Purpose To determine normative data for gait speed and height-normalized gait speed in community-dwelling older men and women. Materials and Methods In this cross-sectional study, we recruited 565 men and women aged ≥60 years old. Age was calculated from the date of birth and further classified into four categories: (1) 60–65 years, (2) 66–70 years, (3) 71–75 years and (4) ≥76 years. Gait speed was assessed by a pressure platform (ZEBRIS, Munich, Germany) in meters per second (m/s). Height and weight were objectively measured. Height-normalized gait speed was calculated by dividing gait speed by height. We created the 20th, 40th, 60th and 80th percentile curves for both outcome measures using Cole’s Lambda (L), Mu (M) and Sigma (S) method. Results Mean gait speed and height-normalized gait speed was 1.24 (standard deviation 0.28) and 0.75 (0.17). Significant age-related decline in gait speed for both sexes was observed ( p < 0.001). Being a woman ( β = - 0.09, p < 0.001), being older ( β = - 0.02, p < 0.001) and having higher body mass index values ( β = - 0.02, p < 0.001) were significantly associated with slower gait speed. Conclusion Gait speed significantly declines with age in both older men and women. Providing normative data can be used in screening and monitoring “slow” walkers to prevent from foot pain and higher risk of falls.
Purpose To examine the associations between gait speed and sleep quality in first-year university students, according to gender. Methods In this cross-sectional study, we recruited 193 first-year university students [mean age±standard deviation (SD): 19.6±1.1 years; mean height: 178.0±10.5 cm; mean weight: 74.0±11.0 kg; 26.9% women). Sleep quality was assessed using the Pittsburgh Sleep Quality questionnaire, with a lower score indicating “better” sleep quality. Gait speed was measured using the Zebris pressure platform. The associations were examined with generalized linear models and multiple regression analysis. Results In the unadjusted model, faster participants had significantly “better” sleep quality ( β =−3.15, 95% CI −3.82 to −2.47, p <0.001). When the model was adjusted for sex, age, body-mass index, self-rated health, smoking status, and psychological distress, faster participants remained having “better” sleep quality ( β =−2.88, 95% CI −3.53 to −2.22, p <0.001). Conclusion This study shows that sleep quality can be predicted by gait speed in the first-year university students.
Purpose: The current study aimed to investigate the normative data for blood pressure. Materials and Methods: From 2017 to 2020, 2032 men and women classified as ‘war veterans’ were recruited (mean age ± standard deviation (SD): 60.97 ± 7.98 years; mean stature: 172.50 ± 9.10 cm; mean body mass: 90.25 ± 36.45 kg; mean body-mass index: 29.66 ± 5.59 kg/m2; 29.9% women). Their systolic and diastolic blood pressures were measured three times. The procedure was carried out according to the American Heart Organization. The sex-specific and age-specific normative data for the 5th, 25th, 50th (median), 75th, and 90th percentiles for systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (measured as SBP-DBP) and mid-BP (the average of SBP and DBP) were presented. Results: The men had higher SBP (p < 0.001), DBP (p < 0.001), pulse pressure (p < 0.001) and mid-BP (p < 0.001) compared to the women. The age-specific differences showed that older individuals had higher values of SBP (p < 0.001), pulse pressure (p < 0.001), and mid-BP (p < 0.001), while no significant differences for DBP (p = 0.496) were observed. Conclusions: This is the first study providing sex-specific and age-specific normative data for blood pressure in war veterans.
The main purpose of the study was to determine whether a pistol shooting efficiency score could be predicted by plantar force distribution patterns. In this cross-sectional study, participants were special police male officers (N = 30), members of the Anti-Terrorist Unit ‘Lučko’ (agemean±SD = 40 ± 6 years, heightmean±SD = 180 ± 5 cm, weightmean±SD = 89 ± 8 kg). Shooting efficiency at a target 10 m away was tested on a scale from 0 to 5, while standing on a Zebris pedobarographic platform. Higher absolute (N; β = −0.19, p = 0.002) and relative (%; β = −0.12, p = 0.043) forces beneath the hindfoot were associated with poorer shooting efficiency. A significant positive association between the relative force beneath the forefoot and shooting efficiency was found, i.e., higher relative forces beneath the forefoot region exhibited better shooting values (β = 0.12, p = 0.043). When the force was normalized by weight (N/kg), similar associations remained. This study shows that higher force values under the hindfoot region may lead to a lower shooting performance, while higher force values under the forefoot region can increase shooting performance.
The main purpose of the study was to explore whether gait velocity predicts the level of separate and overall physical fitness. In this study, we asked one hundred and twenty older adults over the age of 60 (mean ± SD age 71 ± 7 38 years, height 159 ± 21 cm, weight 70 ± 13 kg) to complete a Senior Fitness Test battery to assess the level of physical fitness and walked across the Zebris pressure platform (Munich, Germany) to measure gait velocity. To calculate overall physical fitness, we summed z-score values of each physical fitness test. Pearson’s coefficient (r) was used to determine the level of correlation and coefficient of determination (r2) for variance explained between gait velocity and physical fitness. Respondents conducted a battery of six tests: “chair stand in 30 s”, “arm curl in 30 s”, “2–minute step test”, “chair sit-and-reach test”, “back scratch test” and “8-feet up-and-go test”. Gait velocity was significantly correlated with chair stand in 30 sec (r=0.45, r2=20%, p<0.001), arm curl in 30 sec (r=0.56, r2=31%, p<0.001), 2-minute step test (r=0.44, r2=19%, p<0.001), chair sit-and-reach test (r=0.46, r2=21%, p<0.001), back scratch test (r=0.30, r2=9%, p<0.001) and 8-feet up-and-go test (r=-0.23, r2=5%, p=0.011). Gait velocity was not significantly correlated with waist circumference (r=0.12, r2=1%, p=0.189). Overall physical fitness was strongly correlated with gait velocity (r=0.75, r2=56%, p<0.001). In conclusion this study shows that gait velocity may be an easy and quick screening tool to predict the level of separate and overall physical fitness in a sample of older adults. Keywords: elderly, speed, performance, correlation, tool
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