Variability of neck and trunk movement during single- and dual-task gait in people with chronic neck pain

The impact of neck pain on gait health: a systematic review and meta-analysis

BACKGROUND

Evidence exists demonstrating the negative impacts of chronic musculoskeletal pain on key measures of gait. Despite neck pain being the second most common musculoskeletal pain condition, there is a paucity of evidence exploring the impacts of neck pain specifically on these outcomes. The aims of this work were to systematically review the current evidence of the associations between chronic neck pain and measures of gait health and to conduct meta-analysis for quantitative assessment of the effect sizes under different walking conditions.

METHODS

Systematic review was conducted following PRISMA guidelines. Databases searched included MEDLINE, Embase, Web of Science, CINAHL, and PEDro. Eligible study designs included observational studies consisting of an exposure group with chronic neck pain and control group without chronic neck pain and primary outcomes relating to gait health. For outcomes amenable to meta-analysis, a random-effects model was used to derive summary estimates of Hedge's g depicted graphically with forest plots. Other gait outcomes were narratively summarized. Risk of bias was also assessed.

RESULTS

The original search yielded 1918 articles; 12 met final eligibility criteria including 10 cross-sectional studies. Outcomes were grouped first by the five domains of gait: pace, rhythm, asymmetry, variability, and postural control; and second by the tested walking conditions. Meta-analyses for gait speed revealed large effect-sizes indicating that individuals with chronic neck pain had slower measures of gait and lower measures of cadence. Gait outcomes that were narratively summarized supported these findings.

CONCLUSION

The quantitative and qualitative findings of this systematic review and meta-analysis suggest a negative impact of CNNP on measures of gait health, particularly gait speed, under various walking conditions. However, broad interpretation of these results should be cautious. Testing gait under dual task conditions may be particularly sensitive to the impact of CNNP, and future work is needed to better understand how pain disrupts this important functionality of the locomotor system. Additionally, consideration should be made to assess measures of variability and investigate these relationships in the older adult population.

Construct validity of the Mini-BESTest in individuals with chronic pain in specialized pain care

BACKGROUND

Balance assessment scales are important clinical tests to identify balance impairments. Chronic pain (> 3 months) is associated with impaired dynamic balance; however, very few balance assessment scales are psychometrically evaluated for the population. The purpose of this study was to evaluate the construct validity and internal consistency of the Mini-BESTest for individuals with chronic pain in specialized pain care.

METHODS

In this cross-sectional study, 180 individuals with chronic pain (> 3 months) were assessed with the Mini-BESTest and included in the analyses. For construct validity, five alternative factor structures were evaluated using a confirmatory factor analysis. In addition, we tested the a priori hypotheses about convergent validity with the 10-meter walk test, and divergent validity with the Brief Pain Inventory (BPI): pain intensity, the Tampa Scale of Kinesiophobia-11 (TSK-11), and the Pain Catastrophizing Scale (PCS-SW). Internal consistency was evaluated for the model with the best fit.

RESULTS

A one-factor model with added covariance via the modification indices showed adequate fit indices. In line with our hypotheses, Mini-BESTest showed convergent validity (r_s = > 0.70) with the 10-meter walk test, and divergent validity (r_s = < 0.50) with BPI pain intensity, TSK-11, and PCS-SW. Internal consistency for the one-factor model was good (α = 0.92).

CONCLUSIONS

Our study supported the construct validity and internal consistency of the Mini-BESTest for measuring balance in individuals with chronic pain, who were referred to specialized pain care. The one-factor model showed an adequate fit. In comparison, models with subscales did not reach convergence, or showed high correlations between subscales, implying that Mini-BESTest is measuring one construct in this sample. We, therefore, propose using the total score, instead of subscale scores, for individuals with chronic pain. However, further studies are necessary to establish the reliability of the Mini-BESTest in the population.

