Gait asymmetry in spatiotemporal and kinetic variables does not increase running-related injury risk in lower limbs: a secondary analysis of a randomised trial including 800+ recreational runners.

Objective: To investigate asymmetry in spatiotemporal and kinetic variables in 800+ recreational runners, identify determinants of asymmetry, investigate if asymmetry is related to greater running injury risk and compare spatiotemporal and kinetic variables between the involved and uninvolved limb at baseline in runners having sustained an injury during follow-up. Methods: 836 healthy recreational runners (38.6% women) were tested on an instrumented treadmill at their preferred running speed at baseline and followed up for 6 months. From ground reaction force recordings, spatiotemporal and kinetic variables were derived for each lower limb. The Symmetry Index was computed for each variable. Correlations and multiple regression analyses were performed to identify potential determinants of asymmetry. Cox regression analyses investigated the association between asymmetry and injury risk. Analysis of variance for repeated measures was used to compare the involved and uninvolved limbs in runners who had sustained injuries during follow-up. Results: 107 participants reported at least one running-related injury. Leg length discrepancy and fat mass were the most common determinants of asymmetry, but all correlation coefficients were negligible (0.01-0.13) and explained variance was very low (multivariable-adjusted R2<0.01-0.03). Greater asymmetry for flight time and peak breaking force was associated with lower injury risk (HR (95% CI): 0.80 (0.64 to 0.99) and 0.96 (0.93 to 0.98), respectively). No between-limb differences were observed in runners having sustained an injury. Conclusion: Gait asymmetry was not associated with higher injury risk for investigated spatiotemporal and kinetic variables. Trial registration number: NCT03115437.

Reference Values and Determinants of Spatiotemporal and Kinetic Variables in Recreational Runners.

Background: Identifying atypical lower limb biomechanics may help prevent the occurrence or recurrence of running-related injuries. No reference values for spatiotemporal or kinetic variables in healthy recreational runners are available in the scientific literature to support clinical management. Purpose: To (1) present speed- and sex-stratified reference values for spatiotemporal and kinetic variables in healthy adult recreational runners; (2) identify the determinants of these biomechanical variables; and (3) develop reference regression equations that can be used as a guide in a clinical context. Study Design: Descriptive laboratory study. Methods: This study involved 860 healthy recreational runners (age, 19-65 years [38.5% women]) tested on an instrumented treadmill at their preferred running speed in randomly allocated, standardized running shoes with either hard or soft cushioning. Twelve common spatiotemporal and kinetic variables-including contact time, flight time, duty factor, vertical oscillation, step cadence, step length, vertical impact peak (VIP), time to VIP, vertical average loading rate, vertical stiffness, peak vertical ground-reaction force (GRF), and peak braking force-were derived from GRF recordings. Reference values for each biomechanical variable were calculated using descriptive statistics and stratified by sex and running speed category (≤7, 8, 9, 10, 11, 12, 13, 14, and ≥15 km/h). Correlations and multiple regression analyses were performed to identify potential determinants independently associated with each biomechanical variable and generate reference equations. Results: The mean running speed was 10.5 ± 1.3 km/h and 9 ± 1.1 km/h in men and women, respectively. While all potential predictors were significantly correlated with many of the 12 biomechanical variables, only running speed showed high correlations (r > 0.7). The adjusted R2 of the multiple regression equations ranged from 0.19 to 0.88. Conclusion: This study provides reference values and equations that may guide clinicians and researchers in interpreting spatiotemporal and kinetic variables in recreational runners. Clinical Relevance: The reference values can be used as targets for clinicians working with recreational runners in cases where there is a clinical suspicion of a causal relationship between atypical biomechanics and running-related injury.

Lower impact forces but greater burden for the musculoskeletal system in running shoes with greater cushioning stiffness.

