups makes BFP a viable test protocol for the assessment of neuromuscular functioning in iMS. Future studies should investigate the relationship between the upper extremity neuromuscular quickness and other functional and clinical tests of upper extremity to further signify the importance of the clinical testing of BFP. Regarding the relationships with EDSS, the studied outcome measures obtained in upper extremity muscle groups showed no correlations. This finding was rather expected considering that EDSS uses a non-linear ordinal scale and is heavily weighted toward walking performance [9,46]. Moreover, there might be a higher variability in the self-assessed EDSS used in this study as compared to the same assessment done by an experienced neurologist. Future studies should investigate the possible relationships among the outcome measure obtained in BFP protocol assessed in lower extremity muscle group (e.g., knee extensors and flexors) and standard clinical and functional test of lower extremity (e.g., EDSS, timed 25-ft walk test). 4.4. Studied BFP protocol All subjects completed the experimental protocol without any adverse effects. Including the familiarization, the BFP protocol lasted < 20 min for each subject, which might be longer than the time required to complete 9-HPT (around 5 min). Our results revealed that BFP protocol was not associated with fatigue, which is an important aspect of the testing protocol considering the potential detrimental effects of fatigue on motor function in iMS. Overall, considering the similar effect size observed in the outcome measure of motor control of hand function and higher effect sizes observed in the outcome measures of neuromuscular quickness than those observed from the standard clinical test (i.e., 9-HPT), we recommend future studies to use BFP protocol in both upper and lower extremity muscle groups to evaluate motor functioning in iMS. Future studies could also simplify the testing protocol by using a simpler testing device (e.g., single force transducer or standard isokinetic device). 4.5. Study limitations The main limitation of this study is the small sample size. However, even with a small number of participants, the observed statistical significances in the studied variables along with the observed high effect sizes indicate that the number of participants was enough to test the aims of the present study. However, future studies with larger sample sizes are required to validate the observed correlations between the standard clinical test and the studied outcome measures of BFP. The second limitation was that the patients of this study were not recruited to represent a specific form of multiple sclerosis (10 relapsing-remitting, 1 secondary-progressive, and 1 primary-progressive) and represented mildly affected patients. Therefore, the findings of this study can be generalized neither to a certain form of the disease nor to moderately or highly involved iMS. Future studies should compare different types of multiple sclerosis and iMS with different levels of motor impairment to explore the sensitivity of the outcome variables of this protocol in detecting the effects of the disease progression. The third limitation of this study is that we did not collect data on the physical activity levels of our participants, which is commonly reduced in iMS [47] and could be a factor contributing to reductions in the outcome measures pertaining to neuromuscular quickness [48].
4.3. Correlations with a clinical test We found that both RFD-SF and RFR-SF obtained from LF are correlated to the performance obtained from the standard functional test of upper extremity (i.e., 9-HPT). Previous studies documented positive relationships between isometric RFD obtained from knee extensor and flexors with the lower extremity functional capacity as measured from walking speed and distance [2], sit to stand [45], and foot-tap speed [41]. To our knowledge, this study is the first one showing that both the ability to quickly generate and relax muscle forces in the upper extremity can moderately predict the performance of iMS during a
5. Conclusion Overall, the findings of this study suggest that BFP protocol provides 6
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highly sensitive and objective outcome measures for the clinical assessment of motor function in iMS. These outcome measures may be part of future proof of concept studies that aim to evaluate the effects of therapeutic interventions, detect the differences between the episodes of relapsing and remitting periods, and monitor disease progression in iMS. Finally, the outcome measures obtained from BFP protocol can also be used in different populations with similar motor deficiencies (e.g., Parkinson's disease, stroke, and traumatic brain injuries).
