Analysis of Common Exercise Modalities in Improving Athletic Performance in Older Adults: A Narrative Review.

ABSTRACT: Exercise leads to robust cardiovascular, musculoskeletal, and psychological benefits that improve quality of life and longevity for older adults, but accompanying improvements in athletic parameters are less well explored. The aim of this review is to summarize some of the most common exercise modalities, namely, Pilates, martial arts (tai chi, Japanese-style karate, hard martial arts), locomotion (brisk walking/jogging and running), Masters sports, resistance training, and high-intensity interval training, in improving athletic performance for older adults. Regular participation in these activities can have robust yet unique impacts on physical performance that prolong exercise participation. In particular, this review will explore benefits in cardiorespiratory fitness, power, strength, flexibility, and balance, thereby hopefully improving endurance, exercise adherence, and overall fall risk. A narrative literature review was performed to explore benefits, pitfalls, and recommendations for some of the most popular exercise modalities for older adults.

Posterior intra-articular hip injections: a pilot study investigating a new approach.

PURPOSE: The purpose of this study was to investigate a novel posterior intra-articular hip injection approach while discussing the clinical pearls for methodology, accuracy, and associated adverse events. The goal was to investigate if this approach could potentially be used in those where the typical anterior access would be challenging. MATERIALS AND METHODS: 10 adults (age > 18 years) who were referred to a tertiary outpatient clinic for hip injection due to hip osteoarthritis or femoral acetabular impingement with labral tearing to a single academic sports medicine clinic. Patients were placed in prone position. Posterior structures were identified using a curvilinear transducer. A 22 gauge 3.5-6-inch spinal needle was advanced in-plane to the transducer towards the hip capsule at the head-neck junction of the femur from inferolateral to superomedial. A fluoroscopic arthrogram was obtained to evaluate needle placement accuracy. Location of the needle, direction of contrast flow, the rate of conversion to an anterior portal, and adverse events were analyzed. RESULTS: A fluoroscopic arthrogram was obtained after the first attempt in nine patients (90%). One patient (10%) was converted to the anterior approach due to inability to obtain the expected arthrogram. Two patients (20%) demonstrated mild adverse events. CONCLUSION: Ultrasound-guided posterior approach hip injection is accurate and without serious adverse events in our study. The posterior approach can be considered as an alternative approach for patients who cannot tolerate or may have technical difficulty with an anterior approach.

Embracing firefly flash pattern variability with data-driven species classification.

Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.

Popular Dietary Trends' Impact on Athletic Performance: A Critical Analysis Review.

BACKGROUND: Nutrition fuels optimal performance for athletes. With increased research developments, numerous diets available, and publicity from professional athletes, a review of dietary patterns impact on athletic performance is warranted. RESULTS: The Mediterranean diet is a low inflammatory diet linked to improved power and muscle endurance and body composition. Ketogenic diets are restrictive of carbohydrates and proteins. Though both show no decrements in weight loss, ketogenic diets, which is a more restrictive form of low-carbohydrate diets, can be more difficult to follow. High-protein and protein-paced versions of low-carbohydrate diets have also shown to benefit athletic performance. Plant-based diets have many variations. Vegans are at risk of micronutrient deficiencies and decreased leucine content, and therefore, decreased muscle protein synthesis. However, the literature has not shown decreases in performance compared to omnivores. Intermittent fasting has many different versions, which may not suit those with comorbidities or specific needs as well as lead to decreases in sprint speed and worsening time to exhaustion. CONCLUSIONS: This paper critically evaluates the research on diets in relation to athletic performance and details some of the potential risks that should be monitored. No one diet is universally recommend for athletes; however, this article provides the information for athletes to analyze, in conjunction with medical professional counsel, their own diet and consider sustainable changes that can help achieve performance and body habitus goals.

Collecting-Gathering Biophysics of the Blackworm Lumbriculus variegatus.

