Integrating Intuition and Information for the Development of Coaching Expertise

In high-performance sport, coaching decisions must blend skilled intuition, honed through decades of experience, with an ever-expanding availability of augmented information obtained from a range of performance and practice monitoring systems. Expert intuition, though crucial, is susceptible to cognitive biases like representativeness and availability, especially in unfamiliar contexts, leading to an "illusion of validity”. Conversely the influx of objective data from athlete training and performance monitoring technologies aims to augment these judgments. In this current opinion we debate the impact of the ever-growing availability of augmented information on the development of coaches’ skilled intuition. In this current opinion we argue that augmented information, while not a replacement for intuition, can either hinder expertise by fostering dependency or enhance it by serving as a calibration point for reflective practice. By challenging biases and refining internal models, objective data can lead to more accurate a priori judgments. Therefore, we contend that the central challenge lies in fostering a symbiotic relationship between intuitive expertise and augmented information, moving beyond a "man vs. machine" debate to leverage both for optimal coaching efficacy and athlete development

Design considerations and analysis planning of a phase 2a proof of concept study in rheumatoid arthritis in the presence of possible non-monotonicity

BACKGROUND: It is important to quantify the dose response for a drug in phase 2a clinical trials so the optimal doses can then be selected for subsequent late phase trials. In a phase 2a clinical trial of new lead drug being developed for the treatment of rheumatoid arthritis (RA), a U-shaped dose response curve was observed. In the light of this result further research was undertaken to design an efficient phase 2a proof of concept (PoC) trial for a follow-on compound using the lessons learnt from the lead compound. METHODS: The planned analysis for the Phase 2a trial for GSK123456 was a Bayesian Emax model which assumes the dose-response relationship follows a monotonic sigmoid "S" shaped curve. This model was found to be suboptimal to model the U-shaped dose response observed in the data from this trial and alternatives approaches were needed to be considered for the next compound for which a Normal dynamic linear model (NDLM) is proposed. This paper compares the statistical properties of the Bayesian Emax model and NDLM model and both models are evaluated using simulation in the context of adaptive Phase 2a PoC design under a variety of assumed dose response curves: linear, Emax model, U-shaped model, and flat response. RESULTS: It is shown that the NDLM method is flexible and can handle a wide variety of dose-responses, including monotonic and non-monotonic relationships. In comparison to the NDLM model the Emax model excelled with higher probability of selecting ED90 and smaller average sample size, when the true dose response followed Emax like curve. In addition, the type I error, probability of incorrectly concluding a drug may work when it does not, is inflated with the Bayesian NDLM model in all scenarios which would represent a development risk to pharmaceutical company. The bias, which is the difference between the estimated effect from the Emax and NDLM models and the simulated value, is comparable if the true dose response follows a placebo like curve, an Emax like curve, or log linear shape curve under fixed dose allocation, no adaptive allocation, half adaptive and adaptive scenarios. The bias though is significantly increased for the Emax model if the true dose response follows a U-shaped curve. CONCLUSIONS: In most cases the Bayesian Emax model works effectively and efficiently, with low bias and good probability of success in case of monotonic dose response. However, if there is a belief that the dose response could be non-monotonic then the NDLM is the superior model to assess the dose response

Leadership as social identity management : introducing the Identity Leadership Inventory (ILI) to assess and validate a four-dimensional model

This work has been supported by a grant (FL110100199) from the Australian Research Council awarded to the second author, a grant from the Research Foundation Flanders awarded to the fifth author, and a grant from the National Natural Science Foundation of China (NSFC no. 70962001) awarded to the sixth author.Although nearly two decades of research have provided support for the social identity approach to leadership, most previous work has focused on leaders' identity prototypicality while neglecting the assessment of other equally important dimensions of social identity management. However, recent theoretical developments have argued that in order to mobilize and direct followers' energies, leaders need not only to ‘be one of us’ (identity prototypicality), but also to ‘do it for us’ (identity advancement), to ‘craft a sense of us’ (identity entrepreneurship), and to ‘embed a sense of us’ (identity impresarioship). In the present research we develop and validate an Identity Leadership Inventory (ILI) that assesses these dimensions in different contexts and with diverse samples from the US, China, and Belgium. Study 1 demonstrates that the scale has content validity such that the items meaningfully differentiate between the four dimensions. Studies 2, 3, and 4 provide evidence for the scale's construct validity (distinguishing between dimensions), discriminant validity (distinguishing identity leadership from authentic leadership, leaders' charisma, and perceived leader quality), and criterion validity (relating the ILI to key leadership outcomes). We conclude that by assessing multiple facets of leaders' social identity management the ILI has significant utility for both theory and practice.Publisher PDFPeer reviewe

