Effect of osteopathic manipulation on gait asymmetry

Effect of manual manipulation on mechanical gait parameters

CONTEXT

A variety of manual manipulation techniques are utilized in clinical practice to alleviate pain and improve musculoskeletal function. Many manual practitioners analyze gait patterns and asymmetries in their assessment of the patient, and an increasing number of gait motion capture studies are taking place with recent improvements in motion capture technology. This study is the first systematic review of whether these manual modalities have been shown to produce an objectively measurable change in gait mechanics.

OBJECTIVES

This study was designed to perform a systematic review of the literature to assess the impact of manual medicine modalities on biomechanical parameters of gait.

METHODS

A master search term composed of keywords and Medical Subject Headings (MeSH) search terms from an initial scan of relevant articles was utilized to search six databases. We screened the titles and abstracts of the resulting papers for relevance and then assessed their quality with the Cochrane Risk of Bias Tool. Clinical trials that featured both a manual manipulation intervention and multiple mechanical gait parameters were included. Case reports and other studies that only measured gait speed or other subjective measures of mobility were excluded.

RESULTS

We included 20 studies in our final analysis. They utilize manipulation techniques primarily from osteopathic, chiropractic, massage, and physiotherapy backgrounds. The conditions studied primarily included problems with the back, knee, and ankle, as well as healthy patients and Parkinson's patients. Control groups were highly variable, if not absent. Most studies measured their gait parameters utilizing either multicamera motion capture systems or force platforms.

CONCLUSIONS

Twelve of 20 papers included in the final analysis demonstrated a significant effect of manipulation on gait variables, many of which included either step length, walking speed, or sagittal range of motion (ROM) in joints of the lower extremity. However, the results and study design are too heterogeneous to draw robust conclusions from these studies as a whole. While there are initial indications that certain modalities may yield a change in certain gait parameters, the quality of evidence is low and there is insufficient evidence to conclude that manual therapies induce changes in biomechanical gait parameters. Studies are heterogeneous with respect to the populations studied and the interventions performed. Comparators were variable or absent across the studies, as were the outcome variables measured. More could be learned in the future with consistent methodology around blinding and sham treatment, and if the gait parameters measured were standardized and of a more robust clinical significance.

Reliability and Validity of the Variability Model Testing Procedure for Somatic Dysfunction Assessment: A Comparison with Gait Analysis Parameters in Healthy Subjects

Somatic dysfunction (SD) is an altered body function involving the musculoskeletal system. However, its clinical signs-tissue texture abnormalities, positional asymmetry, restricted range of motion, and tissue tenderness-did not achieve satisfactory results for reliability. A recent theoretical model proposed a revision assessing the movement variability around the joint rest position. The asymmetry and restriction of motion may characterize functional assessment in osteopathic clinical practice, demonstrating the reliability required. Hence, this study investigated the reliability of the new variability model (VM) with gait analysis (GA). Three blind examiners tested 27 young healthy subjects for asymmetry of motion around rest position and the SD grade on six body regions. The results were compared to the VICON procedure for 3D-GA. The inter-rater agreement for the detection of reduced movement variability ranged from 0.78 to 0.54, whereas for SD, grade ranged from 0.64 to 0.47. VM had a sensitivity and specificity of 0.62 and 0.53, respectively, in SD detection compared to step length normality. Global severity grade of SD demonstrated moderate to good correlation with spatial-temporal parameters. The VM showed palpatory reliability and validity with spatial-temporal parameters in GA. Those findings contribute to the innovation for SD examination with implications for the clinical practice.

Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence

A novel approach is presented in this study for the classification of lower limb disorders, with a specific emphasis on the knee, hip, and ankle. The research employs gait analysis and the extraction of PoseNet features from video data in order to effectively identify and categorize these disorders. The PoseNet algorithm facilitates the extraction of key body joint movements and positions from videos in a non-invasive and user-friendly manner, thereby offering a comprehensive representation of lower limb movements. The features that are extracted are subsequently standardized and employed as inputs for a range of machine learning algorithms, such as Random Forest, Extra Tree Classifier, Multilayer Perceptron, Artificial Neural Networks, and Convolutional Neural Networks. The models undergo training and testing processes using a dataset consisting of 174 real patients and normal individuals collected at the Tehsil Headquarter Hospital Sadiq Abad. The evaluation of their performance is conducted through the utilization of K-fold cross-validation. The findings exhibit a notable level of accuracy and precision in the classification of various lower limb disorders. Notably, the Artificial Neural Networks model achieves the highest accuracy rate of 98.84%. The proposed methodology exhibits potential in enhancing the diagnosis and treatment planning of lower limb disorders. It presents a non-invasive and efficient method of analyzing gait patterns and identifying particular conditions.

Validity and Reliability of a Smartphone App for Gait and Balance Assessment

Advances in technology provide an opportunity to enhance the accuracy of gait and balance assessment, improving the diagnosis and rehabilitation processes for people with acute or chronic health conditions. This study investigated the validity and reliability of a smartphone-based application to measure postural stability and spatiotemporal aspects of gait during four static balance and two gait tasks. Thirty healthy participants (aged 20–69 years) performed the following tasks: (1) standing on a firm surface with eyes opened, (2) standing on a firm surface with eyes closed, (3) standing on a compliant surface with eyes open, (4) standing on a compliant surface with eyes closed, (5) walking in a straight line, and (6) walking in a straight line while turning their head from side to side. During these tasks, the app quantified the participants’ postural stability and spatiotemporal gait parameters. The concurrent validity of the smartphone app with respect to a 3D motion capture system was evaluated using partial Pearson’s correlations (r_p) and limits of the agreement (LoA%). The within-session test–retest reliability over three repeated measures was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). One-way repeated measures analyses of variance (ANOVAs) were used to evaluate responsiveness to differences across tasks and repetitions. Periodicity index, step length, step time, and walking speed during the gait tasks and postural stability outcomes during the static tasks showed moderate-to-excellent validity (0.55 ≤ r_p ≤ 0.98; 3% ≤ LoA% ≤ 12%) and reliability scores (0.52 ≤ ICC ≤ 0.92; 1% ≤ SEM% ≤ 6%) when the repetition effect was removed. Conversely, step variability and asymmetry parameters during both gait tasks generally showed poor validity and reliability except step length asymmetry, which showed moderate reliability (0.53 ≤ ICC ≤ 0.62) in both tasks when the repetition effect was removed. Postural stability and spatiotemporal gait parameters were found responsive (p < 0.05) to differences across tasks and test repetitions. Along with sound clinical judgement, the app can potentially be used in clinical practice to detect gait and balance impairments and track the effectiveness of rehabilitation programs. Further evaluation and refinement of the app in people with significant gait and balance deficits is needed.