Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism.

Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson's disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. We attempted to establish an automatic FoG detection method from videos taken in uncontrolled environments such as in daily clinical practices. Motion features of 16 patients were extracted from timed-up-and-go test in 109 video data points, through object tracking and three-dimension pose estimation. These motion features were utilized to form the FoG detection model, which combined rule-based and machine learning-based models. The rule-based model distinguished the frames in which the patient was walking from those when the patient has stopped, using the pelvic position coordinates; the machine learning-based model distinguished between FoG and stop using a combined one-dimensional convolutional neural network and long short-term memory (1dCNN-LSTM). The model achieved a high intraclass correlation coefficient of 0.75-0.94 with a manually-annotated duration of FoG and %FoG. This method is novel as it combines object tracking, 3D pose estimation, and expert-guided feature selection in the preprocessing and modeling phases, enabling FoG detection even from videos captured in uncontrolled environments.

Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation.

Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person's abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular video captured by portable devices like smartphones and tablets. We propose an end-to-end model, achieving an accuracy rate of approximately 76.6% in assessing children with Cerebral Palsy (CP) using the Gross Motor Function Classification System (GMFCS). This represents a 5% improvement in accuracy compared to the current state-of-the-art techniques and demonstrates strong agreement with professional assessments, as indicated by the weighted Cohen's Kappa ( κlw = 0.733 ). In addition, we introduce the use of metric learning through triplet loss and self-supervised training to better handle situations with a limited number of training samples and enable confidence estimation. Setting a confidence threshold at 0.95 , we attain an impressive estimation accuracy of 88% . Notably, our method can be efficiently implemented on a wide range of mobile devices, providing real-time or near real-time results.

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects.

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients.

BACKGROUND: Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS: We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS: We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS: Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.

Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis.

Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.

Estimation of Gait Parameters in Huntington's Disease Using Wearable Sensors in the Clinic and Free-living Conditions.

In Huntington's disease (HD), wearable inertial sensors could capture subtle changes in motor function. However, disease-specific validation of methods is necessary. This study presents an algorithm for walking bout and gait event detection in HD using a leg-worn accelerometer, validated only in the clinic and deployed in free-living conditions. Seventeen HD participants wore shank- and thigh-worn tri-axial accelerometers, and a wrist-worn device during two-minute walk tests in the clinic, with video reference data for validation. Thirteen participants wore one of the thigh-worn tri-axial accelerometers (AP: ActivPAL4) and the wrist-worn device for 7 days under free-living conditions, with proprietary AP data used as reference. Gait events were detected from shank and thigh acceleration using the Teager-Kaiser energy operator combined with unsupervised clustering. Estimated step count (SC) and temporal gait parameters were compared with reference data. In the clinic, low mean absolute percentage errors were observed for stride (shank/thigh: 0.6/0.9%) and stance (shank/thigh: 3.3/7.1%) times, and SC (shank/thigh: 3.1%). Similar errors were observed for proprietary AP SC (3.2%), with higher errors observed for the wrist-worn device (10.9%). At home, excellent agreement was observed between the proposed algorithm and AP software for SC and time spent walking (ICC [Formula: see text]). The wrist-worn device overestimated SC by 34.2%. The presented algorithm additionally allowed stride and stance time estimation, whose variability correlated significantly with clinical motor scores. The results demonstrate a new method for accurate estimation of HD gait parameters in the clinic and free-living conditions, using a single accelerometer worn on either the thigh or shank.

Temporal and spatial characteristics of plantar pressure center trajectory for identifying early Parkinson's disease gait.

Gait analysis can be utilized as an effective method for identifying Parkinson's disease (PD) [1]. However, research methods based on the time-domain gait feature analysis are influenced by population characteristics such as individual height, age, and weight, which unfavorably affect PD diagnostic decision-making.

Vision-based motion capture for the gait analysis of neurodegenerative diseases: A review.

BACKGROUND: Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming. RESEARCH QUESTION: This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice. METHODS: The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles. RESULTS: A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability. SIGNIFICANCE: Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

Stroke walking and balance characteristics via principal component analysis.

