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Assessment of the cervical spine mobility by immersive and non-immersive virtual reality
Journal of Electromyography and Kinesiology 51 (2020) 102397
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Assessment of the cervical spine mobility by immersive and non-immersive virtual reality
T
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Pawel Kipera, , Alfonc Babab, Mahmoud Alheloua, Giorgia Pregnolatoa, Lorenza Maistrelloa, ⁎ Michela Agostinia, Andrea Turollaa, a b
Laboratory of Neurorehabilitation Technologies, San Camillo IRCCS srl, via Alberoni 70, 30126 Venice, Italy Rehabilitation Unit, Azienda Ospedaliera Universitaria di Padova, Padova, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Virtual reality Motion analysis Range of motion Cervical spine Validity
Introduction: Despite many devices are helpful for motion analysis, there is still no established standard technique for the assessment of cervical spine mobility. Objective: To compare differences in using immersive or non-immersive virtual reality (VR) for the assessment of the sensorimotor movement of the cervical spine in healthy subjects. Methods: Thirty-five healthy adults were asked to perform head rotation, flexion, extension, lateral flexion, reaching and repositioning tasks with the head. The same tasks were performed interacting with both nonimmersive and immersive virtual reality. Random sequence determined which of the environments was used as first assessment. Range of motion and kinematics i.e. number of completed targets, time of execution (seconds), spatial length (cm), angle distance (°), jerk of the cervical spine, were automatically computed by a 6D electromagnetic motion tracking system. Results: The following variables were significantly larger in immersive than non-immersive VR: head right rotation (p = 0.027), extension (p = 0.047), flexion (p = 0.000), time (p = 0.001), spatial length (p = 0.004), jerk target (p = 0.032), trajectory repositioning (p = 0.003), jerk target repositioning (p = 0.007). A regression model showed that assessment in both VR environments can be influenced by dependent and independent variables. Conclusions: Immersive VR provided more accurate measurement of cervical spine than non-immersive VR in healthy adults.
1. Introduction The cervical spine can be considered as a functional “dynamic bridge” composed by seven vertebrae, thirty-seven distinct joints and thirty pairs of muscles that cooperate with the aim to orient the head smoothly in three dimensional space by controlling 600 movements per hour on average (Neumann, 2010). Spatial orientation of the head depends both on cervical spine mobility and integration of inputs coming from the somatosensory and the vestibular systems. These two systems are essential to detect correct head positioning and body movements, which are vital to keep the correct posture and coordinate head-eye movements (Neumann, 2010). Thus, integrity of the cervical spine is essential for balance of the whole body and spatial exploration with the upper limb. Impairment of cervical spine mobility is often related with neck pain (Sarig-Bahat et al., 2010). Therefore, instrumental measurement
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methods may provide the potential for better accurate clinical outcomes for the optimal assessment of physiotherapy modalities. Many studies reported an instrumental approach to measure the range of motion (ROM) of cervical spine mobility. These measures have been acquired by means of a variety of technologies including eye-balling (Morphett et al., 2003), dynamic radiographs (Simpson et al., 2008), goniometers and inclinometers (both digital or analogue) (Sjolander et al., 2008), functional computed tomography (fCT) (Laker and Concannon, 2011), ultrasonic (Dvir and Prushansky, 2000), optic (Marcotte et al., 2002) and electromagnetic motion tracking systems for three-dimensional (3D) analyses combined or not with both immersive and non-immersive virtual reality environments (Jordan et al., 2000). Although use of several devices can be helpful for motion analysis, there is still no established “gold standard technique” for the assessment of cervical spine mobility. To date, goniometers and inclinometers are the most popular devices used in clinical settings. Nevertheless, these technologies
Corresponding authors at: San Camillo IRCCS srl, via Alberoni 70, 30126 Venezia, Italy. E-mail addresses: [email protected] (P. Kiper), [email protected] (A. Turolla).
https://doi.org/10.1016/j.jelekin.2020.102397 Received 23 January 2019; Received in revised form 20 January 2020; Accepted 22 January 2020 1050-6411/ © 2020 Elsevier Ltd. All rights reserved.
Journal of Electromyography and Kinesiology 51 (2020) 102397
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2.2. Experimental procedure
usually allow measurement of no more than two-dimensional ROM quotes in static conditions (Michiels et al., 2013; Voss et al., 2012). Conversely, the wide use of motion tracking systems able to measure 3 to 6 degrees of freedom dynamically, while performing standard ROM assessment, are limited due to technical complexities in preparing the setup and high costs. Thus, motion tracking systems are primarily used in research. Recently, the use of virtual environments has emerged as a promising methodology to detect valid measures of neck mobility (Gelalis et al., 2009; Sarig-Bahat et al., 2010). Virtual reality (VR) is defined as a high-end user-computer interface that involves real-time simulation and interactions through multiple sensorial channels (Burdea and Coiffet, 2003). VR can be also defined as a mental experience able to provide to the subject a sense of “being” in a virtual world (i.e. presence) (Luque-Moreno et al., 2016). Importantly, the sense of “presence” is dynamic, so the subject can react and change instantly his/her motor behavior. To date, there are several technologies allowing mediation of the visual interaction, each of them provide a different level of VR interaction and, thus, a variable degree of immersion. What determines the sense of presence in VR is the level of immersion provided, which in turn depends on the system used. Depending on systems’ characteristics virtual reality can be divided into three main categories like; immersive (i.e. head mounted displays – HMD) (Xu et al., 2015), non-immersive (i.e. monitors or wall projections to display 3D images on a 2D surface) (Kiper et al., 2018) and augmented (i.e. enhanced perception of reality mixed with computer-generated sensory input) (Kiper et al., 2013; Laver et al., 2017). Non-immersive VR has been already used for the assessment of the cervical ROM in several studies (Sarig Bahat et al., 2016). Sarig-Bahat et al. compared nonimmersive VR with conventional measures for the assessment of the cervical ROM. The results of this study suggest that non-immersive VR is more repeatable and sensible than conventional assessment, due to the possibility to embed innovative electromagnetic motion tracking technologies (Sarig-Bahat et al., 2009). Whereas, the cervical spine mobility was tested in immersive VR through HMD device (Oculus Rift), showing accuracy in displaying virtual environments and its potential for future clinical use (Xu et al., 2015; Chen et al., 2015). Nevertheless, detection of kinematic parameters is still poor. The possibility to merge different VR modalities (e.g. immersive and non-immersive) may have great potential for cervical spine assessment, but the overall accuracy of such embedding need to be studied thoroughly. The aim of this study was to test the reliability of a protocol for the assessment of the sensorimotor movement of the cervical spine in a population of healthy subjects and to compare whether using immersive or non-immersive VR for visualization affects the performance.
At first instance, the study purpose and setting were explained and consent to participate was obtained from all the participants, and then a diagnostic interview was carried out to check the existence of possible exclusion criteria. Afterwards, the experimental setup was equipped on the subject and a quick warm-up of the virtual scenarios was conducted before ROM assessment to make the subject familiar with the experimental setting. Assessment within non-immersive environment was performed using the Virtual Reality Rehabilitation System – VRRS® (Khymeia Group, Noventa Padovana, Italy), which consisted of a computer connected to a 3D motion–tracking system (Polhemus G4® Colchester, Vermont, US) and a high–resolution LCD monitor to display the virtual scenarios. The whole protocol was implemented like a customized unique application to be run within the VRRS® environment. The electromagnetic sensor for the head was fixed on a sizeable helmet wore by the subject to be placed on the apex. While, the sternum sensor was fixed on a suitable vest wore by the participant to stay at the level of the third sternocostal joint. Whereas, for assessment within immersive VR the Oculus Rift (Oculus VR Inc, Irvine, CA, US) was used as HMD to visualize the same VRRS scenarios. The same motion tracking system was used to detect kinematics of the cervical spine regardless of the VR environment administered. All participants underwent both immersive (VRRS displayed through Oculus Rift) and non-immersive (VRRS displayed on computer screen) assessment. To minimize biases related to tasks familiarization a simple random sequence was used to allocate the first environment (i.e. immersive or non-immersive) to be assigned for each subject. 2.3. Outcome measures During assessment, the subjects were seated with their back supported and hips and knees flexed to 90°, the hands were placed on the knees and feet resting on the ground (Fig. 1). Prior to assessment, each participant had the possibility to familiarize themselves with the system by performing 2–3 attempts. At the beginning of each test, participants were instructed to place their head in neutral position, which was set as the absolute reference position for that task. Both assessments consisted of the same five exercises, but displayed in immersive or non-immersive environment, accordingly (Figs. 2 and 3, respectively). The whole protocol consisted of 46 tasks grouped in the following tests:
• head rotation (5 repetitions to the right and 5 to the left) – parti-
2. Material and methods
• • •
2.1. Participants Thirty-five asymptomatic volunteers were recruited according to the following exclusion criteria: self-reported history of neck trauma, spinal surgery, primary pathologies of the spine (e.g. instability, stenosis), neuromuscular diseases, diagnosed fibromyalgia, migraine, neoplasms, vestibular disorders, pregnancy, and depression. All volunteers were assessed by the same assessor and were asked to fill a case report form with additional information required such as; handedness, presence of scoliosis, neck fatigue in the last three hours, visual deficit (e.g. myopia, astigmatism, hyperopia, presbyopia), missed diopters. Informed consent was obtained from all healthy volunteers who participated in this study. This study enrolled only healthy volunteers for clinical assessment without therapeutic, physical or mental interference. The procedures of enrolment and data acquisition/storing were executed in accordance with the ethical standards on human experimentation and in accordance with the Declaration of Helsinki 1975, revised Hong Kong 1989.
