Scholarly article on topic 'Assessing the Reliability of Ultrasound Imaging to Examine Radial Nerve Excursion'

Assessing the Reliability of Ultrasound Imaging to Examine Radial Nerve Excursion Academic research paper on "Medical engineering"

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{"Ultrasound imaging" / "Radial nerve" / "Nerve mobilisation" / Reliability}

Abstract of research paper on Medical engineering, author of scientific article — Ben Kasehagen, Richard Ellis, Grant Mawston, Scott Allen, Wayne Hing

Abstract Ultrasound imaging allows cost effective in vivo analysis for quantifying peripheral nerve excursion. This study used ultrasound imaging to quantify longitudinal radial nerve excursion during various active and passive wrist movements in healthy participants. Frame-by-frame cross-correlation software allowed calculation of nerve excursion from video sequences. The reliability of ultrasound measurement of longitudinal radial nerve excursion was moderate to high (intraclass correlation coefficient range = 0.63–0.86, standard error of measurement 0.19–0.48). Radial nerve excursion ranged from 0.41 to 4.03 mm induced by wrist flexion and 0.28 to 2.91 mm induced by wrist ulnar deviation. No significant difference was seen in radial nerve excursion during either wrist movement (p > 0.05). Wrist movements performed in forearm supination produced larger overall nerve excursion (1.41 ± 0.32 mm) compared with those performed in forearm pronation (1.06 ± 0.31 mm) (p < 0.01). Real-time ultrasound is a reliable, cost-effective, in vivo method for analysis of radial nerve excursion.

Academic research paper on topic "Assessing the Reliability of Ultrasound Imaging to Examine Radial Nerve Excursion"

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Ultrasound in Med. & Biol., Vol. 42, No. 7, pp. 1651-1659, 2016 © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

0301-5629/$ - see front matter

http://dx.doi.org/10.1016/j.ultrasmedbio.2016.02.013

• Original Contribution

ASSESSING THE RELIABILITY OF ULTRASOUND IMAGING TO EXAMINE

RADIAL NERVE EXCURSION

Ben Kasehagen,* Richard Ellis,y Grant Mawston/ Scott Allen,z and Wayne Hing*

* Faculty of Health Sciences and Medicine, Bond University Institute of Health and Sport, Gold Coast, Robina, Queensland, Australia; y Health and Rehabilitation Research Institute, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand; and z Sound Experience, Mt. Albert, Auckland, New Zealand

(Received 13 August 2015; revised 17 February 2016; in final form 21 February 2016)

Abstract—Ultrasound imaging allows cost effective in vivo analysis for quantifying peripheral nerve excursion. This study used ultrasound imaging to quantify longitudinal radial nerve excursion during various active and passive wrist movements in healthy participants. Frame-by-frame cross-correlation software allowed calculation of nerve excursion from video sequences. The reliability of ultrasound measurement of longitudinal radial nerve excursion was moderate to high (intraclass correlation coefficient range = 0.63-0.86, standard error of measurement 0.19-0.48). Radial nerve excursion ranged from 0.41 to 4.03 mm induced by wrist flexion and 0.28 to 2.91 mm induced by wrist ulnar deviation. No significant difference was seen in radial nerve excursion during either wrist movement (p > 0.05). Wrist movements performed in forearm supination produced larger overall nerve excursion (1.41 ± 0.32 mm) compared with those performed in forearm pronation (1.06 ± 0.31 mm) (p < 0.01). Real-time ultrasound is a reliable, cost-effective, in vivo method for analysis of radial nerve excursion. (E-mail: richard.ellis@ aut.ac.nz) © 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Key Words: Ultrasound imaging, Radial nerve, Nerve mobilisation, Reliability.

INTRODUCTION

Compressive peripheral neuropathies are a common cause of musculoskeletal disorders of the upper limb. The most common peripheral neuropathy is carpal tunnel syndrome (CTS), which has a lifetime risk of approximately 10% (Winterton and Farnell 2013). There is compelling evidence that impaired median nerve excursion (or movement) is an important aetiological factor for CTS (Filius et al. 2013; Hough et al. 2007; Liong et al. 2014). However, links between peripheral neuropathies of the forearm and impaired nerve excursion have yet to be established.