In a recent randomised trial investigating running shoe cushioning, injury risk was greater in recreational runners who trained in the shoe version with greater cushioning stiffness (Stiff) compared to those using the Soft version. However, vertical impact peak force (VIPF) was lower in the Stiff version. To investigate further the mechanisms involved in the protective effect of greater cushioning, the present study used an intra-subject design and analysed the differences in running kinematics and kinetics between the Stiff and Soft shoe versions on a subsample of 41 runners from the previous trial. Data were recorded in the two shoe conditions using an instrumented treadmill at 10 km.h-1. VIPF was confirmed to be lower in the Stiff version compared to the Soft version (1.39 ± 0.25 vs. 1.50 ± 0.25 BW, respectively; p = 0.009, d = 0.42), but not difference was observed in vertical loading rate (p = 0.255 and 0.897 for vertical average and instantaneous loading rate, respectively). Ankle eversion maximal velocity was not different (p = 0.099), but the Stiff version induced greater ankle negative work (-0.55 ± 0.09 vs. -0.52 ± 0.10 J.kg-1; p = 0.009, d = 0.32), maximal ankle negative power (-7.21 ± 1.90 vs. -6.96 ± 1.92 W.kg-1; p = 0.037, d = 0.13) and maximal hip extension moment (1.25 ± 0.32 vs.1.18 ± 0.30 N.m.kg-1; p = 0.009, d = 0.22). Our results suggest that the Stiff shoe version is related to increased mechanical burden for the musculoskeletal system, especially around the ankle joint.Trial registration: ClinicalTrials.gov identifier: NCT03115437.

Spatiotemporal and Ground-Reaction Force Characteristics as Risk Factors for Running-Related Injury: A Secondary Analysis of a Randomized Trial Including 800+ Recreational Runners.

BACKGROUND: Running biomechanics may play a role in running-related injury development, but to date, only a few modifiable factors have been prospectively associated with injury risk. PURPOSE: To identify risk factors among spatiotemporal and ground-reaction force characteristics in recreational runners and to investigate whether shoe cushioning modifies the association between running biomechanics and injury risk. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Recreational runners (N = 848) were tested on an instrumented treadmill at their preferred running speed in randomly allocated, standardized running shoes (with either hard or soft cushioning). Typical kinetic and spatiotemporal metrics were derived from ground-reaction force recordings. Participants were subsequently followed up for 6 months regarding running activity and injury. Cox regression models for competing risk were used to investigate the association between biomechanical risk factors and injury risk, including stratified analyses by shoe version. RESULTS: In the crude analysis, greater injury risk was found for greater step length (subhazard rate ratio [SHR], 1.01; 95% CI, 1.00-1.02; P = .038), longer flight time (SHR, 1.00; 95% CI, 1.00-1.01; P = .028), shorter contact time (SHR, 0.99; 95% CI, 0.99-1.00; P = .030), and lower duty factor (defined as the ratio between contact time and stride time; SHR, 0.95; 95% CI, 0.91-0.98; P = .005). In the stratified analyses by shoe version, adjusted for previous injury and running speed, lower duty factor was associated with greater injury risk in those using the soft shoes (SHR, 0.92; 95% CI, 0.85-0.99; P = .042) but not in those using the hard shoes (SHR, 0.97; 95% CI, 0.91-1.04; P = .348). CONCLUSION: Lower duty factor is an injury risk factor, especially for softer shoe use. Contrary to widespread beliefs, vertical impact peak, loading rate, and step rate were not injury risk factors in recreational runners. REGISTRATION: NCT03115437 (ClinicalTrials.gov identifier).

Relevance of Frequency-Domain Analyses to Relate Shoe Cushioning, Ground Impact Forces and Running Injury Risk: A Secondary Analysis of a Randomized Trial With 800+ Recreational Runners.

