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Introduction/aims: Fluctuating symptoms and fatigue are common issues in myasthenia gravis (MG), but it is unclear if these symptoms are related to physical activity or sleep patterns. This study sought to determine the day-to-day relationship between patient-reported symptoms and physical activity and sleep over 12 weeks., Methods: Sixteen participants with generalized MG wore a wrist-mounted accelerometer continuously for the study duration and reported their symptoms and fatigue each evening. Cumulative link mixed models were used to analyze whether clinical and demographic characteristics, physical activity, and sleep were related to symptom severity and fatigue over the study period. Three types of models were constructed: a cohort model, a model in which data was scaled to each participant, and individual models., Results: The cohort model indicated that higher disease severity, female sex, more comorbidities, less physical activity, more inactive time, and lower quantity of sleep were significantly associated with increased symptom severity and fatigue (p < .05). However, in the within-participant scaled model, there were almost no significant associations with physical activity or sleep. In the individual models, some participants showed similar results to the cohort model, but others showed no associations or the opposite response in some variables., Discussion: While physical activity and sleep were associated with self-reported symptoms and fatigue within this population, this was not necessarily applicable to individuals. This demonstrates the importance of an individualized analysis for determining how physical activity and sleep may impact outcomes in MG, with implications for clinical and self-management., (© 2024 The Author(s). Muscle & Nerve published by Wiley Periodicals LLC.)

Introduction: The Información de la Enfermedad de Alzheimer para Latinos (IDEAL) Study is a randomized clinical trial investigating the psychosocial, behavioral, and cognitive impacts of apolipoprotein E ( APOE ) genotype disclosure for late-onset Alzheimer's disease (AD) among Latinos., Methods: We used address-based sampling to recruit English- and Spanish-speaking Latinos aged 40-64 living in northern Manhattan for a community-based Baseline Survey about their knowledge and opinions about AD. Participants eligible for the clinical trial were invited to complete an Introductory Session, including AD and genetics education and informed consent, before undergoing genotyping for APOE . Participants were then randomized to learn their risk of AD by age 85 (range: 21%-55%) based on either Latino ethnicity and family history alone, or the same factors and their APOE genotype. Risk information is provided in a semi-structured genetic counseling session. Psychological impacts, health-related behavioral changes, and cognitive performance are evaluated 6 weeks, 9 months, and 15 months later via surveys and qualitative interviews. To promote cultural competence, study materials were developed by a multidisciplinary team including bilingual and bicultural staff, Latinx content experts, and genetic counselors., Results: We sent invitations to 91,433 households; 5542 (6.1%) responded, 2120 completed the Baseline Survey (78.5% online; 21.5% via computer-assisted telephone interview), and 2087 were deemed eligible, yielding a response rate of 2.3%. Many participants expressed appreciation for the opportunity to contribute to AD research. We randomized 374 participants for the clinical trial., Discussion: We describe the study design, recruitment and retention strategies, and interventions employed in the IDEAL Study. Our design provides a framework for future studies using rigorous mixed methods. Our findings may facilitate the development of culturally-sensitive educational materials about AD and genetic testing, as well as genetic counseling protocols, to improve coping and adjustment in response to receiving risk information., Highlights: The Información de la Enfermedad de Alzheimer para Latinos (IDEAL) Study investigates apolipoprotein E ( APOE ) genotype disclosure among Latinos using mixed methods.We recruited adults 40-64 years of age without Alzheimer's disease (AD) for a community-based survey and randomized trial.Trial participants receives AD risk estimates with or without APOE genotypes.Psychosocial, behavioral, and cognitive impacts are assessed over 15 months.Findings may inform AD educational materials and genetic counseling protocols., Competing Interests: Neither the study's principal investigators (R.O. and K.S.) nor any of the other coauthors has any conflict of interest to disclose. Author disclosures are available in the supporting information., (© 2024 The Author(s). Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Quantitative biomechanical gait analysis is an important clinical and research tool for injury and disease diagnosis and treatment. However, one major criticism is that gait analysis laboratories largely operate in isolation and there is a lack of benchmark datasets, which can be used to advance research and statistical methodologies. To address this, we present an open biomechanics dataset of n = 1798 healthy and injured, young and older adults during treadmill walking and/or running at a range of gait speeds. The full dataset is available on Figshare+ and data files are contained within a series of zipped folders with folder names representing the subject ID. Each subject ID folder contains walking and/or running data containing raw marker trajectory data along with metadata for each participant. Five tutorials are also provided, demonstrating aspects such as loading data files, sample analyses of discrete variables, and calculating joint angles from code along with covering more complex topics such as principal component analysis for dimensionality reduction, statistical parametric mapping, and conducting unsupervised clustering., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

The limited sample size in gait studies has hampered progress in the field. This challenge could be addressed through multicenter studies, thereby leveraging data sets from different laboratories. This study compared 3-dimensional lower-extremity running kinematics between the Biomechanics and Motor Control Laboratory, Federal University of ABC (Brazil), and the Running Injury Clinic, University of Calgary (Canada). Three-dimensional lower-extremity kinematics from 23 male runners were collected from each laboratory using comparable instrumentation and experimental procedures. The 3-dimensional hip, knee, and ankle angles were compared within and between centers using root-mean-square deviation. Two-sample t tests Statistical Parametric Mapping tested the hypothesis that the data from both laboratories were not different. The sagittal plane hip, knee, and ankle angles were similar between laboratories, while notable differences were observed for frontal (hip and ankle) and transverse (hip and knee) plane angles. The average interlaboratory root-mean-square deviation (2.6°) was lower than the intralaboratory root-mean-square deviation (Biomechanics and Motor Control = 4.8°, Running Injury Clinic = 5.6°), with the ankle transverse angle displaying the smallest, and the knee transverse angle displaying the largest variability. This study demonstrates the potential of combining gait kinematics data from different laboratories to increase sample size, but frontal and transverse plane data should be considered with caution.

The capacity to perceive tactile input at the fingertips, referred to as tactile sensitivity, is known to diminish with age due to regressive changes to mechanoreceptor density and morphology. Sensitivity is measured as perceptual responses to stimuli of varying intensity. Contrary to traditional sensitivity monitoring instruments, smartphones are uniquely suited for remote assessment and have shown to deliver highly calibrated stimuli along a broad spectrum of intensity, which may improve test reliability. The aim of this study was to evaluate a vibration-emitting smartphone application, the Vibratus App, as a mode of estimating tactile sensory thresholds in the aging adult. The peripheral nerve function of 40 neurologically healthy volunteers (ages 18-71) was measured using monofilaments, a 128-Hz tuning fork, the Vibratus App, and nerve conduction studies (NCS). Between group differences were analyzed to determine each measurement's sensitivity to age. Spearman correlation coefficients depicted the associative strength between hand-held measurements and sensory nerve action potential (SNAP) amplitude. Inter-rater reliability of traditional instruments and the software-operated smartphone were assessed by intraclass correlation coefficient (ICC 2,k ). Measurements taken with Vibratus App were significantly different between age groups (p < 0.001). The inter-rater reliability of monofilament, smartphone vibration, and tuning fork testing was moderate to good (ICC 2,k  = 0.65, 0.69, and 0.79, respectively). The findings of this study support further investigation of smartphones as sensitivity monitoring devices for at home monitoring of skin sensitivity., (© 2024. The Author(s).)