It has been hypothesised that impaired peripheral nerve excursion may also be a factor in other peripheral neuropathies. For example, compression of the radial nerve with associated movement impairment has been implicated in several clinical conditions such as radial tunnel syndrome

Address correspondence to: Richard Ellis, Health and Rehabilitation Research Institute, School of Clinical Sciences, Auckland University of Technology, Private Bag 92006, Auckland 1020, New Zealand. E-mail: richard.ellis@aut.ac.nz

(Cleary 2006), posterior interosseous nerve entrapment (Djurdjevic et al. 2014) and superficial radial nerve compression (Dang and Rodner 2009). The epidemiology for radial nerve compressive neuropathies is uncertain.

It has been suggested that reduced nerve excursion alters nerve function by increasing the neural tension, which may adversely contribute to pain (Dilley et al. 2008; Erel et al. 2003). Therefore, a method to allow quantification of nerve excursion would be of value, particularly for those conditions where nerve excursion is believed to be impaired.

In recent times the resolution and imaging capabilities of diagnostic ultrasound technology has greatly improved (Bianchi 2008). The unique ability of ultrasound imaging (USI) to provide an accurate and cost effective assessment of nerve movement, both real-time and in vivo, has made it a viable and effective tool for imaging in clinical practice (Bianchi 2008; Heinemeyer and Reimers 1999). USI is the preferred method for evaluating peripheral nerve morphology and motion (Tagliafico and Martinoli 2013) and has been shown to assist in the diagnosis of compressive neuropathies (Bargallo et al. 2010; Wiesler et al. 2006). For example,

the use of USI to quantify median nerve excursion has become an integral part of the diagnostic screening for CTS (McDonagh et al. 2015; Uchiyama et al. 2010). The value of USI to assess radial nerve excursion, from a clinical perspective, is yet to be established. The assessment of nerve mechanics using USI offers potential advantages over other forms of static imaging techniques in that it allows a more dynamic and functional clinical assessment for compressive neuropathies.

Several studies have shown USI to reliably quantify excursion for the median (Coppieters et al. 2009), ulnar (Dilley et al. 2007), sciatic (Ellis et al. 2008; Ellis et al. 2012) and tibial nerves (Carroll et al. 2012; Ellis et al. 2008). To date, the majority of research has investigated the median nerve due to its association in common neuropathies (e.g., CTS) and ease of location (Coppieters et al. 2009; Dilley et al. 2003). Although there have been reports of cadaver research to examine radial nerve excursion (Wright et al. 2005), there is a lack of in vivo research investigating excursion of the radial nerve.

USI has been advocated for the diagnostic assessment of CTS (McDonagh et al. 2015; Uchiyama et al. 2010). It is possible that the assessment of radial nerve excursion may also become an important diagnostic tool for conditions where radial nerve dysfunction is perceived. It is important to determine the reliability of USI to examine radial nerve excursion and to establish normative data for radial nerve excursion in healthy populations before its use in clinical populations can be fully realised. Therefore, there were two objectives of the present study. The first was to determine the test-retest reliability of measuring radial nerve excursion. It was hypothesised that the reliability of assessing radial nerve excursion with USI would show similar high levels of reliability as has been seen for other peripheral nerves (e.g., median, sciatic, tibial, etc.). The second objective of this study was to quantify the extent of radial nerve excursion for different combinations of movement at the forearm and wrist using USI.

MATERIALS AND METHODS

Study design

A controlled laboratory cross-sectional study using a single-group, within-participant comparison was utilised for this research.

Participants

Thirty participants were recruited for this study from a population of convenience. Recruitment of participants was conducted through the use of advertisements placed on university student noticeboards and social

media sites. Participants were included if they were healthy individuals aged 18-50 y. Participants were excluded if they had a history of significant/major trauma or surgery to the spine, shoulder, elbow or wrist regions; symptoms consistent with radial nerve impairment (e.g., paraesthesia, weakness, etc.); or a known history of a neurologic disorder or known conditions that may negatively affect the nervous system (such as diabetes melli-tus). Informed consent was obtained from all participants before testing. Ethics approval was provided by the Auckland University of Technology Ethics Committee.

Equipment and procedures

Participant set-up. Participants were positioned in supine with their arm supported by a table adjacent to the plinth with the shoulder in 45° abduction and the elbow held in full extension. The wrist was unsupported, over the edge of the table, to allow full movement at the wrist. Shoulder and elbow position were reassessed between each test condition to ensure no movement of the shoulder and elbow joint had occurred during the testing procedure.