Effectiveness of a new model of primary care management on knee pain and function in patients with knee osteoarthritis: Protocol for THE PARTNER STUDY

© 2018 The Author(s). Background: To increase the uptake of key clinical recommendations for non-surgical management of knee osteoarthritis (OA) and improve patient outcomes, we developed a new model of service delivery (PARTNER model) and an intervention to implement the model in the Australian primary care setting. We will evaluate the effectiveness and cost-effectiveness of this model compared to usual general practice care. Methods: We will conduct a mixed-methods study, including a two-arm, cluster randomised controlled trial, with quantitative, qualitative and economic evaluations. We will recruit 44 general practices and 572 patients with knee OA in urban and regional practices in Victoria and New South Wales. The interventions will target both general practitioners (GPs) and their patients at the practice level. Practices will be randomised at a 1:1 ratio. Patients will be recruited if they are aged =45 years and have experienced knee pain =4/10 on a numerical rating scale for more than three months. Outcomes are self-reported, patient-level validated measures with the primary outcomes being change in pain and function at 12 months. Secondary outcomes will be assessed at 6 and 12 months. The implementation intervention will support and provide education to intervention group GPs to deliver effective management for patients with knee OA using tailored online training and electronic medical record support. Participants with knee OA will have an initial GP visit to confirm their diagnosis and receive management according to GP intervention or control group allocation. As part of the intervention group GP management, participants with knee OA will be referred to a centralised multidisciplinary service: the PARTNER Care Support Team (CST). The CST will be trained in behaviour change support and evidence-based knee OA management. They will work with patients to develop a collaborative action plan focussed on key self-management behaviours, and communicate with the patients' GPs. Patients receiving care by intervention group GPs will receive tailored OA educational materials, a leg muscle strengthening program, and access to a weight-loss program as appropriate and agreed. GPs in the control group will receive no additional training and their patients will receive usual care. Discussion: This project aims to address a major evidence-to-practice gap in primary care management of OA by evaluating a new service delivery model implemented with an intervention targeting GP practice behaviours to improve the health of people with knee OA. Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12617001595303, date of registration 1/12/2017

Modelling implicit pre-cues and collision avoidance in a driving simulator: A pilot study

