Piotr Szaflik

Purpose: The Up and Go test is a widely used tool among physical therapists for assessing functional mobility. Advances in inertial measurement unit sensors (IMU) enable the analysis not only of total walk duration, but also of the duration of individual walk phases, as well as the determination of spatiotemporal and kinematic parameters for each phase. Consequently, the IMU-supported version of the test up and go, referred to as the instrumented up and go, enables a substantially more comprehensive analysis of locomotor patterns. The aim of this study was to evaluate the extent to which spatiotemporal and kinematic parameters obtained during the instrumented timed up nad go test correspond to those observed during walking in older adults. Methods: The study included 30 adults aged over 60 years, who performed the instrumented up and go test as well as a 10-m walk test at a self-selected walking speed. The study involved the analysis of selected spatiotemporal parameters, including cadence, velocity, stride length, and the percentage contribution of gait phases, as well as analysis of pelvic tilt and lower-limb kinematics, including ranges of motion. Results: The results indicate that while the instrumented up and go test does not directly reflect natural walk patterns, selected parameters may still provide complementary information relevant to functional mobility assessment in older adults. Conclusions: The instrumented up and go test should not be considered a substitute for standard walk analysis.

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.

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.

Handgrip strength (HGS) is a key indicator of muscular fitness and physical health. Recently, attention has focused on HGS asymmetry as a potential marker of functional impairment. The aim of the study was to analyze differences in handgrip strength between the dominant and non-dominant limbs and to assess the influence of age and sex on asymmetry levels within the Polish population. A total of 341 participants (170 women and 171 men) were divided into two groups: younger (18–35 years) and older (50+). HGS was measured using a Noraxon digital dynamometer, and asymmetry was defined as the ratio of dominant to non-dominant handgrip strength. Most of the participants (approximately 70%) exhibited greater strength in their dominant hand, with a mean asymmetry values ranging from 17% to 19%. Older adults showed lower overall HGS than younger adults, however, the asymmetry did not differ significantly between sexes or age groups. A small asymmetry (≤10%) was the most common among participants. The Polish adult population shows moderate asymmetry in handgrip strength, with a predominance of the dominant hand, regardless of age or gender. Measuring grip strength asymmetry may provide useful information for assessing the function of the upper limbs and could be important for monitoring physical fitness, identifying neuromuscular disorders, and preventing the decline of motor function in older adults.

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: 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.