Development of the Consumer Refrigerator Safety Questionnaire (CRSQ): A Measure of Consumer Perceptions and Practices

Food preparation and storage behaviors in the home deviating from the ‘best practice’ food safety recommendations may result in food borne illnesses. Currently, there are limited tools available to fully evaluate the consumer knowledge, perceptions and behavior in the area of refrigerator safety. The current study aimed to develop a valid and reliable tool in the form of a questionnaire (CFSQCRSQ) for assessing systematically all these aspects. Items relating to refrigerator safety knowledge (n=17), perceptions (n=46), reported behavior (n=30) were developed and pilot tested by an expert reference group and various consumer groups to assess face and content validity (n=20), item difficulty and item consistency (n=55) and construct validity (n=23). The findings showed that the CFSQCRSQ has acceptable face and content validity with acceptable levels of item difficulty. Item consistency was observed for 12 out of 15 refrigerator safety knowledge. Further, all five of the subscales of consumer perceptions of refrigerator safety practices relating to risk of developing foodborne disease food poisoning showed acceptable internal consistency (Cronbach’s α value > 0.8). Construct validity of the CFSQCRSQ was shown to be very good (p=0.022). The CFSQCRSQ exhibited acceptable test-retest reliability at 14 days with majority of knowledge items (93.3%) and reported behavior items (96.4%) having correlation coefficients of greater than 0.70. Overall, the CFSQCRSQ was deemed valid and reliable in assessing refrigerator safety knowledge and behavior and therefore has the potential for future use in identifying groups of individuals at increased risk of deviating from recommended refrigerator safety practices as well as the assessment of refrigerator safety knowledge, behavior for use before and after an intervention.

Systematic review of tools to measure outcomes for young children with autism spectrum disorder

BACKGROUND: The needs of children with autism spectrum disorder (ASD) are complex and this is reflected in the number and diversity of outcomes assessed and measurement tools used to collect evidence about children's progress. Relevant outcomes include improvement in core ASD impairments, such as communication, social awareness, sensory sensitivities and repetitiveness; skills such as social functioning and play; participation outcomes such as social inclusion; and parent and family impact.

OBJECTIVES: To examine the measurement properties of tools used to measure progress and outcomes in children with ASD up to the age of 6 years. To identify outcome areas regarded as important by people with ASD and parents.

METHODS: The MeASURe (Measurement in Autism Spectrum disorder Under Review) research collaboration included ASD experts and review methodologists. We undertook systematic review of tools used in ASD early intervention and observational studies from 1992 to 2013; systematic review, using the COSMIN checklist (Consensus-based Standards for the selection of health Measurement Instruments) of papers addressing the measurement properties of identified tools in children with ASD; and synthesis of evidence and gaps. The review design and process was informed throughout by consultation with stakeholders including parents, young people with ASD, clinicians and researchers.

RESULTS: The conceptual framework developed for the review was drawn from the International Classification of Functioning, Disability and Health, including the domains 'Impairments', 'Activity Level Indicators', 'Participation', and 'Family Measures'. In review 1, 10,154 papers were sifted - 3091 by full text - and data extracted from 184; in total, 131 tools were identified, excluding observational coding, study-specific measures and those not in English. In review 2, 2665 papers were sifted and data concerning measurement properties of 57 (43%) tools were extracted from 128 papers. Evidence for the measurement properties of the reviewed tools was combined with information about their accessibility and presentation. Twelve tools were identified as having the strongest supporting evidence, the majority measuring autism characteristics and problem behaviour. The patchy evidence and limited scope of outcomes measured mean these tools do not constitute a 'recommended battery' for use. In particular, there is little evidence that the identified tools would be good at detecting change in intervention studies. The obvious gaps in available outcome measurement include well-being and participation outcomes for children, and family quality-of-life outcomes, domains particularly valued by our informants (young people with ASD and parents).

CONCLUSIONS: This is the first systematic review of the quality and appropriateness of tools designed to monitor progress and outcomes of young children with ASD. Although it was not possible to recommend fully robust tools at this stage, the review consolidates what is known about the field and will act as a benchmark for future developments. With input from parents and other stakeholders, recommendations are made about priority targets for research.

FUTURE WORK: Priorities include development of a tool to measure child quality of life in ASD, and validation of a potential primary outcome tool for trials of early social communication intervention.

STUDY REGISTRATION: This study is registered as PROSPERO CRD42012002223.

FUNDING: The National Institute for Health Research Health Technology Assessment programme.

Systematic Review of the Measurement Properties of Tools Used to Measure Behaviour Problems in Young Children with Autism

Background
Behaviour problems are common in young children with autism spectrum disorder (ASD). There are many different tools used to measure behavior problems but little is known about their validity for the population.

