ECSS Paris 2023: CP-BM03
INTRODUCTION: The 6-Minute Walk Test (6MWT) is commonly used to assess cardiorespiratory functional capacity in healthy1 and clinical populations. However, its interpretation concerning movement information typically relies solely on total distance covered, providing little information about locomotor and pacing strategies. Although changes in walking patterns (e.g., step frequency and amplitude) are known to be related to the onset of fatigue, these alterations are not captured in the 6MWT evaluation. Thus, this study aimed to quantify within‑ and between‑subject variability in walking patterns throughout the 6MWT. METHODS: Thirty-two apparently healthy participants (age = 28 ± 8.93, BMI = 24.83 ± 4.15) completed a 6MWT while wearing an ankle accelerometer (tri-axial, 100 Hz, GT3X, ActiGraph). The test was conducted on a 30-meter course marked at 5 and 25 meters. Thus, 20-meter segments were recorded as participants crossed both marks. The first segment served as an individualized reference template, which was compared with the subsequent segments. Time and frequency domain gait features such as amplitude, energy, step frequency, and morphological similarity metrics were computed for each segment. Within‑ and between‑subject variability was quantified using coefficients of variation (CoV) and intraclass correlation coefficients (ICC) with 95% confidence intervals (RStudio 2025.09.2+418). RESULTS: Within-subject variability across all segments ranged between 2.8 to 5.6% CoV and ranged from 0.61 to 0.93 ICC. Step amplitude and energy showed the highest stability (ICC > 0.90). While step frequency had the highest variability (ICC: 0.61-0.72), possibly reflecting pacing adjustments. As expected, between‑subject variability was larger than within variability (6.1-21.2% CoV), with step frequency exhibiting the greatest inter‑individual variability (21.2%). CONCLUSION: Six minutes of standardized walking are sufficient to reveal meaningful within‑subject variability in frequency‑related gait features. These intra‑ and inter‑individual gait differences remain underexplored, yet they may offer valuable insights into locomotor control and cardiorespiratory fitness beyond total distance walked. References 1- Albergoni A, Hettinga FJ, Stut W, Sartor F. Factors Influencing Walking and Exercise Adherence in Healthy Older Adults Using Monitoring and Interfacing Technology: Preliminary Evidence. Int J Environ Res Public Health. 2020 Aug 24;17(17):6142. doi: 10.3390/ijerph17176142. PMID: 32846988; PMCID: PMC7503601.
Read CV Moira RaimoECSS Paris 2023: CP-BM03
INTRODUCTION: Parkinson's disease (PD) is characterized by motor and cognitive deficits that become markedly evident during dual-task (DT) walking, where a secondary cognitive or motor task is performed simultaneously. While spatiotemporal gait alterations under DT are well-documented, a comprehensive synthesis of the underlying biomechanical and neurophysiological changes is lacking. This systematic review aimed to characterize the biomechanical gait alterations under DT conditions in individuals with PD, identify associated neurophysiological correlates, and evaluate the methodological quality of the existing evidence. METHODS: A systematic literature search was conducted across five databases (PubMed, Scopus, Web of Science, Cochrane Library, EBSCO) from 2000 to 2025, following PRISMA guidelines. Forty-four studies meeting the PECOS inclusion criteria were included. Studies involved PD patients in the “ON” medication state during DT walking, compared to healthy controls. Methodological quality was assessed using the AXIS tool and a customized DT-checklist, and the review protocol was registered in PROSPERO (CRD420251004404). Meta-analyses, meta-regressions, and subgroup analyses examining the effects of disease severity, DT type, and walking modality are currently in progress. RESULTS: Preliminary synthesis indicates that DT consistently exacerbates gait impairments in PD, with reductions in stride length (10–20%), gait speed (15–30%), and knee joint range of motion (5–12°), alongside increased stride time variability (20–40%). Cognitive DTs, such as Stroop or serial subtraction tasks, induced greater deterioration than motor DTs, particularly in individuals with the Postural Instability/Gait Difficulty phenotype (+35% step variability) and those experiencing freezing of gait (−28% speed). Overground walking imposed greater DT costs (−22% speed) than treadmill walking, and motion capture systems were more sensitive than walkway-based measures in detecting kinematic alterations. Neurophysiological studies using fNIRS revealed compensatory hyperactivation of the prefrontal cortex, suggesting a shift from automatic to controlled movement. External cueing and dopaminergic medication partially mitigated DT-related deficits. CONCLUSION: DT walking in PD is characterized by shortened strides, slowed gait, increased variability, and a stiffened gait pattern, driven by heightened cognitive control and compensatory prefrontal activation. These preliminary findings highlight the potential of DT assessment as a functional biomarker and underscore the need for standardized protocols and integration of cognitive load measures to guide precision rehabilitation. Ongoing meta-analyses and subgroup investigations will provide further quantitative confirmation of these effects.
Read CV Andrea PaternoECSS Paris 2023: CP-BM03
INTRODUCTION: Locomotion with body borne loads may change the kinematic characteristics of human body, and further impairing exercise performance of human body. During uphill walking,the body needs to do extra work against gravity,the inclined plane also effects the posture of the body. However, in the uphill walking, the effect of different loads on joint is not clear. The purpose of this study was to determined the characteristic of peak flexion angle of hip, knee, and ankle during uphill walking with different body borne load. METHODS: Ten male subjects were selected. Age range: 23-26 years old; height: 176.6 ± 5.0cm; weight: 71 ± 8.3kg. The load weight is 0kg (none load), 15kg(medium load) and 30kg(heavy load) respectively. The load was put on the subjects in the form of weight-bearing vests, and the weight was evenly distributed in front, back, left and right of the subjects, the test was carried out on a treadmill with a walking speed of 4km/h, the angle of the treadmill is 8 degrees. Participants had biomechanical data recorded during uphill walking. Specific variables include peak flexion angle of joint (hip, knee and ankle), which collected by 8 lens infrared motion capture system (Motion analysis, Raptor-4; USA). We selected the data of three left single steps during support phase walking and take the average value. R 4.2.2 was used to process the analysis data, and repeated measures ANOVA was user to test the effects of load configuration. RESULTS: During the uphill walking, peak hip flexion angle was 133.9 ± 6.3, 132.1 ± 8.8 and 138.2 ± 7.1 for the none, medium and heavy loads, respectively. Peak knee flexion angle was 139.2 ± 6.1, 139.9 ± 6.5 and 136.0 ± 6.3 for the none, medium and heavy loads, where the angle of heavy load is significantly lower than that of no load (p = 0.03). Peak ankle flexion angle was 88.8 ± 7.6, 85.7 ± 9.7 and 84.3 ± 8.9 for the none, medium and heavy loads.. Body borne load significantly decreased peak ankle flexion angle (p = 0.012) during the uphill walking. No significant trend were observed in peak hip (p = 0.084) and knee (p = 0.240) flexion angle with load. The current outcomes suggest that adding body borne load does not significantly shift kinematic characteristic of hip and knee during uphill walking. The findings of ankle and knee are in agreement with previous experimental evidence in flat ground, But the trend of hip is different from their results . The decrease of ankle flexion angle may be to better adapt to uphill movement. CONCLUSION: The load carrier adopted a stable biomechanical profile in hip and knee to maintain performance of the uphill walking. Greater weight bearing will result in greater ankle flexion angle.
Read CV Zhengyan TangECSS Paris 2023: CP-BM03