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Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contribution MU designed the study. DB recruited participants. MU collected and analyzed data. MU, PF, and DB interpreted the results of the experiment. MU wrote the first draft of the manuscript. PF contributed to editing of the text. Declaration of Competing Interest None. The authors declare that there is no conflict of interest. Acknowledgements We are thankful for the volunteers of this study. We appreciated the comments by Dr. Erin Pletcher on this manuscript. References [1] C. Heesen, J. Bohm, C. Reich, J. Kasper, M. Goebel, S.M. Gold, Patient perception of bodily functions in multiple sclerosis: gait and visual function are the most valuable, Mult. Scler. 14 (7) (2008) 988–991, https://doi.org/10.1177/1352458508088916. [2] T. Kjolhede, K. Vissing, D. Langeskov-Christensen, E. Stenager, T. Petersen, U. Dalgas, Relationship between muscle strength parameters and functional capacity in persons with mild to moderate degree multiple sclerosis, Mult. Scler. Relat. Disord. 4 (2) (2015) 151–158, https://doi.org/10.1016/j.msard.2015.01.002. [3] G.H. Kraft, D. Amtmann, S.E. Bennett, M. Finlayson, M.H. Sutliff, M. Tullman, et al., Assessment of upper extremity function in multiple sclerosis: review and opinion, Postgrad. Med. 126 (5) (2014) 102–108, https://doi.org/10.3810/pgm.2014.09. 2803. [4] V. Iyengar, M.J. Santos, M. Ko, A.S. Aruin, Grip force control in individuals with multiple sclerosis, Neurorehabil. Neural Repair 23 (8) (2009) 855–861, https://doi. org/10.1177/1545968309338194. [5] V. Krishnan, S. Jaric, Hand function in multiple sclerosis: force coordination in manipulation tasks, Clin. Neurophysiol. 119 (10) (2008) 2274–2281, https://doi. org/10.1016/j.clinph.2008.06.011. [6] A. Guclu-Gunduz, S. Citaker, B. Nazliel, C. Irkec, Upper extremity function and its relation with hand sensation and upper extremity strength in patients with multiple sclerosis, Neurorehabilitation 30 (4) (2012) (369–74 6p). [7] N. Yozbatiran, F. Baskurt, Z. Baskurt, S. Ozakbas, E. Idiman, Motor assessment of upper extremity function and its relation with fatigue, cognitive function and quality of life in Multiple Sclerosis patients, J. Neurol. Sci. 246 (1–2) (2006) 117–122, https://doi.org/10.1016/j.jns.2006.02.018. [8] M. Jorgensen, U. Dalgas, I. Wens, L.G. Hvid, Muscle strength and power in persons with multiple sclerosis - a systematic review and meta-analysis, J. Neurol. Sci. 376 (2017) 225–241, https://doi.org/10.1016/j.jns.2017.03.022. [9] J.A. Cohen, S.C. Reingold, C.H. Polman, J.S. Wolinsky, International advisory committee on clinical trials in multiple, Disability outcome measures in multiple sclerosis clinical trials: current status and future prospects, Lancet Neurol. 11 (5) (2012) 467–476, https://doi.org/10.1016/S1474-4422(12)70059-5. [10] M.D. Goldman, R.W. Motl, R.A. Rudick, Possible clinical outcome measures for clinical trials in patients with multiple sclerosis, Ther. Adv. Neurol. Disord. 3 (4) (2010) 229–239, https://doi.org/10.1177/1756285610374117. [11] K. Allgower, C. Kern, J. Hermsdorfer, Predictive and reactive grip force responses to rapid load increases in people with multiple sclerosis, Arch. Phys. Med. Rehabil. 98 (3) (2017) 525–533, https://doi.org/10.1016/j.apmr.2016.08.465. [12] R.S. Johansson, G. Westling, Roles of Glabrous skin receptors and sensorimotor memory in automatic-control of precision grip when lifting rougher or more slippery objects, Exp. Brain Res. 56 (3) (1984) 550–564. [13] G. Westling, R.S. Johansson, Factors influencing the force control during precision grip, Exp. Brain Res. 53 (2) (1984) 277–284.
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