Reduced muscle strength can affect daily activities, so monitoring is essential. Methods such as isokinetic dynamometers assess maximum muscle force, while hand dynamometers measure hand grip strength. Research has investigated hand grip strength as a predictor of upper, lower and total strength, but the results remain unsystematic. The aim of this study was to systematize the current state of knowledge to test whether hand grip strength can be used as an indicator of overall strength and functional performance. A review of studies from 2000 to 2023 was conducted using: ScienceDirect, PubMed and GoogleScholar. The focus was on articles that examined the relationship between hand grip strength and muscle torque. The following keywords, entered in different configurations, were used in the search: ’hand grip’, ’muscle strength’, ’correlation’ and ’association’. A total of 22 studies were selected for analysis, involving 752 healthy adults, 30 physically active people, 120 ill adults, 2965 older people and 112 ill older people. The analysis looked at associations between hand grip strength and muscle moments of the trunk, limbs and knee joint. The results from the literature indicate that the value of the correlation between hand grip strength and muscle torque varies (from low to high) depending on the test method or group. Therefore, caution should be exercised when using hand grip strength as the sole measure of overall strength and functional performance.

Due to the increasing number of people requiring rehabilitation and an aging society, the need to streamline, improve, and, above all, increase the availability of rehabilitation has been identified. In the present study, research was conducted to evaluate the feasibility of adapting an industrial robot to assist in the rehabilitation process. The study included four measurement series, which consisted of ten repetitions of Proprioception Neuromuscular Facilitation (PNF) movements. The first two series were performed with the assistance of a physiotherapist, the next two with the support of the Cobot UR10e. The lower limb movement was analyzed using the Noraxon Ultium Motion system using inertial sensors (IMU). The study analyzed the following parameters: hip flexion and abduction angles; knee flexion and rotation angles; ankle dorsiflexion angle; and motion cycle. Based on the results, it can be seen that the robot reproduces physiotherapeutic movements more precisely and with greater repeatability. The robot reproduced the movements in the hip and knee joints very well. From the analysis, it can be concluded that with proper adaptation, the robot could be used in the rehabilitation process.

Background/Objectives: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system that typically affects adults aged 20–50. Its early stages can be difficult to diagnose due to the variable clinical course, although subtle impairments often appear in balance and motor control. The Timed Up and Go (TUG) test is commonly used to assess functional mobility; however, traditional evaluation based solely on total test duration may not be sensitive to early gait alterations. The use of inertial measurement units enables instrumented analysis of individual TUG subphases (iTUG). The aim of this study was determine whether iTUG parameters can help detect balance and movement difficulties indicative of early-stage MS. Methods: A total of 30 healthy people and 30 people in the early stages of MS with an expanded disability status score between 1 and 2 were included. The iTUG was performed using three Noraxon inertial sensors placed on the feet and upper spine. Results: No significant differences were observed in total iTUG duration between the MS and control groups (p = 0.888). In contrast, individuals with MS demonstrated significant differences in spatiotemporal gait parameters, trunk flexion range of motion (p = 0.003), number of steps during gait (p = 0.004), and turning velocity compared with healthy controls (p = 0.008). Conclusions: Analysis of iTUG duration is not enough to identify subtle gait and balance impairments in individuals with early-stage MS. Parameters that should be considered when performing an iTUG for the assessment of early stages of MS are spatiotemporal parameters, number of steps, and speed of rotation and subphase times.

Background: Recent research highlights uncertainties surrounding the metabolic effects of nicotine in young adults, particularly among people who use e-cigarettes. While traditional smoking is known to alter body composition, the metabolic impact of using e-cigarettes remains less understood. Methods: In this cross-sectional study, body composition (via bioelectrical impedance analysis) and lifestyle data were collected from 60 university students (mean age: 21.7 ± 1.9 years), who were classified as people who use e-cigarettes exclusively, people who smoke cigarettes exclusively, or people who have never used nicotine products. To address confounding by sex and age, inverse probability of treatment weighting (IPTW) was applied. Results: After adjustment, people who use e-cigarettes had significantly higher body fat percentage compared to people who have never used nicotine (β = 5.45, p = 0.001), while no significant differences were found between people who smoke cigarettes and other groups. Energy drink consumption was also positively associated with body fat percentage and metabolic age. Machine learning models, particularly k-nearest neighbors, achieved moderate classification accuracy (up to 72%) in distinguishing people who use nicotine from people who have never used nicotine based on physiological and lifestyle features. Conclusions: It is important to note that the majority of participants were metabolically healthy, and the observed differences occurred within a clinically normal range. While these findings suggest associations between e-cigarette use and higher adiposity in young adults, no causal inferences can be made due to the observational design. Further longitudinal studies are needed to explore the potential metabolic implications of nicotine use.

The growing number of patients in need of rehabilitation, largely due to an aging population and the increasing incidence of strokes, drives the search for more effective therapeutic methods. Stroke remains a leading cause of adult disability, increasing demand for rehabilitation services. Robotic-assisted therapy presents a promising solution by offering precision and repeatability, complementing traditional methods. This study compared traditional rehabilitation led by a physiotherapist with robotic-assisted therapy using the UR10e robot. The research consisted of two stages: in the first, a physiotherapist guided passive upper limb movements, and in the second, the same movements were replicated by the UR10e robot with a specialized adapter for arm positioning. Movements were measured using the Noraxon Ultium Motion system, analyzing flexion, extension, and rotation angles at the shoulder and elbow joints.

Background: The use of inertial measurement units (IMUs) in the Timed Up and Go (TUG) test enables the quantitative assessment of functional performance and mobility. It allows for the determination not only of the total test completion time, but also of the durations of individual phases, as well as the derivation of spatiotemporal gait parameters and turning velocity. The aim of this review article was to compile parameters of the instrumented Timed Up and Go (iTUG) test and to identify the parameters most commonly analyzed in populations of healthy adults, older adults, and patients with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, and ScienceDirect databases. The authors included studies in which commercial IMUs were used during the TUG test and quantitative parameters were analyzed. Methodological quality was assessed using the JBI Critical Appraisal Checklist for cross-sectional studies. Results: A total of 36 studies were included in the review. Only those disease entities represented by at least four studies were included in the tabular analysis. The study presents results for a total of 1268 individuals, including 192 healthy adults, 514 older adults, 230 patients with multiple sclerosis (MS), and 332 patients with Parkinson’s disease (PD). The analysis showed that temporal parameters, particularly the total test duration and the durations of individual phases, were the most commonly reported across all populations. Conclusions: Turning-related parameters were analyzed frequently, whereas spatiotemporal parameters were assessed less often. The results indicate a lack of standardization both in the selection of iTUG parameters as well as in the measurement methods and systems used.