Cushioning systems in running shoes are used assuming that ground impact forces relate to injury risk and that cushioning materials reduce these impact forces. In our recent trial, the more cushioned shoe version was associated with lower injury risk. However, vertical impact peak force was higher in participants with the Soft shoe version. The primary objective of this study was to investigate the effect of shoe cushioning on the time, magnitude and frequency characteristics of peak forces using frequency-domain analysis by comparing the two study groups from our recent trial (Hard and Soft shoe group, respectively). The secondary objective was to investigate if force characteristics are prospectively associated with the risk of running-related injury. This is a secondary analysis of a double-blinded randomized trial on shoe cushioning with a biomechanical running analysis at baseline and a 6-month follow-up on running exposure and injury. Participants (n = 848) were tested on an instrumented treadmill at their preferred running speed in their randomly allocated shoe condition. The vertical ground reaction force signal for each stance phase was decomposed into the frequency domain using the discrete Fourier transform. Both components were recomposed into the time domain using the inverse Fourier transform. An analysis of variance was used to compare force characteristics between the two study groups. Cox regression analysis was used to investigate the association between force characteristics and injury risk. Participants using the Soft shoes displayed lower impact peak force (p < 0.001, d = 0.23), longer time to peak force (p < 0.001, d = 0.25), and lower average loading rate (p < 0.001, d = 0.18) of the high frequency signal compared to those using the Hard shoes. Participants with low average and instantaneous loading rate of the high frequency signal had lower injury risk [Sub hazard rate ratio (SHR) = 0.49 and 0.55; 95% Confidence Interval (CI) = 0.25-0.97 and 0.30-0.99, respectively], and those with early occurrence of impact peak force (high frequency signal) had greater injury risk (SHR = 1.60; 95% CI = 1.05-2.53). Our findings may explain the protective effect of the Soft shoe version previously observed. The present study also demonstrates that frequency-domain analyses may provide clinically relevant impact force characteristics. Clinical Trial Registration: https://clinicaltrials.gov/, identifier: 9NCT03115437.

Smartphone-Based Interventions for Physical Activity Promotion: Scoping Review of the Evidence Over the Last 10 Years.

BACKGROUND: Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. OBJECTIVE: The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. METHODS: We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. RESULTS: Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. CONCLUSIONS: Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Effect of shoe cushioning on landing impact forces and spatiotemporal parameters during running: results from a randomized trial including 800+ recreational runners.

AbstractIn a recent randomized trial including 800+ recreational runners, injury risk was lower in those who received the Soft shoe version compared to those using the Hard version (Hazard ratio = 1.52; 95% Confidence Interval = 1.07-2.16). Here, we investigated the effect of shoe cushioning on ground reaction forces (GRF) and spatiotemporal parameters in the same cohort, with a special focus on Vertical Impact Peak Force (VIPF) and Vertical Instantaneous Loading Rate (VILR). Healthy runners (n = 848) randomly received one of two shoe prototypes that differed only in their cushioning properties (Global stiffness: 61 ± 3 and 95 ± 6 N/mm in the Soft and Hard versions, respectively). Participants were tested on an instrumented treadmill at their preferred running speed. GRF data was recorded over 2 min. VIPF was higher in the Soft shoe group compared to the Hard shoe group (1.53 ± 0.21 vs. 1.44 ± 0.23 BW, respectively; p < 0.001). However, the proportion of steps with detectable VIPF was lower in the Soft shoe group (84 vs. 97%, respectively; p < 0.001) and Time to VIPF was longer (46.9 ± 8.5 vs. 43.4 ± 7.4 milliseconds, respectively; p < 0.001). No significant differences were observed for VILR (60.1 ± 13.8 vs. 58.9 ± 15.6 BW/s for Soft and Hard shoe group, respectively; p = 0.070) or any other kinetic variable. These results show that the beneficial effect of greater shoe cushioning on injury risk in the present cohort is not associated with attenuated VIPF and VILR. These GRF metrics may be inappropriate markers of the shoe cushioning-injury risk relationship, while delayed VIPF and the proportion of steps displaying a VIPF could be more relevant.Trial registration: ClinicalTrials.gov identifier: NCT03115437..

Association between a national public health campaign for physical activity for patients with chronic diseases and the participation in Phase III cardiac rehabilitation in Luxembourg.