It is well-established that pre-cues, including those observed in an implicit manner, can affect motor skills and reaction times. However, little research currently exists on how pre-cues influence complex motor skills such as driving a car at high speed. This pilot study investigates the effect of implicit pre-cues on collision avoidance under a repeat trial experiment design using a car driving simulator. Seventeen par- ticipants (aged 23.8 ± 4.2 years) were included in this investigation, which consisted of four different one-kilometre driving scenarios. This investigation considers two of the four scenarios. Two scenarios had the stimulus of a child crossing the road, however only one of these scenarios had an implicit pre-cue appear before the stimulus. The remaining two scenarios had no stimulus or pre-cue and were included to reduce any learning effect by participants. The proportion of participants who had a collision differed significantly between scenarios with and without a pre-cue. The primary effect size of the pre-cue is modelled using a logis- tic regression and distributions for point estimators are obtained from bootstrapping results. A power analysis exploring different primary effect sizes is performed to inform sample size considerations for repeat studies. Implications for motor control, such as experiment design and statistical modelling methods, are discussed to inform future large scale trials. References J. A. Barela, A. A. Rocha, A. R. Novak, J. Fransen, and G. A. Figueiredo. Age differences in the use of implicit visual cues in a response time task. Braz. J. Motor Behav. 13.2 (2019), pp. 86–93. doi: 10.20338/bjmb.v13i2.139 J. Cohen. Statistical power analysis for the behavioral sciences. Routledge, 1988. doi: 10.4324/9780203771587 U. Eversheim and O. Bock. The role of precues in the preparation of motor responses in humans. J. Mot. Behav. 34.3 (2002), pp. 271–276. doi: 10.1080/00222890209601945 D. G. Jenkins and P. F. Quintana-Ascencio. A solution to minimum sample size for regressions. PLOS One 15.2 (2020), e0229345. doi: 10.1371/journal.pone.0229345 J. Jiang. Linear and generalized linear mixed models and their applications. Springer Series in Statistics. Springer, 2007. doi: 10.1007/978-0-387-47946-0 C. Kistin and M. Silverstein. Pilot studies: A critical but potentially misused component of interventional research. JAMA 314.15 (2015), pp. 1561–1562. doi: 10.1001/jama.2015.10962 H. C. Kraemer, J. Mintz, A. Noda, J. Tinklenberg, and J. A. Yesavage. Caution regarding the use of pilot studies to guide power calculations for study proposals. Arch. Gen. Psych. 63.5 (2006), pp. 484–489. doi: 10.1001/archpsyc.63.5.484 J. A. Nelder and R. W. M. Wedderburn. Generalized linear models. J. Roy. Stat. Soc. 135.3 (1972), pp. 370–384. doi: 10.2307/2344614 R. Stine. An introduction to bootstrap methods: Examples and ideas. Soc. Meth. Res. 18.2–3 (1989), pp. 243–291. doi: 10.1177/004912418901800200

Burden of reduced work productivity among people with chronic knee pain : a systematic review

Objective: The aims of this systematic review were to determine the prevalence of reduced work productivity among people with chronic knee pain as well as specifically categorise determinants of work productivity losses into individual, disease and work-related factors, conduct an evaluation of study methodological quality and present a best-evidence synthesis. Methods: We searched the literature using combinations of key words such as knee pain, knee osteoarthritis, absenteeism (days taken off work) and presenteeism (reduced productivity while at work) for observational studies published in English. Methodological quality appraisal and a best-evidence synthesis were used to pool the study findings. Results: The studies were conducted exclusively in high income countries of North America, Western Europe and Hong Kong. 17 studies were included in the review, 10 measuring absenteeism and six measuring presenteeism. Of the 10 studies reporting absenteeism, seven found a 12-month absenteeism prevalence ranging from 5% to 22%. Only two studies evaluated presenteeism prevalence and reported a range from 66% to 71%. Using best-evidence synthesis: three high quality cohort studies and three cross-sectional studies provided strong evidence that knee pain or knee osteoarthritis was associated with absenteeism; two high quality cross-sectional studies and one cohort study provided limited evidence for an association with presenteeism; one cross-sectional study provided limited evidence for an association among age, high job demands and low coworker support and absenteeism among nurses with knee pain. No studies examined individual or work-related factors associated with presenteeism. Conclusions: A number of high quality studies consistently demonstrated that chronic knee pain or knee osteoarthritis is associated with absenteeism. However, data are lacking regarding presenteeism and individual or work-related risk factors for reduced work productivity among older workers with chronic knee pain. Systematic review registration number: PROSPERO registry number: CRD42013004137

Median Aggregation of Distribution Functions

When multiple redundant probabilistic judgments are obtained from subject matter experts, it is common practice to aggregate their differing views into a single probability or distribution. Although many methods have been proposed for mathematical aggregation, no single procedure has gained universal acceptance. The most widely used procedure is simple arithmetic averaging, which has both desirable and undesirable properties. Here we propose an alternative for aggregating distribution functions that is based on the median cumulative probabilities at fixed values of the variable. It is shown that aggregating cumulative probabilities by medians is equivalent, under certain conditions, to aggregating quantiles. Moreover, the median aggregate has better calibration than mean aggregation of probabilities when the experts are independent and well calibrated and produces sharper aggregate distributions for well-calibrated and independent experts when they report a common location-scale distribution. We also compare median aggregation to mean aggregation of quantiles. </jats:p