Patient preferences for receiving remote communication support for lifestyle physical activity behaviour change: The perspective of patients with musculoskeletal disorders from three hospital services

This study examined patients’ preference ratings for receiving support via remote communication to increase their lifestyle physical activity. Methods People with musculoskeletal disorders ( n=221 of 296 eligible) accessing one of three clinics provided preference ratings for “how much” they wanted to receive physical activity support via five potential communication modalities. The five ratings were generated on a horizontal analogue rating scale (0 represented “not at all”; 10 represented “very much”). Results Most (n=155, 70%) desired referral to a physical activity promoting intervention. “Print and post” communications had the highest median preference rating (7/10), followed by email and telephone (both 5/10), text messaging (1/10), and private Internet-based social network messages (0/10). Desire to be referred was associated with higher preference for printed materials (coefficient = 2.739, p<0.001), telephone calls (coefficient = 3.000, p<0.001), and email (coefficient = 2.059, p=0.02). Older age was associated with lower preference for email (coefficient = −0.100, p<0.001), texting (coefficient = −0.096, p<0.001), and social network messages (coefficient = −0.065, p<0.001). Conclusion Patients desiring support to be physically active indicated preferences for interventions with communication via print, email, or telephone calls.

Relational accountability in Indigenizing visual research for participatory communication

This article argues that an indigenous approach to communication research allows us to re-think academic approaches of engaging in and evaluating participatory communication research. It takes as its case study the Komuniti Tok Piksa project undertaken in the Highlands of Papua New Guinea. The project explores ways in which visual methods when paired with a community action approach embedded within an indigenous framework can be used to facilitate social change through meaningful participation. It involves communities to narrate their experiences in regard to HIV and AIDS and assists them in designing and recording their own messages. Local researchers are trained in using visual tools to facilitate this engagement with the communities.

Patient Safety - A Psychological Perspective

This comprehensive book takes a psychological perspective on patient safety. It is based on the most recent theoretical and empirical research evidence from psychology (including clinical, work, and organizational psychology) and adjacent social and behavioral sciences such as human factors. Factors that influence safety-related experiences, behaviors, and outcomes of patients and professionals working in clinical settings such as medical practices and hospitals are reviewed, structured, and critically evaluated. Consistent with the complexity of the topic, the author takes a multi-level approach to patient safety, which includes a review of individual, team, and organizational factors and outcomes. The book describes how these factors, by themselves and in combination, can facilitate or impede patient safety. Individual factors include safety-relevant knowledge, skills, abilities, and personality traits such as conscientiousness and emotional stability. Team factors include group communication, training, and leadership. Finally, organizational factors include the safety culture and climate. Throughout the book, different evidence-based intervention programs are described that can help practitioners promote patient safety and prevent accidents. The book is a valuable resource for both researchers and practitioners interested in understanding, maintaining, and improving patient safety in a variety of applied settings. It is based on the most up-to-date research evidence from psychology and neighboring disciplines, and it is written in a clear and non-technical language understandable for a wide audience.

The use of neurocognitive methods in assessing health communication messages: A systematic review

We review 20 studies that examined persuasive processing and outcomes of health messages using neurocognitive measures. The results suggest that cognitive processes and neural activity in regions thought to reflect self-related processing may be more prominent in the persuasive process of self-relevant messages. Furthermore, activity in the medial prefrontal cortex (MPFC), the superior temporal gyrus, and the middle frontal gyrus were identified as predictors of message effectiveness, with the MPFC accounting for additional variance in behaviour change beyond that accounted for by self-report measures. Incorporating neurocognitive measures may provide a more comprehensive understanding of the processing and outcomes of health messages.

Complexity of coffee flavor: A compositional and sensory perspective

For the consumer, flavor is arguably the most important aspect of a good coffee. Coffee flavor is extremely complex and arises from numerous chemical, biological and physical influences of cultivar, coffee cherry maturity, geographical growing location, production, processing, roasting and cup preparation. Not surprisingly there is a large volume of research published detailing the volatile and non-volatile compounds in coffee and that are likely to be playing a role in coffee flavor. Further, there is much published on the sensory properties of coffee. Nevertheless, the link between flavor components and the sensory properties expressed in the complex matrix of coffee is yet to be fully understood. This paper provides an overview of the chemical components that are thought to be involved in the flavor and sensory quality of Arabica coffee.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Matrix Decomposition Methods for Data Mining : Computational Complexity and Algorithms

Matrix decompositions, where a given matrix is represented as a product of two other matrices, are regularly used in data mining. Most matrix decompositions have their roots in linear algebra, but the needs of data mining are not always those of linear algebra. In data mining one needs to have results that are interpretable -- and what is considered interpretable in data mining can be very different to what is considered interpretable in linear algebra. --- The purpose of this thesis is to study matrix decompositions that directly address the issue of interpretability. An example is a decomposition of binary matrices where the factor matrices are assumed to be binary and the matrix multiplication is Boolean. The restriction to binary factor matrices increases interpretability -- factor matrices are of the same type as the original matrix -- and allows the use of Boolean matrix multiplication, which is often more intuitive than normal matrix multiplication with binary matrices. Also several other decomposition methods are described, and the computational complexity of computing them is studied together with the hardness of approximating the related optimization problems. Based on these studies, algorithms for constructing the decompositions are proposed. Constructing the decompositions turns out to be computationally hard, and the proposed algorithms are mostly based on various heuristics. Nevertheless, the algorithms are shown to be capable of finding good results in empirical experiments conducted with both synthetic and real-world data.