Balance impairment is associated gait dysfunction with several quantitative spatiotemporal gait parameters in patients with stroke. However, the link between balance impairments and joint kinematics during walking remains unclear. Clinical assessments and gait measurements using motion analysis system was conducted in 44 stroke patients. This study utilised principal component analysis to identify key joint kinematics characteristics of patients with stroke during walking using average joint angles of pelvis and bilateral lower limbs in every gait-cycle percentile related to balance impairments. Reconstructed kinematics showed the differences in joint kinematics in both paretic and nonparetic lower limbs that can be distinguished by balance impairment, particularly in the sagittal planes during swing phase. The impaired balance group exhibited greater joint variability in both the paretic and nonparetic limbs in the sagittal plane during entire gait phase and during terminal swing phase respectively compared with those with high balance scores. This study provides a more comprehensive understanding of stroke hemiparesis gait patterns and suggests considering both nonparetic and paretic limb function, as well as bilateral coordination in clinical practice. Principal component analysis can be a useful assessment tool to distinguish differences in balance impairment and dynamic symmetry during gait in patients with stroke.

Combined diagnosis for Parkinson's disease via gait and eye movement disorders.

BACKGROUND AND OBJECTIVES: With the discovery of the potential role of gait and eye movement disorders in Parkinson's disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD. METHODS: We enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages. RESULTS: A total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients. CONCLUSION: We demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.

Predicting post-surgical outcomes in idiopathic normal pressure hydrocephalus using clinically important changes from the cerebrospinal fluid tap test.

OBJECTIVE: Patients diagnosed with idiopathic Normal Pressure Hydrocephalus (iNPH) typically experience symptom improvements after undergoing a cerebrospinal fluid-tap test (CSF-TT), These improvements are recognized as indicative of potential improvements following surgical intervention. As gait disturbance is the most common iNPH symptom, gait improvements are of predominant interest. The purpose of this study was to examine if clinically important changes in gait and balance from CSF-TT predict meaningful changes following surgery. METHOD: The study involved analysis of data collected in a prospective observational study for 34 iNPH patients who underwent a CSF-TT and subsequent surgery. Linear regression, logistic regression and classification trees were used for predictive modelling comparing changes from CSF-TT with post-surgical changes in Tinetti, Timed Up and Go (TUG) and Berg Balance Scale (BBS) outcomes. RESULTS: Predictive models for minimal clinically important differences (MCIDs) from CSF-TT to surgery were significant for Tinetti (odds ratio = 1.42, p = 0.02) and BBS (odds ratio = 1.57, p < 0.01). Four items on Tinetti and two items on BBS were identified with a predictive accuracy of 79% and 76% respectively. BBS has the highest sensitivity (85%) and negative predictive value (77%). TUG had a 100% specificity and 100% positive predictive value. The predictive model using MCIDs for TUG was not significant (odds ratio = 1.13, p = 0.06). CONCLUSION: Clinically important changes from CSF-TT are useful in predicting post-surgical outcomes in iNPH patients. Tinetti and BBS, both have predictive value using MCID scores as cut off values, of which BBS is a stronger outcome measure for prediction.

Predicting slight freezing of gait in Parkinson's disease with anticipatory postural adjustments and limits of stability.

INTRODUCTION: Gait initiation (GI) includes automatic and voluntary movements. However, research on their impact on the first step in patients with Parkinson's disease (PD) and their relationship to freezing of gait (FOG) is lacking. We examined the effects of automatic movements (anticipatory postural adjustments [APAs]) and voluntary movements (limits of stability [LOS]) on the first step (first-step duration and first-step range of motion), along with their early recognition and prediction of slight FOG. METHODS: Twenty-three patients with PD and slight freezing (PD + FOG) and 25 non-freezing patients with PD (PD-FOG) were tested while off medications and compared with 24 healthy controls (HC). All participants completed a 7-m Stand and Walk Test (7 m SAW) and wore inertial sensors to quantify the APAs and first step. LOS was quantified by dynamic posturography in different directions using a pressure platform. We compared differences among all three groups, analysed correlations, and evaluated their predictive value for slight FOG. RESULTS: In PD + FOG, APAs and LOS were worse than those in the PD-FOG and HC groups (p < 0.001), and the first step was worse than that in HC (p < 0.001). APAs were correlated mainly with the first-step duration. APAs and LOS were correlated with the first-step range of motion. APAs have been recognized as independent predictors of FOG, and their combination with LOS enhances predictive sensitivity. CONCLUSION: APAs and LOS in patients with PD directly affect the first step during GI. In addition, the combination of APAs and LOS helped predict slight FOG.

The validation of a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q).

