Translating training in the NYU Caregiver Intervention in Australia: Maintaining fidelity and meeting graduate standards in an online continuing professional education setting

The aim of this study was to develop an Internet-based self-directed training program for Australian healthcare workers to facilitate learning and competence in delivery of a proven intervention for caregivers of people with dementia: The New York University Caregiver Intervention (NYUCI). The NYUCI is a nonpharmacological, multicomponent intervention for spousal caregivers. It is aimed at maintaining well-being by increasing social support and decreasing family discord, thereby delaying or avoiding nursing home placement of the person with dementia. Training in the NYUCI in the United States has, until now, been conducted in person to trainee practitioners. The Internet-based intervention was developed simultaneously for trainees in the U.S. and Australia. In Australia, due to population geography, community healthcare workers, who provide support to older adult caregivers of people with dementia, live and work in many regional and rural areas. Therefore, it was especially important to have online training available to make it possible to realize the health and economic benefits of using an existing evidence-based intervention. This study aimed to transfer knowledge of training in, and delivery of, the NYUCI for an Australian context and consumers. This article details the considerations given to contextual differences and to learners’ skillset differences in translating the NYUCI for Australia.

Trainee-therapists are not all equal: Examination of therapeutic efficiency, effectiveness and early client dropout after 12 months of clinical training

Objective Contemporary research demonstrates the feasibility of assessing therapeutic performance of trainee-therapists through the use of objective measures of client treatment outcome. Further, significant variation between individual therapists based on their client treatment outcomes has been demonstrated. This study sets out to determine whether a reliable composite measure of therapeutic efficiency, effectiveness and early dropout can be developed and used to objectively compare trainee-therapists against each other. Design and methods Treatment outcomes of 611 clients receiving treatment from 58 trainee-therapists enrolled in a professional training programme were tracked with the OQ-45.2 over a 6-year period to assess therapeutic efficiency, therapeutic effectiveness and early client dropout. Results Significant variation between trainee-therapists was observed for each index. Findings of a moderately strong correlation between therapeutic efficiency and effectiveness enabled the ranking of trainee-therapists based upon a composite measure of these indexes. A non-significant correlation was found between early client dropout and measures of therapeutic effectiveness and efficiency. Conclusions The findings stress the importance of utilizing objective measures to track the treatment outcomes. Despite all trainee-therapists being enrolled in the same training programme, significant variation between trainee-therapists' therapeutic efficiency and effectiveness was found to exist. Practitioner points Developing of potential benchmarking tools that enable trainee-therapists, supervisors and educational institutions to quickly assess therapeutic performance can become part of a holistic assessment of a trainee-therapist's clinical development. Despite an inherent optimistic belief that therapists do not cause harm, there appears to be a small and significant proportion of trainee-therapists who consistently evidence little therapeutic change. Considerable variability in trainee-therapists' therapeutic efficiency and effectiveness can exist in the one training programme. Early client dropout may not be associated with therapists' therapeutic effectiveness and efficiency.

A pilot study investigating the efficacy of an imagery-based motivational intervention for smoking cessation

This pilot study evaluated the potential efficacy of an imagery-based intervention called Functional Imagery Training (FIT) as a therapeutic approach to smoking cessation. FIT showed promising results in reducing cigarette use, managing craving, and promoting abstinence among smokers when compared to a control condition, and may play a role in maintaining smokers' motivation to quit. This study was the first of its kind, and paves the way for future investigations into FIT as a smoking cessation intervention.

A virtual radiation therapy workflow training simulation

Aim Simulation forms an increasingly vital component of clinical skills development in a wide range of professional disciplines. Simulation of clinical techniques and equipment is designed to better prepare students for placement by providing an opportunity to learn technical skills in a “safe” academic environment. In radiotherapy training over the last decade or so this has predominantly comprised treatment planning software and small ancillary equipment such as mould room apparatus. Recent virtual reality developments have dramatically changed this approach. Innovative new simulation applications and file processing and interrogation software have helped to fill in the gaps to provide a streamlined virtual workflow solution. This paper outlines the innovations that have enabled this, along with an evaluation of the impact on students and educators. Method Virtual reality software and workflow applications have been developed to enable the following steps of radiation therapy to be simulated in an academic environment: CT scanning using a 3D virtual CT scanner simulation; batch CT duplication; treatment planning; 3D plan evaluation using a virtual linear accelerator; quantitative plan assessment, patient setup with lasers; and image guided radiotherapy software. Results Evaluation of the impact of the virtual reality workflow system highlighted substantial time saving for academic staff as well as positive feedback from students relating to preparation for clinical placements. Students valued practice in the “safe” environment and the opportunity to understand the clinical workflow ahead of clinical department experience. Conclusion Simulation of most of the radiation therapy workflow and tasks is feasible using a raft of virtual reality simulation applications and supporting software. Benefits of this approach include time-saving, embedding of a case-study based approach, increased student confidence, and optimal use of the clinical environment. Ongoing work seeks to determine the impact of simulation on clinical skills.