Objectives
To evaluate the measurement properties of behaviour problems tools used in evaluation of intervention or observational research studies with children with ASD up to the age of six years.

Methods
Behaviour measurement tools were identified as part of a larger, two stage, systematic review. First, sixteen major electronic databases, as well as grey literature and research registers were searched, and tools used listed and categorized. Second, using methodological filters, we searched for articles examining the measurement properties of the tools in use with young children with ASD in ERIC, MEDLINE, EMBASE, CINAHL, and PsycINFO. The quality of these papers was then evaluated using the COSMIN checklist.

Results
We identified twelve tools which had been used to measure behaviour problems in young children with ASD, and fifteen studies which investigated the measurement properties of six of these tools. There was no evidence available for the remaining six tools. Two questionnaires were found to be the most robust in their measurement properties, the Child Behavior Checklist and the Home Situations Questionnaire—Pervasive Developmental Disorders version.

Conclusions
We found patchy evidence on reliability and validity, for only a few of the tools used to measure behaviour problems in young children with ASD. More systematic research is required on measurement properties of tools for use in this population, in particular to establish responsiveness to change which is essential in measurement of outcomes of intervention.

PROSPERO Registration Number

CRD42012002223

Fuzzy Ensembles for Embedding Adaptive Behaviours in Semi-Autonomous Avatars in 3D Virtual Worlds

Semi-autonomous avatars should be both realistic and believable. The goal is to learn from and reproduce the behaviours of the user-controlled input to enable semi-autonomous avatars to plausibly interact with their human-controlled counterparts. A powerful tool for embedding autonomous behaviour is learning by imitation. Hence, in this paper an ensemble of fuzzy inference systems cluster the user input data to identify natural groupings within the data to describe the users movement and actions in a more abstract way. Multiple clustering algorithms are investigated along with a neuro-fuzzy classifier; and an ensemble of fuzzy systems are evaluated.

An Empirical Evaluation of the Reversal Theory State Measure: Tracking Changes in the Experience of Video Commercials of Three Leading Running Brands

Abstract of paper delivered at the 17th International Reversal Theory Conference, Day 3, session 4, 01.07.15

An Empirical Evaluation of the Reversal Theory State Measure Using Three Running Brand Video Commercials

This paper reports on the first known empirical use of the Reversal Theory State Measure (RTSM) since its publication by Desselles et al. (2014). The RTSM was employed to track responses to three purposely-selected video commercials in a between-subjects design. Results of the study provide empirical support for the central conceptual premise of reversal theory, the experience of metamotivational reversals and the ability of the RTSM to capture them. The RTSM was also found to be psychometrically sound after adjustments were made to two of its three component subscales. Detailed account and rationale is provided for the analytical process of assessing the psychometric robustness of the RTSM, with a number of techniques and interpretations relating to component structure and reliability discussed. Agreeability and critique of the two available versions of the RTSM – the bundled and the branched – is also examined. Researchers are encouraged to assist development of the RTSM through further use, taking into account the analysis and recommendations presented.

Estimating fuzzy membership functions parameters by the levenberg-marquardt algorithm

In previous papers from the authors fuzzy model identification methods were discussed. The bacterial algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg-Marquardt algorithm was also proposed for determining membership functions in fuzzy systems. In this paper the Levenberg-Marquardt technique is improved to optimise the membership functions in the fuzzy rules without Ruspini-partition. The class of membership functions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear functions as well.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

B-spline and neuro-fuzzy models design with function and derivative equalities

The design of neuro-fuzzy models is still a complex problem, as it involves not only the determination of the model parameters, but also its structure. Of special importance is the incorporation of a priori information in the design process. In this paper two known design algorithms for B-spline models will be updated to account for function and derivatives equality restrictions, which are important when the neural model is used for performing single or multi-objective optimization on-line.

Bacterial algorithm applied for fuzzy rule extraction

This paper presents a method of using the so-colled "bacterial algorithm" (4,5) for extracting a fuzzy rule base from a training set. The bewly proposed bacterial evolutionary algorithm (BEA) is shown. In our application one bacterium corresponds to a fuzzy rule system.

Anytime information processing based on fuzzy and neural network models

In modern measurement and control systems, the available time and resources are often not only limited, but could change during the operation of the system. In these cases, the so-called anytime algorithms could be used advantageously. While diflerent soft computing methods are wide-spreadly used in system modeling, their usability in these cases are limited.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Training neuro-fuzzy models using evolution based algorithms

The normal design process for neural networks or fuzzy systems involve two different phases: the determination of the best topology, which can be seen as a system identification problem, and the determination of its parameters, which can be envisaged as a parameter estimation problem. This latter issue, the determination of the model parameters (linear weights and interior knots) is the simplest task and is usually solved using gradient or hybrid schemes. The former issue, the topology determination, is an extremely complex task, especially if dealing with real-world problems.