Many organisms exhibit collecting and gathering behaviors as a foraging and survival method. Benthic macroinvertebrates are classified as collector-gatherers due to their collection of particulate matter. Among these, the aquatic oligochaete Lumbriculus variegatus (California blackworms) demonstrates the ability to ingest both organic and inorganic materials, including microplastics. However, earlier studies have only qualitatively described their collecting behaviors for such materials. The mechanism by which blackworms consolidate discrete particles into a larger clump remains unexplored quantitatively. In this study, we analyze a group of blackworms in a large arena with an aqueous algae solution (organic particles) and find that their relative collecting efficiency is proportional to population size. We found that doubling the population size (N = 25-N = 50) results in a decrease in time to reach consolidation by more than half. Microscopic examination of individual blackworms reveals that both algae and microplastics physically adhere to the worm's body and form clumps due to external mucus secretions by the worms. Our observations also indicate that this clumping behavior reduces the worm's exploration of its environment, possibly due to thigmotaxis. To validate these observed biophysical mechanisms, we create an active polymer model of a worm moving in a field of particulate debris. We simulate its adhesive nature by implementing a short-range attraction between the worm and the nearest surrounding particles. Our findings indicate an increase in gathering efficiency when we add an attractive force between particles, simulating the worm's mucosal secretions. Our work provides a detailed understanding of the complex mechanisms underlying the collecting-gathering behavior in L. variegatus, informing the design of bioinspired synthetic collector systems, and advances our understanding of the ecological impacts of microplastics on benthic invertebrates.

Collecting-Gathering Biophysics of the Blackworm L. variegatus.

Many organisms exhibit collecting and gathering behaviors as a foraging and survival method. Certain benthic macroinvertebrates are classified as collector-gatherers due to their collection of particulate matter as a food source, such as the aquatic oligochaete Lumbriculus variegatus (California blackworms). Blackworms demonstrate the ability to ingest organic and inorganic materials, including microplastics, but previous work has only qualitatively described their possible collecting behaviors for such materials. The mechanism through which blackworms consolidate discrete particles into a larger clumps remains unexplored quantitatively. By analyzing a group of blackworms in a large arena with an aqueous algae solution, we discover that their relative collecting efficiency is proportional to population size. Examining individual blackworms under a microscope reveals that both algae and microplastics physically adhere to the worm's body due to external mucus secretions, which cause the materials to clump around the worm. We observe that this clumping reduces the worm's exploration of its environment, potentially due to thigmotaxis. To validate the observed biophysical mechanisms, we create an active polymer model of a worm moving in a field of particulate debris with a short-range attractive force on its body to simulate its adhesive nature. We find that the attractive force increases gathering efficiency. This study offers insights into the mechanisms of collecting-gathering behavior, informing the design of robotic systems, as well as advancing our understanding the ecological impacts of microplastics on benthic invertebrates.

Artificial Intelligence System for Automatic Quantitative Analysis and Radiology Reporting of Leg Length Radiographs.

Leg length discrepancies are common orthopedic problems with the potential for poor functional outcomes. These are frequently assessed using bilateral leg length radiographs. The objective was to determine whether an artificial intelligence (AI)-based image analysis system can accurately interpret long leg length radiographic images. We built an end-to-end system to analyze leg length radiographs and generate reports like radiologists, which involves measurement of lengths (femur, tibia, entire leg) and angles (mechanical axis and pelvic tilt), describes presence and location of orthopedic hardware, and reports laterality discrepancies. After IRB approval, a dataset of 1,726 extremities (863 images) from consecutive examinations at a tertiary referral center was retrospectively acquired and partitioned into train/validation and test sets. The training set was annotated and used to train a fasterRCNN-ResNet101 object detection convolutional neural network. A second-stage classifier using a EfficientNet-D0 model was trained to recognize the presence or absence of hardware within extracted joint image patches. The system was deployed in a custom web application that generated a preliminary radiology report. Performance of the system was evaluated using a holdout 220 image test set, annotated by 3 musculoskeletal fellowship trained radiologists. At the object detection level, the system demonstrated a recall of 0.98 and precision of 0.96 in detecting anatomic landmarks. Correlation coefficients between radiologist and AI-generated measurements for femur, tibia, and whole-leg lengths were > 0.99, with mean error of < 1%. Correlation coefficients for mechanical axis angle and pelvic tilt were 0.98 and 0.86, respectively, with mean absolute error of < 1°. AI hardware detection demonstrated an accuracy of 99.8%. Automatic quantitative and qualitative analysis of leg length radiographs using deep learning is feasible and holds potential in improving radiologist workflow.

