Exploring the relationship between stride, stature and hand size for forensic assessment

Guest, Richard; Miguel-Hurtado, Oscar; Stevenage, Sarah V.; Black, Sue

doi:10.1016/j.jflm.2017.08.006

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…A proxy of the standard 6MWD was obtained by identifying the 6-minute window with the maximum cumulative number of steps during a given week. The step count was converted to a distance from the following relations: step length (m) = 0.413 x height (m) for females and 0.415 x height (m) for males 39 . The free-living 6MWD (FL6MWD) was then obtained from the step length and the total number of steps in the 6 min window.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Zahradka

et al. 2022

npj Digit. Med.

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Physical health status defines an individual’s ability to perform normal activities of daily living and is usually assessed in clinical settings by questionnaires and/or by validated tests, e.g. timed walk tests. These measurements have relatively low information content and are usually limited in frequency. Wearable sensors, such as activity monitors, enable remote measurement of parameters associated with physical activity but have not been widely explored beyond measurement of daily step count. Here we report on results from a cohort of 22 individuals with Pulmonary Arterial Hypertension (PAH) who were provided with a Fitbit activity monitor (Fitbit Charge HR®) between two clinic visits (18.4 ± 12.2 weeks). At each clinical visit, a maximum of 26 measurements were recorded (19 categorical and 7 continuous). From analysis of the minute-to-minute step rate and heart rate we derive several metrics associated with physical activity and cardiovascular function. These metrics are used to identify subgroups within the cohort and to compare to clinical parameters. Several Fitbit metrics are strongly correlated to continuous clinical parameters. Using a thresholding approach, we show that many Fitbit metrics result in statistically significant differences in clinical parameters between subgroups, including those associated with physical status, cardiovascular function, pulmonary function, as well as biomarkers from blood tests. These results highlight the fact that daily step count is only one of many metrics that can be derived from activity monitors.

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Section: Methodsmentioning

confidence: 99%

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Zahradka

et al. 2022

npj Digit. Med.

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“…Two consecutive peaks are detected as steps occur, and the step interval time between the peaks is measured in 0.1-second increments. There are several ways to estimate stride length, and WELT adopts a method that uses a sex-based constant and height [ 16 , 17 ]. As a result, it is possible to continuously measure gait speed that changes with the step interval time.…”

Section: Methodsmentioning

confidence: 99%

Accuracy and Diversity of Wearable Device–Based Gait Speed Measurement Among Older Men: Observational Study

Kang¹,

Kang²,

Roh³

et al. 2021

J Med Internet Res

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Background Gait speed measurements are widely used in clinical practice, as slow gait is a major predictor of frailty and a diagnostic criterion for sarcopenia. With the development of wearable devices, it is possible to estimate the gait speed in daily life by simply wearing the device. Objective This study aims to accurately determine the characteristics of daily life gait speed and analyze their association with sarcopenia. Methods We invited community-dwelling men aged >50 years who had visited the outpatient clinic at a tertiary university hospital to participate in the study. Daily life gait speed was assessed using a wearable smart belt (WELT) for a period of 4 weeks. Data from participants who wore the smart belt for at least 10 days during this period were included. After 4 weeks, data from a survey about medical and social history, usual gait speed measurements, handgrip strength measurements, and dual-energy x-ray absorptiometry were analyzed. Results A total of 217,578 daily life gait speed measurements from 106 participants (mean age 71.1, SD 7.6 years) were analyzed. The mean daily life gait speed was 1.23 (SD 0.26) m/s. The daily life gait speed of the participants varied according to the time of the day and day of the week. Daily life gait speed significantly slowed down with age (P<.001). Participants with sarcopenia had significantly lower mean daily life gait speed (mean 1.12, SD 0.11 m/s) than participants without sarcopenia (mean 1.23, SD 0.08 m/s; P<.001). Analysis of factors related to mean daily life gait speed showed that age and skeletal muscle mass of the lower limbs were significantly associated characteristics. Conclusions More diverse and accurate information about gait speed can be obtained by measuring daily life gait speed using a wearable device over an appropriate period, compared with one-time measurements performed in a laboratory setting. Importantly, in addition to age, daily life gait speed is significantly associated with skeletal muscle mass of the lower limbs.