Movements of the wrist were used to induce movement of the radial nerve. Two different forearm positions were used for all of the wrist movements: forearm supination and pronation. These two positions have been suggested to expose the radial nerve to different levels of strain (Nee et al. 2012; Wright et al. 2005), which may, in turn, influence radial nerve excursion. The order of forearm positions (pronation or supination) adopted during testing was determined using a random number generator. In all participants, the right arm was imaged.

Wrist movements performed. Several movements of the wrist were utilised to induce excursion of the radial nerve. All wrist movements were performed both actively (by the participant) and passively (by the research assistant) within the participant's maximum tolerable range of motion (ROM). Using a participant's tolerable ROM has previously been shown to produce reliable results when assessing peripheral nerve excursion (Ellis et al. 2015). Wrist ROM was recorded with an electrogoniom-eter (Penny and Giles, Newport, UK). The electrogoni-ometer was calibrated against a manual goniometer before each testing session at 0° and 45° wrist extension or ulnar deviation (depending on the condition). The use of each participant's maximal tolerable ROM was selected because it offered the potential for greater nerve excursion compared to standardising ROM, as previously shown for assessment of the sciatic nerve (Ellis et al. 2015). All movements were performed in a thermoplastic splint that held the metacarpophalangeal joints in a standardised position of 30° of flexion but allowed free wrist

motion. This method allowed the wrist joint to remain the focus for inducement of radial nerve excursion. The test conditions included forearm pronation: (i) active and passive wrist flexion (maximum tolerable extension to maximum tolerable flexion) and (ii) active and passive wrist ulnar deviation (maximum tolerable radial deviation to maximum tolerable ulnar deviation); and forearm supination: (i) active and passive wrist flexion (maximum tolerable extension to maximum tolerable flexion) and (ii) active and passive wrist ulnar deviation (maximum tolerable radial deviation to maximum tolerable ulnar deviation).

Each movement was performed in a controlled manner over 4 s using a metronome to assist with timing. A single assessor first demonstrated each task, which was followed by a familiarisation trial to allow the participant to practice the movement and correct timing. Randomisation of test conditions minimised the potential for ordered effects. Up to four trials were recorded for each test condition. Two optimal trials were necessary for analysis. An optimal trial required good visualisation of the radial nerve throughout the entire cine-loop during USI assessment. Where more than two optimal trials were available for analysis, two trials were randomly selected for nerve excursion assessment.

Ultrasound imaging and analysis

A sonographer with 10 y of experience in USI (including research involving the assessment of peripheral nerve morphology, nerve excursion and biome-chanics) performed all scans. The sonographer was blinded to all radial nerve excursion measurements both at the time and also for offline assessment. B-mode real-time USI was performed using a Philips iU22 (Philips Medical Systems Co., Eindhoven, The Netherlands) ultrasound machine with a 12-5 MHz, 50-mm, linear array transducer.

The location for radial nerve imaging was selected following pilot testing that identified a suitable location whereby the nerve was visible throughout the full range for each of the test movements. Anatomic variations of the radial nerve, in particular the location of the bifurcation into the superficial and deep branches, have been reported previously in the literature (Benham et al. 2012). To ensure that the radial nerve itself was imaged proximal to the bifurcation, initial identification was made in the transverse plane. Once identified and then viewed in the longitudinal plane, USI recordings of longitudinal radial nerve excursion were collected approximately 1-5 cm proximal to the humeroulnar joint (proximal to the bifurcation) (as seen in Fig. 1).

Each ultrasound cine-loop was divided into successive digital frames. Longitudinal radial nerve excursion was assessed using a method of frame-by-frame cross-cor-

Fig. 1. Scanning position of the radial nerve.

relation analysis performed in MATLAB (MathWorks, Natick, MA, USA) as previously described by Dilley et al. (2001). This method was used to determine the relative movement of grey-scale features (speckle tracking) between successive frames throughout a sequence of ultrasound images (Dilley et al. 2001). Dilley et al. (2001) have successfully established the accuracy of this method of speckle tracking. By in vitro means, they established the accuracy of measuring movement of phantoms (string and avian nerve) over a known and standardised distance with a variation of less than 26 mm.