Mapping outcomes for recovery of consciousness in studies from 1986 to 2020: a scoping review protocol.

INTRODUCTION: Historically, heterogeneous outcome assessments have been used to measure recovery of consciousness in patients with disorders of consciousness (DoC) following traumatic brain injury (TBI), making it difficult to compare across studies. To date, however, there is no comprehensive review of clinical outcome assessments that are used in intervention studies of adults with DoC. The objective of this scoping review is to develop a comprehensive inventory of clinical outcome assessments for recovery of consciousness that have been used in clinical studies of adults with DoC following TBI. METHODS AND ANALYSIS: The methodological framework for this review is: (1) identify the research questions, (2) identify relevant studies, (3) select studies, (4) chart the data, (5) collate, summarise and report results and (6) consult stakeholders to drive knowledge translation. We will identify relevant studies by searching the following electronic bibliographic databases: PubMed, Scopus, EMBASE, PsycINFO and The Cochrane Library (including Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials and Cochrane Methodology Register). Criteria for article inclusion are published in the English-language, peer-reviewed studies of interventions aimed at facilitating recovery of consciousness among adults (> 18 years) with DoC following a severe TBI, published from January 1986 to December 2020. Articles meeting inclusion criteria at this stage will undergo a full text review. We will chart the data by applying the WHO International Classification of Functioning, Disability and Health Framework to identify the content areas of clinical outcome assessments. To support knowledge translation efforts, we will involve clinicians and researchers experienced in TBI care throughout the project from conceptualisation of the study through dissemination of results. ETHICS AND DISSEMINATION: No ethical approval is required for this study as it is not determined to be human subjects research. Results will be presented at national conferences and published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: CRD42017058383.

Risk Factors for Readmissions After Total Joint Replacement: A Meta-Analysis.

¼: We performed a systematic review and meta-analysis of predictive modeling studies examining the risk of readmission after total hip arthroplasty (THA) and total knee arthroplasty (TKA) in order to synthesize key risk factors and evaluate their pooled effects. Our analysis entailed 15 compliant studies for qualitative review and 17 compliant studies for quantitative meta-analysis. ¼: A qualitative review of 15 predictive modeling studies highlighted 5 key risk factors for risk of readmission after THA and/or TKA: age, length of stay, readmission reduction policy, use of peripheral nerve block, and type of joint replacement procedure. ¼: A meta-analysis of 17 studies unveiled 3 significant risk factors: discharge to a skilled nursing facility rather than to home (approximately 61% higher risk), surgery at a low- or medium-procedure-volume hospital (approximately 26% higher risk), and the presence of patient obesity (approximately 34% higher risk). We demonstrated clinically meaningful relationships between these factors and moderator variables of procedure type, source of data used for model-building, and the proportion of male patients in the cohort. ¼: We found that many studies did not adhere to gold-standard criteria for reporting and study construction based on the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) and NOS (Newcastle-Ottawa Scale) methodologies. ¼: We recommend that these risk factors be considered in clinical practice and future work alike as they relate to surgical, discharge, and care decision-making. Future work should also prioritize greater observance of gold-standard reporting criteria for predictive models.

Risk Factors for Readmission After Knee Arthroplasty Based on Predictive Models: A Systematic Review.

