The positive effects of population-based preferential sampling in environmental epidemiology.

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.

A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions.

The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or conceding the next goal at any time instance. In this work, we develop a comprehensive analysis framework for the EPV, providing soccer practitioners with the ability to evaluate the impact of observed and potential actions, both visually and analytically. The EPV expression is decomposed into a series of subcomponents that model the influence of passes, ball drives and shot actions on the expected outcome of a possession. We show we can learn from spatiotemporal tracking data and obtain calibrated models for all the components of the EPV. For the components related with passes, we produce visually-interpretable probability surfaces from a series of deep neural network architectures built on top of flexible representations of game states. Additionally, we present a series of novel practical applications providing coaches with an enriched interpretation of specific game situations. This is, to our knowledge, the first EPV approach in soccer that uses this decomposition and incorporates the dynamics of the 22 players and the ball through tracking data.

Training Load and Its Role in Injury Prevention, Part 2: Conceptual and Methodologic Pitfalls.

In part 2 of this clinical commentary, we highlight the conceptual and methodologic pitfalls evident in current training-load-injury research. These limitations make these studies unsuitable for determining how to use new metrics such as acute workload, chronic workload, and their ratio for reducing injury risk. The main overarching concerns are the lack of a conceptual framework and reference models that do not allow for appropriate interpretation of the results to define a causal structure. The lack of any conceptual framework also gives investigators too many degrees of freedom, which can dramatically increase the risk of false discoveries and confirmation bias by forcing the interpretation of results toward common beliefs and accepted training principles. Specifically, we underline methodologic concerns relating to (1) measure of exposures, (2) pitfalls of using ratios, (3) training-load measures, (4) time windows, (5) discretization and reference category, (6) injury definitions, (7) unclear analyses, (8) sample size and generalizability, (9) missing data, and (10) standards and quality of reporting. Given the pitfalls of previous studies, we need to return to our practices before this research influx began, when practitioners relied on traditional training principles (eg, overload progression) and adjusted training loads based on athletes' responses. Training-load measures cannot tell us whether the variations are increasing or decreasing the injury risk; we recommend that practitioners still rely on their expert knowledge and experience.

Training Load and Injury Part 2: Questionable Research Practices Hijack the Truth and Mislead Well-Intentioned Clinicians.

BACKGROUND: In this clinical commentary, we highlight issues related to conceptual foundations and methods used in training load and injury research. We focus on sources of degrees of freedom that can favor questionable research practices such as P hacking and hypothesizing after the results are known, which can undermine the trustworthiness of research findings. CLINICAL QUESTION: Is the methodological rigor of studies in the training load and injury field sufficient to inform training-related decisions in clinical practice? KEY RESULTS: The absence of a clear conceptual framework, causal structure, and reliable methods can promote questionable research practices, selective reporting, and confirmation bias. The fact that well-accepted training principles (eg, overload progression) are in line with some study findings may simply be a consequence of confirmation bias, resulting from cherry picking and emphasizing results that align with popular beliefs. Identifying evidence-based practical applications, grounded in high-quality research, is not currently possible. The strongest recommendation we can make for the clinician is grounded in common sense: "Do not train too much, too soon"-not because it has been confirmed by studies, but because it reflects accepted generic training principles. CLINICAL APPLICATION: The training load and injury research field has fundamental conceptual and methodological weaknesses. Therefore, making decisions about planning and modifying training programs for injury reduction in clinical practice, based on available studies, is premature. Clinicians should continue to rely on best practice, experience, and well-known training principles, and consider the potential influence of contextual factors when planning and monitoring training loads. J Orthop Sports Phys Ther 2020;50(10):577-584. Epub 1 Aug 2020. doi:10.2519/jospt.2020.9211.

Training Load and Injury Part 1: The Devil Is in the Detail-Challenges to Applying the Current Research in the Training Load and Injury Field.

BACKGROUND: This article sets the scene for a critique of the research underpinning 2 common clinical assumptions: (1) training workload is a key factor influencing sports injury risk, and (2) training workload can be manipulated to reduce injury risk. In this clinical commentary, we address why it is important for clinicians to critically evaluate the evidence behind research conclusions. CLINICAL QUESTION: Has research been designed and conducted well enough to help clinicians answer the questions, "What is the relationship between training workload and sports injury risk?" and "Can the metrics based on training workload be used to decrease injury risk?" KEY RESULTS: In the past decade, many sports injury researchers have developed new measures of exposure, based on internal and external training workload, to study the relationship between training load and injury. Some of these metrics may have been embraced by researchers and clinicians because (1) they are apparently supported by the scientific literature, (2) they are simple to calculate and use (averages and their ratio), and (3) there is an apparent reasonable rationale/narrative to support using workload metrics. However, intentional or unintentional questionable research practices and overinterpretation of research results undermine the trustworthiness of research in the training load and sports injury field. CLINICAL APPLICATION: Clinicians should always aim to critically examine the credibility of the evidence behind a research conclusion before implementing research findings in practice. Something that initially looks promising and inviting might not be as revolutionary or useful as one first anticipated. J Orthop Sports Phys Ther 2020;50(10):574-576. Epub 1 Aug 2020. doi:10.2519/jospt.2020.9675.