OBJECTIVE: This study aimed to develop a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q) and investigate its validity and reliability. METHODS: After translating the NFOG-Q according to a standardised protocol, 56 patients with Parkinson's disease (PD) were administered it. Additionally, the MDS-UPDRS parts II and III, Hoehn and Yahr (H&Y) stage, and number of falls over 1 month were evaluated. Spearman's correlation coefficients (rho) were used to determine construct validity, and Cronbach's alpha (α) was used to examine reliability. RESULTS: The interquartile range of the NFOG-Q scores was 10.0-25.3 (range 0-29). The NFOG-Q scores were strongly correlated with the MDS-UPDRS part II, items 2.12 (walking and balance), 2.13 (freezing), 3.11 (freezing of gait), and 3.12 (postural stability) and the postural instability and gait difficulty score (rho = 0.515-0.669), but only moderately related to the MDS-UPDRS item 3.10 (gait), number of falls, disease duration, H&Y stage, and time of the Timed Up-and-Go test (rho = 0.319-0.434). No significant correlations were observed between age and the time of the 10-m walk test. The internal consistency was excellent (α = 0.96). CONCLUSIONS: The Japanese version of the NFOG-Q is a valid and reliable tool for assessing the severity of freezing in patients with PD.

Patterns and assessment of spastic hemiplegic gait.

Hemiparetic gait disorders are common in stroke survivors. A circumductory gait is often considered the typical hemiparetic gait. In clinical practice, a wide spectrum of abnormal gait patterns is observed, depending on the severity of weakness and spasticity, and the anatomical distribution of spasticity. Muscle strength is the key determinant of gait disorders in hemiparetic stroke survivors. Spasticity and its associated involuntary activation of synergistic spastic muscles often alter posture of involved joint(s) and subsequently the alignment of hip, knee, and ankle joints, resulting in abnormal gait patterns. Due to combinations of various levels of muscle weakness and spasticity and their interactions with ground reaction force, presentations of gait disorders are variable. From a neuromechanical perspective, a stepwise visual gait analysis approach is proposed to identify primary underlying causes. In this approach, the pelvic and hip joint movement is examined first. The pelvic girdle constitutes three kinematic determinants. Its abnormality determines the body vector and compensatory kinetic chain reactions in the knee and ankle joints. The second step is to assess the ankle and foot complex abnormality. The last step is to examine abnormality of the knee joint. Assessment of muscle strength and spasticity of hip, knee, and ankle/foot joints needs to be performed before these steps. Lidocaine nerve blocks can be a useful diagnostic tool. Recognizing different patterns and identifying the primary causes are critical to developing clinical interventions to improve gait functions.

Digital Gait Measures Capture 1-Year Progression in Early-Stage Spinocerebellar Ataxia Type 2.

BACKGROUND: With disease-modifying drugs in reach for cerebellar ataxias, fine-grained digital health measures are highly warranted to complement clinical and patient-reported outcome measures in upcoming treatment trials and treatment monitoring. These measures need to demonstrate sensitivity to capture change, in particular in the early stages of the disease. OBJECTIVE: Our aim is to unravel gait measures sensitive to longitudinal change in the-particularly trial-relevant-early stage of spinocerebellar ataxia type 2 (SCA2). METHODS: We performed a multicenter longitudinal study with combined cross-sectional and 1-year interval longitudinal analysis in early-stage SCA2 participants (n = 23, including nine pre-ataxic expansion carriers; median, ATXN2 CAG repeat expansion 38 ± 2; median, Scale for the Assessment and Rating of Ataxia [SARA] score 4.8 ± 4.3). Gait was assessed using three wearable motion sensors during a 2-minute walk, with analyses focused on gait measures of spatio-temporal variability that have shown sensitivity to ataxia severity (eg, lateral step deviation). RESULTS: We found significant changes for gait measures between baseline and 1-year follow-up with large effect sizes (lateral step deviation P = 0.0001, effect size rprb = 0.78), whereas the SARA score showed no change (P = 0.67). Sample size estimation indicates a required cohort size of n = 43 to detect a 50% reduction in natural progression. Test-retest reliability and minimal detectable change analysis confirm the accuracy of detecting 50% of the identified 1-year change. CONCLUSIONS: Gait measures assessed by wearable sensors can capture natural progression in early-stage SCA2 within just 1 year-in contrast to a clinical ataxia outcome. Lateral step deviation represents a promising outcome measure for upcoming multicenter interventional trials, particularly in the early stages of cerebellar ataxia. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops.

