Transference of outdoor gait-training to treadmill running biomechanics and strength measures: A randomized controlled trial.

Outdoor gait-training has been successful in improving pain and reducing contact time during outdoor running for runners with exercise-related lower leg pain (ERLLP). However, it is unclear if these adaptations translate to gold standard treadmill running and clinical strength assessments. The study purpose was to assess the influence of a 4-week outdoor gait-training intervention with home exercises (FBHE) on treadmill running biomechanics and lower extremity strength compared to home exercises alone (HE) among runners with ERLLP. Seventeen runners with ERLLP were randomly allocated to FBHE and HE groups (FBHE: 3 M, 6F, 23 ± 4 years, 22.0 ± 4.6 kg/m2; HE: 3 M, 5F, 25 ± 5 years, 24.0 ± 4.0 kg/m2). Both groups completed eight sessions of home exercises over 4 weeks. The FBHE group received gait-training through wearable sensors to reduce contact time. Treadmill running gait and clinical strength assessments were conducted at baseline and 4-weeks. Multivariate repeated measures analyses of variance were used to assess the influence of group and timepoint for all outcomes. The FBHE group demonstrated significantly decreased contact time at 4-weeks compared to baseline and the HE group (Mean Difference [MD] range: -42 ms - -39 ms; p-range: <0.001-0.02). The FBHE group had significantly increased cadence (MD: +21 steps/min; p = 0.003) and decreased loading impulse (MD: -51, p < 0.001) during treadmill running at 4-weeks compared to the HE group. Strength did not significantly differ adjusting for multiple comparisons (p > 0.007). The outdoor FBHE intervention transferred to favorable changes in treadmill running biomechanics. Clinicians treating runners with ERLLP patients should implement data-driven outdoor gait-training to maximize patient benefits across running locations.

Sensor-based gait training to reduce contact time for runners with exercise-related lower leg pain: a randomised controlled trial.

Objectives: To assess the effects of a 4-week randomised controlled trial comparing an outdoor gait-training programme to reduce contact time in conjunction with home exercises (contact time gait-training feedback with home exercises (FBHE)) to home exercises (HEs) alone for runners with exercise-related lower leg pain on sensor-derived biomechanics and patient-reported outcomes. Design: Randomised controlled trial. Setting: Laboratory and field-based study. Participants: 20 runners with exercise-related lower leg pain were randomly allocated into FBHE (4 male (M), 6 female (F), 23±4 years, 22.0±4.3 kg/m2) or HE groups (3 M, 7 F, 25±5 years, 23.6±3.9 kg/m2). Interventions: Both groups completed eight sessions of HEs over 4 weeks. The FBHE group received vibrotactile feedback through wearable sensors to reduce contact time during outdoor running. Primary and secondary outcome measures: Patient-reported outcome measures (PROMs) and outdoor gait assessments were conducted for both groups at baseline and 4 weeks. PROMs were repeated at 6 weeks, and feedback retention was assessed at 6 weeks for the FBHE group. Repeated measures analyses of variance were used to assess the influence of group and timepoint on primary outcomes. Results: The FBHE group reported increased function and recovery on PROMs beyond the HE group at 6 weeks (p<0.001). There was a significant group by time interaction for Global Rating of Change (p=0.004) and contact time (p=0.002); the FBHE group reported greater subjective improvement and reduced contact time at 4 and 6 weeks compared with the HE group and compared with baseline. The FBHE group had increased cadence (mean difference: 7 steps/min, p=0.01) at 4 weeks during outdoor running compared with baseline. Conclusion: FBHE was more effective than HE alone for runners with exercise-related lower leg pain, manifested with improved PROMs, reduced contact time and increased cadence. Trial registration number: NCT04270565.

An analysis of the Clinical and Translational Science Award pilot project portfolio using data from Research Performance Progress Reports.

Introduction: Pilot projects ("pilots") are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators. Methods: To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions. Results: The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs. Conclusion: Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.

Prospective running assessments among division I cross-country athletes.

