Aming Lu

Cognitive tasks play a pivotal role in posture control among young adults. This study examined how concurrent cognitive tasks alter balance stability and sensory integration during single-leg stance by analyzing center-of-pressure trajectories and wavelet spectra to elucidate the neurobehavioral mechanisms underlying dual-task balance degradation. A cohort of 24 young adults completed both single postural control tasks and dual cognitive–postural tasks on a force plate. COP data and wavelet decomposition energy were computed and analyzed. The results revealed significant differences between the dual-task and single-task groups for Lxy, Ly, Vxy, and Vy (p < 0.05). Energy content analysis showed that the dual-task group had significantly different energy ratios across four frequency bands along the x-axis (p < 0.05). Our findings showed that dual-task conditions impair postural control in young adults, increasing anteroposterior sway and altering mediolateral energy patterns. This suggests a shift toward proprioceptive reliance during cognitive division, revealing cognitive–postural interference. These results support using dual-task assessments for fall risk evaluation and inform interventions for populations requiring cognitive–motor integration.

Background: Existing literature provides inconclusive evidence regarding the impact of pain on movement patterns, especially in medial meniscus injuries. This study investigated how pain induced by medial meniscus injuries affects walking movement patterns, focusing on the biomechanical mechanisms. The goal was to develop targeted rehabilitation.Methods: Thirty control participants (15 male, 15 female), 23 individuals with medial meniscus injury but no pain (11 male, 12 female), and 51 individuals with medial meniscus injury and pain (24 male, 27 female) were recruited. Gait data was collected using eight inertial measurement units and a video camera. Pain characteristics were assessed using the Visual Analog Scale (VAS) score, Tampa Scale for Kinesiophobia (TSK), Pain Catastrophizing Scale, and pain duration score. Statistical analyses were conducted using a one-way ANOVA to compare movement patterns among the three groups. Bivariate correlation analyses were performed within the pain group to examine the relationship between pain characteristics and movement patterns. The p was set at 0.05.Results: (1) ANOVA among the groups revealed significant differences (p < 0.05) in several parameters: a shorter swing phase, reduced hip and knee angles, increased variability index, increased calf-foot mean absolute relative phase (MARP) during the support phase, and decreased calf-foot MARP during the swing phase were associated with pain. (2) Within the pain group, pain in knee extension (PKE) was negatively correlated with hip and ankle angles, stride length, and thigh-calf MARP during the support phase (p < 0.05). The VAS exhibited a negative correlation with knee angle and stride length, and a positive correlation with shock attenuation (p < 0.05). The TSK showed a positive correlation with hip and knee angles, and calf-foot MARP during the swing phase, while it was negatively correlated with stride length (p < 0.05).Conclusion: Medial meniscus injury-induced pain has several adverse effects, including prolonged walking swing periods, reduced angulation, and increased variability while positively influencing coordination and shock attenuation. Pain intensity, kinesiophobia, and pain freedom contribute to these changes. Therapists should focus on pain management and movement pattern retraining to develop personalized rehabilitation. The angle of the swing phase should be emphasized during retraining.Clinical Trial Registration: https://www.chictr.org.cn/showproj.html?proj=65961, identifier ChiCTR2000041087

Objective: This study aimed to establish a dominant and non-dominant limb muscle strength evaluation model to evaluate the asymmetry of lower extremity muscle strength. Methods: The hip, knee, ankle flexors and extensors of 86 runners were evaluated separately in different contraction modes and at different movement speeds. A principal component analysis was used to establish a model for evaluating dominant and non-dominant lower extremity muscle strength and to comprehensively evaluate the asymmetry of lower extremity muscle strength. Results: Six main factors were present in both dominant and non-dominant indicators of lower extremity muscle strength, with dominant indicators of lower extremity muscle strength explaining 80.413% of the total variance and non-dominant indicators explaining 78.607% of the total variance. Conclusions: In a population of healthy male runners, there were differences in the symmetry of lower limbs in the comprehensive assessment model. The main contribution of the non-dominant side was the knee muscles, and the dominant side was the hip and knee muscles, so this difference should be considered in constructing future muscle strength evaluation models. It is critical to understanding the design and function of the human muscle system, and can reduce the number of meaningful tests we perform on diverse populations and help us reduce asymmetry.

Background: With the advent of global aging, the health of the older population has become a critical public health challenge. The purpose of this study was to investigate the effect of dual-tasking on gait performance in patients with sarcopenia. Methods: Thirty participants with sarcopenia (age: 70.73 ± 4.12 yr, MMSE score: 26.90 ± 3.00), including 14 males and 16 females, were selected according to the diagnostic criteria of the Asian Working Group on Sarcopenia. All participants were instructed to perform the gait test in three modes: single task (ST), cognitive–motor dual task (CMDT), and motor–motor dual task (MMDT). Statistical analyses were performed using one-way ANOVA to evaluate the effects of different task types on gait parameters of the participants. Results: (1) Compared with ST walking, gait frequency, step length, and step speed decreased, and the gait cycle and double-support phase increased in patients with sarcopenia during dual-task walking (p < 0.05); (2) Compared with ST walking, gait variability indices such as stride frequency, stride length, and support period significantly increased in patients with sarcopenia during dual-task walking (p < 0.05). Conclusions: The increased difficulty in postural control caused by dual-task interference may reduce the safety of motor strategies in patients with sarcopenia and increase the risk of falls. Future studies should focus on the effects of exercise interventions on multitasking patterns in people with sarcopenia to promote balance function in these populations.

