Ratio data: Understanding pitfalls and knowing when to standardise

Ratios represent a single-value metric but consist of two component parts: a numerator variable and a denominator variable. Strictly speaking, a ratio is defined as: “the quantitative relation between two amounts showing the number of times one value contains or is contained by another”. When we discuss symmetry in sport science, we are generally comparing values of some metric between left and right sides or between agonist and antagonist muscles. The typical practice is to express the comparison as a ratio (differences are also a way of standardizing under different assumptions), such as the injured limb having only 60% of the strength of the uninjured limb. Conceptually though, we are using the ratio as one way to standardize the value of one variable with respect to another. Despite their common use, the interpretation of ratio standardisation, whether for symmetry or other reasons, often provides challenges, some of which are not always obvious to practitioners. Typically, when monitoring a change in ratios, if an intervention affects both the numerator and denominator, there will likely be challenges in interpreting the ratio appropriately. Therefore, the aim of this editorial is to use some examples to highlight when using this form of standardisation may be helpful, and when using it can lead to misinterpretations

Dynamic Poisson Factorization

Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be generated without order. These assumptions limit the explorative and predictive capabilities of such models, since users' interests and item popularity may evolve over time. To address this, we propose dPF, a dynamic matrix factorization model based on the recent Poisson factorization model for recommendations. dPF models the time evolving latent factors with a Kalman filter and the actions with Poisson distributions. We derive a scalable variational inference algorithm to infer the latent factors. Finally, we demonstrate dPF on 10 years of user click data from arXiv.org, one of the largest repository of scientific papers and a formidable source of information about the behavior of scientists. Empirically we show performance improvement over both static and, more recently proposed, dynamic recommendation models. We also provide a thorough exploration of the inferred posteriors over the latent variables.Comment: RecSys 201

Neural Networks

We present an overview of current research on artificial neural networks, emphasizing a statistical perspective. We view neural networks as parameterized graphs that make probabilistic assumptions about data, and view learning algorithms as methods for finding parameter values that look probable in the light of the data. We discuss basic issues in representation and learning, and treat some of the practical issues that arise in fitting networks to data. We also discuss links between neural networks and the general formalism of graphical models

Test–retest reliability of the magnitude and direction of asymmetry in the countermovement jump, drop jump, and countermovement rebound jump

This study aimed to investigate the test–retest reliability of three bilateral jump tests to assess asymmetry and determine the consistency of both the magnitude and direction of asymmetry between two testing sessions. Thirty-three participants performed the countermovement jump (CMJ), drop jump (DJ), and countermovement rebound jump (CMRJ—jump 1: CMRJ1; jump 2: CMRJ2) over two sessions. Inter-limb asymmetry was calculated for kinetic metrics, including the mean propulsive force, net braking impulse, and net propulsive impulse. Test reliability was computed using intraclass correlation coefficients (ICC), coefficients of variation (CV), and standard error of measurement. Furthermore, analysis of variance was used to determine the systematic bias between jump types and sessions. Kappa coefficients were utilised to assess the consistency of asymmetry favouring the same limb. Results showed poor to excellent reliability for all jump tests between sessions (ICC range = 0.19–0.99, CV range = 2.80–11.09%). A significantly higher magnitude of asymmetry was revealed for the net braking impulse during the DJ compared to the CMRJ2 (p ≥ 0.014, g ≤ 0.53). When computing the direction of asymmetry between test sessions, Kappa coefficients revealed that levels of agreement were substantial (Kappa = 0.63–0.70) for the CMJ, moderate to almost perfect (Kappa = 0.59–0.94) for the CMRJ1, moderate to almost perfect (Kappa = 0.58–0.81) for the DJ, and slight to moderate for the CMRJ2 (Kappa = 0.19–0.57). These results underscore the variable nature of both the magnitude and direction of asymmetry during jump testing. Thus, practitioners should carefully choose evaluation methods and metrics characterised by low variability to ensure robust asymmetry assessments

A framework to guide practitioners when selecting metrics during the countermovement and drop jump tests

Researchers and practitioners have highlighted the necessity to monitor jump strategy metrics as well as the commonly reported outcome measures during the countermovement (CMJ) and drop jump (DJ) tests. However, there is a risk of confusion for practitioners, given the vast range of metrics that now seem to be on offer via analysis software when collecting data from force platforms. As such, practitioners may benefit from a framework that can help guide metric selection for commonly used jump tests, which is the primary purpose of this article. To contextualise the proposed framework, we have provided two examples for how this could work: one for the CMJ and one for the DJ, noting that these tests are commonly utilized by practitioners during routine testing across a range of sport performance and clinical settings

An assessment of the hopping strategy and inter-limb asymmetry during the triple hop test: a test–retest pilot study

