Ensemble approach for predicting the diagnosis of osteoarthritis using physical activity factors.

BACKGROUND: Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity. OBJECTIVES: this study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and identify important variables in constructing the model through permutation importance. METHODS: By using the recursive feature elimination, cross-validated technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining RandomForest, XGBoost, and LightGBM algorithms was constructed. The predictive performance and permutation importance of each variable were evaluated. RESULTS: The variables selected to construct the model were age, sex, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and sex (0.034). CONCLUSION: The performance of the model for predicting OA was relatively good. If this model is continuously used and updated, it could be used to predict OA diagnosis, and the predictive performance of the OA model may be further improved.

Comparison of wrist range of motion and muscle strength in assembly workers with and without lateral epicondylitis.

BACKGROUND: Lateral epicondylitis (LE), also called tennis elbow, is a common musculoskeletal disorder that causes pain in the elbow area and is highly prevalent in assembly workers who repeatedly move their wrists. OBJECTIVE: The purpose of this study was to compare the wrist ROM and muscle strength of assembly workers with and without LE. METHODS: Forty-five male assembly line workers (23 with LE) participated in the study. Participants had their wrist range of motion (flexion, extension, ulnar deviation, and radial deviation) and strength (wrist flexors, extensors, and hand grip) measured using Smart KEMA sensors. RESULTS: Workers with LE showed significantly reduced wrist extension and radial deviation ROM compared to workers without LE, with no significant differences in wrist flexion and ulnar deviation ROM between groups. Workers with LE had significantly lower wrist extensor strength compared to workers without LE, and there was no significant difference in wrist flexor and grip strength between the two groups. CONCLUSIONS: For workers with LE, the difference in wrist ROM and muscle strength will be useful for planning intervention and evaluating treatment outcomes for assembly workers with LE.

Comparative analysis of machine learning models for efficient low back pain prediction using demographic and lifestyle factors.

BACKGROUND: Low back pain (LBP) is one of the most frequently occurring musculoskeletal disorders, and factors such as lifestyle as well as individual characteristics are associated with LBP. OBJECTIVE: The purpose of this study was to develop and compare efficient low back pain prediction models using easily obtainable demographic and lifestyle factors. METHODS: Data from adult men and women aged 50 years or older collected from the Korean National Health and Nutrition Examination Survey (KNHANES) were used. The dataset included 22 predictor variables, including demographic, physical activity, occupational, and lifestyle factors. Four machine learning algorithms, including XGBoost, LGBM, CatBoost, and RandomForest, were used to develop predictive models. RESULTS: All models achieved an accuracy greater than 0.8, with the LGBM model outperforming the others with an accuracy of 0.830. The CatBoost model had the highest sensitivity (0.804), while the LGBM model showed the highest specificity (0.884) and F1-Score (0.821). Feature importance analysis revealed that EQ-5D was the most critical variable across all models. CONCLUSION: In this study, an efficient LBP prediction model was developed using easily accessible variables. Using this model, it may be helpful to identify the risk of LBP in advance or establish prevention strategies in subjects who have difficulty accessing medical facilities.

Bioceramic-mediated chondrocyte hypertrophy promotes calcified cartilage formation for rabbit osteochondral defect repair.

Osteochondral tissue is a highly specialized and complex tissue composed of articular cartilage and subchondral bone that are separated by a calcified cartilage interface. Multilayered or gradient scaffolds, often in conjunction with stem cells and growth factors, have been developed to mimic the respective layers for osteochondral defect repair. In this study, we designed a hyaline cartilage-hypertrophic cartilage bilayer graft (RGD/RGDW) with chondrocytes. Previously, we demonstrated that RGD peptide-modified chondroitin sulfate cryogel (RGD group) is chondro-conductive and capable of hyaline cartilage formation. Here, we incorporated whitlockite (WH), a Mg2+-containing calcium phosphate, into RGD cryogel (RGDW group) to induce chondrocyte hypertrophy and form collagen X-rich hypertrophic cartilage. This is the first study to use WH to produce hypertrophic cartilage. Chondrocytes-laden RGDW cryogel exhibited significantly upregulated expression of hypertrophy markers in vitro and formed ectopic hypertrophic cartilage in vivo, which mineralized into calcified cartilage in bone microenvironment. Subsequently, RGD cryogel and RGDW cryogel were combined into bilayer (RGD/RGDW group) and implanted into rabbit osteochondral defect, where RGD layer supports hyaline cartilage regeneration and bioceramic-containing RGDW layer promotes calcified cartilage formation. While the RGD group (monolayer) formed hyaline-like neotissue that extends into the subchondral bone, the RGD/RGDW group (bilayer) regenerated hyaline cartilage tissue confined to its respective layer and promoted osseointegration for integrative defect repair.

Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.

OBJECTIVES: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advanced relationship exists between neck pain and craniocervical alignment, which requires a deeper exploration of diverse postures and movement patterns using advanced techniques, such as clustering analysis. We aimed to explore the complex relationship between craniocervical alignment, and neck pain and to categorize alignment patterns in individuals with nonspecific neck pain using the K-means algorithm. METHODS: This study included 229 office workers with nonspecific neck pain who applied unsupervised machine learning techniques. The craniocervical angles (CCA) during rest, protraction, and retraction were measured using two-dimensional video analysis, and neck pain severity was assessed using the Northwick Park Neck Pain Questionnaire (NPQ). CCA during sitting upright in a comfortable position was assessed to evaluate the resting CCA. The average of midpoints between repeated protraction and retraction measures was considered as the midpoint CCA. The K-means algorithm helped categorize participants into alignment clusters based on age, sex and CCA data. RESULTS: We found no significant correlation between NPQ scores and CCA data, challenging the traditional understanding of neck pain and alignment. We observed a significant difference in age (F = 140.14, p < 0.001), NPQ total score (F = 115.83, p < 0.001), resting CCA (F = 79.22, p < 0.001), CCA during protraction (F = 33.98, p < 0.001), CCA during retraction (F = 40.40, p < 0.001), and midpoint CCA (F = 66.92, p < 0.001) among the three clusters and healthy controls. Cluster 1 was characterized by the lowest resting and midpoint CCA, and CCA during pro- and -retraction, indicating a significant forward head posture and a pattern of retraction restriction. Cluster 2, the oldest group, showed CCA measurements similar to healthy controls, yet reported the highest NPQ scores. Cluster 3 exhibited the highest CCA during protraction and retraction, suggesting a limitation in protraction movement. DISCUSSION: Analyzing 229 office workers, three distinct alignment patterns were identified, each with unique postural characteristics; therefore, treatments addressing posture should be individualized and not generalized across the population.

Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

AIM: As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity-related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. METHODS: A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi-layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. RESULTS: The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity-related variables were also used in constructing the model. CONCLUSIONS: The sarcopenia prediction model, which consisted of only physical characteristics and activity-related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; 24: 595-602.

CSF pulsations measured in Parkinson's disease patients using EPI-based fMRI data.

Introduction: Cerebrospinal fluid (CSF) flow is involved in brain waste clearance and may be impaired in neurodegenerative diseases such as Parkinson's disease. This study aims to investigate the relationship between the CSF pulsation and the development of dementia in Parkinson's disease (PD) patients using EPI-based fMRI. Methods: We measured CSF pulsation in the 4th ventricle of 17 healthy controls and 35 PD patients using a novel CSF pulsation index termed "CSFpulse" based on echo-planar imaging (EPI)-based fMRI. The PD patients were classified into a PD with dementia high-risk group (PDD-H, n = 19) and a low risk group (PDD-L, n = 16), depending on their development of dementia within 5 years after initial brain imaging. The size of the 4th ventricle was measured using intensity-based thresholding. Results: We found that CSF pulsation was significantly higher in PD patients than in healthy controls, and that PD patients with high risk of dementia (PDD-H) had the highest CSF pulsation. We also observed an enlargement of the 4th ventricle in PD patients compared to healthy controls. Conclusion: Our results suggest that CSF pulsation may be a potential biomarker for PD progression and cognitive decline, and that EPI-based fMRI can be a useful tool for studying CSF flow and brain function in PD.