In regard to the assessment of in vivo nerve movement with USI, this technique of speckle tracking via frame-by-frame cross-correlation analysis has become widely used. Several studies have used this technique and have shown it to be reliable for the assessment of the median (Coppieters et al. 2009), sciatic (Coppieters et al. 2015; Ellis et al. 2008, 2012; Ridehalgh et al. 2012), tibial (Boyd et al. 2012; Boyd and Dilley 2014; Ellis et al. 2008; Shum et al. 2013), common peroneal (Boyd et al. 2012) and posterior tibial (Carroll et al. 2012) nerves. Furthermore, this technique has been validated against median nerve movement assessment, via indwelling nerve markers, from whole-body, embalmed cadavers (Meng et al. 2015).

For the digital frames from each cine-loop, regions of interest were selected within the radial nerve from which pixel movements were tracked (Fig. 2). A correlation coefficient was calculated for each individual pixel shift. The peak of a quadratic equation fitted to the maximum three correlation coefficients is

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a Forearm PRONATION

' Skin

L12-5/MSK Sup

Fig. 2. Ultrasound images of radial nerve in forearm pronation (a) and forearm supination (b). Yellow boxes depict ROIs. White boxes depict pixel movement analysis, over 4 s, using frame-by-frame cross-correlation software (Dilley et al. 2001). ROI = region of interest.

MVIC to indicate passive movement has been previously reported (Reid and McNair 2004).

Each participant's skin was prepared by shaving, skin abrasion and cleaning with isopropyl alcohol in accordance with international EMG recommendations (Hermens et al. 2000). AG/AgCl bipolar surface electrodes (Delsys DE02.3; Delsys Inc., Boston, MA, USA) were placed parallel to the direction of muscle fibres with a reference electrode attached over the ulnar styloid process. The forearm extensor bundle was located dorsally approximately 5 cm distal from the humeroulnar joint, while the forearm flexor bundle was located ventrally approximately 5 cm distal from the humeroul-nar joint (Cram and Criswell 2011). An inter-electrode resistance of less than 10 kU was considered acceptable for recording purposes.

EMG signals were collected at a sampling rate of 1000 Hz and amplified (500 X) using a Bortec Biomedical amplifier (Bortec Biomedical Ltd, Alberta, Canada). The raw EMG data were demeaned, bandpass filtered (10-500 Hz) using a Butterworth 4th order filter. For the two MVIC trials the root mean square (RMS) value of a 2-s epoc of EMG activity for both the wrist extensors and wrist flexors was calculated using LabVIEW software (National Instruments Corporation, Austin, TX, USA). RMS values were also calculated from resting (no muscle activity) EMG data. EMG data during the performance of each subsequent test condition were then normalised (EMGN) to the highest MVIC using the following formula:

%EMGN =

Test condition RMS-Resting RMS MVIC RMS-Resting RMS

equivalent to the pixel shift between adjacent frames (Dilley et al. 2001). Pixel shift measurements from stationary structures (e.g., subcutaneous layers, bones, etc.) were subtracted from the pixel shift measurements of the nerve, therefore eliminating any movement of the ultrasound transducer from the analysis (Dilley et al. 2001; Ellis et al. 2008). A separate assessor to the sonographer analysed all of the excursion data and was blind to all test conditions.

Electromyography (EMG) measurements

Surface EMG recordings were used to quantify the level of activity of the forearm extensor bundle (wrist extensors) and flexor bundle (wrist flexors) during the performance of active movements, and to determine that muscle activation was not evident (<1% maximum voluntary isometric contraction [MVIC]) in those wrist movements considered "passive." The value of <1%

Statistical analysis

All statistical analyses were conducted using Statistical Package for the Social Sciences software (SPSS 20.0, IBM Corp., Armonk, NY, USA) with an alpha level of 0.05. Descriptive statistics expressed as means ± standard deviation (SD) were calculated for participant demographic characteristics and anthropo-metric measures (age, sex, height and body mass). Normality of data was confirmed by performing a Shapiro-Wilk test.

In line with recommendations for ease of comparing USI reliability studies, the following statistics and plots were calculated to determine the test-retest reliability of using USI to assess radial nerve excursion: intra-class correlation coefficients (ICC), standard error of measurement (SEM), minimal detectable change (MDC) and Bland-Altman plots (Whittaker et al. 2007; Whittaker and Stokes 2011).

To establish test-retest reliability of radial nerve excursion measurement, a two-way random, single measure ICC (2,1), with 95% confidence interval was calculated. The strength of agreement has been described as very low if the correlation ranged from 0 to 0.29, a low correlation if 0.30-0.49, a moderate correlation if 0.50-0.69, a high correlation if 0.70-0.89 and a very high correlation if 0.90-1.00 (Munro 2004).

