Getting your life back on track after stroke : a phase II multi-centered, single-blind, randomized, controlled trial of the Stroke Self-Management Program vs. the Stanford Chronic Condition Self-Management Program or standard care in stroke survivors

Background Self-management is seen as a primary mechanism to support the optimization of care for people with chronic diseases such as symptomatic vascular disease. There are no established and evidence-based stroke-specific chronic disease self-management programs. Our aim is to evaluate whether a stroke-specific program is safe and feasible as part of a Phase II randomized-controlled clinical trial.
Methods Stroke survivors are recruited from a variety of sources including: hospital stroke services, local paper advertisements, Stroke South Australia newsletter (volunteer peer support organization), Divisions of General Practice, and community service providers across Adelaide, South Australia. Subjects are invited to participate in a multi-center, single-blind, randomized, controlled trial. Eligible participants are randomized to either;
• standard care,
• standard care plus a six week generic chronic condition self-management group education program, or,
• standard care plus an eight week stroke specific self-management education group program.
Interventions are conducted after discharge from hospital. Participants are assessed at baseline, immediate post intervention and six months.
Study Outcomes The primary outcome measures determine study feasibility and safety, measuring, recruitment, participation, compliance and adverse events.
Secondary outcomes include:
• positive and active engagement in life measured by the Health Education Impact Questionnaire,
• improvements in quality of life measured by the Assessment of Quality of Life instrument,
• improvements in mood measured by the Irritability, Depression and Anxiety Scale,
• health resource utilization measured by a participant held diary and safety.

Conclusion The results of this study will determine whether a definitive Phase III efficacy trial is justified.

Effect of clinician designation on emergency department fast track performance

Objective To examine the effect of clinician designation on emergency department (ED) fast track performance.
Design and Setting A retrospective audit of patients managed in the fast track area of an ED in metropolitan Melbourne, Australia.
Participants Patients triaged to ED fast track from 1 January 2008 to 31 December 2008 (n=8714).
Main Outcome Measures Waiting times in relation to Australasian triage scale (ATS) recommendations and ED length of stay (LOS) for non-admitted patients were examined for each clinician group.
Results Compliance with ATS waiting time recommendations was highest (82.5%) for emergency nurse practitioners/candidates and lowest (48.2%) for junior medical officers. Median ED LOS was less than 3 h for non-admitted patients, and 85.8% of non-admitted fast track patients (n=6278) left the ED within 4 h. Patients managed by emergency nurse practitioners/ candidates had the shortest ED LOS (median 1.7 h) and patients managed by junior medical officers and locum medical officers the longest ED LOS (median 2.7 h) (x²=498.539, df=6, p<0.001).
Conclusions Clinician designation does impact on waiting times and, to a lesser extent, ED LOS for patients managed in ED fast track systems. Future research should focus on obtaining a better understanding of the relationship between clinician expertise, time-based performance measures and quality of care indicators.

Using multiple windows to track concept drift

In this paper we present a multiple window incremental learning algorithm that distinguishes between virtual concept drift and real concept drift. The algorithm is unsupervised and uses a novel approach to tracking concept drift that involves the use of competing windows to interpret the data. Unlike previous methods which use a single window to determine the drift in the data, our algorithm uses three windows of different sizes to estimate the change in the data. The advantage of this approach is that it allows the system to progressively adapt and predict the change thus enabling it to deal more effectively with different types of drift. We give a detailed description of the algorithm and present the results obtained from its application to two real world problems: background image processing and sound recognition. We also compare its performance with FLORA, an existing concept drift tracking algorithm.

Using selective memory to track concept drift effectively

In this paper we describe a supervised learning algorithm that uses selective memory to track concept drift. Unlike previous methods to track concept drift that use window heuristics to adapt to changes, we present an improved approach that discriminates between the instances observed. The advantage of this method is that it allows the system to both adapt to and track drift more accurately as well as filter the noise in the data more effectively. We present the algorithm and compare its performance with FLORA a well known concept drift tracking algorithm.

Coordination of multiple cameras to track multiple people

In this paper, we present a distributed surveillance system that uses multiple cheap static cameras to track multiple people in indoor environments. The system has a set of Camera Processing Modules and a Central Module to coordinate the tracking tasks among the cameras. Since each object in the scene can be tracked by a number of cameras, the problem is how to choose the most appropriate camera for each object. We propose a novel algorithm to allocate objects to cameras using the object-to-camera distance while taking into account occlusion. The algorithm attempts to assign objects in the overlapping fields of view to the nearest camera which can see the object without occlusion. Experimental results show that the system can coordinate cameras to track people properly and can deal well with occlusion.