Purpose: This study explored employee health behavior changes and health care utilization after workplace genetic testing (wGT). Wellness-program-associated wGT seeks to improve employee health, but the related health implications are unknown., Methods: Employees of a large US health care system offering wGT (cancer, heart disease, and pharmacogenomics [PGx]) were sent electronic surveys. Self-reported data from those who received test results were analyzed. Descriptive statistics characterized responses, whereas logistic regression analyses explored correlates of responses to wGT., Results: 53.9% (n = 418/776) of respondents (88.3% female, mean age = 44 years) reported receiving wGT results. 12.0% (n = 48/399) received results indicating increased risk (IR) of cancer, 9.5% (n = 38/398) had IR of heart disease, and 31.4% (n = 125/398) received informative PGx results. IR results for cancer and/or heart disease (n = 67) were associated with health behavior changes (adjusted odds ratio: 3.23; 95% CI 1.75, 6.13; P < .001) and health care utilization (adjusted odds ratio: 8.60; 95% CI 4.43, 17.5; P < .001). Informative PGx results (n = 125) were associated with medication changes (PGx-informative: 15.2%; PGx-uninformative: 4.8%; P = .002)., Conclusion: This study explored employee responses to wGT, contributing to the understanding of the ethical and social implications of wGT. Receiving IR results from wGT may promote health behavior changes and health care utilization in employees., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Suppression of noise from recorded signals is a critically important data processing step for biomechanical analyses. While a wide variety of filtering or smoothing spline methods are available, the majority of these are not well suited for the analysis of signals with rapidly changing derivatives such as the processing of motion data for impact-like events. This is because commonly used low-pass filtering approaches or smoothing splines typically assume a single fixed cut-off frequency or regularization penalty which fails to describe rapid changes in the underlying function. To overcome these limitations we examine a class of adaptive penalized splines (APS) that extend commonly used penalized spline smoothers by inferring temporal adaptations in regularization penalty from observed data. Three variations of APS are examined in which temporal variation of spline penalization is described via either a series of independent random variables, an autoregressive process or a smooth cubic spline. Comparing the performance of APS on simulated datasets is promising with APS reducing RMSE by 48%-183% compared to a widely used Butterworth filtering approach. When inferring acceleration from noisy measurements describing the position of a pendulum impacting a barrier we observe between a 13% (independent variables) to 28% (spline) reduction in RMSE when compared to a 4th order Butterworth filter with optimally selected cut-off frequency. In addition to considerable improvement in RMSE, APS can provide estimates of uncertainty for fitted curves and generated quantities such as peak accelerations or durations of stationary periods. As a result, we suggest that researchers should consider the use of APS if features such as impact peaks, rates of loading, or periods of negligible acceleration are of interest., Competing Interests: Declaration of competing interest The authors have no competing or financial interests to disclose., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Rotationally resolved Fourier-transform spectra of laser-induced fluorescence A1Σu+∼b3Πu→X1Σg+ of K2 molecules were recorded and analyzed, yielding 4053 term values of the spin-orbit (SO) coupled A ∼ b complex of the 39K2 isotopologue with ∼0.01 cm-1 accuracy. Their compilation with 1739 term values from previously published sources allowed them to cover the energy range [9955, 17 436] cm-1 from the bottom of the lower-lying b3Πu state up to the vicinity of the atomic asymptote 4s2S12 + 4p2P12, with a rotational quantum number J ∈ [0, 149]. The experimental data were processed by a direct 6 × 6 coupled-channel (CC) deperturbation treatment, which accounted explicitly for both SO and electronic-rotational interactions between all six e-symmetry states: A1Σu+(0u+), b3Πu(0u+,1u,2u), c3Σu(1u), and B1Πu(1u). The initial parameters of the global deperturbation model have been estimated in the framework of ab initio electronic structure calculations applying multi-reference configuration-interaction and coupled-clusters methods. The interatomic potentials analytically defined for A and b states, as well as SO-splitting of the triplet b state and A ∼ b SO-coupling functions, have been particularly refined to fit the 5792 term values of the 39K2 isotopologue, whereas the rest parameters were fixed on their ab initio values. The resulting mass-invariant parameters of the 6 × 6 CC model reproduced the overall rovibronic term energies of the A ∼ b complex of 39K2 with accuracy, which is well within the experimental errors. The quality of the deperturbation analysis was independently confirmed by comparison with the present obtained 705 and 14 term values of respective 39K41K and 41K2 isotopologues, as well as by agreement between measured and predicted relative intensity distributions in long A ∼ b → X(vX) band progressions. This deperturbation analysis provided the refined dissociation energy Tdis = 17 474.569(5) cm-1 and the long-range coefficient C3Σ = 5.501(4) × 105 cm-1 Å3 relevant to the non-relativistic atomic limit 4s + 4p. The derived Tdis yielded the accurate well depth De = 4450.910(5) cm-1 for the ground X1Σg+ state, whereas the new C3Σ value yielded the improved estimates for atomic K(4p2P12;32) radiative lifetimes, τ12 = 26.67(3) and τ32 = 26.32(3) ns., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Running biomechanics are affected by fatiguing or prolonged runs. However, no evidence to date has conclusively linked this effect to running-related injury (RRI) development or performance implications. Previous investigations using subject-specific models in running have demonstrated higher accuracy than group-based models, however, this has been infrequently applied to fatigue. In this study, two experiments were conducted to determine whether subject-specific models outperformed group-based models to classify running biomechanics during non-fatigued and fatigued conditions. In the first experiment, 16 participants performed four treadmill runs at or around the maximal lactate steady state. In the second experiment, nine participants performed five prolonged runs using commercial wearable devices. For each experiment, two segments were extracted from each trial from early and late in the run. For each participant, a random forest model was applied with a leave-one-run-out cross-validation to classify between the early (non-fatigued) and late (fatigued) segments. Additionally, group-based classifiers with a leave-one-subject-out cross validation were constructed. For experiment 1, mean classification accuracies for the single-subject and group-based classifiers were 68.2 ± 8.2% and 57.0 ± 8.9%, respectively. For experiment 2, mean classification accuracies for the single-subject and group-based classifiers were 68.9 ± 17.1% and 61.5 ± 11.7%, respectively. Variable importance rankings were consistent within participants, but these rankings differed from each participant to those of the group. Although the classification accuracies were relatively low, these findings highlight the advantage of subject-specific classifiers to detect changes in running biomechanics with fatigue and indicate the potential of using big data and wearable technology approaches in future research to determine possible connections between biomechanics and RRI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Dimmick, van Rassel, MacInnis and Ferber.)

Introduction: Wearable technology (WT) has become common place in sport. Increased affordability has allowed WT to reach the wrists and bodies of grassroots and community athletes. While WT is commonly used by sport populations to monitor training load, the use of WT among dancers and dance teachers is unknown. Therefore, the purpose of this study is to explore the perspectives of dancers, dance teachers, and dance parents on using WT in the dance studio environment. Methods: Dancers (aged 14+), dance teachers (aged 18+), and dance parents (with a child <18 years registered in a dance program) were recruited from local dance studios (including those offering vocational programs and/or professional training opportunities), and dance retail stores. Participants provided informed consent/assent and completed a one-time online survey about their attitudes, self-efficacy, motivations, barriers, and current practices of using WT in the studio. Results: Sixty-seven participants (19 dancers, 32 dance teachers, and 16 dance parents) completed the survey. Attitudes toward using WT were similar across all groups (mean score range = 34-38/45). Thirteen dancers (68%), 29 teachers (91%), and 7 dance parents reporting on behalf of their children (47%) were permitted to use WT in the studio. Smartwatches were the most common WT used in the studio by dancers (7/9) and teachers (13/17), while dance parents reported that their children primarily used wristband activity trackers (3/4). Among all groups, the primary reason for using WT was to track personalized training data, with calories, total duration, and heart rate being the most important perceived metrics for improving dancing. Conclusion: Across all groups, attitudes toward WT were modest. Prevalence of WT use in the dance studio varied, with wrist-based gadgets being the most common. As WT research continues in dance populations, it will be important for future studies to consider studio permissions as well as participants' existing WT use practices., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