BACKGROUND: An increase in the aging yet active US population will continue to make total knee arthroplasty (TKA) procedures routine in the coming decades. For such joint procedures, the Centers for Medicare and Medicaid Services introduced programs such as the Comprehensive Care for Joint Replacement to emphasize accountable and efficient transitions of care. Accordingly, many studies have proposed models using risk factors for predicting readmissions after the procedure. We performed a systematic review of TKA literature to identify such models and risk factors therein using a reliable appraisal tool for their quality assessment. METHODS: Five databases were searched to identify studies that examined correlations between post-TKA readmission and risk factors using multivariate models. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology and Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis criteria established for quality assessment of prognostic studies. RESULTS: Of 29 models in the final selection, 6 models reported performance using a C-statistic, ranging from 0.51 to 0.76, and 2 studies used a validation cohort for assessment. The average 30-day and 90-day readmission rates across the studies were 5.33% and 7.12%, respectively. Three new significant risk factors were discovered. CONCLUSIONS: Current models for TKA readmissions lack in performance measurement and reporting when assessed with established criteria. In addition to using new techniques for better performance, work is needed to build models that follow the systematic process of calibration, external validation, and reporting for pursuing their deployment in clinical settings.

Predictive models for identifying risk of readmission after index hospitalization for hip arthroplasty: A systematic review.

BACKGROUND: An aging United States population profoundly impacts healthcare from both a medical and financial standpoint, especially with an increase in related procedures such as Total Hip Arthroplasty (THA). The Hospital Readmission Reduction Program and Comprehensive Care for Joint Replacement Program incentivize hospitals to decrease post-operative readmissions by correlating reimbursements with smoother care transitions, thereby decreasing hospital burden and improving quantifiable patient outcomes. Many studies have proposed predictive models built upon risk factors for predicting 30-day THA readmissions. QUESTIONS: (1) Are there validated statistical models that predict 30-day readmissions for THA patients when appraised with a standards-based, reliable assessment tool?. (2) Which evidence-based factors are significant and have support across models for predicting risk of 30-day readmissions post-THA? METHODS: Five major electronic databases were searched to identify studies that examined correlations between post-THA readmission and risk factors using multivariate models. We rigorously applied the PRISMA methodology and TRIPOD criteria for assessment of the prognostic studies. RESULTS: We found 26 studies that offered predictive models, of which two presented models tested with validation cohorts. In addition to the many factors grouped into demographic, administrative, and clinical categories, bleeding disorder, higher ASA status, discharge disposition, and functional status appeared to have broad and significant support across the studies. CONCLUSIONS: Reporting of recent predictive models establishing risk factors for 30-day THA readmissions against the current standard could be improved. Aside from building better performing models, more work is needed to follow the thorough process of undergoing calibration, external validation, and integration with existing EHR systems for pursuing their use in clinical settings. There are several risk factors that are significant in multiple models; these factors should be closely examined clinically and leveraged in future risk modeling efforts.

Characterization of fracture in topology-optimized bioinspired networks.

Designing strong and robust bioinspired structures requires an understanding of how function arises from the architecture and geometry of materials found in nature. We draw from trabecular bone, a lightweight bone tissue that exhibits a complex, anisotropic microarchitecture, to generate networked structures using multiobjective topology optimization. Starting from an identical volume, we generate multiple different models by varying the objective weights for compliance, surface area, and stability. We examine the relative effects of these objectives on how resultant models respond to simulated mechanical loading and element failure. We adapt a network-based method developed initially in the context of modeling trabecular bone to describe the topology-optimized structures with a graph-theoretical framework, and we use community detection to characterize locations of fracture. This complementary combination of computational methods can provide valuable insights into the strength of bioinspired structures and mechanisms of fracture.

Functional workspace and patient-reported outcomes improve after reverse and total shoulder arthroplasty.