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cipants were asked to rotate the head from the neutral position without trunk movement. head flexion and extension (5 flexions and 5 extensions) – participants were asked to execute maximal head flexion and extension, head lateral flexion (5 right and 5 left side bending) – participants were asked to touch the shoulder with the ear, reaching (head movements toward 8 targets placed along a circular perimeter each 45°, visualized one-by-one in random order) – participants were asked to reach eight targets overall, which were visualised randomly (in a form of 3D cube) in the following positions; 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°. repositioning (head movement toward the same 8 targets as for reaching and following reposition to the start point) – participants were asked to reach eight targets overall, which were visualised randomly (in a form of 3D cube) in the following positions; 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, and return to the starting point as close as possible without its visualization.
Kinematic parameters were automatically computed by the VRRS and the following were considered for the analyses: maximum and average degree of ROM of each exercise repetitions (i.e. rotation, 2
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Fig. 1. Setting for the cervical spine assessment. 1 – assessment in immersive VR (Oculus Rift), 2 – graphical setting, 3 – assessment in non-immersive VR (VRRS), 4 and 5 – position for assessment in immersive VR, 6 and 7 – position for assessment in non-immersive VR.
flexion, extension, lateral flexion); number of completed targets (No), time of execution (seconds) – i.e. mean duration of task, spatial length (mm) – i.e. path length of a 1000 mm pointer moving from head, angle distance (°) – i.e. angular repositioning distance, and jerk for reaching and repositioning tasks (s2) – i.e. jerk smoothness parameter.
were statistically different in the comparison between measurements in immersive or non-immersive VR. Finally, a multivariate linear regression model was used to infer any potential relationship between dependent and independent variables, moreover the values of variance explained for each outcome survived in the model were considered as measures of reliability. Statistical significance was set at p < 0.05 and RStudio package software was used for the analyses.
2.4. Statistical analysis
3. Results
The distribution skewness was studied with the Shapiro-Wilk test and according to the results parametric (t-student) or non-parametric (Wilcoxon-Mann-Whitney) tests were used to determine if the outcomes
Thirty-five healthy participants (i.e. 14 males and 21 females) were
Fig. 2. Assessment in non-immersive VR environment through VRRS. (A) Head rotation, (B) head lateral flexion, (C) head flexion, (D) head extension, (E) reaching, (F) repositioning. 3
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Fig. 3. Assessment in immersive VR environment through Oculus Rift. (A) Head rotation, (B) head lateral flexion, (C) head flexion, (D) head extension, (E) reaching, (F) repositioning.
explained of goodness-of-fit analysis) in relation to results obtained from regression model. The regression models are presented in Table 3 and Table 4. Moreover, we analyzed which one of the parameters had a highest weight (positive or negative) on each single regression model. In depth, in immersive VR the jerk target (1.35) and jerk target repositioning (−1.18) had a highest positive and negative weight for right rotation, respectively. Further, the right rotation (0.73) showed highest weight for left rotation parameter. Flexion (0.57) and spatial length (−0.46) resulted with the highest positive and negative weight for right lateral flexion, respectively. Whereas, for left lateral flexion, parameters like head flexion (0.41, highest positive) and jerk target repositioning (−0.35, highest negative) showed the highest weight. The jerk target repositioning (1.18) had the highest positive weight and fatigue (−1.15) activity in last 3 h had the highest negative weight for regression model of head extension. Whereas, for head flexion, the right rotation (0.63, highest positive) and sex (i.e. man; −0.51, highest negative) resulted with the highest weight. Kinematics parameters were also affected by both demographic and kinematic variables in immersive VR and were as follows: for time of task execution, the highest weight on regression model were observed by jerk target repositioning (1.13, highest positive) and fatigue (−0.71, highest negative) activity in last 3 h. For spatial length, the trajectory repositioning (0.42) and height (−0.34) had highest positive and negative weight on regression model, respectively. For jerk target, the highest weight on regression model had jerk target repositioning (0.89, highest positive) and body weight (−0.32, highest negative). For angle distance, the right handedness (−1.33) had highest negative weight on regression model. For trajectory repositioning, only the spatial length (0.40) resulted with highest positive weight. Finally, for jerk target repositioning, the highest weight on regression model had jerk target repositioning (0.57, highest positive) and body mass index (−0.55, highest negative). In non-immersive VR the following variables had positive highest weight on regression models: for head right rotation it was a left
Table 1 Demographic characteristics of participants at baseline. Demographic values
Mean
SD
Median
CI 95%
Age Height Weight
28.97 1.72 67.54
7.43 0.09 14.38
27.0 1.7 65.0
26.85 – 31.09 1.03 – 1.45 63.43 – 71.65
Data are displayed as mean, standard deviation (SD), median and 95% confidence interval (CI).
recruited for the cervical spine assessment within immersive and nonimmersive VR environments. The demographic characteristics of the sample are reported in Table 1. The age range was 20 to 60 years (i.e. 20–30 = 24 subjects; 30–40 = 7 subjects; 40–50 = 3; 50–60 = 1). None of the participants reported cervical trauma, migraine, neoplasms, spinal surgery, cervical disorders, neuromuscular diseases, eye disorders, vascular disorders, pregnancy, or psychiatric pathologies. Furthermore, six volunteers were left-handed, twenty reported visual defects, and five presented mild scoliosis. The kinematics results obtained from the assessment protocol are reported in Table 2. Comparison analysis demonstrated that head right rotation, extension and flexion, as well as time, spatial length, jerk target, trajectory repositioning and jerk target repositioning differed significantly, and were better for immersive than non-immersive VR assessment. The multivariate linear regression showed that such results were determined by the following characteristics: sex, age, height, weight, body mass index, scoliosis, profession (i.e. sedentary or non-sedentary), visual deficit, handedness, fatigue activity in last 3 h, kind of virtual environment (i.e. immersive or non-immersive), head rotation, flexion, extension and lateral flexion, as well as time of execution, spatial length, angle distance, jerk for reaching and repositioning tasks. Further the reliability of assessment protocol showed moderate to high tested reliability of the explained variance (presented as the variance 4
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Table 2 Results of the cervical spine assessment in different VR environments. Kinematic parameters
Immersive VR
Right rotation (°) Left rotation (°) Right lateral flexion (°) Left lateral flexion (°) Extension (°) Flexion (°) Time (s) Spatial length (mm) Jerk Target (s2) Angle distance (°) Trajectory repositioning (mm) Jerk target repositioning (s2)
Non-immersive VR