OBJECTIVE: To examine modifiable and nonmodifiable factors for associations with dance-related injury among preprofessional ballet dancers over 5 academic years. DESIGN: Prospective cohort study. METHODS: Full-time preprofessional ballet dancers (n = 452; 399 female; median age [range], 15 years [11-21]) participated across 5 academic years at a vocational school. Participants completed baseline screening and online weekly injury questionnaires including dance exposure (hours/week). Zero-inflated Poisson regression models were used to examine associations between potential risk factors measured at baseline and self-reported dance-related injury. RESULTS: In count model coefficients, left one leg standing score (log coefficient estimate, -0.249 [95% CI: -0.478, -0.02]; P = .033) and right unipedal dynamic balance time (log coefficient estimate, -0.0294 [95% CI: -0.048, -0.01]; P >.001) carried a protective effect with increased years of training when adjusted for Athletic Coping Skills Inventory (ACSI) score. A significant association was found for left unipedal dynamic balance time and dance-related injury (log coefficient estimate, 0.013 [95% CI: 0.000, 0.026]; P = .045) when adjusted for years of training and ACSI score. There were no significant associations between dance-related injury and ankle and hip range of motion, active straight leg raise, or Y Balance Test measures. CONCLUSION: When adjusted for years of previous dance training and psychological coping skills, there was a significant association between limb-specific lumbopelvic control and dynamic balance tasks, as well as self-reported dance-related injury in preprofessional ballet. J Orthop Sports Phys Ther 2023;53(11):703-711. Epub 3 October 2023. doi:10.2519/jospt.2023.11835 .

Change of direction (COD) maneuvers in soccer create tactical advantages, but also expose the player to an increased risk of injury. COD ability is commonly tested with pre-planned drills including cuts greater than 90°. These tests do not take into consideration positional differences players encounter during games. This case-series study used principal component analysis (PCA) to examine situational differences during COD movements between playing positions in youth soccer games. For each of the four teams included (26 females, 27 males), one game was analyzed using video-analysis. Two independent reviewers identified situational patterns and a PCA was used to examine differences between playing positions. Three principal components explained 89% of the variation in the data and were categorized as the total quantity of CODs, attacking/goal-scoring and defensive reacting types of CODs. One-way ANOVA on the individual principal component (PC) scores showed significant differences (p < 0.05) between centre midfielders, goalkeepers, and centrebacks in the quantity of CODs (PC1), and between wingers and fullbacks and centre backs in attacking/goal-scoring CODs (PC2), whereas PC3 was not different between playing positions. Differences between playing positions suggest that training and testing protocols in soccer could be enhanced to better match the individual and playing position-based needs., Competing Interests: The authors declare no conflict of interest., (Copyright: © Academy of Physical Education in Katowice.)

Objectives: Pre-professional dance is high-risk, with injury incidence up to 4.7 injuries/1000 dance hours. Pre-season screening measures have been utilized to assess risk factors for dance-related injury, however normative values haven't been established for a pre-professional ballet population. The purpose of this study was to establish normative values of ankle and hip joint range of motion (ROM), lumbopelvic control, and dynamic balance pre-season screening measures for pre-professional ballet dancers., Methods: 498 adolescent pre-professional ballet dancers [n = 219 junior division (194 female, 25 male; mean age: 12.9±0.9 year); n = 281 senior division (238 female, 41 male; mean age: 16.8±1.5 year)] participated in baseline screening tests across 5 seasons (2015-2019). Baseline measures took place at the beginning of each academic year: ankle ROM [dorsiflexion (deg); plantarflexion (PF) (deg)], total active turnout (TAT) (deg), lumbopelvic control [active straight leg raise (ASLR) (score); one leg standing test (OLS) (score)], and dynamic balance [unipedal balance (sec); Y-Balance Test (cm)]., Results: Percentiles for ankle dorsiflexion ranged from 28.2° (male senior division, 10th percentile) to 63.3° (female junior division, 100th percentile). For PF, percentiles ranged from 77.5 to 111.8° (male junior division, 10th percentile; male senior division, 100th percentile). Percentiles for TAT for all participants ranged between 121.1° and 131.0°. For the ASLR, the proportion of participants moving with compensation (pelvis shifting) was between 64.0% and 82.2%. For OLS, 19.7% to 56.1% of dancers had a positive score (hip hiking). Percentiles for dynamic balance ranged from 3.5 to 17.1 seconds (unipedal dynamic balance) and 75.8 to 103.3 cm (YBT composite reach score) across all groups., Conclusion: The establishment of normative values of pre-season screening measures among a pre-professional ballet population can be used to determine areas to target during training, recognize individuals with possible injury risk, and inform return to dance protocols following injury. Comparison with other dancer/athletic populations will also provide insight into the performance of dancers and identify areas in need of improvement.

Dance is a popular physical activity. Increased dance training has been associated with an increased risk of injury. Given the established association between training load (TL) and injury in sport, knowledge of how TL is currently being measured in dance is critical. The objective of this study is to summarise published literature examining TL monitoring in dance settings. Six prominent databases (CINAHL, EMBASE, Medline, ProQuest, Scopus, SportDiscus) were searched and nine dance-specific journals were handsearched up to May 2022. Selected studies met inclusion criteria, where original TL data were collected from at least one dancer in a class, rehearsal and/or performance. Studies were excluded if TL was not captured in a dance class, rehearsal or performance. Two reviewers independently assessed each record for inclusion at title, abstract and full-text screening stages. Study quality was assessed using Joanna Briggs Institute Critical Appraisal Tool checklists for each study design. The 199 included studies reported on female dancers (61%), ballet genre (55%) and the professional level (31%). Dance hours were the most common tool used to measure TL (90%), followed by heart rate (20%), and portable metabolic systems (9%). The most common metric for each tool was mean weekly hours (n=381; median=9.5 hours, range=0.2-48.7 hours), mean heart rate (n=143) and mean oxygen consumption (n=93). Further research on TL is needed in dance, including a consensus on what tools and metrics are best suited for TL monitoring in dance., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Wireless accelerometers with various operating ranges have been used to measure tibial acceleration. Accelerometers with a low operating range output distorted signals and have been found to result in inaccurate measurements of peaks. A restoration algorithm using spline interpolation has been proposed to restore the distorted signal. This algorithm has been validated for axial peaks within the range of 15.0-15.9 g . However, the accuracy of peaks of higher magnitude and the resultant peaks have not been reported. The purpose of the present study is to evaluate the measurement agreement of the restored peaks using a low-range accelerometer (±16 g ) against peaks sampled using a high-range accelerometer (±200 g ). The measurement agreement of both the axial and resultant peaks were examined. In total, 24 runners were equipped with 2 tri-axial accelerometers at their tibia and completed an outdoor running assessment. The accelerometer with an operating range of ±200 g was used as reference. The results of this study showed an average difference of -1.40 ± 4.52 g and -1.23 ± 5.48 g for axial and resultant peaks. Based on our findings, the restoration algorithm could skew data and potentially lead to incorrect conclusions if used without caution.