The SEM (SEM = SDpooled X 01 - ICC) was calculated to identify the range of measurement errors between trials (Coppieters et al. 2009). The MDC at the 95% confidence interval (MDC = 1.96 X 02$ SEM) was calculated to identify the degree of change required to exceed trial-to-trial variability (Coppieters et al. 2009). A Bland-Altman plot was developed to graphically demonstrate the reliability between two measurement trials within a selected condition (Bland and Altman 1986).

On confirming data normality, parametric analyses were performed to provide quantitative assessment of radial nerve excursion between the test conditions. Repeated measures analysis of variance with Bonferroni correction was performed to determine mean differences between position (forearm pronation and supination), movements (wrist flexion and wrist ulnar deviation) and contraction types (active and passive).

RESULTS

Participant demographic characteristics

Thirty participants (18 females, 12 males) were recruited into this study. The key demographic data (mean ± SD) included an age of 29.8 ± 8.4 y (range 18.5-48.9 y), height 1.73 ± 0.09 m and mass 70.1 ± 17.1 kg.

Reliability of USI of radial nerve excursion

The reliability of measuring radial nerve excursion ranged from moderate to high depending on the test condition (Table 1). Measurement of radial nerve excursion whilst the forearm was supinated during passive ulnar deviation was the only measure that demonstrated moderate

reliability (ICC = 0.63). The remaining test conditions demonstrated high reliability (ICC = 0.70-0.86) for radial nerve excursion measurement.

The SEM for active and passive movements ranged from 0.16 to 0.30 mm and 0.22 to 0.40 mm, respectively (Table 1). The MDC ranged from 0.44 to 1.11 mm for all conditions (Table 1). A Bland-Altman plot representing within-session analysis of a selected condition (pronation, passive wrist flexion) is shown in Figure 3.

Graphical representation of matched wrist ROM, muscle EMG and radial nerve excursion data is presented for one participant in Figure 4.

Longitudinal radial nerve excursion

Data representing longitudinal nerve excursion are presented in Table 2 and Figure 5. With respect to participant positioning, forearm supination exhibited significantly larger overall nerve excursion (mean ± SD, 1.41 ± 0.32 mm) compared with forearm pronation (1.06 ± 0.31 mm) (p < 0.01). Similarly, passive movements (1.42 ± 0.42) produced significantly greater nerve excursion than active movements (1.04 ± 0.28 mm) (p < 0.01). There was no statistically significant difference in radial nerve excursion between wrist flexion (1.29 ± 0.05 mm) and ulnar deviation (1.19 ± 0.07 mm) (p = 0.20). When observing combined movements, the greatest amount of longitudinal radial nerve excursion occurred in the supinated position during passive wrist flexion (1.78 ± 0.69 mm) and passive wrist ulnar deviatin (1.69 ± 0.66 mm). The smallest amount of radial nerve excursion occurred in pronation during passive wrist ulnar deviation (0.82 ± 0.60 mm).

Forearm muscle activation

During all passive movements, the average muscle activation levels of the wrist flexors and extensors was less than 1% of MVIC (mean ± SD; 0.86% ± 1.05%). Active movements involving wrist flexion elicited the highest EMG recordings in the wrist flexor muscle group. However, these activation levels did not exceed 7.8% of MVIC. Similarly, wrist extensor activation levels were

Table 1. ICC of test-retest reliability for longitudinal nerve movement for each condition

Forearm position Wrist movement ICC 95% CI SEM (mm) MDC (mm)

Pronation Wrist flexion Active 0.72 0.49-0.86 0.19 0.53

Passive 0.77 0.57-0.88 0.48 0.80

Wrist ulnar deviation Active 0.85 0.71-0.93 0.20 0.56

Passive 0.86 0.73-0.93 0.22 0.62

Supination Wrist flexion Active 0.79 0.60-0.88 0.16 0.44

Passive 0.76 0.56-0.88 0.34 0.49

Wrist ulnar deviation Active 0.70 0.46-0.84 0.30 0.84

Passive 0.63 0.36-0.81 0.40 1.11

CI = confidence interval; ICC = intra-class correlation coefficient; MDC = minimal detectable change; SEM = standard error measurement.

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Average (mm)

Fig. 3. Bland-Altman plot (difference vs. average) for all trials consisting of the passive movement of wrist flexion with forearm pronation.