Mean
SD
CI 95%
Mean
SD
CI 95%
Δ
p-value
74.66 73.66 38.37 39.62 59.23 66.67 18.21 10,470 58.51 2.88 10,340 47.77
10.90 11.42 8.33 6.49 11.00 10.18 5.26 1303.39 31.72 1.16 1986.45 29.32
71.55–77.78 70.40–76.93 35.98–40.75 37.76–41.47 56.08–62.37 63.76–69.58 16.70–19.71 10101.9–10846.9 49.44–67.57 2.54–3.20 9776.89–10912.42 39.39–56.14
72.13 73.66 38.49 40.65 56.83 60.89 19.79 9852 48.39 3.16 9969 38.53
7.88 8.03 6.27 5.43 11.04 8.90 18.80 769.57 24.71 1.91 782.11 23.90
69.88–74.38 71.37–75.96 36.70–40.28 39.10–42.20 53.67–59.98 58.34–63.43 18.26–21.31 9632.3–10072.2 41.32–55.45 2.61–3.70 9745.70–10192.79 31.70–45.36
2.530 −0.001 −0.124 −1.032 2.401 5.786 −1.577 622.192 10.121 −0.281 375.411 9.238
0.027* 0.999 0.910 0.291 0.047* 0.001* 0.001* 0.004* 0.032* 0.903 0.003* 0.007*
Data are displayed as mean, standard deviation (SD) and 95% confidence interval (CI). The Wilcoxon-Mann-Whitney test or t-student test were used for comparison between measurement modalities (* p < 0.05). (°) – degree, (s) – seconds, (No) – number, (mm) – millimetres, (s2) – second squared. Table 3 Relationship between qualitative and quantitative parameters in immersive virtual environment. Parameters
Regression model
% Variance explained
p-value
Right rotation
65.4 + 10.52*Sex: M - 0.54*Age + 0.53*Weight - 2.93*BMI −4.5*Visual Deficit: yes − 8.09*Non-Immersive VR + 0.32*Flexion + 0.81* Left rotation - 0.29* Right lateral flexion – 0.98*Time + 0.45*Jerk Target – 0.43* Jerk target repositioning −11 + 0.79* Right rotation + 0.28*Extension + 0.45* Time 50.36–8.9*Scoliosis: yes − 5.33*Profession: sedentary − 0.35*Age + 0.49*Flexion − 0.003* Spatial length 25.69 + 0.27*Flexion – 0.08* Jerk target repositioning 123.89 + Sex: M − 0.91*Age − 1.82*BMI − 41.33*Fatigue: yes − 10.84*Non-Immersive RV −10.02* Right rotation + 1.09* Left rotation + 0.56*Flexion − 1.08*Time − 0.002* Spatial length + 0.38*Jerk Target − 64–10.32*Sex: M + 0.74*Age + 1.01*BMI + 5.62*Profession: Sedentary + 13.56*Scoliosis: yes + 0.56* Right rotation + 0.59* Right lateral flexion + 0.002* Spatial length 20.18 + 3.8*Sex: M − 0.59*BMI −12.19*Fatigue: yes − 2.31*Non-Immersive VR + 0.29* Left rotation − 0.11*Extension − 0.17* Right rotation + 0.17*Jerk Target 16530–5187* Height + 0.28* Trajectory repositioning − 22.48–0.73*Weight + 2.82*BMI + 7.64*Visual Deficit: yes + 0.48* Right lateral flexion − 0.39*Extension + 1.17*Time + 0.96* Jerk target repositioning 3.58 – 3.40* Handedness 3912.49 + 0.61* Spatial length 40.15 + 1.2*Weight − 5.12*BMI − 17.35*Scoliosis: yes + 38.97*Fatigue: yes − 0.69* Right lateral flexion + 0.44*Extension + 0.69*Jerk Target
0.925
0.359
0.83 0.62
0.763 0.904
0.30 0.76
0.165 0.599
0.84
0.880
0.87
0.487
0.28 0.98
0.050 * 0.296
0.28 0.16 0.97
0.030* 0.050* 0.225
Left rotation Right lateral flexion Left lateral flexion Extension
Flexion Time
Spatial length Jerk Target Angle distance Trajectory repositioning Jerk target repositioning
The outcomes are displayed with equation of the regression models. The Normality test was applied on model’s residuals and significance was established at p < 0.05*. Sex (M/F) = male/female, Profession (S/NS) = sedentary/non-sedentary, Handedness (R/L) = right/left. Table 4 Relationship between qualitative and quantitative parameters in non-immersive virtual environment. Parameters
Regression model
% Variance explained
p-value
Right rotation Left rotation Right lateral flexion Left lateral flexion Extension Flexion Time Spatial length
17.7 + 0.76* Left rotation + 0.15*Jerk Target −0.23* Jerk target repositioning 2.79 + 0.79* Right rotation + 0.27* Extension – 0.24*Jerk Target + 0.26* Jerk target repositioning 07.95 + 0.75 * Left lateral flexion 18.98 + 0.56* Right lateral flexion 10.02 + 0.72* Right lateral flexion + 0.93*Time 29.88 + 0.29*Age + 0.31* Right rotation 26.35 + 0.27* Left rotation −0.28* Right lateral flexion −0.002* Spatial length + 0.08*Jerk Target 3035.76–40.48*Age + 18.39*Weight − 62.39* Left lateral flexion + 44.43* Right lateral flexion + 0.76* Trajectory repositioning −4.39 + 0.37*Extension + 0.84* Jerk target repositioning 2.83 + 4.46*Scoliosis: yes − 3.66*Fatigue: yes 8638.11 + 45.95*Age −84.06 + 53.88*Height + 32.65*Fatigue: yes + 0.56*Jerk Target
0.66 0.76 0.42 0.42 0.97 0.98 0.48 0.61
0.839 0.947 0.848 0.694 0.175 0.307 0.113 0.040*
0.72 0.31 0.16 0.82
0.002* 0.050 0.143 0.002*
Jerk Target Angle distance Trajectory repositioning Jerk target repositioning
The outcomes are displayed with equation of the regression models. The Normality test was applied on model’s residuals and significance was established at p < 0.05*. Sex (M/F) = male/female, Profession (S/NS) = sedentary/non-sedentary, Handedness (R/L) = right/left.
5
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which is correlated with physical activity, is important and can have negative side effects if not controlled. The analyzed parameters were also influenced positively or negatively by kinematic variables within regression models, and this relationship depends on its correlation. For example, left rotation influenced positively right rotation and vice versa. Finally, non-immersive VR resulted negatively influenced right head rotation, extension, and time of task execution in the regression model of the immersive VR. These findings confirm results obtained from comparison of both groups where the immersive VR resulted more accurate for cervical spine assessment. In the non-immersive VR the regression models showed slightly different results, i.e. mostly kinematics affected the parameters that were analyzed. Moreover, in non-immersive VR height resulted to affect kinematics positively (i.e. taller subjects). This could be due to the fact that non-immersive VR is presented on computer screen providing some limitation of a visual field. Further, the presence of scoliosis affected negatively cervical kinematics (i.e. angle distance). At the end also in the non-immersive VR, age resulted to be a factor that can influence cervical kinematics (i.e. flexion, spatial length, trajectory repositioning). The use of electromagnetic tracking systems was developed to assess cervical ROM and kinematics under conditions that are similar to daily life function. Moreover, development of the bioengineering field has provided possibilities to create artificial 3D environments similar to the real one. Such development has promoted new clinical methods based on VR, providing new insights on human kinematics. The assessment and treatment of cervical spine became very important in designing rehabilitation programmes for patients with neck pain. In the last 20 years various similar measuring methods have been used, however the accuracy measurement level between those methods differ substantially (Michiels et al., 2013). So, a reliable and valid method seems to be essential for accurate clinical diagnostic. Several studies showed that VR-based assessment was found to be more precise than the conventional evaluation (Sarig Bahat et al., 2010). Motion tracking systems showed also inter- and intra-tester reliability (Sarig-Bahat et al., 2009). Further, in this study we found that immersion has an impact on results, as well. Thus, the assessment with different sensory conditions that are created in VR environment may influence more accurately cervical motion diagnostic. In addition, some studies reported effectiveness of VR in neck pain treatment and provided evidence of improvement in velocity and smoothness of the cervical motion which were restricted due to the neck pain (Sarig Bahat et al., 2015). The kinematic assessment is undoubtedly needed in clinical setting for precise and accurate diagnostic, and the immersive VR should be considered for future evaluation in this field.