Running is a popular form of physical activity with a high incidence of running-related injuries. However, the etiology of running-related injuries remains elusive, possibly due to the heterogeneity of movement patterns. The purpose of this study was to investigate whether different clusters existed within a large group of injured and uninjured runners based on their kinetic gait patterns. A sample of 134 injured and uninjured runners were acquired from an existing database and 12 discrete kinetic and spatiotemporal variables which are commonly associated with running injuries were extracted from the ground reaction force waveforms. A principal components analysis followed by an unsupervised hierarchical cluster analysis was performed. The results revealed two distinct clusters of runners which were not associated with injury status (OR = 1.14 [0.57, 2.30], χ 2  = 0.143, p = 0.706) or sex (OR = 1.72 [0.85, 3.49], χ 2  = 2.3258, p = 0.127). These results suggest that while there appeared to be evidence for two distinct clusters within a large sample of injured and uninjured runners, there is no association between the kinetic variables and running related injuries., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Introduction: Pre-professional ballet dancers are at high-risk for injury, with injury rates ranging from 1.4 to 4.7 injuries/1000 dance-hours. In dance injury epidemiology, multi-year studies are limited, and findings are inconsistent. Thus, the extent to which injury estimates range from year to year in a pre-professional ballet program is currently unknown., Objective: The aim of this study was to establish the extent and characteristics of injuries in pre-professional adolescent ballet dancers across five academic training years., Methods: 452 female and male pre-professional ballet dancers (median age, 15 years; range, 11-20 years) participated across five academic years at a vocational ballet school. Participants completed an online weekly injury questionnaire (OSTRC-Q) and self-reported dance hours questionnaire., Results: Questionnaire response was 91.4%. Depending on the definition of injury, yearly injury prevalence ranged from 32.1% (145/452; time-loss) to 67.4% (305/452; all-complaints) across the 5 years. Yearly injury rates ranged from 0.76 (95%CI: 0.66, 0.86; time-loss) to 2.54 (95%CI: 2.37, 2.73; all-complaints) per 1000 dance-hours. The ankle was the most reported injury location (range: 16-33%)., Conclusions: Injury prevalence and injury rate estimates remained high across five academic years in a pre-professional ballet population. Injury estimates were highest when an all-complaints definition was employed., Level of Evidence: Therapy/Prevention, Aetiology/Harm, level 2b., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Recent research indicates that distance running stride-to-stride variability (SSV) is related to performance and injury. Previous studies have primarily focused on stride characteristics (stride length and time). We assessed SSV for sagittal plane joint angles with the primary purpose of testing for significant differences among the lower body joints. The secondary purpose was to determine if strong correlations exist among joint SSV measures. Thirty recreational adult runners participated in the study (8 females, 22 males, 39 ± 10 years; 53.1 ± 25.7 km/week). A 6-camera motion capture system (200 Hz) collected kinematic data during treadmill running at a preferred pace. A 2 by 3 repeated measures factorial ANOVA (phase-stance, swing; joint-hip, knee, ankle) was run ( p = 0.05). There was a significant interaction effect (p < 0.001) and post hoc analysis revealed knee swing to be the most variable condition by far. For all three joints, there were strong correlations between stance and swing SSV (r = 0.80 to r = 0.88) and correlations among the joints were moderate to strong (r = 0.55 to 0.86). This study helps to better understand the joints/phases that contribute most to variability in the overall stride. Also, the strong correlations suggest that runners appear to have an overall SSV pattern that is similar across joints/phases.

Bayesian methods have recently been proposed to solve inverse kinematics problems for marker based motion capture. The objective is to find the posterior distribution, a probabilistic summary of our knowledge and corresponding uncertainty about the model parameters such as joint angles, segment angles, segment translations, and marker positions. To date, Bayesian inverse kinematics models have focused on a frame by frame solution, which if repeatedly applied gives estimates that are discontinuous in time. We propose to overcome this limitation for continuous, planar inverse kinematics problems via the use of finite basis representations to model latent kinematic quantities as smooth, continuous functions. Our generalised smoothing approach is able to accurately approximate the solution to planar inverse kinematics problems defined by simple systems of ordinary differential equations in addition to considerably more complex systems such as a planar analysis of human gait. Improvements in accuracy are considerable with a decrease in average RMSE of 0.025 rad observed when estimating ankle joint angle for a randomly selected running stride with the proposed generalised smoothing approach compared to previous time-independent approaches. In addition, the generalised smoothing approach is able to effectively estimate kinematic parameters in the presence of missing data along with derivatives of kinematic quantities without the need for prior filtering or gap-filling of data., (Copyright © 2022. Published by Elsevier Ltd.)

Background: Three-dimensional (3D) motion measured at the lower back during walking can describe the regularity and symmetry of gait that may be related to osteoarthritis (OA) and functional status. However, gait speed and inherent sex differences, regardless of the presence of OA, may confound these measures. Therefore, there is a need to understand the effect of OA separately among males and females, without the confounding influence of gait speed., Objective: To investigate the difference in 3D gait regularity and symmetry measures between gait speed-matched males and females with and without knee OA., Method: Gait regularity and symmetry were computed as autocorrelations of pelvic accelerations during treadmill walking in four groups of older adults: healthy asymptomatic females (AsymF; n = 44), healthy asymptomatic males (AsymM; n = 45), females diagnosed with knee OA (OAF; n = 44), and males diagnosed with knee OA (OAM; n = 45). Data were obtained from a larger research database, allowing for the matching of gait speed between groups. The main effect of OA, sex, and interaction effect between them was examined for the 3D gait regularity and symmetry measures at an alpha level of 0.05., Results: There was no main effect of OA on any variable, but there was a significant main effect of sex on mediolateral and anteroposterior gait regularity measures. Specifically, females demonstrated significantly greater gait regularity, most notably in the mediolateral directions compared to males., Conclusion: Older adult females were found to display significantly greater mediolateral gait regularity as compared to males, regardless of the presence of OA. Further, this difference exists among matched gait speeds, suggesting it is not the result of gait speed. Overall, these results highlight the importance of sex-specific analyses and considering gait speed when examining gait acceleration patterns near the center of mass for both cross sectional and longitudinal gait assessments., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The purpose of this study was to determine if fatigue-related changes in biomechanics derived from an inertial measurement unit (IMU) placed at the center of mass (CoM) are reliable day-to-day. Sixteen runners performed two runs at maximal lactate steady state (MLSS) on a treadmill, one run 5% above MLSS speed, and one run 5% below MLSS speed while wearing a CoM-mounted IMU. Trials were performed to volitional exhaustion or a specified termination time. IMU features were derived from each axis and the resultant. Feature means were calculated for each subject during non-fatigued and fatigued states. Comparisons were performed between the two trials at MLSS and between all four trials. The only significant fatigue state × trial interaction was the 25th percentile of the results when comparing all trials. There were no main effects for trial for either comparison method. There were main effects for fatigue state for most features in both comparison methods. Reliability, measured by an intraclass coefficient (ICC), was good-to-excellent for most features. These results suggest that fatigue-related changes in biomechanics derived from a CoM-mounted IMU are reliable day-to-day when participants ran at or around MLSS and are not significantly affected by slight deviations in speed.

