Chronic non-specific low back pain (CNSLBP) is a highly prevalent musculoskeletal condition that has a great socioeconomic impact on health systems. Instead of focusing on mechanical causes and direct workload in the development of CNSLBP, genetics, psychosocial environment, lifestyle and quality of life are coming to the forefront in its approach. The main objective was to analyze whether interventions aimed at modifying lifestyle can be effective in improving pain intensity and functional disability in CNSLBP. A search in PubMed, Web of Science, Scopus and SportDiscus databases was performed. Both a univariate and a multivariate network meta-analysis were applied with the difference pre/post-treatment. A total of 20 studies were included for qualitative analysis, of which 16 were randomized clinical trials with a moderate–high methodological quality and were part of the quantitative analysis. The interventions that had the greatest effect in reducing pain intensity were cognitive therapy combined with functional exercise programs, lumbar stabilization exercise and resistance exercise; meanwhile, for functional disability, they were functional exercise programs, aerobic exercise and standard care. In conclusion, a multimodal intervention aimed at changing one’s lifestyle that encompasses cognitive, behavioral, and physical aspects seems to be highly effective in improving pain intensity and functional disability caused by CNSLBP; however, it is not yet known if these improvements are maintained in the long term.

Non-specific low back pain (NSLBP) is a highly prevalent condition that implies substantial expenses and affects quality of life in terms of occupational and recreational activities, physical and psychological health, and general well-being. The diagnosis and treatment are challenging processes due to the unknown underlying causes of the condition. Recently, sensors have been included in clinical practice to implement its management. In this review, we furthered knowledge about the potential benefits of sensors such as force platforms, video systems, electromyography, or inertial measure systems in the assessment process of NSLBP. We concluded that sensors could identify specific characteristics of this population like impaired range of movement, decreased stability, or disturbed back muscular activation. Sensors could provide sufferers with earlier diagnosis, prevention strategies to avoid chronic transition, and more efficient treatment approaches. Nevertheless, the review has limitations that need to be considered in the interpretation of results.

Background/Objectives: Strokes remain a major global health concern, contributing significantly to disability and healthcare costs. Currently, there are no established indicators to accurately assess the degree of muscle tissue impairment in stroke-affected individuals. However, ultrasound imaging with an echotexture analysis shows potential as a quantitative tool to assess muscle tissue quality. This study aimed to identify specific echotexture features in the gastrocnemius medialis that effectively characterize muscle impairment in post-stroke individuals. Methods: An observational study was conducted with 22 post-stroke individuals. A total of 21 echotexture features were extracted and analyzed, including first-order metrics, a grey-level co-occurrence matrix, and a grey-level run length matrix. The modified Heckmatt scale was also applied to correlate with the most informative echotexture features. Results: Among the features analyzed, echovariation (EV), echointensity, and kurtosis emerged as the most informative indicators of muscle tissue quality. The EV was highlighted as the primary feature due to its strong and significant correlation with the modified Heckmatt scale (r = −0.81, p < 0.001) and its clinical and technical robustness. Lower EV values were associated with poorer muscle tissue quality, while higher values indicated better quality. Conclusions: The EV may be used as a quantitative indicator for characterizing the gastrocnemius medialis muscle tissue quality in post-stroke individuals, offering a more nuanced assessment than traditional qualitative scales. Future studies should investigate the correlation between the EV and other clinical outcomes and explore its potential to monitor the treatment efficacy, enhancing its applicability in clinical practice.

Background/Objectives: This study explores the integration of wearable sensors and artificial intelligence (AI) for Human Activity Recognition (HAR) in the diagnosis and rehabilitation of Parkinson’s disease (PD). The objective was to develop a proof-of-concept model based on internal reproducibility, without external generalization, that is capable of distinguishing pathological movements from healthy ones while ensuring clinical relevance and patient safety. Methods: Nine subjects, including eight patients with Parkinson’s disease and one healthy control, were included. Motion data were collected using the Motigravity platform, which integrates inertial sensors in a controlled environment. The signals were automatically segmented into fixed-length windows, with poor-quality segments excluded through preprocessing. A hybrid CNN-LSTM (Convolutional Neural Networks—Long Short-Term Memory) model was trained to classify motion patterns, leveraging convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. The Motigravity system provided a controlled hypogravity environment for data collection and rehabilitation exercises. Results: The proposed CNN-LSTM model achieved a validation accuracy of 100%, demonstrating classification potential. The Motigravity system contributed to improved data reliability and ensured patient safety. Despite increasing class imbalance in extended experiments, the model consistently maintained perfect accuracy, suggesting strong generalizability after external validation to overcome the limitations. Conclusions: Integrating AI and wearable sensors has significant potential to improve the HAR-based classification of movement impairments and guide rehabilitation strategies in PD. While challenges such as dataset size remain, expanding real-world validation and enhancing automated segmentation could further improve clinical impact. Future research should explore larger cohorts, extend the model to other neurodegenerative diseases, and evaluate its integration into clinical rehabilitation workflows.

Chronic pain exerts an enormous personal and economic burden, with sleep disturbances being one of the most reported problems by adults with chronic pain. The aim of this study was to analyse whether different physical therapy interventions could lead to improvements in sleep quality and pain intensity in individuals with chronic pain, as well as if there is any association. A systematic review and a univariate and multivariate meta-analysis were carried out according to the PRISMA guidelines. A search in PubMed, Scopus and Web of Science databases was performed. Six randomised controlled trials were included in the review and four of them were included in the meta-analysis; all of them with a moderate to high methodological quality. Data from adult participants with chronic pain after different physical therapy interventions was extracted. For the meta-analysis, the Insomnia Severity Index and the Numerical Rating Scale were considered. Results from the qualitative and quantitative analysis showed that most of the physical therapy interventions included had higher improvements in the intervention group than in the control group, although the effect size was not statistically significant (univariate for sleep quality: -0.08 [-0.34, 0.18], p = 0.46; univariate for pain intensity: -0.47 [-1.24, 0.30], p = 0.18; multivariate for both outcomes: -0.27). More studies are still needed to determine which physical therapy interventions are effective to improve sleep in people with chronic pain and if there are patients with specific characteristics who may benefit more than others.