rotation (0.77); for head left rotation it was a jerk target repositioning (0.83); for right lateral flexion it was a left lateral flexion (0.65); for left lateral flexion it was a right lateral flexion (0.56); for head extension it was a time (0.44); for head flexion it was age (0.26) and right rotation (0.26); for time it was a left rotation (0.41); for spatial length it was a spatial length (0.77); for jerk target it was a jerk target repositioning (0.82); for angle distance it was a scoliosis (0.83); for trajectory repositioning it was age (0.44), and for jerk target repositioning it was a jerk target (0.58). Whereas, the highest negative weight on regression models was not observed in all analyzed variables, as follows: for head right rotation it was a jerk target repositioning (−0.71); for head left rotation it was a jerk target (−0.77); for time it was a right lateral flexion (−0.34); for spatial length it was a left lateral flexion (−0.44) and for angle distance it was a fatigue activity in last 3 h (−0.55). 4. Discussion This study compared two novel VR-based methods used for cervical spine ROM assessment. The results of this study showed significant difference between evaluations executed in two different VR environments. Importantly, this variation was observed between sensorimotor movements and biomechanical parameters. For example, the same task executed in immersive VR resulted in longer trajectory for head flexion, extension, and right rotation than in non-immersive VR. This could be due to both VR immersion (i.e. the same task visualization but devoid of external stimuli) and limits in ROM acquisition, created for example by the perception of several sensors placed on the helmet and participant’s body. Apart from ROM trajectory, also overall spatial length resulted longer in immersive VR. However, longer trajectory was not related with time of task execution needed for its completion in immersive VR. The time of task execution resulted inferior in immersive VR and significantly different between VR environments. Further, the trajectory repositioning, jerk target, and jerk target repositioning showed higher accuracy in immersive VR. This could be due to the fact that immersive VR is able to create a high level of real world simulation by producing a 3D computer-generated environment. Thus, in the immersive VR the user is completely isolated from the surrounding environment and this high level of immersion is devoid of visual references (except those created by a software), which, in turn, are vital for good integration between the cervical spine, somatosensory and vestibular systems. Therefore, the modality in which the test is administered can affect significantly the dynamic performance related to the head movement control and its orientation in the space. Furthermore, the multivariate linear regression analysis showed that independent and dependent variables like sex, age, height, weight, body mass index (BMI), scoliosis, profession (i.e. sedentary or non-sedentary), visual deficit, handedness, fatigue activity in last 3 h and kind of virtual environment (i.e. immersive or non-immersive) can affect kinematic parameters in immersive VR environment. In depth, male gender seems to affect positively right head rotation, extension and time of task execution, and negatively head flexion. Participants’ age affected negatively analyzed variables except head flexion, which can be correlated with modification of the body posture in adult age. Presence of scoliosis reduced right lateral flexion and jerk target repositioning but increased head flexion. This could be due to permanently structured and incorrect trunk position. Professions related to the sedentary work influenced negatively kinematics of right head rotation and augmented head flexion. Thus, possibly frequent position changing in non-sedentary professions can help to keep higher level of cervical motion. Visual deficit influenced negatively right head rotation and jerk target, which could be associated with visual perturbations that creates immersive VR as well as with limitations of visual field. Whereas, BMI affected negatively right head rotation, extension, flexion, time of task execution and jerk target repositioning but affected positively jerk target. These results show that adequate level of BMI,
4.1. Limitations of the study and further research perspectives. Some limitations need to be addressed. Firstly, the number of righthanded subjects were superior to the subjects with left hand dominance. Thus, its comparison can provide different effect related to handedness. Secondly, the study lacks of the comparison with subjects affected by cervical spine pathologies. However, this population has been included in the ongoing trial. Thirdly, mainly young subjects were assessed in the study, thus a wider sample should be considered, consisting of individuals from multiple age groups. Fourthly, this study did not analyze the level of scoliosis of each single participant, but only its incidence was reported. This should be taken into consideration for future studies. Finally, the history of neck trauma, spinal surgery, primary pathologies of the spine, neuromuscular diseases, diagnosed fibromyalgia, migraine, neoplasms, vestibular disorders, pregnancy, and depression were self-reported by a healthy volunteers. 5. Conclusion Both immersive and non-immersive VR are useful for cervical spine 6
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assessment. The immersive VR provide more accurately measure of cervical spine ROM and kinematics, than non-immersive VR. The diagnostic potential of immersive VR should be confirmed in future research considering subjects affected by cervical spine pathologies.
and other imaging modalities for neck pain. Phys. Med. Rehabil. Clin. N Am. 22 (3), 411–428. Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M., 2017. Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. 11, CD008349. Luque-Moreno, C., Oliva-Pascual-Vaca, A., Kiper, P., Rodriguez-Blanco, C., Agostini, M., Turolla, A., 2016. Virtual Reality to Assess and Treat Lower Extremity Disorders in Post-stroke Patients. Methods Inf. Med. 55 (1), 89–92. Marcotte, J., Normand, M.C., Black, P., 2002. The kinematics of motion palpation and its effect on the reliability for cervical spine rotation. J. Manipulative Physiol. Ther. 25 (7), E7. Michiels, S., De Hertogh, W., Truijen, S., November, D., Wuyts, F., Van de Heyning, P., 2013. The assessment of cervical sensory motor control: a systematic review focusing on measuring methods and their clinimetric characteristics. Gait Posture 38 (1), 1–7. Morphett, A.L., Crawford, C.M., Lee, D., 2003. The use of electromagnetic tracking technology for measurement of passive cervical range of motion: a pilot study. J. Manipulative Physiol. Ther. 26 (3), 152–159. Neumann, D.A., 2010. Kinesiology of the Musculoskeletal System, 2nd. Elsevier, St. Louis. Sarig-Bahat, H., Weiss, P. L. & Laufer, Y. 2009. Cervical motion assessment using virtual reality. Spine (Phila Pa 1976) 34(10), pp. 1018–1024. Sarig-Bahat, H., Weiss, P. L. & Laufer, Y. 2010. Neck pain assessment in a virtual environment. Spine (Phila Pa 1976) 35(4), pp. E105–12. Sarig Bahat, H., Sprecher, E., Sela, I., Treleaven, J., 2016. Neck motion kinematics: an inter-tester reliability study using an interactive neck VR assessment in asymptomatic individuals. Eur. Spine J. 25 (7), 2139–2148. Sarig Bahat, H., Takasaki, H., Chen, X., Bet-Or, Y., Treleaven, J., 2015. Cervical kinematic training with and without interactive VR training for chronic neck pain - a randomized clinical trial. Man Ther. 20 (1), 68–78. Sarig Bahat, H., Weiss, P.L., Laufer, Y., 2010. The effect of neck pain on cervical kinematics, as assessed in a virtual environment. Arch. Phys. Med. Rehabil. 91 (12), 1884–1890. Simpson, A.K., Biswas, D., Emerson, J.W., Lawrence, B.D., Grauer, J.N., 2008. Quantifying the effects of age, gender, degeneration, and adjacent level degeneration on cervical spine range of motion using multivariate analyses. Spine (Phila Pa 1976) 33(2), pp. 183–186. Sjolander, P., Michaelson, P., Jaric, S., Djupsjobacka, M., 2008. Sensorimotor disturbances in chronic neck pain–range of motion, peak velocity, smoothness of movement, and repositioning acuity. Man Ther. 13 (2), 122–131. Voss, S., Page, M., Benger, J., 2012. Methods for evaluating cervical range of motion in trauma settings. Scand J. Trauma Resusc. Emerg. Med. 20(50). Xu, X., Chen, K.B., Lin, J.H., Radwin, R.G., 2015. The accuracy of the Oculus Rift virtual reality head-mounted display during cervical spine mobility measurement. J. Biomech. 48 (4), 721–724.
Funding None. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We would like to acknowledge all study participants, Dr. Katie Palmer for language revision and Luca Cecchinato, Laura Maserati, Pietro Foppolo for their assistance in data collection. References Burdea, G.C., Coiffet, P., 2003. Virtual Reality Technology 2th. John Wiley & Sons Inc, Hoboken, New Jersey. Chen, K.B., Xu, X., Lin, J.H., Radwin, R.G., 2015. Evaluation of older driver head functional range of motion using portable immersive virtual reality. Exp. Gerontol. 70, 150–156. Dvir, Z., Prushansky, T., 2000. Reproducibility and instrument validity of a new ultrasonography-based system for measuring cervical spine kinematics. Clin. Biomech. (Bristol, Avon) 15 (9), 658–664. Gelalis, I.D., DeFrate, L.E., Stafilas, K.S., Pakos, E.E., Kang, J.D., Gilbertson, L.G., 2009. Three-dimensional analysis of cervical spine motion: reliability of a computer assisted magnetic tracking device compared to inclinometer. Eur. Spine J. 18 (2), 276–281. Jordan, K., Dziedzic, K., Jones, P.W., Ong, B.N., Dawes, P.T., 2000. The reliability of the three-dimensional FASTRAK measurement system in measuring cervical spine and shoulder range of motion in healthy subjects. Rheumatology (Oxford) 39 (4), 382–388. Kiper, P., Szczudlik, A., Agostini, M., Opara, J., Nowobilski, R., Ventura, L., Tonin, P., Turolla, A., 2018. Virtual Reality for Upper Limb Rehabilitation in Subacute and Chronic Stroke: A Randomized Controlled Trial. Arch. Phys. Med. Rehabil. 99 (5), 834–842. Kiper, P., Szczudlik, A., Mirek, E., Nowobilski, R., Opara, J., Agostini, M., Tonin, P., Turolla, A., 2013. The application of virtual reality in neuro-rehabilitation: motor relearning supported by innovative technologies. Med. Rehabil. 17 (4), 29–36. Laker, S.R., Concannon, L.G., 2011. Radiologic evaluation of the neck: a review of radiography, ultrasonography, computed tomography, magnetic resonance imaging,
Pawel Kiper is a postdoctoral researcher at the Laboratory of Neurorehabilitation Technologies of the San Camillo IRCCS s.r.l. Hospital. He started professional career by obtaining the title of Pharmacy Technician in 2002. Simultaneously in the years 20012004 he joint undergraduate studies in Physiotherapy at the Rzeszow University, Poland. In 2006 he defended his MSc thesis at the Rzeszow University, Poland. In 2013 he completed his PhD research and obtained the title of Doctor of Health Sciences at the Jagiellonian University Medical College, Krakow, Poland. In 2016 he completed postgraduate studies in Management and Coordination Functions in the Health Professions at the University Unitelma Sapienza in Rome, Italy. His main interests are in the area of innovative technologies applied to neurological disorders.