Review and evaluation of mindfulness-based iPhone apps

Background There is growing evidence for the positive impact of mindfulness on wellbeing. Mindfulness-based mobile apps may have potential as an alternative delivery medium for training. While there are hundreds of such apps, there is little information on their quality. Objective This study aimed to conduct a systematic review of mindfulness-based iPhone mobile apps and to evaluate their quality using a recently-developed expert rating scale, the Mobile Application Rating Scale (MARS). It also aimed to describe features of selected high-quality mindfulness apps. Methods A search for “mindfulness” was conducted in iTunes and Google Apps Marketplace. Apps that provided mindfulness training and education were included. Those containing only reminders, timers or guided meditation tracks were excluded. An expert rater reviewed and rated app quality using the MARS engagement, functionality, visual aesthetics, information quality and subjective quality subscales. A second rater provided MARS ratings on 30% of the apps for inter-rater reliability purposes. Results The “mindfulness” search identified 700 apps. However, 94 were duplicates, 6 were not accessible and 40 were not in English. Of the remaining 560, 23 apps met inclusion criteria and were reviewed. The median MARS score was 3.2 (out of 5.0), which exceeded the minimum acceptable score (3.0). The Headspace app had the highest average score (4.0), followed by Smiling Mind (3.7), iMindfulness (3.5) and Mindfulness Daily (3.5). There was a high level of inter-rater reliability between the two MARS raters. Conclusions Though many apps claim to be mindfulness-related, most were guided meditation apps, timers, or reminders. Very few had high ratings on the MARS subscales of visual aesthetics, engagement, functionality or information quality. Little evidence is available on the efficacy of the apps in developing mindfulness.

Interval training intensity affects energy intake compensation in obese men

Abstract PURPOSE: Compensatory responses may attenuate the effectiveness of exercise training in weight management. The aim of this study was to compare the effect of moderate- and high-intensity interval training on eating behavior compensation. METHODS: Using a crossover design, 10 overweight and obese men participated in 4-week moderate (MIIT) and high (HIIT) intensity interval training. MIIT consisted of 5-min cycling stages at ± 20% of mechanical work at 45%VO(2)peak, and HIIT consisted of alternate 30-s work at 90%VO(2)peak and 30-s rests, for 30 to 45 min. Assessments included a constant-load exercise test at 45%VO(2)peak for 45 min followed by 60-min recovery. Appetite sensations were measured during the exercise test using a Visual Analog Scale. Food preferences (liking and wanting) were assessed using a computer-based paradigm, and this paradigm uses 20 photographic food stimuli varying along two dimensions, fat (high or low) and taste (sweet or nonsweet). An ad libitum test meal was provided after the constant-load exercise test. RESULTS: Exercise-induced hunger and desire to eat decreased after HIIT, and the difference between MIIT and HIIT in desire to eat approached significance (p = .07). Exercise-induced liking for high-fat nonsweet food tended to increase after MIIT and decreased after HIIT (p = .09). Fat intake decreased by 16% after HIIT, and increased by 38% after MIIT, with the difference between MIIT and HIIT approaching significance (p = .07). CONCLUSIONS: This study provides evidence that energy intake compensation differs between MIIT and HIIT.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

Transition: Exchange establishing a visual arts practice based on personal pedagogy

There is a perceived tension in the relationship between the roles of art teacher and artist that led to the question: can an art teacher use their professional training and experience to establish an authentic artistic identity? This self-study tracked and analysed how the process of making her own art enabled an art teacher to also identify as an artist. Drawing on Lamina, the public exhibition of her multimedia artworks, the final exegesis proposes five conditions for art teachers in developing their own art practice: developing an identity as artist, using time and space mindfully, tolerating uncertainty, mentoring, and privileging the process.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Development and evaluation of training in culturally specific screening and brief intervention for hospital patients with alcohol-related injuries

Objective To evaluate health practitioners’ confidence and knowledge of alcohol screening, brief intervention and referral after training in a culturally adapted intervention on alcohol misuse and well-being issues for trauma patients. Design Mixed methods, involving semi-structured interviews at baseline and a post-workshop questionnaire. Setting: Targeted acute care within a remote area major tertiary referral hospital. Participants Ten key informants and 69 questionnaire respondents from relevant community services and hospital-based health care professionals. Intervention Screening and brief intervention training workshops and resources for 59 hospital staff. Main outcome measures Self-reported staff knowledge of alcohol screening, brief intervention and referral, and satisfaction with workshop content and format. Results After training, 44% of participants reported being motivated to implement alcohol screening and intervention. Satisfaction with training was high, and most participants reported that their knowledge of screening and brief intervention was improved. Conclusion Targeted educational interventions can improve the knowledge and confidence of inpatient staff who manage patients at high risk of alcohol use disorder. Further research is needed to determine the duration of the effect and influence on practice behaviour. Ongoing integrated training, linked with systemic support and established quality improvement processes, is required to facilitate sustained change and widespread dissemination.