International collaborative project to compare and track the nutritional composition of fast foods

Background
Chronic diseases are the leading cause of premature death and disability in the world with over-nutrition a primary cause of diet-related ill health. Excess quantities of energy, saturated fat, sugar and salt derived from fast foods contribute importantly to this disease burden. Our objective is to collate and compare nutrient composition data for fast foods as a means of supporting improvements in product formulation.
Methods/design
Surveys of fast foods will be done in each participating country each year. Information on the nutrient composition for each product will be sought either through direct chemical analysis, from fast food companies, in-store materials or from company websites. Foods will be categorized into major groups for the primary analyses which will compare mean levels of saturated fat, sugar, sodium, energy and serving size at baseline and over time. Countries currently involved include Australia, New Zealand, France, UK, USA, India, Spain, China and Canada, with more anticipated to follow.
Discussion
This collaborative approach to the collation and sharing of data will enable low-cost tracking of fast food composition around the world. This project represents a significant step forward in the objective and transparent monitoring of industry and government commitments to improve the quality of fast foods.

Expansion of congested railway capacity using triple-track line operation based on hourly passenger demand

This paper proposes a method of improving level of service in congested urban railways by means of a triple-track line operation for a highly dense urban area with special travel demand characteristics. Where the future travel demand forecasts show sluggish growth or no growth at all, there is little to no incentives for heavy railway investments like quadruple-track extension and construction of new railway routes to alleviate current railway congestion problems. In such a situation, triple-track line operation can be the best alternative due to its moderate investment cost and ease in land acquisition for just an additional single track along the existing tracks. Our simulation investigation in one of the congested railway lines in Tokyo showed that triple track line operation increases railway capacity by 26% and shortens travel time by 38% in peak direction during morning peak hours. These results are encouraging and are useful for removing current railways problems in Tokyo and in similar urban situations elsewhere.

How many seabirds do we need to track to define home-range area?

In recent years, marine predator and seabird tracking studies have become ever more popular. However, they are often conducted without first considering how many individuals should be tracked and for how long they should be tracked in order to make reliable predictions of a population's home-range area. Home-range area analysis of two seabird-tracking data sets was used to define the area of active use (where birds spent 100% of their time) and the core foraging area (where birds spent 50% of their time). Analysis was conducted on the first foraging trip undertaken by the birds and then the first two, three and four foraging trips combined. Appropriate asymptotic models were applied to the data, and the calculated home-range areas were plotted as a function of an increasing number of individuals and trips included in the sample. Data were extrapolated from these models to predict the area of active use and the core foraging area of the colonies sampled. Significant variability was found in the home-range area predictions made by analysis of the first foraging trip and the first four foraging trips combined. For shags, the first foraging trip predicted a 56% smaller area of active use when compared to the predictions made by combining the first four foraging trips. For kittiwakes, a 43% smaller area was predicted when comparing the first foraging trip with the four combined trips. The number of individuals that would be required to predict the home range area of the colony depends greatly on the number of trips included in the analysis. This analysis predicted that 39 (confidence interval 29-73) shags and 83 (CI: 109-161) kittiwakes would be required to predict 95% of the area of active use when the first four foraging trips are included in the sample compared with 135 (CI 96-156) shags and 248 (164-484) kittiwakes when only the first trip is included in the analysis. Synthesis and applications. Seabird and marine mammal tracking studies are increasingly being used to aid the designation of marine conservation zones and to predict important foraging areas. We suggest that many studies may be underestimating the size of these foraging areas and that better estimates could be made by considering both the duration and number of data logger deployments. Researchers intending to draw conclusions from tracking data should conduct a similar analysis of their data as used in this study to determine the reliability of their home-range area predictions.

A machine learning approach to predicting winning patterns in track cycling omnium

This paper presents work on using Machine Learning approaches for predicting performance patterns of medalists in Track Cycling Omnium championships. The omnium is a newly introduced track cycling competition to be included in the London 2012 Olympic Games. It involves six individual events and, therefore, requires strategic planning for riders and coaches to achieve the best overall standing in terms of the ranking, speed, and time in each individual component. We carried out unsupervised, supervised, and statistical analyses on the men’s and women’s historical competition data in the World Championships since 2008 to find winning patterns for each gender in terms of the ranking of riders in each individual event. Our results demonstrate that both sprint and endurance capacities are required for both men and women to win a medal in the omnium. Sprint ability is shown to have slightly more influence in deciding the medalists of the omnium competitions.

Supporting athlete selection and strategic planning in track cycling omnium: A statistical and machine learning approach

This article describes the implementation of machine learning techniques that assist cycling experts in the crucial decision-making processes for athlete selection and strategic planning in the track cycling omnium. The omnium is a multi-event competition that was included in the Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and opinion. They rarely have access to knowledge that helps predict athletic performances. The omnium presents a unique and complex decision-making challenge as it is not clear what type of athlete is best suited to the omnium (e.g., sprint or endurance specialist) and tactical decisions made by the coach and athlete during the event will have significant effects on the overall performance of the athlete. In the present work, a variety of machine learning techniques were used to analyze omnium competition data from the World Championships since 2007. The analysis indicates that sprint events have slightly more influence in determining the medalists, than endurance-based events. Using a probabilistic analysis, we created a model of performance prediction that provides an unprecedented level of supporting information that assists coaches with strategic and tactical decisions during the omnium.