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Contents lists available at ScienceDirect
Journal of Electromyography and Kinesiology journal homepage: www.elsevier.com/locate/jelekin
Assessment of the cervical spine mobility by immersive and non-immersive virtual reality
T
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Pawel Kipera, , Alfonc Babab, Mahmoud Alheloua, Giorgia Pregnolatoa, Lorenza Maistrelloa, ⁎ Michela Agostinia, Andrea Turollaa, a b
Laboratory of Neurorehabilitation Technologies, San Camillo IRCCS srl, via Alberoni 70, 30126 Venice, Italy Rehabilitation Unit, Azienda Ospedaliera Universitaria di Padova, Padova, Italy
A R T I C LE I N FO
A B S T R A C T
Keywords: Virtual reality Motion analysis Range of motion Cervical spine Validity
Introduction: Despite many devices are helpful for motion analysis, there is still no established standard technique for the assessment of cervical spine mobility. Objective: To compare differences in using immersive or non-immersive virtual reality (VR) for the assessment of the sensorimotor movement of the cervical spine in healthy subjects. Methods: Thirty-five healthy adults were asked to perform head rotation, flexion, extension, lateral flexion, reaching and repositioning tasks with the head. The same tasks were performed interacting with both nonimmersive and immersive virtual reality. Random sequence determined which of the environments was used as first assessment. Range of motion and kinematics i.e. number of completed targets, time of execution (seconds), spatial length (cm), angle distance (°), jerk of the cervical spine, were automatically computed by a 6D electromagnetic motion tracking system. Results: The following variables were significantly larger in immersive than non-immersive VR: head right rotation (p = 0.027), extension (p = 0.047), flexion (p = 0.000), time (p = 0.001), spatial length (p = 0.004), jerk target (p = 0.032), trajectory repositioning (p = 0.003), jerk target repositioning (p = 0.007). A regression model showed that assessment in both VR environments can be influenced by dependent and independent variables. Conclusions: Immersive VR provided more accurate measurement of cervical spine than non-immersive VR in healthy adults.
1. Introduction The cervical spine can be considered as a functional “dynamic bridge” composed by seven vertebrae, thirty-seven distinct joints and thirty pairs of muscles that cooperate with the aim to orient the head smoothly in three dimensional space by controlling 600 movements per hour on average (Neumann, 2010). Spatial orientation of the head depends both on cervical spine mobility and integration of inputs coming from the somatosensory and the vestibular systems. These two systems are essential to detect correct head positioning and body movements, which are vital to keep the correct posture and coordinate head-eye movements (Neumann, 2010). Thus, integrity of the cervical spine is essential for balance of the whole body and spatial exploration with the upper limb. Impairment of cervical spine mobility is often related with neck pain (Sarig-Bahat et al., 2010). Therefore, instrumental measurement
⁎
methods may provide the potential for better accurate clinical outcomes for the optimal assessment of physiotherapy modalities. Many studies reported an instrumental approach to measure the range of motion (ROM) of cervical spine mobility. These measures have been acquired by means of a variety of technologies including eye-balling (Morphett et al., 2003), dynamic radiographs (Simpson et al., 2008), goniometers and inclinometers (both digital or analogue) (Sjolander et al., 2008), functional computed tomography (fCT) (Laker and Concannon, 2011), ultrasonic (Dvir and Prushansky, 2000), optic (Marcotte et al., 2002) and electromagnetic motion tracking systems for three-dimensional (3D) analyses combined or not with both immersive and non-immersive virtual reality environments (Jordan et al., 2000). Although use of several devices can be helpful for motion analysis, there is still no established “gold standard technique” for the assessment of cervical spine mobility. To date, goniometers and inclinometers are the most popular devices used in clinical settings. Nevertheless, these technologies
Corresponding authors at: San Camillo IRCCS srl, via Alberoni 70, 30126 Venezia, Italy. E-mail addresses: [email protected] (P. Kiper), [email protected] (A. Turolla).
https://doi.org/10.1016/j.jelekin.2020.102397 Received 23 January 2019; Received in revised form 20 January 2020; Accepted 22 January 2020 1050-6411/ © 2020 Elsevier Ltd. All rights reserved.
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2.2. Experimental procedure
usually allow measurement of no more than two-dimensional ROM quotes in static conditions (Michiels et al., 2013; Voss et al., 2012). Conversely, the wide use of motion tracking systems able to measure 3 to 6 degrees of freedom dynamically, while performing standard ROM assessment, are limited due to technical complexities in preparing the setup and high costs. Thus, motion tracking systems are primarily used in research. Recently, the use of virtual environments has emerged as a promising methodology to detect valid measures of neck mobility (Gelalis et al., 2009; Sarig-Bahat et al., 2010). Virtual reality (VR) is defined as a high-end user-computer interface that involves real-time simulation and interactions through multiple sensorial channels (Burdea and Coiffet, 2003). VR can be also defined as a mental experience able to provide to the subject a sense of “being” in a virtual world (i.e. presence) (Luque-Moreno et al., 2016). Importantly, the sense of “presence” is dynamic, so the subject can react and change instantly his/her motor behavior. To date, there are several technologies allowing mediation of the visual interaction, each of them provide a different level of VR interaction and, thus, a variable degree of immersion. What determines the sense of presence in VR is the level of immersion provided, which in turn depends on the system used. Depending on systems’ characteristics virtual reality can be divided into three main categories like; immersive (i.e. head mounted displays – HMD) (Xu et al., 2015), non-immersive (i.e. monitors or wall projections to display 3D images on a 2D surface) (Kiper et al., 2018) and augmented (i.e. enhanced perception of reality mixed with computer-generated sensory input) (Kiper et al., 2013; Laver et al., 2017). Non-immersive VR has been already used for the assessment of the cervical ROM in several studies (Sarig Bahat et al., 2016). Sarig-Bahat et al. compared nonimmersive VR with conventional measures for the assessment of the cervical ROM. The results of this study suggest that non-immersive VR is more repeatable and sensible than conventional assessment, due to the possibility to embed innovative electromagnetic motion tracking technologies (Sarig-Bahat et al., 2009). Whereas, the cervical spine mobility was tested in immersive VR through HMD device (Oculus Rift), showing accuracy in displaying virtual environments and its potential for future clinical use (Xu et al., 2015; Chen et al., 2015). Nevertheless, detection of kinematic parameters is still poor. The possibility to merge different VR modalities (e.g. immersive and non-immersive) may have great potential for cervical spine assessment, but the overall accuracy of such embedding need to be studied thoroughly. The aim of this study was to test the reliability of a protocol for the assessment of the sensorimotor movement of the cervical spine in a population of healthy subjects and to compare whether using immersive or non-immersive VR for visualization affects the performance.
At first instance, the study purpose and setting were explained and consent to participate was obtained from all the participants, and then a diagnostic interview was carried out to check the existence of possible exclusion criteria. Afterwards, the experimental setup was equipped on the subject and a quick warm-up of the virtual scenarios was conducted before ROM assessment to make the subject familiar with the experimental setting. Assessment within non-immersive environment was performed using the Virtual Reality Rehabilitation System – VRRS® (Khymeia Group, Noventa Padovana, Italy), which consisted of a computer connected to a 3D motion–tracking system (Polhemus G4® Colchester, Vermont, US) and a high–resolution LCD monitor to display the virtual scenarios. The whole protocol was implemented like a customized unique application to be run within the VRRS® environment. The electromagnetic sensor for the head was fixed on a sizeable helmet wore by the subject to be placed on the apex. While, the sternum sensor was fixed on a suitable vest wore by the participant to stay at the level of the third sternocostal joint. Whereas, for assessment within immersive VR the Oculus Rift (Oculus VR Inc, Irvine, CA, US) was used as HMD to visualize the same VRRS scenarios. The same motion tracking system was used to detect kinematics of the cervical spine regardless of the VR environment administered. All participants underwent both immersive (VRRS displayed through Oculus Rift) and non-immersive (VRRS displayed on computer screen) assessment. To minimize biases related to tasks familiarization a simple random sequence was used to allocate the first environment (i.e. immersive or non-immersive) to be assigned for each subject. 2.3. Outcome measures During assessment, the subjects were seated with their back supported and hips and knees flexed to 90°, the hands were placed on the knees and feet resting on the ground (Fig. 1). Prior to assessment, each participant had the possibility to familiarize themselves with the system by performing 2–3 attempts. At the beginning of each test, participants were instructed to place their head in neutral position, which was set as the absolute reference position for that task. Both assessments consisted of the same five exercises, but displayed in immersive or non-immersive environment, accordingly (Figs. 2 and 3, respectively). The whole protocol consisted of 46 tasks grouped in the following tests:
• head rotation (5 repetitions to the right and 5 to the left) – parti-
2. Material and methods
• • •
2.1. Participants Thirty-five asymptomatic volunteers were recruited according to the following exclusion criteria: self-reported history of neck trauma, spinal surgery, primary pathologies of the spine (e.g. instability, stenosis), neuromuscular diseases, diagnosed fibromyalgia, migraine, neoplasms, vestibular disorders, pregnancy, and depression. All volunteers were assessed by the same assessor and were asked to fill a case report form with additional information required such as; handedness, presence of scoliosis, neck fatigue in the last three hours, visual deficit (e.g. myopia, astigmatism, hyperopia, presbyopia), missed diopters. Informed consent was obtained from all healthy volunteers who participated in this study. This study enrolled only healthy volunteers for clinical assessment without therapeutic, physical or mental interference. The procedures of enrolment and data acquisition/storing were executed in accordance with the ethical standards on human experimentation and in accordance with the Declaration of Helsinki 1975, revised Hong Kong 1989.