Background: The related functions of skeletal muscle and brain decrease significantly with age, and muscle-brain-related diseases are primarily associated with each other. Exercise can promote the secretion of myokines in skeletal muscle, showing a beneficial effect on the function of both, reflecting muscle-brain crosstalk. However, the key mechanism of action of exercise-regulated myokines in muscle-brain diseases remains unclear. Summary: This review is intended to sort out and explore the key mechanism of the effect of exercise regulatory myokines on muscle-brain diseases through summarizing the relevant literature on the level of motor regulatory myokines in recent years and pay special attention to the impact of exercise type, intensity, and duration on myokine expression levels. Key Messages: The mechanism by which exercise regulates myokine levels in muscle-brain diseases is explained, and an effective exercise prescription for myokine expression that is more suitable for the elderly based on relevant literature is proposed. This work may hold certain value for subsequent exercise treatment of chronic diseases in the elderly and for further research on muscle-brain crosstalk.

The application of multi-tasking (MT), especially dual-tasking (DT), in frail older adults is currently gaining attention. The aim was to review the application of the MT mode on cognition and lower limb function in frail older adults, including the MT test and MT training. By searching five electronic databases, Scopus, PubMed, PEDro, Web of Science and the Chinese electronic database, a total of 18 studies were finally included in this study, with 7 articles on MT testing and 11 articles on MT training. The results of the study showed that the current testing and training of MT is mainly based on the DT mode, with a wide variety of test types and protocols, as well as a variety of outcomes. The included studies suggested that DT can be used as a test to assess cognitive and lower limb function in the frail population and that an MT (DT) training program with an intervention period of ≥3 months or a duration of ≥60 min per session could improve cognitive and lower limb function in the frail population, thereby reducing the risk of falls. Further research is required to explore the effects of different types of MT and task prioritization in frail older adults.

Objective: To examine the effectiveness of virtual reality (VR)-based rehabilitation training in improving cognition, motor function, and daily functioning in patients with mild cognitive impairment and dementia. Data sources: A systematic review of published literature was conducted using PubMed, Web of Science, Elsevier, Embase, Cochrane, CNKI, Networked Digital Library of Theses and Dissertations. Methods: The search period was from inception to 7 October 2023. Eligible studies were randomized controlled trials evaluating the efficacy of VR-based rehabilitation training in patients with mild cognitive impairment or dementia versus control subjects. Methodologic quality was assessed with the Cochrane risk of bias tool, and outcomes were calculated as the standard mean difference between participant groups with 95% confidence interval. Results: A total of 21 randomized controlled trials with 1138 patients were included. The meta-analysis showed that VR-based rehabilitation training had significant effects on Montreal Cognitive Assessment (SMD: 0.50; 95%CI: 0.05 to 0.95; P = 0.030), Trail-making test A (SMD: −0.38; 95%CI: −0.61 to −0.14; P = 0.002), and Berg Balance Scale scores (SMD: 0.79; 95%CI: 0.13 to 1.45; P = 0.020). A subgroup analysis revealed that the type of VR, and duration and frequency of interventions had statistically significant effects on cognition and motor function. Conclusion: VR-based rehabilitation training is a beneficial nonpharmacologic approach for managing mild cognitive impairment or dementia. Immersive VR-based training had greater effects on cognition and motor function than non-immersive VR-based training, but non-immersive VR-based training was more convenient for patients with limitations imposed by their disease. Also, an intervention lasting 5–8 weeks and for >30 min at a frequency of ≥3 times/week achieved the best results. It indicated that a longer intervention cycle may not achieve the best intervention effect and training duration and schedule should be carefully considered when managing patients.

Introduction: Running is one of the most popular sports in the world, but it also increases the risk of injury. The purpose of this study was to establish a modeling approach for IMU-based subdivided action pattern evaluation and to investigate the classification performance of different deep models for predicting running fatigue.Methods: Nineteen healthy male runners were recruited for this study, and the raw time series data were recorded during the pre-fatigue, mid-fatigue, and post-fatigue states during running to construct a running fatigue dataset based on multiple IMUs. In addition to the IMU time series data, each participant’s training level was monitored as an indicator of their level of physical fatigue.Results: The dataset was examined using single-layer LSTM (S_LSTM), CNN, dual-layer LSTM (D_LSTM), single-layer LSTM plus attention model (LSTM + Attention), CNN, and LSTM hybrid model (LSTM + CNN) to classify running fatigue and fatigue levels.Discussion: Based on this dataset, this study proposes a deep learning model with constant length interception of the raw IMU data as input. The use of deep learning models can achieve good classification results for runner fatigue recognition. Both CNN and LSTM can effectively complete the classification of fatigue IMU data, the attention mechanism can effectively improve the processing efficiency of LSTM on the raw IMU data, and the hybrid model of CNN and LSTM is superior to the independent model, which can better extract the features of raw IMU data for fatigue classification. This study will provide some reference for many future action pattern studies based on deep learning.