Wrist ROM

Mean ± SD wrist extension to flexion and wrist radial to ulnar deviation ROM (when active and passive ROM data were pooled) were 124° ± 12° and 54° ± 7°, respectively. This is in line with previous research in normative populations (Klum et al. 2012). When comparing the total wrist ROM (mean) between the two forearm positions, there was a difference (p < 0.01) in ROM of 14° ± 7°, with the greatest ROM occurring in the pronated position compared to the supi-nated position. Passive movements elicited significantly greater overall wrist ROM compared with active movements (8° ± 8°, p < 0.01), with the exception of radial to ulnar deviation when in pronation (Table 1).

highest during the performance of active movements but were significantly less than that recorded in the wrist flexors (p < 0.05).

Fig. 4. Matched data for wrist ROM, muscle activation and radial nerve excursion for one participant (n = 1) during passive wrist flexion in supination. A = start of wrist movement, B = end of wrist movement. EMG = electromyography; MVIC = maximum voluntary isometric contraction; ROM = range of motion; RMS = root mean square. (Note: for EMG data, 50 ms RMS epochs of EMG expressed as percentage of MVIC.)

DISCUSSION

The primary objective of this study was to establish the reliability of using USI to measure longitudinal radial nerve excursion. Our main finding indicates that the USI technique used in the present study had a moderate to high level of reliability (ICC = 0.63-0.86) for quantifying longitudinal radial nerve excursion. This finding is similar to many studies that have examined longitudinal nerve movement using the same techniques. Reliability for assessing in vivo longitudinal nerve movement using USI has been reported as high for the sciatic (Ellis et al. 2008) and tibial (Boyd and Dilley 2014; Shum et al. 2013) nerves and very high for the median (Coppieters et al. 2009), sciatic (Coppieters et al. 2015; Ellis et al. 2012; Ridehalgh et al. 2012), tibial (Boyd et al. 2012) and posterior tibial (Carroll et al. 2012) nerves. The present study is the first to present findings for in vivo assessment of radial nerve excursion using USI.

A secondary objective of this study was to quantify longitudinal excursion of the radial nerve induced by wrist movements. Furthermore, there was interest in assessing the impact of participant positioning (forearm pronation and supination) and muscle contraction type (active and passive wrist movements) upon the range of radial nerve excursion.

With respect to the forearm position, there was significantly greater radial nerve excursion induced by movements performed in supination compared to pronation. These differences may be partly explained by greater tension imposed on the radial nerve when the forearm is pronated. Although the present study did not examine radial nerve tension or strain, cadaver research has shown that the radial nerve is exposed to greater strain when the forearm is pronated (Wright et al. 2005). Furthermore, in vivo studies of other peripheral nerves have reported a reduction in nerve excursion when nerve tracts are exposed to greater strain (Coppieters and Butler

Table 2. Amount of radial nerve movement (mm) measured for each condition (wrist range of motion (°) recorded throughout

each movement)

Radial nerve Radial nerve excursion Wrist ROM, degrees

Forearm position Wrist movement excursion, mm (mean ± SD) range, mm (mean ± SD)

Pronation Wrist flexion Active 0.93 (0.36) 0.43-1.95 132 (11)

Passive 1.42 (0.60) 0.41-2.90 141 (15)

Wrist ulnar deviation Active 1.07 (0.52) 0.34-2.07 59 (9)

Passive 0.82 (0.60) 0.28-2.89 56 (8)

Supination Wrist flexion Active 1.01 (0.35) 0.49-1.85 107 (19)

Passive 1.78 (0.69) 0.68-4.03 122 (16)

Wrist ulnar deviation Active 1.16(0.55) 0.49-2.70 48 (8)

Passive 1.69 (0.66) 0.65-2.91 55 (8)

ROM = range of motion; SD = standard deviation.

2008; Dilley et al. 2003; Dilley et al. 2007). Therefore, it was not an unexpected finding that the lowest levels of radial nerve excursion occurred when the forearm was pronated.

No difference was seen in radial nerve excursion when induced via wrist flexion compared to wrist ulnar deviation despite the fact that there was a significant difference in wrist ROM between the different movements. These findings are in agreement with Wright et al. (2005), who demonstrated similar levels of radial nerve excursion during wrist flexion and ulnar deviation proximal to the elbow joint in vitro.