BACKGROUND: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. METHODS: Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC). RESULTS: For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data. CONCLUSION: A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.

Force Control Issues in Upper and Lower Limbs in Parkinson's Disease and Freezing of Gait.

Parkinson's Disease (PD) has been found to cause force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop a debilitating symptom called freezing of gait (FOG), which has been linked to force control problems in the lower limbs, and some may only have a limited response to the gold standard pharmaceutical therapy, levodopa, resulting in partially levodopa-responsive FOG (PLR-FOG). There has been limited research on investigating upper-limb force control in people with PD with PLR-FOG, and without FOG. In this pilot study, force control was explored using an upper-and-lower-limb haptics-enabled robot in a reaching task while people with PD with and without PLR-FOG were on their levodopa medication. A healthy control group was used for reference, and each cohort completed the task at three different levels of assistance provided by the robot. Similar significant proportional force control deficits were found in the upper and lower limbs in patients with PLR-FOG versus those without FOG. Some aspects of force control were found to be retained, including an ability to increase or decrease force in response to changes in resistance while completing a reaching task. Overall, these results suggest there are force control deficits in both the upper and lower limbs in people with PLR-FOG.

[Additional functional symptoms in Parkinson's disease and tremor syndromes]. / Zusätzliche funktionelle Symptome bei Parkinson und Tremorsyndromen.

Patients with Parkinson's Disease or a tremor syndrome may present with additional functional movement disorders. The differential diagnosis is particularly difficult. In some cases, functional symptoms occur either before the manifestation of the organic disease or can emerge as an additional symptom after Parkinson's disease or tremor became apparent. In patients with Parkinson's disease the prevalence for additional functional symptoms is 7 %. In the case that patients with Parkinson's diseases have one side that is more severely affected, additional functional motor symptoms such as functional rest tremor also occur on that same, predominantly affected side. Functional gait disorders occur frequently. Clinically, patients appear notably slow in automatized, daily tasks. Their speech is more whispering than hypophonic, bradykinesia during finger tapping manifest without a decrement. The Dopamintransporterszintigraphy (123) I FP-CIT SPECT; DaTSCANTM) may be helpful to differentiate between functional Parkinsonism and Parkinson's disease. Functional tremor in patients with an organic tremor syndrome is diagnosed with the same distraction techniques as in solely functional tremor. This includes cognitive, motor, and suggestive distraction maneuvers. In some cases, additional neurophysiological investigations such as accelerometry are useful for the differential diagnosis. It is most important to identify patients with additional functional symptoms in non-functional movement disorders, because the therapeutic approach differs and a multi professional team is required to initiate effective treatment strategies.

Update on the reliability of gait analysis interpretation in cerebral palsy: Inter-institution agreement.

BACKGROUND: Studies have shown good reliability for gait analysis interpretation among surgeons from the same institution. However, reliability among surgeons from different institutions remains to be determined. RESEARCH QUESTION: Is gait analysis interpretation by surgeons from different institutions as reliable as it is for surgeons from the same institution? METHODS: Gait analysis data for 67 patients with cerebral palsy (CP) were reviewed prospectively by two orthopedic surgeons from different institutions in the same state, each with > 10 years' experience interpreting gait analysis data. The surgeons identified gait problems and made treatment recommendations for each patient using a rating form. Percent agreement between raters was calculated for each problem and treatment, and compared to expected agreement based on chance using Cohen's kappa. RESULTS: For problem identification, the greatest agreement was seen for equinus (85% agreement), calcaneus (88%), in-toeing (89%), and out-toeing (90%). Agreement for the remaining problems ranged between 66-78%. Percent agreement was significantly higher than expected due to chance for all issues (p ≤ 0.01) with modest kappa values ranging from 0.12 to 0.51. Agreement between surgeons for treatment recommendations was highest for triceps surae lengthening (89% agreement), tibial derotation osteotomy (90%), and foot osteotomy (87%). Agreement for the remaining treatments ranged between 72-78%. Percent agreement for all treatments was significantly higher than the expected values (p ≤ 0.002) with modest kappa values ranging from 0.22 to 0.52. SIGNIFICANCE: Previous research established that computerized gait analysis data interpretation is reliable for surgeons within a single institution. The current study demonstrates that gait analysis interpretation can also be reliable among surgeons from different institutions. Future research should examine reliability among physicians from more institutions to confirm these results.