Towards vision-based deep reinforcement learning for robotic motion control

This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.

Assessing the effectiveness of High Intensity Interval Training (HIIT) for smoking cessation in women: HIIT to quit study protocol

Background: Smoking and physical inactivity are major risk factors for heart disease. Linking strategies that promote improvements in fitness and assist quitting smoking has potential to address both these risk factors simultaneously. The objective of this study is to compare the effects of two exercise interventions (high intensity interval training (HIIT) and lifestyle physical activity) on smoking cessation in female smokers. Method/design: This study will use a randomised controlled trial design. Participants: Women aged 18–55 years who smoke ≥ 5 cigarettes/day, and want to quit smoking. Intervention: all participants will receive usual care for quitting smoking. Group 1 - will complete two gym-based supervised HIIT sessions/week and one home-based HIIT session/week. At each training session participants will be asked to complete four 4-min (4 × 4 min) intervals at approximately 90 % of maximum heart rate interspersed with 3- min recovery periods. Group 2 - participants will receive a resource pack and pedometer, and will be asked to use the 10,000 steps log book to record steps and other physical activities. The aim will be to increase daily steps to 10,000 steps/day. Analysis will be intention to treat and measures will include smoking cessation, withdrawal and cravings, fitness, physical activity, and well-being. Discussion: The study builds on previous research suggesting that exercise intensity may influence the efficacy of exercise as a smoking cessation intervention. The hypothesis is that HIIT will improve fitness and assist women to quit smoking.

vSpeak: Edge detection based feature extraction for sign to text conversion

This paper presents 'vSpeak', the first initiative taken in Pakistan for ICT enabled conversion of dynamic Sign Urdu gestures into natural language sentences. To realize this, vSpeak has adopted a novel approach for feature extraction using edge detection and image compression which gives input to the Artificial Neural Network that recognizes the gesture. This technique caters for the blurred images as well. The training and testing is currently being performed on a dataset of 200 patterns of 20 words from Sign Urdu with target accuracy of 90% and above.

Blended learning in medical imaging pre-clinical skills training – does it work?

Undergraduate Medical Imaging (MI)students at QUT attend their first clinical placement towards the end of semester two. Students undertake two (pre)clinical skills development units – one theory and one practical. Students gain good contextual and theoretical knowledge during these units via a blended learning model with multiple learning methods employed. Students attend theory lectures, practical sessions, tutorial sessions in both a simulated and virtual environment and also attend pre-clinical scenario based tutorial sessions. The aim of this project is to evaluate the use of blended learning in the context of 1st year Medical Imaging Radiographic Technique and its effectiveness in preparing students for their first clinical experience. It is hoped that the multiple teaching methods employed within the pre-clinical training unit at QUT builds students clinical skills prior to the real situation. A quantitative approach will be taken, evaluating via pre and post clinical placement surveys. This data will be correlated with data gained in the previous year on the effectiveness of this training approach prior to clinical placement. In 2014 59 students were surveyed prior to their clinical placement demonstrated positive benefits of using a variety of learning tools to enhance their learning. 98.31%(n=58)of students agreed or strongly agreed that the theory lectures were a useful tool to enhance their learning. This was followed closely by 97% (n=57) of the students realising the value of performing role-play simulation prior to clinical placement. Tutorial engagement was considered useful for 93.22% (n=55) whilst 88.14% (n=52) reasoned that the x-raying of phantoms in the simulated radiographic laboratory was beneficial. Self-directed learning yielded 86.44% (n=51). The virtual reality simulation software was valuable for 72.41% (n=42) of the students. Of the 4 students that disagreed or strongly disagreed with the usefulness of any tool they strongly agreed to the usefulness of a minimum of one other learning tool. The impact of the blended learning model to meet diverse student needs continues to be positive with students engaging in most offerings. Students largely prefer pre -clinical scenario based practical and tutorial sessions where 'real-world’ situations are discussed.

Grand Push Auto: A car based exertion game

Grand Push Auto is an exertion game in which players aim to push a full sized car to ever increasing speeds. The re-appropriation of a car as essentially a large weight allows us to create a highly portable and distributable exertion game in which the main game element has a weight of over 1000 kilograms. In this paper we discuss initial experiences with GPA, and present 3 questions for ongoing study which have been identified from our early testing: How might we appropriate existing objects in exertion game design, and does appropriation change how we think about these objects in different contexts, for example environmental awareness? How does this relate to more traditional sled based weight training? How can we create exertion games that allow truly brutal levels of force?