The impact of prolonged experimental neck pain on walking stability and gait kinematics - A parallel-group study

BACKGROUND

Neck pain is a common problem in the general population, and movement adaptations are a natural response to pain. Previous studies have reported reduced trunk rotation during walking in those suffering from clinical neck pain. However, it is unknown how soon after the onset of pain, movement adaptations are adopted. This study investigated the effect of prolonged experimental neck pain four days after pain onset on gait kinematics during walking.

METHODS

Forty healthy participants were randomized to receive injections of nerve-growth-factor or a control injection of isotonic saline into the right splenius capitis muscle at the end of days 0 and 2. Participants performed two walking tasks, walking and walking while reading on a smartphone, on days 0, 4, and 15. Gait kinematics, spatiotemporal parameters, and gait stability were measured using Xsens Awinda.

FINDINGS

The nerve-growth-factor group reported increased neck pain intensity (median VAS 17.5 [IQR: 2.75-25.75]) on day 4 compared to day 0 and day 15. No pain intensity changes between days were reported for the isotonic-group. For gait kinematics, a main effect of the task was identified, showing that during the smartphone condition, participants had shorter stride lengths and reduced RoM for the trunk, hip, knee, and ankle compared to normal waking (P < 0.006).

INTERPRETATION

Walking while reading on a smartphone, but not mild neck muscle pain, caused changes in the gait kinematics compared to normal walking without neck pain. This finding suggests that movement alterations during walking are not an early feature of prolonged experimental neck pain.

SPATIOTEMPORAL GAIT PARAMETERS, PELVIC MOVEMENTS AND PELVIC SYMMETRY DURING SINGLE AND DUAL TASK IN INDIVIDUALS WITH CHRONIC IDIOPATHIC NECK PAIN

Purpose: Neck pain is associated with gait disturbances. The first aim of this study was to examine spatiotemporal gait parameters, pelvic movements and pelvic symmetry during single and dual task conditions in persons with CINP compared to healthy subjects (HS). The second aim was to investigate the relationship between disability and gait in persons with CINP.Methods: Thirty-one individuals with CINP (mean age 22.06±1.38 years, range 18-56 years) and 24 HS (mean age 22.60±1.51 years, range 18-43 years) participated in the study. CINP group completed Neck Disability Index. All participants performed 10-meter walking test in two conditions: (1) walking with head straight at usual speed, (2) walking with head straight at usual speed while carrying a tray. The BTS G-Walk wearable sensor was used to assess spatiotemporal parameters (speed, cadence, step length), pelvic movements (tilt, obliquity, rotation), and pelvic symmetry. Results: During single task gait, the CINP group had significantly lower walking speed, shorter stride length and worse pelvic tilt symmetry (p&lt;0.05), but pelvic movements did not differ significantly according to the HS (p&lt;0.05). During dual task condition, the CINP group had significantly lower cadence, gait speed and stride length, significantly increased pelvic movements and significantly worse pelvic rotation symmetry compared to HS (p&lt;0.05). Conclusion: Patients with CINP had a slower gait, lower stride length, decreased pelvic symmetry and increased pelvic movements compared to HS. Differences in gait and pelvic movements between the two groups were more pronounced during the dual task gait than single task gait. 

A machine learning approach for the identification of kinematic biomarkers of chronic neck pain during single- and dual-task gait

BACKGROUND

Changes in gait characteristics have been reported in people with chronic neck pain (CNP).

RESEARCH QUESTION

Can we classify people with and without CNP by training machine learning models with Inertial Measurement Units (IMU)-based gait kinematic data?

METHODS

Eighteen asymptomatic individuals and 21 participants with CNP were recruited for the study and performed two gait trajectories, (1) linear walking with their head straight (single-task) and (2) linear walking with continuous head-rotation (dual-task). Kinematic data were recorded from three IMU sensors attached to the forehead, upper thoracic spine (T1), and lower thoracic spine (T12). Temporal and spectral features were extracted to generate the dataset for both single- and dual-task gait. To evaluate the most significant features and simultaneously reduce the dataset size, the Neighbourhood Component Analysis (NCA) method was utilized. Three supervised models were applied, including K-Nearest Neighbour, Support Vector Machine, and Linear Discriminant Analysis to test the performance of the most important temporal and spectral features.