Machine learning models for classifying non-specific neck pain using craniocervical posture and movement.

OBJECTIVE: Physical therapists and clinicians commonly confirm craniocervical posture (CCP), cervical retraction, and craniocervical flexion as screening tests because they contribute to non-specific neck pain (NSNP). We compared the predictive performance of statistical machine learning (ML) models for classifying individuals with and without NSNP using datasets containing CCP and cervical kinematics during pro- and retraction (CKdPR). DESIGN: Exploratory, cross-sectional design. SETTING AND PARTICIPANTS: In total, 773 public service office workers (PSOWs) were screened for eligibility (NSNP, 441; without NSNP, 332). METHODS: We set up five datasets (CCP, cervical kinematics during the protraction, cervical kinematics during the retraction, CKdPR and combination of the CCP and CKdPR). Four ML algorithms-random forest, logistic regression, Extreme Gradient boosting, and support vector machine-were trained. MAIN OUTCOME MEASURES: Model performance were assessed using area under the curve (AUC), accuracy, precision, recall and F1-score. To interpret the predictions, we used Feature permutation importance and SHapley Additive explanation values. RESULTS: The random forest model in the CKdPR dataset classified PSOWs with and without NSNP and achieved the best AUC among the five datasets using the test data (AUC, 0.892 [good]; F1, 0.832). The random forest model in the CCP dataset had the worst AUC among the five datasets using the test data [AUC, 0.738 (fair); F1, 0.715]. CONCLUSION: ML performance was higher for the CKdPR dataset than for the CCP dataset, suggesting that ML algorithms are more suitable than classical statistical methods for developing robust models for classifying PSOWs with and without NSNP.

Geometry-driven self-supervision for 3D human pose estimation.

Although 3D human pose estimation has recently made strides, it is still difficult to precisely recreate a 3D human posture from a single image without the aid of 3D annotation for the following reasons. Firstly, the process of reconstruction inherently suffers from ambiguity, as multiple 3D poses can be projected onto the same 2D pose. Secondly, accurately measuring camera rotation without laborious camera calibration is a difficult task. While some approaches attempt to address these issues using traditional computer vision algorithms, they are not differentiable and cannot be optimized through training. This paper introduces two modules that explicitly leverage geometry to overcome these challenges, without requiring any 3D ground-truth or camera parameters. The first module, known as the relative depth estimation module, effectively mitigates depth ambiguity by narrowing down the possible depths for each joint to only two candidates. The second module, referred to as the differentiable pose alignment module, calculates camera rotation by aligning poses from different views. The use of these geometrically interpretable modules reduces the complexity of training and yields superior performance. By adopting our proposed method, we achieve state-of-the-art results on standard benchmark datasets, surpassing other self-supervised methods and even outperforming several fully-supervised approaches that heavily rely on 3D annotations.

Classification of chronic ankle instability using machine learning technique based on ankle kinematics during heel rise in delivery workers.

Objective: Ankle injuries in delivery workers (DWs) are often caused by trips, and high recurrence rates of ankle sprains are related to chronic ankle instability (CAI). Heel rise requires joint angles and moments similar to those of the terminal stance phase of walking that the foot supinates. Thus, our study aimed to develop, determine, and compare the predictive performance of statistical machine learning models to classify DWs with and without CAI using ankle kinematics during heel rise. Methods: In total, 203 DWs were screened for eligibility. Seven predictors were included in our study (age, work duration, body mass index, calcaneal stance position angle [CSPA] in the initial and terminal positions during heel rise, calcaneal movement during heel rise [CMHR], and plantar flexion angle during heel rise). Six machine learning algorithms, including logistic regression, decision tree, AdaBoost, Extreme Gradient boosting machines, random forest, and support vector machine, were trained. Results: The random forest model (area under the curve [AUC], 0.967 [excellent]; F1, 0.889; accuracy, 0.925) confirmed the best predictive performance in the test datasets among the six machine learning models. For Shapley Additive Explanations, old age, low CMHR, high CSPA in the initial position, high PFA, long work duration, low CSPA in the terminal position, and high body mass index were the most important predictors of CAI in the random forest model. Conclusion: Ankle kinematics during heel rise can be considered in the classification of DWs with and without CAI.