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Anthropometrics and Body Composition Predict Physical Performance and Selection to Attend Special Forces Training in United States Army Soldiers

et al. 2021

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Introduction Anthropometrics and body composition characteristics differentiate many types of athletes and are related to performance on fitness tests and tasks in military personnel. Soldiers competing to enter elite units must demonstrate physical fitness and operational competence across multiple events. Therefore, this study determined whether anthropometrics and body composition predicted physical performance and selection for special forces training among soldiers attending the rigorous Special Forces Assessment and Selection (SFAS) course. Materials and Methods Soldiers attending the SFAS course between May 2015 and March 2017 were enrolled in a longitudinal, observational study. Anthropometrics (height, body mass, and body mass index [BMI]; n = 795) and body composition measured by dual-energy X-ray absorptiometry (percentage body fat, fat mass, lean mass, bone mineral content [BMC], and bone mineral density [BMD]; n = 117) were assessed before the course start. Associations with physical performance were determined with correlation coefficients. Associations with selection were determined with analyses of variance and t-tests; effect sizes were calculated as Cohen’s d. The U.S. Army Research Institute of Environmental Medicine Institutional Review Board (IRB) initially approved this study, and the U.S. Army Medical Research and Development Command IRB approved the continuing review. Results Lower percentage body fat and fat mass predicted better performance on all assessments: Army Physical Fitness Test (APFT), pull-ups, SFAS run, loaded road march, obstacle course, and land navigation (P ≤ .05). Higher lean mass predicted better performance on the loaded road march (P ≤ .05). Lower body mass and BMI predicted better performance on APFT, pull-ups, run, and obstacle course; higher body mass and BMI predicted better performance on the loaded road march (P ≤ .05). Shorter stature predicted better performance on push-ups (APFT) and pull-ups; taller stature predicted better performance on SFAS run and loaded road march (P ≤ .05). On average, the selected soldiers were taller (179.0 ± 6.6 vs. 176.7 ± 6.7 cm), had higher body mass (85.8 ± 8.8 vs. 82.1 ± 9.6 kg), BMI (26.8 ± 2.2 vs. 26.3 ± 2.6 kg/m2), lean mass (67.2 ± 7.3 vs. 61.9 ± 7.6 kg), BMC (3.47 ± 0.40 vs. 3.29 ± 0.56 kg), and BMD (1.34 ± 0.10 vs. 1.28 ± 0.10 g/cm2), and lower percentage body fat (17.3 ± 3.4 vs. 20.1 ± 4.5%) and fat mass (14.2 ± 3.7 vs. 15.8 ± 4.4 kg) (P ≤ .05). Effect sizes were largest for lean mass (Cohen’s d = 0.71) and percentage body fat (d = 0.70), followed by BMD (d = 0.60), body mass (d = 0.40), fat mass (d = 0.39), BMC (d = 0.37), height (d = 0.35), and BMI (d = 0.21). Body mass adjustment attenuated associations between height and selection. Conclusions Anthropometrics and body composition are predictors of physical performance and SFAS success. Since these measures are modifiable (excluding height), they may be the focus of intervention studies aiming to improve performance in arduous military training courses, sports that require competition in multiple events, and occupations that have varied physical demands, such as firefighting, law enforcement, and construction.

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Exploring the relationship between stride, stature and hand size for forensic assessment

Cited by 5 publications

References 22 publications

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Evaluation of physical health status beyond daily step count using a wearable activity sensor

Accuracy and Diversity of Wearable Device–Based Gait Speed Measurement Among Older Men: Observational Study

Anthropometrics and Body Composition Predict Physical Performance and Selection to Attend Special Forces Training in United States Army Soldiers

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