BACKGROUND: A 2-stage national campaign promoting physical activity for patients with chronic diseases (including cardiovascular disease) was implemented in the Grand-Duchy of Luxembourg in 2018. The first stage consisted of national TV and radio advertisements broadcasted from June 15, 2018 to July 29, 2018. The second stage was a promotional mail sent to all medical doctors on September 3, 2018. This study investigated the association between this campaign and the participation of cardiac patients in Phase III cardiac rehabilitation as well as the visibility of a dedicated website (www.sport-sante.lu). METHODS: The daily numbers of participants in the classes of the Phase III cardiac rehabilitation and the visits on www.sport-sante.lu were collected from January 1, 2016 to December 31, 2018. Segmented regression analysis was used to assess the association between the promotional campaign and the participation in the Phase III cardiac rehabilitation as well as the website visibility. RESULTS: The baseline participation rate, which was 30 participants/day, increased temporarily by 11 (p = 0.0267) and 18 (p = 0.0030) participants/day after the first and second stages of the campaign, respectively. The baseline visit rate on www.sport-sante.lu, which was 12 visits/day, increased temporarily by 20 (p < 0.0001) and 15 (p = 0.0002) visits/day after the first and second stages of the campaign, respectively. CONCLUSIONS: The national public health campaign was associated with a limited increased participation in the Phase III cardiac rehabilitation and the website visibility. However, no conclusion can be drawn about causality due to the long and difficult process from health promotion to patient's behavior change.

Motion-Control Shoes Reduce the Risk of Pronation-Related Pathologies in Recreational Runners: A Secondary Analysis of a Randomized Controlled Trial.

OBJECTIVE: To investigate whether motion-control shoes reduce the risk of pronation-related injuries in recreational runners. DESIGN: Secondary analysis of a randomized controlled trial of the effect of shoes on running injuries. METHODS: Three hundred seventy-two recreational runners were randomized to receive either standard neutral or motion-control shoes and were followed up for 6 months regarding running activity and injury. Running injuries that occurred during this period were registered and classified as pronation-related injuries (Achilles tendinopathy, plantar fasciopathy, exercise-related lower-leg pain, and anterior knee pain) or other running-related injuries. With the use of competing risk analysis, the relationship between pronation-related and other running-related injuries and shoe type was evaluated by estimating the cause-specific hazard, controlling for other possible confounders like age, sex, body mass index, previous injury, and sport participation pattern. RESULTS: Twenty-five runners sustained pronation-related running injuries and 68 runners sustained other running-related injuries. Runners wearing the motion-control shoes had a lower risk of pronation-related running injuries compared with runners who wore standard neutral shoes (hazard ratio = 0.41; 95% confidence interval: 0.17, 0.98). There was no effect of shoe type (hazard ratio = 0.68; 95% confidence interval: 0.41, 1.10) on the risk of other running-related injuries. CONCLUSION: Motion-control shoes may reduce the risk of pronation-related running injuries, but did not influence the risk of other running-related injuries. J Orthop Sports Phys Ther 2021;51(3):135-143. Epub 11 Dec 2020. doi:10.2519/jospt.2021.9710.

Can the "Appropriate" Footwear Prevent Injury in Leisure-Time Running? Evidence Versus Beliefs.

Leisure-time running is one of the most popular forms of physical activity around the world. It can be practiced almost everywhere and requires mainly a pair of "appropriate" running shoes. However, the term appropriate is ambiguous, and the properties of running footwear have always generated hot debates among clinicians, coaches, and athletes, whatever the level of practice. As the main interface between the runner's foot and the ground, the shoe potentially plays an important role in managing repetitive external mechanical loads applied to the musculoskeletal system and, thus, in injury prevention. Consequently, over the last decades, running shoes have been prescribed based on matching shoe features to foot morphology. This strategy aligns with the popular belief that footwear is one of the main extrinsic factors influencing running-related injury risk. Despite a seemingly sound strategy for shoe prescription and constant progress in running-footwear technology, the injury rate remains high. Therefore, our aim in this narrative literature review is to clarify whether the prescription of appropriate footwear to prevent injury in running is evidence based, the result of logical fallacy, or just a myth. The literature presented in this review is based on a nonsystematic search of the MEDLINE database and focuses on work investigating the effect of shoe features on injury risk in runners. In addition, key elements for a proper understanding of the literature on running footwear and injury risk are addressed. In this literature review, we outline (1) the main risk factors and the mechanisms underlying the occurrence of running-related injury, (2) important methodologic considerations for generating high-level evidence, (3) the evidence regarding the influence of running-shoe features on injury risk, (4) future directions for research, and (5) final general recommendations.

