Physiological, perceptual, and technical responses to on-court tennis training on hard and clay courts

The aim of this study was to investigate the effect of court surface (clay vs. hard court) on technical, physiological, and perceptual responses to on-court tennis training. Four high-performance junior male players performed 2 identical training sessions on hard and clay courts, respectively. Sessions included both physical conditioning and technical elements as led by the coach. Each session was filmed for later notational analysis of stroke count and error rates. Furthermore, players wore a global positioning satellite device to measure distance covered during each session, while heart rate, countermovement jump distance, and capillary blood measures of metabolites were measured before, during, and after each session. Additionally, a respective coach and athlete rating of perceived exertion (RPE) were measured after each session. Total duration and distance covered during each session were comparable (p > 0.05; d 0.05; d 0.05; d > 0.90). Furthermore, large effects for increased heart rate, blood lactate, and RPE values were evident on clay compared with hard courts (p > 0.05; d > 0.90). Additionally, although player and coach RPE on hard courts were similar, there were large effects for coaches to underrate the RPE of players on clay courts (p > 0.05; d > 0.90). In conclusion, training on clay courts results in trends for increased heart rate, lactate, and RPE values, suggesting that sessions on clay courts tend towards higher physiological and perceptual loads than hard courts. Furthermore, coaches seem effective at rating player RPE on hard courts but may underrate the perceived exertion of sessions on clay courts. © 2013 National Strength and Conditioning Association

Towards ‘Knowledge-Driven ’ Strategic Services Abstract

Lately, there is a growing realisation amongst the healthcare community to leverage upon the vast quantities of healthcare data and enterprise-wide knowledge to transform it into value-added, ‘decision-quality ’ knowledge, vis-à-vis Knowledge-Driven Strategic Healthcare Services, oriented towards healthcare management and planning. In this paper, we firstly present an integrated Knowledge Management Info-structure—a Healthcare Enterprise Memory—with the functionality to acquire, share and operationalise the various modalities of knowledge existent in a healthcare enterprise. Secondly, we focus on a specific component of our Healthcare Enterprise Memory—the Knowledge-Driven Strategic Healthcare Services Info-structure—that effectuates a confluence of Knowledge Discovery in Databases and Knowledge Management techniques. Functionally, the proposed Knowledge-Driven Strategic Healthcare Services Info-structure leverages on existing healthcare knowledge and data bases to derive decision-quality knowledge and then operationalises the acquired knowledge in terms of Strategic Healthcare Services. In conclusion, we argue that the proposed Knowledge-Driven Strategic Healthcare Services Info-structure is an attempt to rethink the possible sources of leverage to improve healthcare delivery, hereby providing a valuable management resource to healthcare policy makers

In 15 th IEEE Symposium on Computer Based Medical Systems (CBMS’2002), Maribor (Slovenia). An Intelligent Agent-based Knowledge Broker for Enterprise-wide Healthcare Knowledge Procurement

Within the confines of a Healthcare Enterprise Memory (HEM), most traditional medical systems do not sufficiently provide the necessary assistance to healthcare practitioners in the handling of critical situations. Furthermore, localized knowledge repositories are often lacking the required knowledge for problem solving. Therefore, in this paper, we present an agentbased knowledge broker called the Intelligent Healthcare Knowledge Assistant (IHKA) for dynamic knowledge gathering, filtering, adaptation and acquisition from a HEM comprising an amalgamation of (i) databases storing empirical knowledge, (ii) case-bases storing experiential knowledge, (iii) scenario-bases storing tacit knowledge and (iv) document-bases storing explicit knowledge. The featured work leverages intelligent agent techniques for autonomous HEM-wide navigation, approximate content matching, inter- and intra-repositories content correlation, and knowledge adaptation and procurement to meet the user’s healthcare knowledge needs. 1