RESULTS

The performance of all classifiers increased after the implementation of NCA. The best performance was achieved by NCA-Support Vector Machine with an accuracy of 86.85%, specificity of 83.30%, and sensitivity of 92.85% during the dual-task gait using only nine features.

SIGNIFICANCE

The results present a data-driven approach and machine learning-based methods to identify test conditions and features from high-dimensional data obtained during gait for the classification of people with and without CNP.

A Comparative study on the assessment of dual task performance in rheumatic diseases

BACKGROUND

In task-oriented studies showed that the chronic pain is effective on dual tasks. Chronic pain is the main health problem that prevents mobility restriction and participation in most rheumatic diseases.

RESEARCH QUESTION

Do rheumatic diseases have an effect on dual task gait performance?

METHODS

This comparative-descriptive study included 75 individuals who aged 18-65 years and divided in two groups as Rheumatic Disease Group (RG; 23 women, 14 men) and Control Group (CG; 20 women, 18 men). The individuals have a chronic pain (> 3.4 cm according to Visual Analogue Scale, VAS) and Standardized Mini Mental State Examination score above 24 were included in this study as the RG. The individuals who were did not have any known disease were included in the CG. The health status of RG was evaluated with the Arthritis Impact Measurement Scale 2 (AIMS-2). The 10-meter Walk Test was applied under single and dual task conditions (dual task cognitive, DT_cognitive; dual task motor, DT_motor) for assessing gait performance.

RESULTS AND SIGNIFICANCE

The mean age of the individuals in the study was 40.6 ± 11.34 years (RG=43.08 ± 11.30; CG=38.18 ± 11.00). There was a significant difference in favor of CG between the groups both in terms of gait speed in DT_cognitive and its cost (p < 0.05). VAS scores correlate with single and DT_cognitive and DT_motor gait parameters (p < 0.05). Many subdivisions of AIMS-2 were associated with single, DT_cognitive and DT_motor gait parameters (p < 0.05). This study concluded that rheumatic diseases may reduce gait performance in concurrent motor-cognitive dual task conditions due to chronic pain. Single and dual task gait parameters may be related with psychosocial factors. Therefore, applications including pain control and biopsychosocial approach may be beneficial in the management gait disturbances and falls due to a rheumatic disease.

Comparison of kinematic similarity index during gait between adults with and without nonspecific chronic neck pain

BACKGROUND

Individuals with nonspecific chronic neck pain (NP) walk with a stiffer spine. However, there is a lack of understanding on kinematic similarities on the limbs during gait between individuals with and without NP.

RESEARCH QUESTION

Are there differences in gait parameters and the kinematic similarity index (SI) between individuals with and without NP?

METHODS

Eighteen individuals with NP and 17 controls participated in this study. A three-dimensional motion capture system and two force plates were utilized to measure kinematic changes of the upper and lower limbs during gait. The gait parameters included cadence, speed, stride length, and step width. The SI calculations were compared based on the response vectors from the NP group and the prototype response vectors from the control participants. The SI values at 5% intervals of the entire gait cycle were compared between groups.

RESULTS

Although the gait parameters were not significantly different between groups, the SI values of the control group were significantly higher than the NP group during gait (0.98 ± 0.02 vs. 0.95 ± 0.03), especially at the midstance (10-30 %) and swing (80-90 %) phases. Also, the standard deviation of the SI decreased in the control group when compared to the NP group (0.02 ± 0.01 vs. 0.04 ± 0.02).

SIGNIFICANCE

The SI was a useful measure to differentiate similarities between groups in the gait cycle at specific phases. These results indicated that the NP group demonstrated a greater variation of walking patterns during the midstance and swing phases and displayed altered compensatory gait. Clinicians need to consider the similarities of the kinematic changes for the NP group to aid in detection of limb motion differences and the resulting gait dysfunction.