Modelling and analysing track cycling Omnium performances using statistical and machine learning techniques

This article describes the utilisation of an unsupervised machine learning technique and statistical approaches (e.g., the Kolmogorov-Smirnov test) that assist cycling experts in the crucial decision-making processes for athlete selection, training, and strategic planning in the track cycling Omnium. The Omnium is a multi-event competition that will be included in the summer Olympic Games for the first time in 2012. Presently, selectors and cycling coaches make decisions based on experience and intuition. They rarely have access to objective data. We analysed both the old five-event (first raced internationally in 2007) and new six-event (first raced internationally in 2011) Omniums and found that the addition of the elimination race component to the Omnium has, contrary to expectations, not favoured track endurance riders. We analysed the Omnium data and also determined the inter-relationships between different individual events as well as between those events and the final standings of riders. In further analysis, we found that there is no maximum ranking (poorest performance) in each individual event that riders can afford whilst still winning a medal. We also found the required times for riders to finish the timed components that are necessary for medal winning. The results of this study consider the scoring system of the Omnium and inform decision-making toward successful participation in future major Omnium competitions.

Analyzing track sprint cyclists' performances using position-specific maximal-torque and power-cadence relationships

Previous studies have demonstrated the importance of maximal Torque-Cadence (T-C) and Power-Cadence (P-C) relationships, for the performances of world class track sprint cyclists. If these relationships are affected by the function of the lower limb muscles, the ability of cyclists to generate torque and power at a given cadence may vary depending on their riding position. During sprint events (individual and team sprints and Keirin), cyclists alternate between standing and seated positions. The T-C and P-C relationships may change with the position adopted by the cyclists. PURPOSE: The aim of this study was to evaluate the necessity to define position specific maximal T-C and P-C relationships. METHODS: Eight junior elite track cyclists from the National Talent Identification squad undertook two inertial-load tests that consisted of four all-out sprints each. One test was undertaken at the velodrome in a standing position on a carbon fibre track bike, and the other test was completed in a seated position on an air-braked stationary ergometer. A calibrated SRM power meter interfaced to a custom instrumentation package was used for all mechanical measurements. Maximal T-C and P-C relationships were analysed to calculate maximal Torque (T0), maximal Power (Pmax) and optimal pedalling cadence (PCopt). RESULTS: All individual T-C and P-C relationships obtained for both body positions were fitted by linear regressions (r2=0.95 ± 0.02) and second order polynomials (r2=0.96 ± 0.01), respectively. T0 was higher (209 ± 2.2N.m vs. 177.0 ± 3.9N.m, p<0.05), PCopt was lower (112.5 ± 11.4rpm vs. 120.1 ± 6.7rpm, p<0.05), and Pmax was higher (1261 ± 235W vs. 1076 ± 183W, p<0.05) in standing position compared to seated position. CONCLUSION: Analysis of track sprint cyclists’ performances can be improved by the determination of position-specific maximal T-C and P-C relationships .

The elimination race in track cycling : patterns and predictors of performance

The track cycling Omnium is a multi-event competition that has recently been expanded to include the Elimination Race (ER), which presents a unique set of physical and tactical demands. The purpose of this research was to characterise the performance attributes of successful and unsuccessful cyclists in the ER, that are also predictive of performance. Video recordings of four international level ERs were analysed. The performance attributes measured related to the cyclists’ velocity and two dimensional position in the peloton. The average velocity of the peloton up to lap 30 (of 50) was relatively high and consistent (52.2±1.5 km/h). After lap 30, there was a significant (p<0.001) change in velocity (49.9±2.4 km/h), characterised by more fluctuations in lap-to-lap velocity. Successful ER cyclists adopted a tactic of remaining in the middle of the peloton, in the lower lanes of the velodrome, thus avoiding the risk of elimination at the rear and the extra effort required to remain on the front of the peloton. Unsuccessful cyclists tended to reside in the rear and upper (higher) portions of the peloton, risking elimination more often and having to ride faster than those in the lower lanes of the velodrome. The physiological demands of the Elimination Race that are determined by velocity, vary throughout the Elimination Race and the pattern of movement within the peloton is different for successful and unsuccessful cyclists. The findings of the present study may confirm some aspects of race tactics that are currently thought to be optimal, but they also reveal novel information that is useful to coaches and cyclists who compete in the Elimination Race.

An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

  Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body’s co-ordination system and the sensor’s co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77◦ for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.