A phase II multicentered, single-blind, randomized, controlled trial of the stroke self-management program

Background and Purpose—The benefits of chronic disease self-management programs for stroke survivors are uncertain because individuals with severe impairments have been excluded from previous research. We undertook a phase II randomized controlled trial to determine whether a self-management program designed for survivors (SSMP; 8 weeks) was safe and feasible compared to standard care (control) or a generic self-management program (generic; 6 weeks).
Methods—Stroke survivors were recruited from 7 South Australian hospitals via a letter or indirectly (eg, newspapers). Eligible participants were randomized at a 1:1:1 ratio of 50 per group. Primary outcomes were recruitment, participation, and participant safety. Secondary outcomes were positive and active engagement in life using the Health Education Impact Questionnaire and characteristics of quality of life and mood at 6 months from program completion.
Results—Of 315 people screened, 149 were eligible and 143 were randomized (48 SSMP, 47 generic, 48 control); mean age was 69 years (SD, 11) and 59% were female. Demographic features were similar between groups and 41% had severe cognitive impairment; 57% accessed the interventions, with 52% SSMP and 38% generic completing >50% of sessions (P=0.18). Thirty-two participants reported adverse events (7 control, 12 generic, 13 SSMP; P=0.3; 34% severe); however, none was attributable to the interventions. Potential benefits for improved mood were found.
Conclusions—SSMP was safe and feasible. Benefits of the stroke-specific program over the generic program included greater participation and completion rates. An efficacy trial is warranted given the forecast growth in the stroke population and improved survival trends.

‘The Snowtown we know and love’ : small newspapers and heinous crimes

This paper examines the role of small newspapers in Australia when bizarre and shocking crimes are committed locally. These crimes often attract intense media attention that casts a net of shame across entire townships through their representation as places of fascination and fear in the public imagination. We take a practice approach in the tradition of Pierre Bourdieu to explore the complex editorial considerations, news judgements and community responsibilities small newspapers must negotiate when covering these stories for local audiences. This study focuses on three towns in regional Australia that have been represented in metropolitan and international news media as ‘dead zones' after shocking crimes: Bowral in NSW, Snowtown in South Australia and Moe in Victoria.

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances.

Using camera motion to identify different types of American football plays

This paper presents a method that uses camera motion parameters to recognise 7 types of American football plays. The approach is based on the motion information extracted from the video and it can identify short and long pass plays, short and long running plays, quarterback sacks, punt plays and kickoff plays. This method has the advantage that it is fast and it does not require player or ball tracking. The system was trained and tested using 782 plays and the results show that the system has an overall classification accuracy of 68%.

Combining NL processing and video data to query American football

We explore the use of natural language understanding and image processing to index and query American Football tapes. We present a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain, and a recognition system which uses the model to recognise American Football plays.