The long-run impact of Information and Communication Technology on economic output: The case of Australia

This paper investigates the cointegration and causal relationships between Information and Communication Technology (ICT) and economic output in Australia using data for about five decades. The framework used in this paper is the single-sector aggregate production function, which is the first comprehensive approach of this kind to include ICT and non-ICT capital and other factors to examine long-run Granger causality. The empirical evidence points to a cointegration relationship between ICT capital and output, and implies that ICT capital Granger causes economic output and multifactor productivity, as does non-ICT capital.

On the contribution of information and communication technology to productivity growth in Australia

This paper revisits the so-called ‘ICT-productivity paradox’ from a long-run perspective by using annual Australian data for 1965–2013. It provides estimates of long-run and short-run elasticities of labour productivity with respect to ICT capital deepening, and explores the nature of long-run causality among productivity growth and ICT and non-ICT capital deepening. The estimates of long-run elasticities are derived by employing both time-series and panel data econometric techniques. The empirical results provide strong confirmatory evidence of the long-run impact of ICT capital deepening on labour productivity in Australia.

Location of concentrators in a computer communication network: a stochastic automation search method

The following problem is considered. Given the locations of the Central Processing Unit (ar;the terminals which have to communicate with it, to determine the number and locations of the concentrators and to assign the terminals to the concentrators in such a way that the total cost is minimized. There is alao a fixed cost associated with each concentrator. There is ail upper limit to the number of terminals which can be connected to a concentrator. The terminals can be connected directly to the CPU also In this paper it is assumed that the concentrators can bo located anywhere in the area A containing the CPU and the terminals. Then this becomes a multimodal optimization problem. In the proposed algorithm a stochastic automaton is used as a search device to locate the minimum of the multimodal cost function . The proposed algorithm involves the following. The area A containing the CPU and the terminals is divided into an arbitrary number of regions (say K). An approximate value for the number of concentrators is assumed (say m). The optimum number is determined by iteration later The m concentrators can be assigned to the K regions in (mk) ways (m > K) or (km) ways (K>m).(All possible assignments are feasible, i.e. a region can contain 0,1,…, to concentrators). Each possible assignment is assumed to represent a state of the stochastic variable structure automaton. To start with, all the states are assigned equal probabilities. At each stage of the search the automaton visits a state according to the current probability distribution. At each visit the automaton selects a 'point' inside that state with uniform probability. The cost associated with that point is calculated and the average cost of that state is updated. Then the probabilities of all the states are updated. The probabilities are taken to bo inversely proportional to the average cost of the states After a certain number of searches the search probabilities become stationary and the automaton visits a particular state again and again. Then the automaton is said to have converged to that state Then by conducting a local gradient search within that state the exact locations of the concentrators are determined This algorithm was applied to a set of test problems and the results were compared with those given by Cooper's (1964, 1967) EAC algorithm and on the average it was found that the proposed algorithm performs better.

Two Stage Transform Vector Quantization of LSFs for Wideband Speech Coding

We investigate the use of a two stage transform vector quantizer (TSTVQ) for coding of line spectral frequency (LSF) parameters in wideband speech coding. The first stage quantizer of TSTVQ, provides better matching of source distribution and the second stage quantizer provides additional coding gain through using an individual cluster specific decorrelating transform and variance normalization. Further coding gain is shown to be achieved by exploiting the slow time-varying nature of speech spectra and thus using inter-frame cluster continuity (ICC) property in the first stage of TSTVQ method. The proposed method saves 3-4 bits and reduces the computational complexity by 58-66%, compared to the traditional split vector quantizer (SVQ), but at the expense of 1.5-2.5 times of memory.

Output feedback control of networked systems with a stochastic communication protocol

This paper addresses an output feedback control problem for a class of networked control systems (NCSs) with a stochastic communication protocol. Under the scenario that only one sensor is allowed to obtain the communication access at each transmission instant, a stochastic communication protocol is first defined, where the communication access is modelled by a discrete-time Markov chain with partly unknown transition probabilities. Secondly, by use of a network-based output feedback control strategy and a time-delay division method, the closed-loop system is modeled as a stochastic system with multi time-varying delays, where the inherent characteristic of the network delay is well considered to improve the control performance. Then, based on the above constructed stochastic model, two sufficient conditions are derived for ensuring the mean-square stability and stabilization of the system under consideration. Finally, two examples are given to show the effectiveness of the proposed method.