All-Solution-Processed High-Performance MoS2 Thin-Film Transistors with a Quasi-2D Perovskite Oxide Dielectric.

Assembling solution-processed van der Waals (vdW) materials into thin films holds great promise for constructing large-scale, high-performance thin-film electronics, especially at low temperatures. While transition metal dichalcogenide thin films assembled in solution have shown potential as channel materials, fully solution-processed vdW electronics have not been achieved due to the absence of suitable dielectric materials and high-temperature processing. In this work, we report on all-solution-processedvdW thin-film transistors (TFTs) comprising molybdenum disulfides (MoS2) as the channel and Dion-Jacobson-phase perovskite oxides as the high-permittivity dielectric. The constituent layers are prepared as colloidal solutions through electrochemical exfoliation of bulk crystals, followed by sequential assembly into a semiconductor/dielectric heterostructure for TFT construction. Notably, all fabrication processes are carried out at temperatures below 250 °C. The fabricated MoS2 TFTs exhibit excellent device characteristics, including high mobility (>10 cm2 V-1 s-1) and an on/off ratio exceeding 106. Additionally, the use of a high-k dielectric allows for operation at low voltage (∼5 V) and leakage current (∼10-11 A), enabling low power consumption. Our demonstration of the low-temperature fabrication of high-performance TFTs presents a cost-effective and scalable approach for heterointegrated thin-film electronics.

Measurement of changes in cerebrospinal fluid pulsation after traumatic brain injury using echo-planar imaging-based functional MRI.

Traumatic brain injury (TBI) is a major public health concern worldwide, with a high incidence and a significant impact on morbidity and mortality. The alteration of cerebrospinal fluid (CSF) dynamics after TBI is a well-known phenomenon; however, the underlying mechanisms and their implications for cognitive function are not fully understood. In this study, we propose a new approach to studying the alteration of CSF dynamics in TBI patients. Our approach involves using conventional echo-planar imaging-based functional MRI with no additional scan, allowing for simultaneous assessment of functional CSF dynamics and blood oxygen level-dependent-based functional brain activities. We utilized two previously suggested indices of (i) CSFpulse, and (ii) correlation between global brain activity and CSF inflow. Using CSFpulse, we demonstrated a significant decrease in CSF pulsation following TBI (p < 0.05), which was consistent with previous studies. Furthermore, we confirmed that the decrease in CSF pulsation was most prominent in the early months after TBI, which could be explained by ependymal ciliary loss, intracranial pressure increment, or aquaporin-4 dysregulation. We also observed a decreasing trend in the correlation between global brain activity and CSF inflow in TBI patients (p < 0.05). Our findings suggest that the decreased CSF pulsation after TBI could lead to the accumulation of toxic substances in the brain and an adverse effect on brain function. Further longitudinal studies with larger sample sizes, TBI biomarker data, and various demographic information are needed to investigate the association between cognitive decline and CSF dynamics after TBI. Overall, this study sheds light on the potential role of altered CSF dynamics in TBI-induced neurologic symptoms and may contribute to the development of novel therapeutic interventions.

Prediction model of subacromial pain syndrome in assembly workers using shoulder range of motion and muscle strength based on support vector machine.

Subacromial pain syndrome (SAPS) is the most common upper-extremity musculoskeletal problem among workers. In this study, a machine learning model was built to predict and classify the presence or absence of SAPS in assembly workers with shoulder joint range of motion (ROM) and muscle strength data using support vector machine (SVM). Permutation importance was used to determine important variables for predicting workers with or without SAPS. The accuracy of the support vector classifier (SVC) polynomial model for classifying workers with SAPS was 82.4%. The important variables in model construction were internal rotation and abduction of shoulder ROM and internal rotation of shoulder muscle strength. It is possible to accurately perform SAPS classification of workers with relatively easy-to-obtain shoulder ROM and muscle strength data using this model. In addition, preventing SAPS in workers is possible by adjusting the factors affecting model building using exercise or rehabilitation programs.Practitioner summary: This study aimed to create a machine learning model that can predict and classify SAPS using shoulder ROM and muscle strength and identify the variables that are of high importance in model construction. This model could be used to predict or classify workers' SAPS and manage or prevent SAPS.