The aims of the present study are to: (1) determine within- and between-session reliability of multiple metrics obtained during the triple hop test; and (2) determine any systematic bias in both the test and inter-limb asymmetry scores for these metrics. Thirteen male young American football athletes performed three trials of a triple hop test on each leg on two separate occasions. In addition to the total distance hopped, manual detection of touch down and toe-off were calculated via video analysis, enabling flight time (for each hop), ground contact time (GCT), reactive strength index (RSI), and leg stiffness (between hops) to be calculated. Results showed all coefficient of variation (CV) values were ≤ 10.67% and intraclass correlation coefficients (ICC) ranged from moderate to excellent (0.53–0.95) in both test sessions. Intrarater reliability showed excellent reliability for all metrics (CV ≤ 3.60%, ICC ≥ 0.97). No systematic bias was evident between test sessions for raw test scores (g = −0.34 to 0.32) or the magnitude of asymmetry (g = −0.19 to 0.43). However, ‘real’ changes in asymmetry (i.e., greater than the CV in session 1) were evident on an individual level for all metrics. For the direction of asymmetry, kappa coefficients revealed poor-to-fair levels of agreement between test sessions for all metrics (K = −0.10 to 0.39), with the exception of the first hop (K = 0.69). These data show that, given the inherent limitations of distance jumped in the triple hop test, practitioners can confidently gather a range of reliable data when computed manually, provided sufficient test familiarization is conducted. In addition, although the magnitude of asymmetry appears to show only small changes between test sessions, limb dominance does appear to fluctuate between test sessions, highlighting the value of also monitoring the direction of the imbalance

Providing patients with direct access to musculoskeletal physiotherapy: the impact on general practice musculoskeletal workload and resource use. The STEMS-2 study.

OBJECTIVES: This study examined the real-world impact of patient direct access to NHS physiotherapy (self-referral) on (a) general practice consultations for musculoskeletal (MSK) conditions and (b) specified clinical management for patients with MSK conditions. DESIGN AND SETTING: Natural experiment in four general practices and the associated physiotherapy service. METHODS: Anonymised routinely collected data were obtained. MSK coded GP consultations, recorded fit notes, MSK-related prescription medication, X-rays and MRI requests, and referrals to secondary care for patients consulting with MSK conditions were identified and trends described across a 6-year period (June 2011 to June 2017). Joinpoint regression analysis was used to identify any significant changes in GP MSK consultation trends before and after the introduction of self-referral to physiotherapy. Physiotherapy service data examined access methods used by patients (GP referred, GP recommended self-referral, true self-referral) and the number of physiotherapy sessions. RESULTS: Direct access resulted in inconsistent impact on general practices. In one arm of the experiment a significant increase in GP consultations was observed and in one arm was stable. Exploratory examination of clinical management showed only requests for X-rays (arm 1) and possibly requests for MRI (arm 2) changed over time. Physiotherapy service referrals showed a low uptake of true self-referral (10% and 6%) in each arm respectively. CONCLUSION: This is the first study to examine the real-world impact of patient direct access to physiotherapy at general practice level. We found no consistent impact of patient direct access on GP MSK workload. Impact on some clinical management was observed but not consistently in the direction suggested by previous studies

Monitoring lower limb biomechanical asymmetry and psychological measures in athletic populations - A scoping review

Background: Lower limb biomechanics, including asymmetry, are frequently monitored to determine sport performance level and injury risk. However, contributing factors extend beyond biomechanical and asymmetry measures to include psychological, sociological, and environmental factors. Unfortunately, inadequate research has been conducted using holistic bio-psycho-social models to characterize sport performance and injury risk. Therefore, this scoping review summarized the research landscape of studies concurrently assessing measures of lower limb biomechanics, asymmetry, and introspective psychological state (e.g., pain, fatigue, perceived exertion, stress, etc.) in healthy, competitive athletes. Methods: A systematic search of Medline, Embase, CINAHL, SPORT Discus, and Web of Science Core Collections was designed and conducted in accordance with PRISMA guidelines. 51 articles were included in this review. Results: Significant relationships between biomechanics (k = 22 studies) or asymmetry (k = 20 studies) and introspective state were found. Increased self-reported pain was associated with decreased range of motion, strength, and increased lower limb asymmetry. Higher ratings of perceived exertion were related to increased lower limb asymmetry, self-reported muscle soreness, and worse jump performance. Few studies (k = 4) monitored athletes longitudinally throughout one or more competitive season(s). Conclusion: This review highlights the need for concurrent analysis of introspective, psychological state, and biomechanical asymmetry measures along with longitudinal research to understand the contributing factors to sport performance and injury risk from bio-psycho-social modeling. In doing so, this framework of bio-psycho-social preventive and prognostic patient-centered practices may provide an actionable means of optimizing health, well-being, and sport performance in competitive athletes