•
cipants were asked to rotate the head from the neutral position without trunk movement. head flexion and extension (5 flexions and 5 extensions) – participants were asked to execute maximal head flexion and extension, head lateral flexion (5 right and 5 left side bending) – participants were asked to touch the shoulder with the ear, reaching (head movements toward 8 targets placed along a circular perimeter each 45°, visualized one-by-one in random order) – participants were asked to reach eight targets overall, which were visualised randomly (in a form of 3D cube) in the following positions; 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°. repositioning (head movement toward the same 8 targets as for reaching and following reposition to the start point) – participants were asked to reach eight targets overall, which were visualised randomly (in a form of 3D cube) in the following positions; 0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°, and return to the starting point as close as possible without its visualization.
Kinematic parameters were automatically computed by the VRRS and the following were considered for the analyses: maximum and average degree of ROM of each exercise repetitions (i.e. rotation, 2
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Fig. 1. Setting for the cervical spine assessment. 1 – assessment in immersive VR (Oculus Rift), 2 – graphical setting, 3 – assessment in non-immersive VR (VRRS), 4 and 5 – position for assessment in immersive VR, 6 and 7 – position for assessment in non-immersive VR.
flexion, extension, lateral flexion); number of completed targets (No), time of execution (seconds) – i.e. mean duration of task, spatial length (mm) – i.e. path length of a 1000 mm pointer moving from head, angle distance (°) – i.e. angular repositioning distance, and jerk for reaching and repositioning tasks (s2) – i.e. jerk smoothness parameter.
were statistically different in the comparison between measurements in immersive or non-immersive VR. Finally, a multivariate linear regression model was used to infer any potential relationship between dependent and independent variables, moreover the values of variance explained for each outcome survived in the model were considered as measures of reliability. Statistical significance was set at p < 0.05 and RStudio package software was used for the analyses.
2.4. Statistical analysis
3. Results
The distribution skewness was studied with the Shapiro-Wilk test and according to the results parametric (t-student) or non-parametric (Wilcoxon-Mann-Whitney) tests were used to determine if the outcomes
Thirty-five healthy participants (i.e. 14 males and 21 females) were
Fig. 2. Assessment in non-immersive VR environment through VRRS. (A) Head rotation, (B) head lateral flexion, (C) head flexion, (D) head extension, (E) reaching, (F) repositioning. 3
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Fig. 3. Assessment in immersive VR environment through Oculus Rift. (A) Head rotation, (B) head lateral flexion, (C) head flexion, (D) head extension, (E) reaching, (F) repositioning.
explained of goodness-of-fit analysis) in relation to results obtained from regression model. The regression models are presented in Table 3 and Table 4. Moreover, we analyzed which one of the parameters had a highest weight (positive or negative) on each single regression model. In depth, in immersive VR the jerk target (1.35) and jerk target repositioning (−1.18) had a highest positive and negative weight for right rotation, respectively. Further, the right rotation (0.73) showed highest weight for left rotation parameter. Flexion (0.57) and spatial length (−0.46) resulted with the highest positive and negative weight for right lateral flexion, respectively. Whereas, for left lateral flexion, parameters like head flexion (0.41, highest positive) and jerk target repositioning (−0.35, highest negative) showed the highest weight. The jerk target repositioning (1.18) had the highest positive weight and fatigue (−1.15) activity in last 3 h had the highest negative weight for regression model of head extension. Whereas, for head flexion, the right rotation (0.63, highest positive) and sex (i.e. man; −0.51, highest negative) resulted with the highest weight. Kinematics parameters were also affected by both demographic and kinematic variables in immersive VR and were as follows: for time of task execution, the highest weight on regression model were observed by jerk target repositioning (1.13, highest positive) and fatigue (−0.71, highest negative) activity in last 3 h. For spatial length, the trajectory repositioning (0.42) and height (−0.34) had highest positive and negative weight on regression model, respectively. For jerk target, the highest weight on regression model had jerk target repositioning (0.89, highest positive) and body weight (−0.32, highest negative). For angle distance, the right handedness (−1.33) had highest negative weight on regression model. For trajectory repositioning, only the spatial length (0.40) resulted with highest positive weight. Finally, for jerk target repositioning, the highest weight on regression model had jerk target repositioning (0.57, highest positive) and body mass index (−0.55, highest negative). In non-immersive VR the following variables had positive highest weight on regression models: for head right rotation it was a left
Table 1 Demographic characteristics of participants at baseline. Demographic values
Mean
SD
Median
CI 95%
Age Height Weight
28.97 1.72 67.54
7.43 0.09 14.38
27.0 1.7 65.0
26.85 – 31.09 1.03 – 1.45 63.43 – 71.65
Data are displayed as mean, standard deviation (SD), median and 95% confidence interval (CI).
recruited for the cervical spine assessment within immersive and nonimmersive VR environments. The demographic characteristics of the sample are reported in Table 1. The age range was 20 to 60 years (i.e. 20–30 = 24 subjects; 30–40 = 7 subjects; 40–50 = 3; 50–60 = 1). None of the participants reported cervical trauma, migraine, neoplasms, spinal surgery, cervical disorders, neuromuscular diseases, eye disorders, vascular disorders, pregnancy, or psychiatric pathologies. Furthermore, six volunteers were left-handed, twenty reported visual defects, and five presented mild scoliosis. The kinematics results obtained from the assessment protocol are reported in Table 2. Comparison analysis demonstrated that head right rotation, extension and flexion, as well as time, spatial length, jerk target, trajectory repositioning and jerk target repositioning differed significantly, and were better for immersive than non-immersive VR assessment. The multivariate linear regression showed that such results were determined by the following characteristics: sex, age, height, weight, body mass index, scoliosis, profession (i.e. sedentary or non-sedentary), visual deficit, handedness, fatigue activity in last 3 h, kind of virtual environment (i.e. immersive or non-immersive), head rotation, flexion, extension and lateral flexion, as well as time of execution, spatial length, angle distance, jerk for reaching and repositioning tasks. Further the reliability of assessment protocol showed moderate to high tested reliability of the explained variance (presented as the variance 4
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Table 2 Results of the cervical spine assessment in different VR environments. Kinematic parameters
Immersive VR
Right rotation (°) Left rotation (°) Right lateral flexion (°) Left lateral flexion (°) Extension (°) Flexion (°) Time (s) Spatial length (mm) Jerk Target (s2) Angle distance (°) Trajectory repositioning (mm) Jerk target repositioning (s2)
Non-immersive VR
Mean
SD
CI 95%