Investigation of balance performance under different sensory and dual-task conditions in patients with chronic neck pain

BACKGROUND

Most studies suggest that people with chronic neck pain (CNP) have decreased balance abilities. However, balance performance during performing concurrent tasks is not clarified.

OBJECTIVES

To investigate balance performance under different sensory and dual-task conditions in people with and without CNP.

METHOD

Twenty-two women with CNP and twenty-two asymptomatic women were tested using the Biodex Stability System. Overall stability index (OSI), anterior/posterior stability index (APSI), medial/lateral stability index (MLSI) were obtained in two sessions: eyes-open and eyes-closed. Both sessions consisted of four conditions: quiet standing, rotating head, counting backward, standing on foam. Higher index scores mean poorer balance.

DESIGN

Case-Control study.

RESULTS

A mixed factorial ANOVA (2 × 8 design) showed that there was a main effect of CNP on OSI, APSI, and MLSI (p < 0.001), which indicates that CNP causes poor balance. Further, there was an interaction between CNP and test conditions for only OSI (p < 0.05). Simple effects tests showed that patients with CNP had higher OSI in all conditions except standing on foam with eyes-open, and quiet standing and counting backward with eyes-closed (p < 0.05). The largest effect size was obtained during rotating head with eyes-open (η²:0.301), followed by counting backward with eyes-open and quiet standing with eyes-open (η²:0.267 and 0.245). Performing a concurrent task, closing eyes, or standing on foam mostly increased OSI in both groups (p < 0.05).

CONCLUSIONS

Patients with CNP have poorer balance under different sensory and dual-task conditions. Addressing balance assessment while performing concurrent tasks, especially head rotations, may offer new insights into the management of CNP.

Machine learning approaches applied in spinal pain research

The purpose of this narrative review is to provide a critical reflection of how analytical machine learning approaches could provide the platform to harness variability of patient presentation to enhance clinical prediction. The review includes a summary of current knowledge on the physiological adaptations present in people with spinal pain. We discuss how contemporary evidence highlights the importance of not relying on single features when characterizing patients given the variability of physiological adaptations present in people with spinal pain. The advantages and disadvantages of current analytical strategies in contemporary basic science and epidemiological research are reviewed and we consider how analytical machine learning approaches could provide the platform to harness the variability of patient presentations to enhance clinical prediction of pain persistence or recurrence. We propose that machine learning techniques can be leveraged to translate a potentially heterogeneous set of variables into clinically useful information with the potential to enhance patient management.

Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach

People with chronic neck pain (CNP) often present with altered gait kinematics. This paper investigates, combines, and compares the kinematic features from linear and nonlinear walking trajectories to design supervised machine learning models which differentiate asymptomatic individuals from those with CNP. For this, 126 features were extracted from seven body segments of 20 asymptomatic subjects and 20 individuals with non-specific CNP. Neighbourhood Component Analysis (NCA) was used to identify body segments and the corresponding significant features which have the maximum discriminative power for conducting classification. We assessed the efficacy of NCA combined with K- Nearest Neighbour (K-NN), Support Vector Machine and Linear Discriminant Analysis. By applying NCA, all classifiers increased their performance for both linear and nonlinear walking trajectories. Notably, features selected by NCA which magnify the classification power of the computational model were solely from the head, trunk and pelvis kinematics. Our results revealed that the nonlinear trajectory provides the best classification performance through the NCA-K-NN algorithms with an accuracy of 90%, specificity of 100% and sensitivity of 83.3%. The selected features by NCA are introduced as key biomarkers of gait kinematics for classifying non-specific CNP. This paper provides insight into changes in gait kinematics which are present in people with non-specific CNP which can be exploited for classification purposes. The result highlights the importance of curvilinear gait kinematic features which potentially could be utilized in future research to predict recurrent episodes of neck pain.