Motivational Interviewing to Increase Physical Activity Behavior in Cancer Patients: A Pilot Randomized Controlled Trials.

OBJECTIVE: This pilot randomized controlled trial (RCT) aimed at evaluating the feasibility and potential efficacy of a motivational interviewing (MI) intervention to increase physical activity (PA) behavior in cancer patients. METHODS: Participants were randomly assigned to an experimental group with standard care plus 12 MI sessions within 12 weeks or a control group with standard care only. The number of recruited participants and the modality of recruitment were recorded to describe the reach of the study. The acceptability of the study was estimated using the attrition rate during the intervention phase. The potential efficacy of the intervention was evaluated by analyzing the PA behavior. RESULTS: Twenty-five participants were recruited within the 16-month recruitment period (1.6 participants per month). Five participants (38.5%) from the experimental group (n = 13) and one participant (8.3%) from the control group (n = 12) dropped out of the study before the end of the intervention phase. No group by time interaction effect for PA behavior was observed at the end of the intervention. CONCLUSION: Due to the low recruitment rate and compliance, no conclusion can be drawn regarding the efficacy of MI to increase PA behavior in cancer patients. Moreover, the current literature cannot provide any evidence on the effectiveness of MI to increase PA in cancer survivors. Future RCTs should consider that the percentage of uninterested patients to join the study may be as high as 60%. Overrecruitment (30% to 40%) is also recommended to accommodate the elevated attrition rate.

Randomised controlled trials (RCTs) in sports injury research: authors-please report the compliance with the intervention.

BACKGROUND: In randomised controlled trials (RCTs) of interventions that aim to prevent sports injuries, the intention-to-treat principle is a recommended analysis method and one emphasised in the Consolidated Standards of Reporting Trials (CONSORT) statement that guides quality reporting of such trials. However, an important element of injury prevention trials-compliance with the intervention-is not always well-reported. The purpose of the present educational review was to describe the compliance during follow-up in eight large-scale sports injury trials and address compliance issues that surfaced. Then, we discuss how readers and researchers might consider interpreting results from intention-to-treat analyses depending on the observed compliance with the intervention. METHODS: Data from seven different randomised trials and one experimental study were included in the present educational review. In the trials that used training programme as an intervention, we defined full compliance as having completed the programme within ±10% of the prescribed running distance (ProjectRun21 (PR21), RUNCLEVER, Start 2 Run) or time-spent-running in minutes (Groningen Novice Running (GRONORUN)) for each planned training session. In the trials using running shoes as the intervention, full compliance was defined as wearing the prescribed running shoe in all running sessions the participants completed during follow-up. RESULTS: In the trials that used a running programme intervention, the number of participants who had been fully compliant was 0 of 839 (0%) at 24-week follow-up in RUNCLEVER, 0 of 612 (0%) at 14-week follow-up in PR21, 12 of 56 (21%) at 4-week follow-up in Start 2 Run and 8 of 532 (1%) at 8-week follow-up in GRONORUN. In the trials using a shoe-related intervention, the numbers of participants who had been fully compliant at the end of follow-up were 207 of 304 (68%) in the 21 week trial, and 322 of 423 (76%), 521 of 577 (90%), 753 of 874 (86%) after 24-week follow-up in the other three trials, respectively. CONCLUSION: The proportion of runners compliant at the end of follow-up ranged from 0% to 21% in the trials using running programme as intervention and from 68% to 90% in the trials using running shoes as intervention. We encourage sports injury researchers to carefully assess and report the compliance with intervention in their articles, use appropriate analytical approaches and take compliance into account when drawing study conclusions. In studies with low compliance, G-estimation may be a useful analytical tool provided certain assumptions are met.