OBJECTIVES: To prospectively monitor biomechanics, session-rating of perceived exertion (sRPE), and wellness in a cohort of collegiate Division-1 cross-country athletes over the course of a single competitive season. DESIGN: Prospective cohort study. METHODS: Healthy Division-1 cross-country athletes (9 males, 13 females) were prospectively followed over a single competitive cross-country season. Wearable sensors were used to collect biomechanics twice per week, along with surveys to assess sRPE and wellness. Mixed model linear regressions were used to assess the relationship among biomechanical measures to sRPE, and to wellness z-scores. RESULTS: Stride length, contact time, impact g, pace, weekly mileage, and running a meet in the day prior to the recorded run explained 25.4% of the variance in sRPE scores across the season (R2 = 0.254, p < 0.001). Contact time and braking g helped explain 3.7% of the variance in wellness (R2 = 0.037, F = 5.70, p = 0.01). CONCLUSIONS: There were several identified associations between gait biomechanics and sRPE, yet minimal associations with wellness measures. These findings suggest there are movement adaptations associated with perceived running intensity, however biomechanical measures alone do not lend additional insight into wellness measures.

Use of wearable sensors to identify biomechanical alterations in runners with Exercise-Related lower leg pain.

Exercise-related lower leg pain (ERLLP) is one of the most prevalent running-related injuries, however little is known about injured runners' mechanics during outdoor running. Establishing biomechanical alterations among ERLLP runners would help guide clinical interventions. Therefore, we sought to a) identify defining biomechanical features among ERLLP runners compared to healthy runners during outdoor running, and b) identify biomechanical thresholds to generate objective gait-training recommendations. Thirty-two ERLLP (13 M, age: 21 ± 5 years, BMI: 22.69 ± 2.25 kg/m2) and 32 healthy runners (13 M, age: 23 ± 6 years, BMI: 22.33 ± 3.20 kg/m2) were assessed using wearable sensors during one week of typical outdoor training. Step-by-step data were extracted to assess kinetic, kinematic, and spatiotemporal measures. Preliminary feature extraction analyses were conducted to determine key biomechanical differences between healthy and ERLLP groups. Analyses of covariance (ANCOVA) and variability assessments were used compare groups on the identified features. Participants were split into 3 pace bands, and mean differences across groups were calculated to establish biomechanical thresholds. Contact time was the key differentiating feature for ERRLP runners. ANCOVA assessments reflected that the ERLLP group had increased contact time (Mean Difference [95% Confidence Interval] = 8 ms [6.9,9.1], p < .001), and approximate entropy analyses reflected greater contact time variability. Contact time differences were dependent upon running pace, with larger between-group differences being exhibited at faster paces. In all, ERLLP runners demonstrated longer contact time than healthy runners during outdoor training. Clinicians should consider contact time when assessing and treating these ERLLP runner patients.

Detecting multivariate cross-correlation between brain regions.

The problem of identifying functional connectivity from multiple time series data recorded in each of two or more brain areas arises in many neuroscientific investigations. For a single stationary time series in each of two brain areas statistical tools such as cross-correlation and Granger causality may be applied. On the other hand, to examine multivariate interactions at a single time point, canonical correlation, which finds the linear combinations of signals that maximize the correlation, may be used. We report here a new method that produces interpretations much like these standard techniques and, in addition, 1) extends the idea of canonical correlation to 3-way arrays (with dimensionality number of signals by number of time points by number of trials), 2) allows for nonstationarity, 3) also allows for nonlinearity, 4) scales well as the number of signals increases, and 5) captures predictive relationships, as is done with Granger causality. We demonstrate the effectiveness of the method through simulation studies and illustrate by analyzing local field potentials recorded from a behaving primate. NEW & NOTEWORTHY Multiple signals recorded from each of multiple brain regions may contain information about cross-region interactions. This article provides a method for visualizing the complicated interdependencies contained in these signals and assessing them statistically. The method combines signals optimally but allows the resulting measure of dependence to change, both within and between regions, as the responses evolve dynamically across time. We demonstrate the effectiveness of the method through numerical simulations and by uncovering a novel connectivity pattern between hippocampus and prefrontal cortex during a declarative memory task.

Computational Neuroscience: Mathematical and Statistical Perspectives.

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

Population Vectors Can Provide Near Optimal Integration of Information.

Much attention has been paid to the question of how Bayesian integration of information could be implemented by a simple neural mechanism. We show that population vectors based on point-process inputs combine evidence in a form that closely resembles Bayesian inference, with each input spike carrying information about the tuning of the input neuron. We also show that population vectors can combine information relatively accurately in the presence of noisy synaptic encoding of tuning curves.