Mean
SD
CI 95%
Δ
p-value
74.66 73.66 38.37 39.62 59.23 66.67 18.21 10,470 58.51 2.88 10,340 47.77
10.90 11.42 8.33 6.49 11.00 10.18 5.26 1303.39 31.72 1.16 1986.45 29.32
71.55–77.78 70.40–76.93 35.98–40.75 37.76–41.47 56.08–62.37 63.76–69.58 16.70–19.71 10101.9–10846.9 49.44–67.57 2.54–3.20 9776.89–10912.42 39.39–56.14
72.13 73.66 38.49 40.65 56.83 60.89 19.79 9852 48.39 3.16 9969 38.53
7.88 8.03 6.27 5.43 11.04 8.90 18.80 769.57 24.71 1.91 782.11 23.90
69.88–74.38 71.37–75.96 36.70–40.28 39.10–42.20 53.67–59.98 58.34–63.43 18.26–21.31 9632.3–10072.2 41.32–55.45 2.61–3.70 9745.70–10192.79 31.70–45.36
2.530 −0.001 −0.124 −1.032 2.401 5.786 −1.577 622.192 10.121 −0.281 375.411 9.238
0.027* 0.999 0.910 0.291 0.047* 0.001* 0.001* 0.004* 0.032* 0.903 0.003* 0.007*
Data are displayed as mean, standard deviation (SD) and 95% confidence interval (CI). The Wilcoxon-Mann-Whitney test or t-student test were used for comparison between measurement modalities (* p < 0.05). (°) – degree, (s) – seconds, (No) – number, (mm) – millimetres, (s2) – second squared. Table 3 Relationship between qualitative and quantitative parameters in immersive virtual environment. Parameters
Regression model
% Variance explained
p-value
Right rotation
65.4 + 10.52*Sex: M - 0.54*Age + 0.53*Weight - 2.93*BMI −4.5*Visual Deficit: yes − 8.09*Non-Immersive VR + 0.32*Flexion + 0.81* Left rotation - 0.29* Right lateral flexion – 0.98*Time + 0.45*Jerk Target – 0.43* Jerk target repositioning −11 + 0.79* Right rotation + 0.28*Extension + 0.45* Time 50.36–8.9*Scoliosis: yes − 5.33*Profession: sedentary − 0.35*Age + 0.49*Flexion − 0.003* Spatial length 25.69 + 0.27*Flexion – 0.08* Jerk target repositioning 123.89 + Sex: M − 0.91*Age − 1.82*BMI − 41.33*Fatigue: yes − 10.84*Non-Immersive RV −10.02* Right rotation + 1.09* Left rotation + 0.56*Flexion − 1.08*Time − 0.002* Spatial length + 0.38*Jerk Target − 64–10.32*Sex: M + 0.74*Age + 1.01*BMI + 5.62*Profession: Sedentary + 13.56*Scoliosis: yes + 0.56* Right rotation + 0.59* Right lateral flexion + 0.002* Spatial length 20.18 + 3.8*Sex: M − 0.59*BMI −12.19*Fatigue: yes − 2.31*Non-Immersive VR + 0.29* Left rotation − 0.11*Extension − 0.17* Right rotation + 0.17*Jerk Target 16530–5187* Height + 0.28* Trajectory repositioning − 22.48–0.73*Weight + 2.82*BMI + 7.64*Visual Deficit: yes + 0.48* Right lateral flexion − 0.39*Extension + 1.17*Time + 0.96* Jerk target repositioning 3.58 – 3.40* Handedness 3912.49 + 0.61* Spatial length 40.15 + 1.2*Weight − 5.12*BMI − 17.35*Scoliosis: yes + 38.97*Fatigue: yes − 0.69* Right lateral flexion + 0.44*Extension + 0.69*Jerk Target
0.925
0.359
0.83 0.62
0.763 0.904
0.30 0.76
0.165 0.599
0.84
0.880
0.87
0.487
0.28 0.98
0.050 * 0.296
0.28 0.16 0.97
0.030* 0.050* 0.225
Left rotation Right lateral flexion Left lateral flexion Extension
Flexion Time
Spatial length Jerk Target Angle distance Trajectory repositioning Jerk target repositioning
The outcomes are displayed with equation of the regression models. The Normality test was applied on model’s residuals and significance was established at p < 0.05*. Sex (M/F) = male/female, Profession (S/NS) = sedentary/non-sedentary, Handedness (R/L) = right/left. Table 4 Relationship between qualitative and quantitative parameters in non-immersive virtual environment. Parameters
Regression model
% Variance explained
p-value
Right rotation Left rotation Right lateral flexion Left lateral flexion Extension Flexion Time Spatial length
17.7 + 0.76* Left rotation + 0.15*Jerk Target −0.23* Jerk target repositioning 2.79 + 0.79* Right rotation + 0.27* Extension – 0.24*Jerk Target + 0.26* Jerk target repositioning 07.95 + 0.75 * Left lateral flexion 18.98 + 0.56* Right lateral flexion 10.02 + 0.72* Right lateral flexion + 0.93*Time 29.88 + 0.29*Age + 0.31* Right rotation 26.35 + 0.27* Left rotation −0.28* Right lateral flexion −0.002* Spatial length + 0.08*Jerk Target 3035.76–40.48*Age + 18.39*Weight − 62.39* Left lateral flexion + 44.43* Right lateral flexion + 0.76* Trajectory repositioning −4.39 + 0.37*Extension + 0.84* Jerk target repositioning 2.83 + 4.46*Scoliosis: yes − 3.66*Fatigue: yes 8638.11 + 45.95*Age −84.06 + 53.88*Height + 32.65*Fatigue: yes + 0.56*Jerk Target
0.66 0.76 0.42 0.42 0.97 0.98 0.48 0.61
0.839 0.947 0.848 0.694 0.175 0.307 0.113 0.040*
0.72 0.31 0.16 0.82
0.002* 0.050 0.143 0.002*
Jerk Target Angle distance Trajectory repositioning Jerk target repositioning
The outcomes are displayed with equation of the regression models. The Normality test was applied on model’s residuals and significance was established at p < 0.05*. Sex (M/F) = male/female, Profession (S/NS) = sedentary/non-sedentary, Handedness (R/L) = right/left.
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which is correlated with physical activity, is important and can have negative side effects if not controlled. The analyzed parameters were also influenced positively or negatively by kinematic variables within regression models, and this relationship depends on its correlation. For example, left rotation influenced positively right rotation and vice versa. Finally, non-immersive VR resulted negatively influenced right head rotation, extension, and time of task execution in the regression model of the immersive VR. These findings confirm results obtained from comparison of both groups where the immersive VR resulted more accurate for cervical spine assessment. In the non-immersive VR the regression models showed slightly different results, i.e. mostly kinematics affected the parameters that were analyzed. Moreover, in non-immersive VR height resulted to affect kinematics positively (i.e. taller subjects). This could be due to the fact that non-immersive VR is presented on computer screen providing some limitation of a visual field. Further, the presence of scoliosis affected negatively cervical kinematics (i.e. angle distance). At the end also in the non-immersive VR, age resulted to be a factor that can influence cervical kinematics (i.e. flexion, spatial length, trajectory repositioning). The use of electromagnetic tracking systems was developed to assess cervical ROM and kinematics under conditions that are similar to daily life function. Moreover, development of the bioengineering field has provided possibilities to create artificial 3D environments similar to the real one. Such development has promoted new clinical methods based on VR, providing new insights on human kinematics. The assessment and treatment of cervical spine became very important in designing rehabilitation programmes for patients with neck pain. In the last 20 years various similar measuring methods have been used, however the accuracy measurement level between those methods differ substantially (Michiels et al., 2013). So, a reliable and valid method seems to be essential for accurate clinical diagnostic. Several studies showed that VR-based assessment was found to be more precise than the conventional evaluation (Sarig Bahat et al., 2010). Motion tracking systems showed also inter- and intra-tester reliability (Sarig-Bahat et al., 2009). Further, in this study we found that immersion has an impact on results, as well. Thus, the assessment with different sensory conditions that are created in VR environment may influence more accurately cervical motion diagnostic. In addition, some studies reported effectiveness of VR in neck pain treatment and provided evidence of improvement in velocity and smoothness of the cervical motion which were restricted due to the neck pain (Sarig Bahat et al., 2015). The kinematic assessment is undoubtedly needed in clinical setting for precise and accurate diagnostic, and the immersive VR should be considered for future evaluation in this field.