Shoe Cushioning Influences the Running Injury Risk According to Body Mass: A Randomized Controlled Trial Involving 848 Recreational Runners.

BACKGROUND: Shoe cushioning is expected to protect runners against repetitive loading of the musculoskeletal system and therefore running-related injuries. Also, it is a common belief that heavier runners should use footwear with increased shock absorption properties to prevent injuries. PURPOSE: The aim of this study was to determine if shoe cushioning influences the injury risk in recreational runners and whether the association depends on the runner's body mass. STUDY DESIGN: Randomized controlled trial; Level of evidence, 1. METHODS: Healthy runners (n = 848) randomly received 1 of 2 shoe prototypes that only differed in their cushioning properties. Global stiffness was 61.3 ± 2.7 and 94.9 ± 5.9 N/mm in the soft and hard versions, respectively. Participants were classified as light or heavy according to their body mass using the median as a cut-off (78.2 and 62.8 kg in male and female runners, respectively). They were followed over 6 months regarding running activity and injury (any physical complaint reducing/interrupting running activity for at least 7 days). Data were analyzed through time-to-event models with the subhazard rate ratio (SHR) and their 95% confidence interval (CI) as measures of association. A stratified analysis was conducted to investigate the effect of shoe cushioning on the injury risk in lighter and heavier runners. RESULTS: The runners who had received the hard shoes had a higher injury risk (SHR, 1.52 [95% CI, 1.07-2.16]), while body mass was not associated with the injury risk (SHR, 1.00 [95% CI, 0.99-1.01]). However, after stratification according to body mass, results showed that lighter runners had a higher injury risk in hard shoes (SHR, 1.80 [95% CI, 1.09-2.98]) while heavier runners did not (SHR, 1.23 [95% CI, 0.75-2.03]). CONCLUSION: The injury risk was higher in participants running in the hard shoes compared with those using the soft shoes. However, the relative protective effect of greater shoe cushioning was found only in lighter runners. REGISTRATION: NCT03115437 (ClinicalTrials.gov identifier).

Sampling Techniques on Collecting Fine Carbon Nanotube Fibers for Exposure Assessment.

Carbon nanotube (CNT) sampling using an open-faced 25 mm cassette fiber sampling method and a newly developed direct sampling device was evaluated for the size fractioned analysis of collected airborne CNT fibers to improve the sampling and analytical methods. The open-faced 25 mm cassette fiber sampling method primarily collected large agglomerates, with the majority of collected particles being larger than two micrometer in size. Most of CNT structures collected by the new direct sampling device were individual fibers and clusters smaller than one micrometer with a high particle number concentration discrepancy compared to the open-faced 25 mm cassette method raising the concern of this sampling method to representatively characterize the respirable size fraction of CNT aerosols. This work demonstrates that a specialized technique is needed for collecting small fibers to provide a more representative estimate of exposure. It is recommended that an additional sampler be used to directly collect and analyze small fibers in addition to the widely accepted sampling method which utilizes an open-faced 25 mm cassette.

Intrahepatic diffuse periportal enhancement patterns on hepatobiliary phase gadoxetate disodium-enhanced liver MR images: Do they correspond to periportal hyperintense patterns on T2-weighted images?