Knee osteoarthritis is a progressive disease mediated by high joint loads. Foot progression angle modifications that reduce the knee adduction moment (KAM), a surrogate of knee loading, have demonstrated efficacy in alleviating pain and improving function. Although changes to the foot progression angle are overall beneficial, KAM reductions are not consistent across patients. Moreover, customized interventions are time-consuming and require instrumentation not commonly available in the clinic. We present a regression model that uses minimal clinical data-a set of six features easily obtained in the clinic-to predict the extent of first peak KAM reduction after toe-in gait retraining. For such a model to generalize, the training data must be large and variable. Given the lack of large public datasets that contain different gaits for the same patient, we generated this dataset synthetically. Insights learned from a ground-truth dataset with both baseline and toe-in gait trials (N = 12) enabled the creation of a large (N = 138) synthetic dataset for training the predictive model. On a test set of data collected by a separate research group (N = 15), the first peak KAM reduction was predicted with a mean absolute error of 0.134% body weight * height (%BW*HT). This error is smaller than the standard deviation of the first peak KAM during baseline walking averaged across test subjects (0.306%BW*HT). This work demonstrates the feasibility of training predictive models with synthetic data and provides clinicians with a new tool to predict the outcome of patient-specific gait retraining without requiring gait lab instrumentation., Competing Interests: The authors have declared that no competing interests exist.

The oscillation continuum in laser-induced fluorescence spectra of bound-free c 3 Σ + → a 3 Σ + and (4) 1 Σ + → X 1 Σ + transitions of the KCs molecule was recorded by a Fourier-transform spectrometer and modeled under the adiabatic approximation. The required interatomic potentials for ground a 3 Σ + and X 1 Σ + states were reconstructed in an analytical Chebyshev-polynomial-expansion form in the framework of the regularization direct-potential-fit procedure based on the simultaneous consideration of experimental line positions from Ferber et al. [Phys. Rev. A 80, 062501 (2009)] and the present ab initio calculation of short-range repulsive potential data. It was proved that the repulsive part over the dissociation limit of the derived a 3 Σ + potential reproduces the experiment better than the potentials reported in the literature. It is also shown that all empirical and semi-empirical potentials available for the X 1 Σ + state reproduce the bound-free (4) 1 Σ + → X 1 Σ + spectrum with equal quality in the range of observations.

Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods' broad training and application in the field., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Inertial measurement units (IMUs) can be used to monitor running biomechanics in real-world settings, but IMUs are often used within a laboratory. The purpose of this scoping review was to describe how IMUs are used to record running biomechanics in both laboratory and real-world conditions. We included peer-reviewed journal articles that used IMUs to assess gait quality during running. We extracted data on running conditions (indoor/outdoor, surface, speed, and distance), device type and location, metrics, participants, and purpose and study design. A total of 231 studies were included. Most (72%) studies were conducted indoors; and in 67% of all studies, the analyzed distance was only one step or stride or <200 m. The most common device type and location combination was a triaxial accelerometer on the shank (18% of device and location combinations). The most common analyzed metric was vertical/axial magnitude, which was reported in 64% of all studies. Most studies (56%) included recreational runners. For the past 20 years, studies using IMUs to record running biomechanics have mainly been conducted indoors, on a treadmill, at prescribed speeds, and over small distances. We suggest that future studies should move out of the lab to less controlled and more real-world environments.

The COVID-19 pandemic caused widespread disruption to many individuals' lifestyles. Social distancing restrictions implemented during this global pandemic may bring potential impact on physical activity habits of the general population. However, running is one of the most popular forms of physical activity worldwide and one in which it could be maintained even during most COVID-19 restrictions. We aimed to determine the impact of COVID-19 restrictions on runners' training habits through analyzing the training records obtained from their GPS enabled wearable trackers. Retrospective and prospective data were collected from an online database (https://wetrac.ucalgary.ca). Runners' training habits, including frequency, intensity and duration of training, weekly mileage and running locations were analyzed and compared 9 months before and after the start of COVID-19 restrictions in March 2020. We found that runners ran 3 km per week more ( p = 0.05, Cohen's d = 0.12) after the start of COVID-19 restrictions, and added 0.3 training sessions per week ( p = 0.03, Cohen's d = 0.14). Moreover, runners ran an additional 0.4 sessions outdoors ( p < 0.01, Cohen's d = 0.21) but there was no significant change in the intensity or duration of training sessions. Our findings suggested that runners adopted slightly different training regimen as a result of COVID-19 restrictions. Our results described the collective changes, irrespective of differences in response measures adopted by various countries or cities during the COVID-19 pandemic., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chan, Peeters, Cheing, Ferber and Cheung.)

Introduction: This study describes practices for disclosing individual research results to participants in Alzheimer's disease research., Methods: An online survey of clinical core leaders at National Institutes of Health-funded Alzheimer's Disease Research Centers in the United States (response rate: 30/31, 97%) examined return of results practices across nine different types of research results., Results: Most centers had returned consensus research diagnoses (83%) and neuropsychological test results (73%), with fewer having shared amyloid positron emission tomography (43%), tau imaging (10%), or apolipoprotein E ( APOE ) genotype (7%) results. Centers reported having disclosed a mean of 3.1 types of results (standard deviation = 2.1; range 0-8). The most commonly cited reason for disclosure was to inform participants' medical decision-making (88%). Disclosure involved multiple professionals and modalities, with neurologists (87%) and in-person visits (85%) most commonplace., Discussion: Centers varied widely as to whether and how they disclosed research results. Diagnostic and cognitive test results were more commonly returned than genetic or biomarker results., Competing Interests: The authors report no financial conflicts of interest. Dr. Roberts has served as a paid advisory board member for the national Alzheimer's Clinical Trials Consortium and NIH‐funded research projects based at Indiana University (U01 AG057195), Washington University (R01AG065234), and Children's National Medical Center (R01 HD095068). He also serves in an unpaid role as co‐chair of the Michigan Dementia Coalition. Dr. Blacker and Ms. Rumbaugh serve in unpaid roles as co‐chairs of the Asymptomatic Subcommittee of the Advisory Group on Risk Evidence Education for Dementia. Dr. Grill has served in an unpaid role on an advisory board convened by the National Institute on Aging. Ms. Ferber reports no relevant disclosures. Dr. Blacker has received consulting fees for legal consultation from Saul Ewing Arnstein & Lehr, LLP, and the estate of Barbara Bartle; and for epidemiology forecasting from Biogen (2015‐18). Dr. Grill has received consulting fees from SiteRx, Flint Rehab, and Cogniciti. Dr. Roberts has received payment from the National Institutes of Health for scientific review activities., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Altered gait variability occurs in those with patellofemoral pain and may be relevant to pain progression. We examined gait kinematic and coordination variability between individuals with acute and chronic patellofemoral pain and healthy controls. Eighty-three patellofemoral pain runners (37 men and 46 women) and 142 healthy controls (52 men and 90 women) ran on a treadmill while 3-dimensional lower limb kinematic data were collected. Patellofemoral pain runners were split into acute (n = 22) and chronic (n = 61) subgroups based on pain duration (< and ≥3 mo, respectively). Approximate entropy assessed continuous hip, knee, and ankle kinematic variability. Vector coding calculated coordination variability for select joint couplings. Variability measures were compared between groups using 1-way analysis of variance and post hoc comparisons with Cohen d effect sizes. The chronic patellofemoral pain subgroup displayed higher frontal plane knee kinematic variability compared with controls (P = .0004, d = 0.550). No statistically significant effects for any coordination variability couplings were identified. Minimal differences in gait variability were detected between those with acute and chronic patellofemoral pain and healthy controls.