BACKGROUND: Low-cost motion analysis systems (LCMASs) have emerged as easy and practical methods to measure the functional workspace (FWS). Thus, we ventured to apply an LCMAS, the Kinect2 gaming camera, to evaluate the FWS in patients with shoulder osteoarthritis (OA) and patients who underwent total shoulder arthroplasty (TSA) or reverse total shoulder arthroplasty (RTSA). METHODS: A cross-sectional study of participants with OA (n = 53), TSA (n = 70), and RTSA (n = 34) was performed. The FWS as measured by an LCMAS, the American Shoulder and Elbow Surgeons (ASES) Standardized Shoulder Assessment Form score, and the Patient-Reported Outcomes Measurement Information System (PROMIS) score were collected. For participants who underwent TSA or RTSA, the FWS was evaluated at 6, 12, and 24 months postoperatively. The correlation of the FWS with the ASES score and PROMIS score was determined. Significance was set at P < .05. RESULTS: Patients who underwent TSA or RTSA had a significantly higher FWS than patients with shoulder OA at almost all time points. Patients who underwent TSA had a significantly higher FWS than patients who underwent RTSA at 24 months after surgery. PROMIS and ASES scores showed strong correlations with the FWS in patients who underwent TSA (R = 0.75 [P < .001] and R = 0.83 [P < .001], respectively) and RTSA (R = 0.84 [P < .001] and R = 0.73 [P < .001], respectively). CONCLUSION: The FWS measured by an LCMAS is an easy and low-cost method to quantify the reachable space of the hand in patients and shows strong correlations with patient-reported outcome measures. This may be a useful tool to assess upper-extremity range of motion before and after shoulder arthroplasty.

Network models for characterization of trabecular bone.

Trabecular bone is a lightweight, compliant material organized as a web of struts and rods (trabeculae) that erode with age and the onset of bone diseases like osteoporosis, leading to increased fracture risk. The traditional diagnostic marker of osteoporosis, bone mineral density (BMD), has been shown in ex vivo experiments to correlate poorly with fracture resistance when considered on its own, while structural features in conjunction with BMD can explain more of the variation in trabecular bone strength. We develop a network-based model of trabecular bone by creating graphs from micro-computed tomography images of human bone, with weighted links representing trabeculae and nodes representing branch points. These graphs enable calculation of quantitative network metrics to characterize trabecular structure. We also create finite element models of the networks in which each link is represented by a beam, facilitating analysis of the mechanical response of the bone samples to simulated loading. We examine the structural and mechanical properties of trabecular bone at the scale of individual trabeculae (of order 0.1 mm) and at the scale of selected volumes of interest (approximately a few mm), referred to as VOIs. At the VOI scale, we find significant correlations between the stiffness of VOIs and 10 different structural metrics. Individually, the volume fraction of each VOI is most strongly correlated to the stiffness of the VOI. We use multiple linear regression to identify the smallest subset of variables needed to capture the variation in stiffness. In a linear fit, we find that node degree, weighted node degree, Z-orientation, weighted Z-orientation, trabecular spacing, link length, and the number of links are the structural metrics that are most significant (p<0.05) in capturing the variation of stiffness in trabecular networks.

Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone.

Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 µm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a 'ratio metric' determined from a few k-space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.

Optimizing Real-Time Vaccine Allocation in a Stochastic SIR Model.

Real-time vaccination following an outbreak can effectively mitigate the damage caused by an infectious disease. However, in many cases, available resources are insufficient to vaccinate the entire at-risk population, logistics result in delayed vaccine deployment, and the interaction between members of different cities facilitates a wide spatial spread of infection. Limited vaccine, time delays, and interaction (or coupling) of cities lead to tradeoffs that impact the overall magnitude of the epidemic. These tradeoffs mandate investigation of optimal strategies that minimize the severity of the epidemic by prioritizing allocation of vaccine to specific subpopulations. We use an SIR model to describe the disease dynamics of an epidemic which breaks out in one city and spreads to another. We solve a master equation to determine the resulting probability distribution of the final epidemic size. We then identify tradeoffs between vaccine, time delay, and coupling, and we determine the optimal vaccination protocols resulting from these tradeoffs.