rotation (0.77); for head left rotation it was a jerk target repositioning (0.83); for right lateral flexion it was a left lateral flexion (0.65); for left lateral flexion it was a right lateral flexion (0.56); for head extension it was a time (0.44); for head flexion it was age (0.26) and right rotation (0.26); for time it was a left rotation (0.41); for spatial length it was a spatial length (0.77); for jerk target it was a jerk target repositioning (0.82); for angle distance it was a scoliosis (0.83); for trajectory repositioning it was age (0.44), and for jerk target repositioning it was a jerk target (0.58). Whereas, the highest negative weight on regression models was not observed in all analyzed variables, as follows: for head right rotation it was a jerk target repositioning (−0.71); for head left rotation it was a jerk target (−0.77); for time it was a right lateral flexion (−0.34); for spatial length it was a left lateral flexion (−0.44) and for angle distance it was a fatigue activity in last 3 h (−0.55). 4. Discussion This study compared two novel VR-based methods used for cervical spine ROM assessment. The results of this study showed significant difference between evaluations executed in two different VR environments. Importantly, this variation was observed between sensorimotor movements and biomechanical parameters. For example, the same task executed in immersive VR resulted in longer trajectory for head flexion, extension, and right rotation than in non-immersive VR. This could be due to both VR immersion (i.e. the same task visualization but devoid of external stimuli) and limits in ROM acquisition, created for example by the perception of several sensors placed on the helmet and participant’s body. Apart from ROM trajectory, also overall spatial length resulted longer in immersive VR. However, longer trajectory was not related with time of task execution needed for its completion in immersive VR. The time of task execution resulted inferior in immersive VR and significantly different between VR environments. Further, the trajectory repositioning, jerk target, and jerk target repositioning showed higher accuracy in immersive VR. This could be due to the fact that immersive VR is able to create a high level of real world simulation by producing a 3D computer-generated environment. Thus, in the immersive VR the user is completely isolated from the surrounding environment and this high level of immersion is devoid of visual references (except those created by a software), which, in turn, are vital for good integration between the cervical spine, somatosensory and vestibular systems. Therefore, the modality in which the test is administered can affect significantly the dynamic performance related to the head movement control and its orientation in the space. Furthermore, the multivariate linear regression analysis showed that independent and dependent variables like sex, age, height, weight, body mass index (BMI), scoliosis, profession (i.e. sedentary or non-sedentary), visual deficit, handedness, fatigue activity in last 3 h and kind of virtual environment (i.e. immersive or non-immersive) can affect kinematic parameters in immersive VR environment. In depth, male gender seems to affect positively right head rotation, extension and time of task execution, and negatively head flexion. Participants’ age affected negatively analyzed variables except head flexion, which can be correlated with modification of the body posture in adult age. Presence of scoliosis reduced right lateral flexion and jerk target repositioning but increased head flexion. This could be due to permanently structured and incorrect trunk position. Professions related to the sedentary work influenced negatively kinematics of right head rotation and augmented head flexion. Thus, possibly frequent position changing in non-sedentary professions can help to keep higher level of cervical motion. Visual deficit influenced negatively right head rotation and jerk target, which could be associated with visual perturbations that creates immersive VR as well as with limitations of visual field. Whereas, BMI affected negatively right head rotation, extension, flexion, time of task execution and jerk target repositioning but affected positively jerk target. These results show that adequate level of BMI,
4.1. Limitations of the study and further research perspectives. Some limitations need to be addressed. Firstly, the number of righthanded subjects were superior to the subjects with left hand dominance. Thus, its comparison can provide different effect related to handedness. Secondly, the study lacks of the comparison with subjects affected by cervical spine pathologies. However, this population has been included in the ongoing trial. Thirdly, mainly young subjects were assessed in the study, thus a wider sample should be considered, consisting of individuals from multiple age groups. Fourthly, this study did not analyze the level of scoliosis of each single participant, but only its incidence was reported. This should be taken into consideration for future studies. Finally, the history of neck trauma, spinal surgery, primary pathologies of the spine, neuromuscular diseases, diagnosed fibromyalgia, migraine, neoplasms, vestibular disorders, pregnancy, and depression were self-reported by a healthy volunteers. 5. Conclusion Both immersive and non-immersive VR are useful for cervical spine 6
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assessment. The immersive VR provide more accurately measure of cervical spine ROM and kinematics, than non-immersive VR. The diagnostic potential of immersive VR should be confirmed in future research considering subjects affected by cervical spine pathologies.
and other imaging modalities for neck pain. Phys. Med. Rehabil. Clin. N Am. 22 (3), 411–428. Laver, K.E., Lange, B., George, S., Deutsch, J.E., Saposnik, G., Crotty, M., 2017. Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. 11, CD008349. Luque-Moreno, C., Oliva-Pascual-Vaca, A., Kiper, P., Rodriguez-Blanco, C., Agostini, M., Turolla, A., 2016. Virtual Reality to Assess and Treat Lower Extremity Disorders in Post-stroke Patients. Methods Inf. Med. 55 (1), 89–92. Marcotte, J., Normand, M.C., Black, P., 2002. The kinematics of motion palpation and its effect on the reliability for cervical spine rotation. J. Manipulative Physiol. Ther. 25 (7), E7. Michiels, S., De Hertogh, W., Truijen, S., November, D., Wuyts, F., Van de Heyning, P., 2013. The assessment of cervical sensory motor control: a systematic review focusing on measuring methods and their clinimetric characteristics. Gait Posture 38 (1), 1–7. Morphett, A.L., Crawford, C.M., Lee, D., 2003. The use of electromagnetic tracking technology for measurement of passive cervical range of motion: a pilot study. J. Manipulative Physiol. Ther. 26 (3), 152–159. Neumann, D.A., 2010. Kinesiology of the Musculoskeletal System, 2nd. Elsevier, St. Louis. Sarig-Bahat, H., Weiss, P. L. & Laufer, Y. 2009. Cervical motion assessment using virtual reality. Spine (Phila Pa 1976) 34(10), pp. 1018–1024. Sarig-Bahat, H., Weiss, P. L. & Laufer, Y. 2010. Neck pain assessment in a virtual environment. Spine (Phila Pa 1976) 35(4), pp. E105–12. Sarig Bahat, H., Sprecher, E., Sela, I., Treleaven, J., 2016. Neck motion kinematics: an inter-tester reliability study using an interactive neck VR assessment in asymptomatic individuals. Eur. Spine J. 25 (7), 2139–2148. Sarig Bahat, H., Takasaki, H., Chen, X., Bet-Or, Y., Treleaven, J., 2015. Cervical kinematic training with and without interactive VR training for chronic neck pain - a randomized clinical trial. Man Ther. 20 (1), 68–78. Sarig Bahat, H., Weiss, P.L., Laufer, Y., 2010. The effect of neck pain on cervical kinematics, as assessed in a virtual environment. Arch. Phys. Med. Rehabil. 91 (12), 1884–1890. Simpson, A.K., Biswas, D., Emerson, J.W., Lawrence, B.D., Grauer, J.N., 2008. Quantifying the effects of age, gender, degeneration, and adjacent level degeneration on cervical spine range of motion using multivariate analyses. Spine (Phila Pa 1976) 33(2), pp. 183–186. Sjolander, P., Michaelson, P., Jaric, S., Djupsjobacka, M., 2008. Sensorimotor disturbances in chronic neck pain–range of motion, peak velocity, smoothness of movement, and repositioning acuity. Man Ther. 13 (2), 122–131. Voss, S., Page, M., Benger, J., 2012. Methods for evaluating cervical range of motion in trauma settings. Scand J. Trauma Resusc. Emerg. Med. 20(50). Xu, X., Chen, K.B., Lin, J.H., Radwin, R.G., 2015. The accuracy of the Oculus Rift virtual reality head-mounted display during cervical spine mobility measurement. J. Biomech. 48 (4), 721–724.
Funding None. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We would like to acknowledge all study participants, Dr. Katie Palmer for language revision and Luca Cecchinato, Laura Maserati, Pietro Foppolo for their assistance in data collection. References Burdea, G.C., Coiffet, P., 2003. Virtual Reality Technology 2th. John Wiley & Sons Inc, Hoboken, New Jersey. Chen, K.B., Xu, X., Lin, J.H., Radwin, R.G., 2015. Evaluation of older driver head functional range of motion using portable immersive virtual reality. Exp. Gerontol. 70, 150–156. Dvir, Z., Prushansky, T., 2000. Reproducibility and instrument validity of a new ultrasonography-based system for measuring cervical spine kinematics. Clin. Biomech. (Bristol, Avon) 15 (9), 658–664. Gelalis, I.D., DeFrate, L.E., Stafilas, K.S., Pakos, E.E., Kang, J.D., Gilbertson, L.G., 2009. Three-dimensional analysis of cervical spine motion: reliability of a computer assisted magnetic tracking device compared to inclinometer. Eur. Spine J. 18 (2), 276–281. Jordan, K., Dziedzic, K., Jones, P.W., Ong, B.N., Dawes, P.T., 2000. The reliability of the three-dimensional FASTRAK measurement system in measuring cervical spine and shoulder range of motion in healthy subjects. Rheumatology (Oxford) 39 (4), 382–388. Kiper, P., Szczudlik, A., Agostini, M., Opara, J., Nowobilski, R., Ventura, L., Tonin, P., Turolla, A., 2018. Virtual Reality for Upper Limb Rehabilitation in Subacute and Chronic Stroke: A Randomized Controlled Trial. Arch. Phys. Med. Rehabil. 99 (5), 834–842. Kiper, P., Szczudlik, A., Mirek, E., Nowobilski, R., Opara, J., Agostini, M., Tonin, P., Turolla, A., 2013. The application of virtual reality in neuro-rehabilitation: motor relearning supported by innovative technologies. Med. Rehabil. 17 (4), 29–36. Laker, S.R., Concannon, L.G., 2011. Radiologic evaluation of the neck: a review of radiography, ultrasonography, computed tomography, magnetic resonance imaging,
Pawel Kiper is a postdoctoral researcher at the Laboratory of Neurorehabilitation Technologies of the San Camillo IRCCS s.r.l. Hospital. He started professional career by obtaining the title of Pharmacy Technician in 2002. Simultaneously in the years 20012004 he joint undergraduate studies in Physiotherapy at the Rzeszow University, Poland. In 2006 he defended his MSc thesis at the Rzeszow University, Poland. In 2013 he completed his PhD research and obtained the title of Doctor of Health Sciences at the Jagiellonian University Medical College, Krakow, Poland. In 2016 he completed postgraduate studies in Management and Coordination Functions in the Health Professions at the University Unitelma Sapienza in Rome, Italy. His main interests are in the area of innovative technologies applied to neurological disorders.
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