The purpose of this study was to investigate the findings of diffuse periportal enhancement in the liver on hepatobiliary phase gadoxetate disodium-enhanced magnetic resonance images by comparing with the finding of periportal hyperintensity on T2-weighted images and to reveal their clinical significance.Nineteen consecutive patients with diffuse periportal enhancement on hepatobiliary phase images constituted the study population. The intrahepatic diffuse periportal enhancement finding was assessed on whether it corresponded to periportal hyperintense patterns on T2-weighted images or not in the location, and the cases were classified into 2 groups according to this characteristic. Signal intensities at the periportal areas were also assessed on T1-, T2-, diffusion-weighted and dynamic images. Furthermore, possible associations between these image findings and the final diagnoses were explored.In 7 of the 19 patients, periportal enhancement area corresponded with the periportal hyperintensity area on T2-weighted images. In the remaining 12 patients, the finding of periportal T2-hyperintensity was absent or the periportal enhancement differed from the periportal T2-hyperintensity in the location. Diseases of the former group comprised autoimmune hepatitis, acute exacerbation of chronic hepatitis and acute alcoholic steatohepatitis, and those of the latter group primary sclerosing cholangitis, autoimmune hepatitis-primary biliary cirrhosis overlap syndrome, and liver cirrhosis with miscellaneous etiology.Diffuse periportal enhancement during the hepatobiliary phase did not always correspond to periportal hyperintensity on T2-weighted images. In the classification based on whether enhancement area corresponded or not, each enhancement pattern appeared in different groups of liver diseases. Specifically, the former (corresponding) was associated with active inflammation such as hepatitis and the latter (not corresponding) was predominantly associated with a chronic change such as cirrhosis. Appropriate recognition of these periportal enhancement patterns may contribute to the improved diagnosis of diffuse liver diseases.

From research to clinic: A sensor reduction method for high-density EEG neurofeedback systems.

OBJECTIVE: To accurately deliver a source-estimated neurofeedback (NF) signal developed on a 128-sensors EEG system on a reduced 32-sensors EEG system. METHODS: A linearly constrained minimum variance beamformer algorithm was used to select the 64 sensors which contributed most highly to the source signal. Monte Carlo-based sampling was then used to randomly generate a large set of reduced 32-sensors montages from the 64 beamformer-selected sensors. The reduced montages were then tested for their ability to reproduce the 128-sensors NF. The high-performing montages were then pooled and analyzed by a k-means clustering machine learning algorithm to produce an optimized reduced 32-sensors montage. RESULTS: Nearly 4500 high-performing montages were discovered from the Monte Carlo sampling. After statistically analyzing this pool of high performing montages, a set of refined 32-sensors montages was generated that could reproduce the 128-sensors NF with greater than 80% accuracy for 72% of the test population. CONCLUSION: Our Monte Carlo reduction method was used to create reliable reduced-sensors montages which could be used to deliver accurate NF in clinical settings. SIGNIFICANCE: A translational pathway is now available by which high-density EEG-based NF measures can be delivered using clinically accessible low-density EEG systems.

Correction to: Rotational profile alterations after anatomic posterolateral corner reconstructions in multiligament injured knees.

The original article can be found online.

Physical activity promotion in primary care: a Utopian quest?

The health benefits of physical activity (PA) are acknowledged and promoted by the scientific community, especially within primary care. However, there is little evidence that such promotion is provided in any consistent or comprehensive format. Brief interventions (i.e. discussion, negotiation or encouragement) and exercise referral schemes (i.e. patients being formally referred to a PA professional) are the two dominant approaches within primary care. These cost-effective interventions can generate positive changes in health outcomes and PA levels in inactive patients who are at increased risk for non-communicable diseases. Their success relies on the acceptability and efficiency of primary care professionals to deliver PA counselling. To this end, appropriate training and financial support are crucial. Similarly, human resourcing and synergy between the different stakeholders must be addressed. To obtain maximum adherence, specific populations should be targeted and interventions adapted to their needs. Key enablers include motivational interviewing, social support and multi-disciplinary approaches. Leadership and lines of accountability must be clearly delineated to ensure the success of the initiatives promoting PA in primary care. The synergic and multisectoral action of several stakeholders, especially healthcare professionals, will help overcome physical inactivity in a sustainable way.

Time-to-event analysis for sports injury research part 1: time-varying exposures.

BACKGROUND: 'How much change in training load is too much before injury is sustained, among different athletes?' is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology. AIM: To discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes. CONTENT: Time-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills. CONCLUSION: To increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data.

Time-to-event analysis for sports injury research part 2: time-varying outcomes.

BACKGROUND: Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain. CONTENT: In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete. CONCLUSION: Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.