Association between pain interference and motoric cognitive risk syndrome in older adults: a population-based cohort study.

OBJECTIVES: Motoric cognitive risk syndrome (MCR) is a pre-dementia condition characterized by subjective complaints in cognition and slow gait. Pain interference has previously been linked with cognitive deterioration; however, its specific relationship with MCR remains unclear. We aimed to examine how pain interference is associated with concurrent and incident MCR. METHODS: This study included older adults aged ≥ 65 years without dementia from the Health and Retirement Study. We combined participants with MCR information in 2006 and 2008 as baseline, and the participants were followed up 4 and 8 years later. The states of pain interference were divided into 3 categories: interfering pain, non-interfering pain, and no pain. Logistic regression analysis was done at baseline to examine the associations between pain interference and concurrent MCR. During the 8-year follow-up, Cox regression analysis was done to investigate the associations between pain interference and incident MCR. RESULTS: The study included 7120 older adults (74.6 ± 6.7 years; 56.8% females) at baseline. The baseline prevalence of MCR was 5.7%. Individuals with interfering pain had a significantly increased risk of MCR (OR = 1.51, 95% CI = 1.17-1.95; p = 0.001). The longitudinal analysis included 4605 participants, and there were 284 (6.2%) MCR cases on follow-up. Participants with interfering pain at baseline had a higher risk for MCR at 8 years of follow-up (HR = 2.02, 95% CI = 1.52-2.69; p < 0.001). CONCLUSIONS: Older adults with interfering pain had a higher risk for MCR versus those with non-interfering pain or without pain. Timely and adequate management of interfering pain may contribute to the prevention and treatment of MCR and its associated adverse outcomes.

Analysis of lumbar spine loading during walking in patients with chronic low back pain and healthy controls: An OpenSim-Based study.

Low back pain (LBP) is one of the most prevalent and disabling disease worldwide. However, the specific biomechanical changes due to LBP are still controversial. The purpose of this study was to estimate the lumbar and lower limb kinematics, lumbar moments and loads, muscle forces and activation during walking in healthy adults and LBP. A total of 18 healthy controls and 19 patients with chronic LBP were tested for walking at a comfortable speed. The kinematic and dynamic data of the subjects were collected by 3D motion capture system and force plates respectively, and then the motion simulation was performed by OpenSim. The OpenSim musculoskeletal model was used to calculate lumbar, hip, knee and ankle joint angle variations, lumbar moments and loads, muscle forces and activation of eight major lumbar muscles. In our results, significant lower lumbar axial rotation angle, lumbar flexion/extension and axial rotation moments, as well as the muscle forces of the four muscles and muscle activation of two muscles were found in patients with LBP than those of the healthy controls (p < 0.05). This study may help providing theoretical support for the evaluation and rehabilitation treatment intervention of patients with LBP.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Pain-Related Risk Factors Among Radiologic Stages of Knee Osteoarthritis: Data From the Osteoarthritis Initiative.

OBJECTIVE: To investigate whether risk factors related to pain vary at different stages of knee osteoarthritis (OA). METHODS: Individuals from the Osteoarthritis Initiative with available Kellgren/Lawrence (K/L) grade and numerical rating scale (NRS) data at baseline were included in this study. Pain severity was classified into 3 categories based on NRS scores: no pain, mild pain, and moderate/severe pain. Knee OA severity was stratified into 4 categories according to the K/L system. Pain risk factors were evaluated using generalized ordinal logistic regression analysis, and a heatmap was created to compare differences in standardized regression coefficients between subgroups of patients with different knee OA severities. RESULTS: A total of 4,446 subjects were included in this study: 1,574 individuals without pain (35.4%), 1,138 individuals with mild pain (25.6%), and 1,734 individuals with moderate/severe pain (39.0%). For the entire population and subjects in the premorbid-stage subgroup, knee injury history, diabetes mellitus, depression, use of nonsteroidal anti-inflammatory drugs (NSAIDs), and valgus malaligned knees were associated with more severe pain. Older age and stronger quadriceps muscles were associated with milder pain. As the disease progressed, the number of significant risk factors decreased. Only age and quadriceps muscle force remained significant in end-stage disease. CONCLUSION: Multiple factors are associated with pain in patients with knee OA. As the disease progresses, the number of significant risk factors gradually reduces. These findings suggest that strategies for managing pain related to knee OA should vary depending on radiographic grades.

Millennial Evolution of a Karst Socio-Ecological System: A Case Study of Guizhou Province, Southwest China.

The dynamic changes in socio-ecological system (SES) have exerted increasing pressures on the natural environment, leading to observable changes in terrestrial surface structure. Therefore, understanding the historical evolution mechanism of social ecosystems is crucial for the future sustainable management of karst regions. However, detailed quantitative analyses of karst socio-ecological system at a long-term scale are lacking. Here, we applied a comprehensive research framework for the SES of karst region to visually analyze the evolution of karst SES over the past 1000 years in Guizhou Province, defining five evolution stages of the karst SES. Concurrently, we characterized the interactive effects of drivers on karst socio-ecological system during every evolutionary stage, and then assess major influences between these stages. Despite rocky desertification as the main effect of karst SES driven by many indicators, the quantitative analysis indicated that human-dominated land-use change explained the expansion of rocky desertification. Although effective implementation of relevant policies partly compensated for increased environmental pressures, continued structure and function shifts in local ecosystem can challenge progress towards sustainability in karst region. Our findings provide scientific references for managers and policymakers to assist them to identify how environmental issues emerged in karst areas and how they should be addressed.