Relationship between core self-evaluation and innovative work behavior: mediating effect of affective organizational commitment and moderating effect of organizational learning capacity.

Focusing on employees, this study examined the respective mediating and moderating effects of affective organizational commitment and organizational learning capacity in the relationship between core self-evaluation and innovation work behavior. We collected data via an online survey from 330 office workers at midsize and large companies in a metropolitan area of South Korea. The results of analyzing the data using PROCESS macro were as follows: (1) core self-evaluation was positively related to innovative work behavior; (2) the relationship was mediated by affective organizational commitment; (3) the relationship was buffered by organizational learning capacity, such that a higher level of organizational learning capacity diminished the impact of core self-evaluation on innovative wok behavior; and (4) the conditional effect of core self-evaluation on innovative work behavior existed only in the group of a low level of organizational learning capacity. Based on these findings, we suggested implications for theory building, research, and practice.

Orthogonal Dual Photocatalysis of Single Atoms on Carbon Nitrides for One-Pot Relay Organic Transformation.

Single-atom photocatalysis has shown potential in various single-step organic transformations, but its use in multistep organic transformations in one reaction systems has rarely been achieved. Herein, we demonstrate atomic site orthogonality in the M1/C3N4 system (where M = Pd or Ni), enabling a cascade photoredox reaction involving oxidative and reductive reactions in a single system. The system utilizes visible-light-generated holes and electrons from C3N4, driving redox reactions (e.g., oxidation and fluorination) at the surface of C3N4 and facilitating cross-coupling reactions (e.g., C-C and C-O bond formation) at the metal site. The concept is generalized to different systems of Pd and Ni, thus making the catalytic site-orthogonal M1/C3N4 system an ideal photocatalyst for improving the efficiency and selectivity of multistep organic transformations.

Gastrodia elata rhizoma ameliorates thioacetamide-induced liver injury in dogs.

Objective: The study aimed to investigate the hepatoprotective effects of Gastrodia elata rhizome (GR) on thioacetamide (TAA)-induced liver injury in dogs. We evaluated serum biochemical and hematological parameters, with emphasis on alanine transaminase (ALT), alanine phosphates (ALP), and nitric oxide (NO) levels, in dogs with TAA-induced liver injury. Materials and Methods: The animals were divided into a control group (Con), TAA group, Silymarin group (Sil, 50 mg/kg), Gastrodia rhizome low dose (GRL) (low) + TAA, GRH (high) + TAA, and GR high-dose group (GRH) control group. GRL and GRH were given daily at 50 and 100 mg/kg, respectively. TAA was given on days 1, 4, and 7 at a dose of 300 mg/kg. Results: GR significantly reduced liver injury in treated animals, as indicated by lowered levels of ALT (about 32% at day 21 in both GRL + TAA and GRH + TAA groups), ALP (about 17% and 21% at day 21 in both GRL + TAA, GRH + TAA groups, respectively), and NO (about 36% at day 21 in both GRL + TAA, GRH + TAA groups) compared to the TAA control group. Hematological parameters showed mild changes during the experiment. High-performance liquid chromatography analysis revealed gastrodin, a major component of the GR extract, constitutes 2.6% of the extract. Conclusion: The GR demonstrated significant hepatoprotective effects against TAA-induced liver injury in dogs. The study provides evidence for the potential therapeutic use of GR in the management of liver diseases.

Non-invasive flow mapping of parasagittal meningeal lymphatics using 2D interslice flow saturation MRI.