Objective: The objective of this scoping review is to map the evidence related to how consumer-targeted wearable and mobile technology is being used to measure and/or promote sleep among adolescents., Introduction: Sleep is a key component of physical and mental health and is required for healthy development in adolescence. Efforts to improve insufficient and poor-quality sleep among adolescents have resulted in limited and temporary enhancements in sleep habits. Since good sleep hygiene is established through the development of daily routines, wearable technology offers a potential solution by providing real-time feedback, allowing adolescents to monitor and manage their sleep habits., Inclusion Criteria: Studies that focus on adolescents between 13 and 24 years who use mobile or wearable technology to measure and/or promote sleep health will be considered for inclusion., Methods: Using a scoping methodology, the authors will conduct a review of studies on the use of commercially available, wearable technology or mobile devices designed to measure and/or improve sleep among adolescents. Literature searched will include published primary studies, reviews, and dissertations from database inception to present. Databases searched will include MEDLINE, Embase, PsycINFO, CINAHL, CENTRAL, SPORTDiscus, JBI Evidence Synthesis, Cochrane Database of Systematic Reviews, Scopus, and ProQuest Dissertations and Theses. The search will be conducted using identified keywords and indexed terms, and studies will be limited to the English language. Data extracted will include study population, methods, description of sleep technology reported, sleep outcomes, and strategies used to promote healthy sleep behaviors. Quality assessment of included studies will be conducted to facilitate data mapping and synthesis., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 JBI.)

Bayesian inference has recently been identified as an approach for estimating a subjects' pose from noisy marker position data. Previous research suggests that Bayesian inference markedly reduces error for inverse kinematic problems relative to traditional least-squares approaches with estimators having reduced variance despite both least-squares and Bayesian estimators being unbiased. This result is surprising as Bayesian estimators are typically similar to least-squares approaches unless highly informative prior distributions are used. As a result the purpose of this work was to examine the sensitivity of Bayesian inverse kinematics solutions to the prior distribution. Our results highlight that Bayesian solutions to inverse kinematics are sensitive to the choice of prior and that the previously reported superior performance of Bayesian inference is likely due to an overly informative prior distribution which unrealistically uses knowledge of the true kinematic pose. When more realistic, 'weakly-informative' priors, which do not use the known kinematic pose are used then any improvements in estimator accuracy are minimal when compared to the traditional least squares approach. However, with appropriate priors, Bayesian inference can propagate uncertainties related to marker position to uncertainty in joint angles, a valuable contribution for kinematic analyses. When using Bayesian methods, we recommend researchers use weakly-informative priors and conduct a sensitivity analysis to highlight the effects of prior choice on analysis outcomes., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Rationale & Objective: People receiving hemodialysis often require urgent care or hospitalizations. It is possible that reductions in a patient's level of physical activity may serve as an "early warning" of clinical deterioration, allowing timely clinical intervention. We explored whether step count could serve as a trigger for deterioration., Study Design: Prospective observational cohort feasibility study., Setting & Participants: We recruited consenting adult participants from outpatient dialysis clinics in Calgary, AB, between June 28, 2019, and October 10, 2019., Exposure and Outcomes: Participants wore a wristband fitness tracker for 4 weeks. Activity data from the trackers were imported weekly into the study database. Demographic, clinical management, functional impairment, and frailty were assessed at baseline. Clinical events (urgent care and emergency department visits and hospitalizations) were monitored during the observation period., Analytical Approach: Box and whisker plots and line plots were used to describe each participant's daily steps. Adjusted rate ratios (and 95 % confidence intervals) were calculated to assess the associations between the number of steps taken each day and potential predictors., Results: Data from 46 patients were included; their median age was 64 years (range, 22 to 85), and 63 % were men. The median number of steps taken per day was 3,133 (range, 248-13,753). Fourteen events among 11 patients were reported. Within patients, step count varied considerably; it was not possible to identify a patient-specific normal range for daily step count. There was no association between step count and the occurrence of clinical events, although the number of events was very small., Limitations: The number of participants was relatively small, and there were too few events to model to examine whether step count could predict clinical deterioration., Conclusions: Within-patient variation in daily step count was too high to generate a normal range for patients. However, patient-specific norms over a longer period (3- or 7-day periods) appear potentially worthy of future study in a larger sample and/or over a longer follow-up., (© 2021 The Authors.)

