Comparison of gender, age, and body mass index for spatiotemporal parameters of bilateral gait pattern

Faster stepping cadence partially explains the higher metabolic cost of walking among females versus males

The metabolic cost of walking (MCOW), or oxygen uptake normalized to distance, provides information on the energy expended during movement. There are conflicting reports as to whether sex differences in MCOW exist, with scarce evidence investigating factors that explain potential sex differences. This study 1) tested the hypothesis that females exhibit a higher MCOW than males, 2) determined whether normalizing to stepping cadence ameliorates the hypothesized sex difference, and 3) explored whether more habitual step counts and time in intensity-related physical activity, and less sedentary time were associated with a decreased MCOW. Seventy-six participants (42 females, 24 ± 5 yr) completed a five-stage, graded treadmill protocol with speeds increasing from 0.89 to 1.79 m/s (6-min walking stage followed by 4-min passive rest). Steady-state oxygen uptake (via indirect calorimetry) and stepping cadence (via manual counts) were determined. Gross and net MCOW, normalized to distance traveled (km) and step-cadence (1,000 steps) were calculated for each stage. Thirty-nine participants (23 females) wore an activPAL on their thigh for 6.9 ± 0.4 days. Normalized to distance, females had greater gross MCOW (J/kg/km) at all speeds (P < 0.014). Normalized to stepping frequency, females exhibited greater gross and net MCOW at 1.12 and 1.79 m/s (J/kg/1,000 steps; P < 0.01) but not at any other speeds (P < 0.075). Stature was negatively associated with free-living cadence (r = -0.347, P = 0.030). Females expend more energy/kilometer traveled than males, but normalizing to stepping cadence attenuated these differences. Such observations provide an explanation for prior work documenting higher MCOW among females and highlight the importance of stepping cadence when assessing the MCOW.NEW & NOTEWORTHY Whether there are sex differences in the metabolic cost of walking (MCOW) and the factors that may contribute to these are unclear. We demonstrate that females exhibit a larger net MCOW than males. These differences were largely attenuated when normalized to stepping cadence. Free-living activity was not associated with MCOW. We demonstrate that stepping cadence, but not free-living activity, partially explains the higher MCOW in females than males.

Beyond timing and step counting in 360° turning-in-place assessment: a scoping review

BACKGROUND

Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed.

METHODS

Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated.

RESULTS

Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation.

CONCLUSION

This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy.

Examining gender differences in gait parameters between non-smoker and smoker participants

Smoking is one of the predictors of decreased cardiopulmonary endurance. Gait disturbance may be due to many reasons, including cardiovascular endurance. This study aimed to determine differences in gait parameters between non-smoker and smoker participants. A cross-sectional design was employed, involving thirty non-smokers and thirty-seven smokers as participants. Detailed interviews were conducted to gather information on smoking habits, status, and history. Gait parameters were measured using a high-quality 3D accelerometer, 3D gyroscope, and barometric pressure sensors (Physilog4 from GaitUp). Anthropometric characteristics were described, and mean values with standard deviations (SD) were calculated. An independent two-tailed t-test was conducted to compare gait parameters between non-smokers and smokers, with statistical significance set at p<0.05. The analysis revealed significant differences in various gait parameters between non-smokers and smokers. Specifically, significant differences were found in cadence (t=9.95, p=0.001), stride length (t=6.85, p=0.001), stride velocity (t=-6.58, p=0.001), stance (t=2.02, p=0.001), swing (t=3.46, p=0.001), foot flat (t=-8.94, p=0.001), pushing (t=3.53, p=0.001), and double support (t=-13.35, p=0.001). However, no significant difference was found between non-smokers and smokers in the loading phase (t=-1.57, p= 0.121). There were significant differences in general and temporal gait parameters between smokers and non-smokers. Gait parameters provide valuable insights for evaluating functional performance and providing objective and quantitative data to assess gait disorders. Future studies should include longitudinal studies with large sample sizes to explore the effects of potential confounders on gait parameters.

Determine the kinematics and kinetics parameters associated with bilateral gait patterns among healthy, overweight, and obese adults

BACKGROUND AND AIM

Biomechanical analysis of gait is important to obtain information regarding the lower limb impairments and dysfunction during locomotion. This study aimed to determine the potential difference among healthy, overweight, and obese participants and their impact on gait parameters by observing the kinematic and kinetics parameters.

METHODS

A cross-sectional study conducted with forty (15 healthy, 12, overweight, 13 obese) male participants. All participants were non-smokers, and their physical activity level was 7000±2142 steps per day. Participants anthropometric characteristics were age:21.57±1.46 years; height:173. 63±6.43 cm, body mass;86.15±23.36 kg., body mass index (BMI) :28.57±7.68 kg/m2, body fat:29.93±9.44%. A bioelectrical impedance device was used to determine participants' body composition and health status. A portable pressure sensor mat (Walkway) from Tekscan was used to measure bilateral gait parameters kinematically and kinetically. One-way analysis of variance was used to determine the differences between groups.

RESULTS

Significant differences were found between health, overweight, and obese participant for different bilateral gait's kinematic and kinetic parameters as cadence, gait velocity, step time, step length, step velocity, step width, stride time, stride length, stride velocity, maximum force, maximum peak pressure, active propulsion, and passive propulsion except impulse at .05 level of significance.

CONCLUSIONS

The findings shows that kinematics and kinetics parameters of gait were affected by the status of their BMI. Current research suggests that increased body weight interferes with normal musculoskeletal function via a range of kinematic and kinetic deficits. More research is required to accurately understand the structural and functional restriction imposed by overweight and obese individuals.