The clearance pathways of brain waste products in humans are still under debate in part due to the lack of noninvasive imaging techniques for meningeal lymphatic vessels (mLVs). In this study, we propose a new noninvasive mLVs imaging technique based on an inter-slice blood perfusion MRI called alternate ascending/descending directional navigation (ALADDIN). ALADDIN with inversion recovery (IR) at single inversion time of 2300 ms (single-TI IR-ALADDIN) clearly demonstrated parasagittal mLVs around the human superior sagittal sinus (SSS) with better detectability and specificity than the previously suggested noninvasive imaging techniques. While in many studies it has been difficult to detect mLVs and confirm their signal source noninvasively, the detection of mLVs in this study was confirmed by their posterior to anterior flow direction and their velocities and morphological features, which were consistent with those from the literature. In addition, IR-ALADDIN was compared with contrast-enhanced black blood imaging to confirm the detection of mLVs and its similarity. For the quantification of flow velocity of mLVs, IR-ALADDIN was performed at three inversion times of 2000, 2300, and 2600 ms (three-TI IR-ALADDIN) for both a flow phantom and humans. For this preliminary result, the flow velocity of the dorsal mLVs in humans ranged between 2.2 and 2.7 mm/s. Overall, (i) the single-TI IR-ALADDIN can be used as a novel non-invasive method to visualize mLVs in the whole brain with scan time of ~ 17 min and (ii) the multi-TI IR-ALADDIN can be used as a way to quantify the flow velocity of mLVs with a scan time of ~ 10 min (or shorter) in a limited coverage. Accordingly, the suggested approach can be applied to noninvasively studying meningeal lymphatic flows in general and also understanding the clearance pathways of waste production through mLVs in humans, which warrants further investigation.

Sensor Data Reconstruction for Dynamic Responses of Structures Using External Feedback of Recurrent Neural Network.

An event of sensor faults in sensor networks deployed in structures might result in the degradation of the structural health monitoring system and lead to difficulties in structural condition assessment. Reconstruction techniques of the data for missing sensor channels were widely adopted to restore a dataset from all sensor channels. In this study, a recurrent neural network (RNN) model combined with external feedback is proposed to enhance the accuracy and effectiveness of sensor data reconstruction for measuring the dynamic responses of structures. The model utilizes spatial correlation rather than spatiotemporal correlation by explicitly feeding the previously reconstructed time series of defective sensor channels back to the input dataset. Because of the nature of spatial correlation, the proposed method generates robust and precise results regardless of the hyperparameters set in the RNN model. To verify the performance of the proposed method, simple RNN, long short-term memory, and gated recurrent unit models were trained using the acceleration datasets obtained from laboratory-scaled three- and six-story shear building frames.

Comparison of shoulder functions between assembly workers with and without subacromial pain syndrome.

BACKGROUND: The occurrence of subacromial pain syndrome (SPS) is associated with the frequent handling and lifting of heavy loads and excessive repetitive work. Thus, assembly workers have a high prevalence of SPS. OBJECTIVE: The purpose of this study was to investigate differences in shoulder ROM, muscle strength, asymmetry ratio, function, productivity, and depression between workers with and without SPS. METHODS: Sixty-seven male workers (35 workers with SPS and 32 workers without SPS) participated in this study. Shoulder internal rotation (SIR), shoulder external rotation (SER), shoulder abduction (SAB), shoulder horizontal adduction ROM and SIR, SER, elbow flexion (EF), scapular depression and adduction, scapular protraction strength were measured. The asymmetry ratio was calculated using the asymmetry ratio formula; shoulder functions were measured using the shoulder pain and disability index (SPADI), disabilities of the arm, shoulder, and hand (DASH), and visual analogue scale (VAS); and Endicott work productivity scale (EWPS). RESULTS: The SPADI (p = 0.001), DASH (p = 0.001), and VAS (p = 0.001) values of workers with SPS were higher than those of workers without SPS. Also, workers with SPS had lower SIR (p = 0.001) and SAB (p = 0.002) ROM compared to workers without SPS. In addition, workers with SPS exhibited lower SIR (p = 0.012) strength than workers without SPS. Workers with SPS had higher asymmetry ratio in SIR (p = 0.015), SER (p = 0.005), and EF (p = 0.008) strength than workers without SPS. CONCLUSIONS: The SIR, SAB ROM, SIR strength, and the asymmetry ratio of SIR, SER, EF strengths could provide an important baseline comparison for the workers with SPS.