The literature that has investigated the effect of active and passive movements and the influence of muscle activity upon nerve excursion is limited. Passive test EMG data would indicate that the wrist extensor and flexor muscles on average activated at less than 1% of MVIC, and therefore it could be assumed that radial nerve techniques performed by a skilled therapist are indeed passive. The significantly reduced nerve excursion demonstrated with active movement may be attributed to the resultant change in physical shape of the forearm muscles during an active contraction or the reduced range of motion observed during active

Active Passive Active Passive Active Passive Active Passive Wrist Wrist Ulnar Ulnar Wrist Wrist Ulnar Ulnar Flexion Flexion Dev. Dev. Flexion Flexion Dev. Dev.

Fig. 5. Comparison of radial nerve movement for each condition (with 95% confidence interval bars included).

testing. This acute physical change of the forearm musculature may indirectly influence the pathway of the radial nerve as it is repositioned in relation to the contracted muscle thereby reducing potential nerve excursion. Whether activation levels as low as those recorded during the active test in the present study induce these morphologic changes warrants further research. It would therefore be more likely that the increased wrist ROM observed in the passive testing permitted greater excursion of the nerve.

The findings in regard to the amount of radial nerve excursion seen between the different conditions may have important clinical implications. For example, neural mobilisation exercises have been advocated for conditions where impaired nerve movement is perceived (Coppieters et al. 2009; Ellis et al. 2012). For neural mobilisation exercises that clinicians prescribe to induce or encourage radial nerve excursion, decisions could be made in regard to the design of exercises based on the findings of this study. For example, if radial nerve excursion was to be maximised, the clinician should consider performing passive movements of the wrist in a supinated forearm position. Selection of which wrist movement to utilise (i.e., wrist flexion or ulnar deviation) could be made based on the functional limitation of the patient as radial nerve movement induced appears to be similar. Although relevant to consider, it must be noted that this study was conducted within a healthy cohort. The possibility remains that these interpretations may not be consistent in a clinical population. This warrants further investigation.

A number of steps were implemented to improve methodological quality while reducing potential sources of bias. First, randomisation of tasks was utilised, which is believed to reduce the learning effect of improved scanning that has been shown to occur in USI studies (Ridehalgh et al. 2012). Following completion of each condition, the shoulder and elbow positions were reassessed, with goniometry, to ensure consistent participant set-up. The sonographer was blinded to all USI

measurements, thereby reducing error bias (Ellis and Hing 2008). Data analysis was performed with the assessor blinded to participant and testing conditions to reduce the level of confounders related to assessor recollection (Ellis et al. 2008, 2012).

It is important to reflect upon the fact that the sample recruited consisted of healthy individuals; therefore, the results may not be confidently extrapolated to clinical populations. However, clinical implications can be drawn from this research as the examined USI technique demonstrated moderate to high levels of reliability when examining radial nerve excursion. This finding could be of value for the assessment of clinical conditions such as radial tunnel syndrome (Cleary 2006), posterior inteross-eous nerve entrapment (Djurdjevic et al. 2014) and superficial radial nerve compression (Dang and Rodner 2009), where radial nerve excursion is perceived to be impaired. This has been the case for the importance of including the USI assessment of the median nerve in CTS (McDonagh et al. 2015; Uchiyama et al. 2010). Furthermore, the establishment of normative values of radial nerve excursion may shed light on eliminating radial nerve dysfunction for conditions that may clinically mimic this, for example C6 radiculopathy and/or radicular pain. Finally, the observed differences in radial nerve excursion during the varied test conditions (i.e., active vs. passive exercises, wrist flexion vs. ulnar deviation and pronation vs. supination) can be taken into account for the design and selection of neural mobilisation exercises.

CONCLUSIONS

The findings of the present study provide evidence that USI and frame-by-frame cross-correlation analysis techniques used in the present study to quantify longitudinal radial nerve excursion has moderate to high reliability. Wrist movements that were performed passively were shown to produce significantly greater radial nerve excursion than those using active movement. Furthermore, wrist movements when performed in forearm supination produced significantly greater nerve excursion than when placed in pronation. No significant differences in radial nerve excursion were demonstrated between the movements of wrist flexion and ulnar deviation. This research provides insight into the typical range of radial nerve excursion in healthy people. This has the potential to inform future research that examines clinical conditions where radial nerve excursion may be impaired.

Acknowledgments—The authors would like to thank Edel Kelly for providing hand splints to assist with testing and to all participants for donating their time.

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