The difficulty of estimating joint kinematics remains a critical barrier toward widespread use of inertial measurement units in biomechanics. Traditional sensor-fusion filters are largely reliant on magnetometer readings, which may be disturbed in uncontrolled environments. Careful sensor-to-segment alignment and calibration strategies are also necessary, which may burden users and lead to further error in uncontrolled settings. We introduce a new framework that combines deep learning and top-down optimization to accurately predict lower extremity joint angles directly from inertial data, without relying on magnetometer readings. We trained deep neural networks on a large set of synthetic inertial data derived from a clinical marker-based motion-tracking database of hundreds of subjects. We used data augmentation techniques and an automated calibration approach to reduce error due to variability in sensor placement and limb alignment. On left-out subjects, lower extremity kinematics could be predicted with a mean (±STD) root mean squared error of less than 1.27° (±0.38°) in flexion/extension, less than 2.52° (±0.98°) in ad/abduction, and less than 3.34° (±1.02°) internal/external rotation, across walking and running trials. Errors decreased exponentially with the amount of training data, confirming the need for large datasets when training deep neural networks. While this framework remains to be validated with true inertial measurement unit data, the results presented here are a promising advance toward convenient estimation of gait kinematics in natural environments. Progress in this direction could enable large-scale studies and offer new perspective into disease progression, patient recovery, and sports biomechanics., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Runners with iliotibial band syndrome display symptoms similar to chronic tendinopathy and distinct gait patterns compared to healthy controls. Although altered pain processing has been demonstrated in chronic tendinopathies, central pain processing and its relationship to motor control has not been measured in iliotibial band syndrome. The purpose of this study was to examine pain sensitivity, hip strength, and gait kinematics in runners with and without iliotibial band syndrome., Methods: Nine female runners with iliotibial band syndrome and eight healthy controls participated. Subjective pain was reported and pressure pain threshold measured at the bilateral foot, tibialis anterior, contralateral hand. Isometric hip strength was assessed. Three-dimensional joint angles were collected while running. Differences in pain and strength were determined using 1-way ANOVAs. Discrete hip and knee joint angles during stance phase were calculated and waveform analysis performed., Findings: Runners with iliotibial band syndrome exhibited bilaterally diminished pain at the foot (injured-limb: 1.54 (SD = 0.51); non-injured limb: 1.54 (SD = 0.55); control: 4.01 (SD = 2.30) kg, P < .001) and ipsilateral tibialis anterior (injured-limb: 2.33 (SD = 1.10); control: 6.13 (SD = 4.89) kg, P = .03). Hip strength was not different between groups. Runners with iliotibial band syndrome had greater hip adduction at touchdown, knee internal rotation during loading, and knee abduction and flexion at toe-off than controls., Interpretation: Runners with iliotibial band syndrome demonstrated expanded somatic pain sensitivity without hip strength differences, but concomitant with altered gait patterns. Bilateral pain symptoms and gait deviations exist in runners with iliotibial band syndrome even with unilateral symptoms, highlighting the importance of bilateral assessment., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: To investigate the validity and reliability of a smartphone application for selected lower-limb kinematics during treadmill running., Design: Validity and reliability study., Setting: Biomechanics laboratory., Participants: Twenty healthy female runners., Main Outcome Measure(s): Sagittal-plane hip, knee, and ankle angle and rearfoot eversion were assessed using the Coach's Eye Smartphone application and a 3D motion capture system. Paired t-test and intraclass correlation coefficients (ICC) established criterion validity of Coach's Eye; ICC determined test-retest and intrarater/interrater reliability. Standard error of measurement (SEM) and minimal detectable change (MDC) were also reported., Results: Significant differences were found between Coach's Eye and 3D measurements for ankle angle at touchdown and knee angle at toe-off (p < 0.05). ICCs for validity of Coach's Eye were excellent for rearfoot eversion at touchdown (ICC = 0.79) and fair-to-good for the other kinematics (range 0.51-0.74), except for hip at touchdown, which was poor (ICC = 0.36). Test-retest (range 0.80-0.92), intrarater (range 0.95-0.99) and interrater (range 0.87-0.94) ICC results were excellent for all selected kinematics., Conclusion: Coach's Eye can be used as a surrogate for 3D measures of knee and rearfoot in/eversion at touchdown, and hip, ankle, and rearfoot in/eversion at toe-off, but not for hip and ankle at touchdown or knee at toe-off. Reliable running kinematics were obtained using Coach's Eye, making it suitable for repeated measures., Competing Interests: Declaration of competing interest This study did not target a product promotion of either the Samsung smartphone or camera or the Coach’s Eye app used in the current study. None of these companies were involved in any part of the research., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns; however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three-dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10-66 years), were clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one-way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies., (© 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Objective: To assess the biomechanical changes following a systematic gait retraining to modify footstrike patterns from rearfoot strike (RFS) to midfoot strike (MFS)., Design: Pre-post interventional study. All participants underwent a gait retraining program designed to modify footstrike pattern to MFS., Setting: Research laboratory., Participants: Twenty habitual RFS male runners participated., Main Outcome Measures: Gait evaluations were conducted before and after the training. Footstrike pattern, vertical loading rates, ankle and knee joint stiffness were compared., Results: Participants' footstrike angle was reduced (p < 0.001, Cohen's d = 1.65) and knee joint stiffness was increased (p = 0.003, Cohen's d = 0.69). No significant difference was found in the vertical loading rates (p > 0.155). Further subgroup analyses were conducted on the respondents (n = 8, 40% of participants) who exhibited MFS for over 80% of their footfalls during the post-training evaluation. Apart from the increased knee joint stiffness (p = 0.005, Cohen's d = 1.14), respondents exhibited a significant reduction in the ankle joint stiffness (p = 0.019, Cohen's d = 1.17) when running with MFS., Conclusions: Gait retraining to promote MFS was effective in reducing runners' footstrike angle, but only 40% of participants responded to this training program. The inconsistent training effect on impact loading suggests a need to develop new training protocols in an effort to prevent running injuries., Competing Interests: Declaration of competing interest None declared., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Traditionally, running biomechanics analyses have been conducted using 3D motion capture during treadmill or indoor overground running. However, most runners complete their runs outdoors. Since changes in running terrain have been shown to influence running gait mechanics, the purpose of this study was to use a machine learning approach to objectively determine relevant accelerometer-based features to discriminate between running patterns in different environments and determine the generalizability of observed differences in running patterns. Center of mass accelerations were recorded for recreational runners in treadmill-only ( n = 28) and sidewalk-only ( n = 25) environments, and an independent group ( n = 16) ran in both treadmill and sidewalk environments. A feature selection algorithm was used to develop a training dataset from treadmill-only and sidewalk-only running. A binary support vector machine model was trained to classify treadmill and sidewalk running. Classification accuracy was determined using 10-fold cross-validation of the training dataset and an independent testing dataset from the runners that ran in both environments. Nine features related to the consistency and variability of center of mass accelerations were selected. Specifically, there was greater ratio of vertical acceleration during treadmill running and a greater ratio of anterior-posterior acceleration during sidewalk running in both the training and testing dataset. Step and stride regularity were significantly greater in the treadmill condition for the vertical axis in both the training and testing dataset, and in the medial-lateral axis for the testing dataset. During sidewalk running, there was significantly greater variability in the magnitude of the vertical and anterior-posterior accelerations for both datasets. The classification accuracy based on 10-fold cross-validation of the training dataset ( M = 93.17%, SD = 2.43%) was greater than the classification accuracy of the independent testing dataset ( M = 83.81%, SD = 3.39%). This approach could be utilized in future analyses to identify relevant differences in running patterns using wearable technology., (Copyright © 2020 Benson, Clermont and Ferber.)

Context: The risk of experiencing an overuse running-related injury can increase with atypical running biomechanics associated with neuromuscular fatigue and/or training errors. While it is important to understand the changes in running biomechanics within a fatigue-inducing run, it may be more clinically relevant to assess gait patterns in the days following a marathon to better evaluate the effects of inadequate recovery on injury., Objective: To use center of mass (CoM) acceleration patterns to investigate changes in running patterns prior to (PRE) and at 2 (POST2) and 7 (POST7) days following a marathon race., Design: Pre-post intervention study., Setting: A 200-m oval track surface., Participants: Seventeen recreational marathon runners (10 females, age = 34.2 [5.67] y; 7 males, age = 47.41 [15.32] y)., Intervention: Marathon race., Main Outcome Measures: An inertial measurement unit was placed near the CoM to collect triaxial acceleration data during overground running for PRE, POST2, and POST7 sessions. Twenty-two features were extracted from the acceleration waveforms to characterize different aspects of running gait. Lower-limb musculoskeletal pain was also recorded at each session with a visual analog scale., Results: At POST2, runners reported higher self-reported pain and exhibited elevated peak mediolateral acceleration with an increased mediolateral ratio of acceleration root mean square compared with PRE. At POST7, pain was reduced and more similar to PRE, with runners demonstrating increased stride regularity in the vertical direction and decreased peak resultant acceleration., Conclusions: The observed changes in CoM motion at POST2 may be associated with atypical running biomechanics that can translate to greater mediolateral impulses, potentially increasing the risk of injury. This study demonstrates the use of an accelerometer as an effective tool to detect atypical CoM motion for runners due to fatigue, recovery, and musculoskeletal pain in real-world environments.

What was once a science fiction fantasy, virtual reality (VR) technology has evolved and come a long way. Together with augmented reality (AR) technology, these simulations of an alternative environment have been incorporated into rehabilitation treatments. The introduction of head-mounted displays has made VR/AR devices more intuitive and compact, and no longer limited to upper-limb rehabilitation. However, there is still limited evidence supporting the use of VR and AR technology during locomotion, especially regarding the safety and efficacy relating to walking biomechanics. Therefore, the objective of this study is to explore the limitations of such technology through gait analysis. In this study, thirteen participants walked on a treadmill in normal, virtual and augmented versions of the laboratory environment. A series of spatiotemporal parameters and lower-limb joint angles were compared between conditions. The center of pressure (CoP) ellipse area (95% confidence ellipse) was significantly different between conditions (p = 0.002). Pairwise comparisons indicated a significantly greater CoP ellipse area for both the AR (p = 0.002) and VR (p = 0.005) conditions when compared to the normal laboratory condition. Furthermore, there was a significant difference in stride length (p<0.001) and cadence (p<0.001) between conditions. No statistically significant difference was found in the hip, knee and ankle joint kinematics between the three conditions (p>0.082), except for maximum ankle plantarflexion (p = 0.001). These differences in CoP ellipse area indicate that users of head-mounted VR/AR devices had difficulty maintaining a stable position on the treadmill. Also, differences in the gait parameters suggest that users walked with an unusual gait pattern which could potentially affect the effectiveness of gait rehabilitation treatments. Based on these results, position guidance in the form of feedback and the use of specialized treadmills should be considered when using head-mounted VR/AR devices., Competing Interests: The authors have declared that no competing interests exist.