The Impact of Residences and Roads on Wind Erosion in a Temperate Grassland Ecosystem: A Spatially Oriented Perspective.

The existence of residences and roads is an important way in which human activity affects wind erosion in arid and semiarid environments. Studies assessing the impact of these elements on wind erosion have only focused on limited plots, and their threat of erosion to the surrounding environment has been ignored by many studies. This study was based on spatially overlayed analysis of independent wind erosion distribution simulated by the revised wind erosion equation (RWEQ) and remote-sensing-image-derived residence and road distribution data. Wind erosion at different distances from residences and roads was quantified at the landscape scale of a typical temperate grassland ecosystem, explicitly demonstrating the crucial impacts of both elements on wind erosion. The results showed that wind erosion weakened as the distance from residences and roads increased due to the priority pathways of human activities, and the wind erosion around the residence was more severe than around the road. Human activities in the buffer zones 0-200 m from the residences most frequently caused severe wind erosion, with a wind soil loss of 25 t ha-1 yr-1 and a wind soil loss of approximately 5.25 t ha-1 yr-1 for 0-60 m from the roads. The characteristics of wind erosion variation in the buffer zones were also affected by residence size and the environments in which the residences were located. The variation in wind erosion was closely related to the road levels. Human activities intensified wind erosion mainly by affecting the soil and vegetation around residences and roads. Ecological management should not be limited to residences and roads but should also protect the surrounding environments. The findings of this study are aimed towards a spatial perspective that can help implement rational and effective environmental management measures for the sustainability of wind-eroded ecosystems.

Spatially explicit quantification of total soil erosion by RTK GPS in wind and water eroded croplands.

Quantification of total soil erosion in wind and water eroded croplands is essential for assessing their contributions and the interaction between them. However, it is difficult to quantify total soil erosion amounts by the traditional monitoring and modelling approaches of wind erosion and water erosion. To address this problem, a Real Time Kinematic Global Positioning System (RTK GPS) was applied for a series of wind and water eroded croplands in the Bashang area in North China to quantify the total soil erosion amount over a period of 44 years. By comparing the elevation of the croplands with a reference surface without erosion, the total soil erosion modulus and its spatial variation were determined. Results showed that the erosion moduli of the six croplands ranged from 1.09 to 45.34 Mg ha-1 y-1 with an average modulus of 17.02 Mg ha-1 y-1. The croplands in the west suffered from more intense wind erosion compared to the middle and eastern areas; this was due to the presence of forest-grasslands, which served as wind breaks for the croplands in the middle and eastern regions. However, the croplands in the east showed the highest total erosion modulus, which was due to the influences of a gully. Within the croplands, the slope areas suffered from intense soil erosion which was mainly owing to water erosion. The reliability and uncertainty of this approach were discussed in terms of the equipment precision, results accuracy, and possible deposition on the reference surface. This study shows that when a suitable reference surface is identified and the erosion amount is considerable, RTK GPS survey can be used as a reliable and effective method to assess the spatially explicit total soil erosion in croplands influenced by both wind and water erosion.

Quantification of river bank erosion by RTK GPS monitoring: case studies along the Ningxia-Inner Mongolia reaches of the Yellow River, China.

The Ningxia-Inner Mongolia reaches of the Yellow River suffer from bank erosion problems; in order to identify the bank erosion dynamics, Real Time Kinematic Global Positioning System (RTK GPS) was applied to monitor bank morphology at three sites: Taole Cropland (TC), Maobula Shrubland (MS), and Maobula Cropland (MC). The measured data were analyzed using the Geographical Information System (GIS) to quantify the volume and amount of bank erosion. To verify the feasibility of other means quantifying bank erosion including remote sensing image interpretation and Bank-Stability and Toe-Erosion Model (BSTEM) simulation, their results were compared with the directly monitored results by RTK GPS. Results show that the bank erosion moduli at the TC, MS, and MC sites are 12,762, 6681 and 44,142 t km-1 a-1 respectively based on RTK GPS measurements from 2011 to 2014, with the bank erosion amount varying between flood and non-flood seasons and among different years. The bank erosion quantified by remote sensing interpretation and BSTEM simulation agreed well with results from RTK GPS measurement. The main factors that influence bank erosion on the upper reaches of the Yellow River include land use in the bank area, bank height, and bank curvature. More rational land use along the Yellow River and stabilization of the river bank are required for this area. This study shows that RTK GPS monitoring is reliable and useful for bank erosion research, which has not yet been fully exploited. There is potential of applying remote sensing and model simulation to determine bank erosion of large rivers, while they should be combined and supported by field investigated data.