The purpose of this study was to use wearable technology data to quantify alterations in subject-specific running patterns throughout a marathon race and to determine if runners could be clustered into subgroups based on similar trends in running gait alterations throughout the marathon. Using a wearable sensor, data were collected for cadence, braking, bounce, pelvic rotation, pelvic drop, and ground contact time for 27 runners. A composite index was calculated based on the "typical" data (4-14 km) for each runner and evaluated for 14 individual 2-km sections thereafter to detect "atypical" data (ie, higher indices). A cluster analysis assigned all runners to a subgroup based on similar trends in running alterations. Results indicated that the indices became significantly higher starting at 20 to 22 km. Cluster 1 exhibited lower indices than cluster 2 throughout the marathon, and the only significant difference in characteristics between clusters was that cluster 1 had a lower age-grade performance score than cluster 2. In summary, this study presented a novel method to investigate the effects of fatigue on running biomechanics using wearable technology in a real-world setting. Recreational runners with higher age-grade performance scores had less atypical running patterns throughout the marathon compared with runners with lower age-grade performance scores.

Purpose: To determine the effects of low-dose caffeine supplementation (3 mg/kg body mass) consumed 1 h before the experiment on rating of perceived exertion (RPE), skills performance (SP), and physicality in male college ice hockey players., Methods: Using a double-blind, placebo-controlled, randomized crossover experimental design, 15 college ice hockey players participated in SP trials and 14 participated in scrimmage (SC) trials on a total of 4 d, with prescribed ice hockey tasks occurring after a 1-h high-intensity practice. In the SP trials, time to complete and error rate for each drill of the validated Western Hockey League Combines Testing Standard were recorded. Peak head accelerations, trunk contacts, and offensive performance were quantified during the SC trials using accelerometery and video analysis. RPE was assessed in both the SP and SC trials., Results: RPE was significantly greater in the caffeine (11.3 [2.0]) than placebo (9.9 [1.9]) condition postpractice (P = .002), with a trend toward greater RPE in caffeine (16.9 [1.8]) than placebo (15.7 [2.8]) post-SC (P = .05). There was a greater number of peak head accelerations in the caffeine (4.35 [0.24]) than placebo (4.14 [0.24]) condition (P = .028). Performance times, error rate, and RPE were not different between intervention conditions during the SP trials (P > .05)., Conclusions: A low dose of caffeine has limited impact on sport-specific skill performance and RPE but may enhance physicality during ice hockey SCs.

Running-related injuries have been associated with excessive foot pronation and high vertical loading rates. Traditional plaster-molded (TPM) foot orthoses are commonly prescribed to minimize these atypical biomechanical patterns. Recently, 3D printed (3DP) orthoses have become popular, yet the functional difference between these two types of orthoses remains unknown. Therefore, this study compared running biomechanics and perceived comfort during treadmill running in three orthotic conditions: 3DP orthoses, TPM orthoses, and a no-orthoses control condition (CON). Thirteen female asymptomatic runners with excessive foot pronation were recruited. Rearfoot eversion angle and velocity (at initial contact and peak) during stance, vertical loading rates, and perceived comfort were compared. Results showed lower peak rearfoot eversion angles during running with TPM ( p =0.001, d =0.38) or 3DP orthoses ( p =0.002, d =0.24) than CON. No differences were observed in other biomechanical parameters among the three conditions ( p >0.05). Running with TPM ( p ≤0.001, d =1.74-1.82) and 3DP orthoses ( p <0.003, d =1.06-1.34) resulted in better perceived comfort in "medial-lateral control" and "heel cushioning" than CON. There were no statistical differences in all parameters between TPM and 3DP orthoses. The present findings indicate improved comfort during running with TPM or 3DP orthoses, which hinted 3DP orthoses could be a viable alternative to TPM orthoses for clinical practice.

The purpose of this study was to examine the effect of speed on coordination and its variability in running gait using vector coding analysis. Lower extremity kinematic data were collected for thirteen recreational runners while running at three different speeds in random order: preferred speed, 15% faster and 15% lower than preferred speed. A dynamical systems approach, using vector coding and circular statistics, were used to quantify coordination and its variability for selected hip-knee and knee-ankle joint couplings. The influence of running speed was calculated from the continuous data sets of the running cycle, allowing for the identification of time percentages where differences existed. Results indicate that increases in running speed produced moderate alterations in the frequency of movement patterns which were not enough to alter classification of coordination. No effects of speed on coordination variability were observed. This study has demonstrated that coordination and coordination variability is generally stable in the range of ±15% around of preferred speed in recreational runners., (Copyright © 2019. Published by Elsevier B.V.)

As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a 'typical' or 'stable' gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2-4 runs were needed to define a stable running pattern for univariate, and 4-5 days were necessary for multivariate analysis across all inclination conditions. Pearson's correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63-0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual's stable gait pattern.

Background: Running-related overuse injuries can result from the combination of extrinsic (e.g., running mileage) and intrinsic risk factors (e.g., biomechanics and gender), but the relationship between these factors is not fully understood. Therefore, the first purpose of this study was to determine whether we could classify higher- and lower-mileage runners according to differences in lower extremity kinematics during the stance and swing phases of running gait. The second purpose was to subgroup the runners by gender and determine whether we could classify higher- and lower-mileage runners in male and female subgroups., Methods: Participants were allocated to the "higher-mileage" group (≥32 km/week; n  = 41 (30 females)) or to the "lower-mileage" group (≤25 km; n  = 40 (29 females)). Three-dimensional kinematic data were collected during 60 s of treadmill running at a self-selected speed (2.61 ± 0.23 m/s). A support vector machine classifier identified kinematic differences between higher- and lower-mileage groups based on principal component scores., Results: Higher- and lower-mileage runners (both genders) could be separated with 92.59% classification accuracy. When subgrouping by gender, higher- and lower-mileage female runners could be separated with 89.83% classification accuracy, and higher- and lower-mileage male runners could be separated with 100% classification accuracy., Conclusion: These results demonstrate there are distinct kinematic differences between subgroups related to both mileage and gender, and that these factors need to be considered in future research.

The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions.

Wearable technology can be used to quantify running biomechanical patterns in a runner's natural environment, however, changes in external factors during outdoor running may influence a runner's typical gait pattern. Therefore, the purpose of this study was to determine how many runs are needed to define a stable or typical running pattern. Six biomechanical variables were recorded using a single wearable sensor placed on the lower back during ten outdoor runs for twelve runners. Univariate and multivariate distributions were created and based on the probability density function, the percent of similar data points (within 95%) from each unique run for the same runner were determined. Stability was defined when the addition of data from a new run resulted in less than a 5% change in the probability density function. To cross-validate, the percent of similar data points at stability was compared between the same and different runners using a repeated-measures MANOVA (Bonferroni-corrected α = 0.007). The maximum number of runs needed to reach stability for univariate and multivariate analyses was four and five, respectively. There was a significant overall effect on similar data points between the same and different runners (p = 0.001), with a greater percent of similar data points for the same runner compared to other runners (p < 0.007). Based on biomechanical data collected using a single wearable sensor placed on the lower back, this is the first study to show that four (univariate) to five (multivariate) runs are needed to establish a stable running pattern in real-world settings., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson's correlation coefficient (r), and a Lin's concordance correlation coefficient (r c ). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r c  = 0.97; 95% LOA = -8.0 mm - 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait., (Copyright © 2019 Elsevier Ltd. All rights reserved.)