Current management of pregnancy-related low back pain: a national cross-sectional survey of UK physiotherapists.

BACKGROUND: Pregnancy-related low back pain (LBP) is very common. Evidence from a systematic review supports the use of exercise and acupuncture, although little is known about the care received by women with pregnancy-related back pain in the UK. OBJECTIVE: To describe current acupuncture and standard care management of pregnancy-related LBP by UK physiotherapists. DESIGN: Cross-sectional survey of physiotherapists with experience of treating women with pregnancy-related LBP from three professional networks of the Chartered Society of Physiotherapy. METHODS: In total, 1093 physiotherapists were mailed a questionnaire. The questionnaire captured respondents' demographic and practice setting information, and experience of managing women with pregnancy-related back pain, and investigated the reported management of pregnancy-related LBP using a patient case vignette of a specific, 'typical' case. RESULTS: The overall response rate was 58% (629/1093). Four hundred and ninety-nine physiotherapists had experience of treating women with pregnancy-related LBP and were included in the analysis. Most respondents worked wholly or partly in the UK National Health Service (78%). Most respondents reported that they treat patients with pregnancy-related LBP in three to four one-to-one treatment sessions over 3 to 6 weeks. The results show that a range of management strategies are employed for pregnancy-related LBP, and multimodal management is common. The most common reported treatment was home exercises (94%), and 24% of physiotherapists reported that they would use acupuncture with the patient described in the vignette. CONCLUSIONS: This study provides the first robust data on the management of pregnancy-related LBP by UK physiotherapists. Multimodal management is common, although exercise is the most frequently used treatment for pregnancy-related LBP. Acupuncture is used less often for this patient group

Providing patients with direct access to musculoskeletal physiotherapy: the impact on general practice musculoskeletal workload and resource use. The STEMS-2 study.

OBJECTIVES: This study examined the real-world impact of patient direct access to NHS physiotherapy (self-referral) on (a) general practice consultations for musculoskeletal (MSK) conditions and (b) specified clinical management for patients with MSK conditions. DESIGN AND SETTING: Natural experiment in four general practices and the associated physiotherapy service. METHODS: Anonymised routinely collected data were obtained. MSK coded GP consultations, recorded fit notes, MSK-related prescription medication, X-rays and MRI requests, and referrals to secondary care for patients consulting with MSK conditions were identified and trends described across a 6-year period (June 2011 to June 2017). Joinpoint regression analysis was used to identify any significant changes in GP MSK consultation trends before and after the introduction of self-referral to physiotherapy. Physiotherapy service data examined access methods used by patients (GP referred, GP recommended self-referral, true self-referral) and the number of physiotherapy sessions. RESULTS: Direct access resulted in inconsistent impact on general practices. In one arm of the experiment a significant increase in GP consultations was observed and in one arm was stable. Exploratory examination of clinical management showed only requests for X-rays (arm 1) and possibly requests for MRI (arm 2) changed over time. Physiotherapy service referrals showed a low uptake of true self-referral (10% and 6%) in each arm respectively. CONCLUSION: This is the first study to examine the real-world impact of patient direct access to physiotherapy at general practice level. We found no consistent impact of patient direct access on GP MSK workload. Impact on some clinical management was observed but not consistently in the direction suggested by previous studies

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

Learning HCI Across Institutions, Disciplines and Countries: A Field Study of Cognitive Styles in Analytical and Creative Tasks

Human-computer interaction (HCI) is increasingly becoming a subject taught in universities around the world. However, little is known of the interactions of the HCI curriculum with students in different types of institutions and disciplines internationally. In order to explore these interactions, we studied the performance of HCI students in design, technology and business faculties in universities in UK, India, Namibia, Mexico and China who participated in a common set of design and evaluation tasks. We obtained participants’ cognitive style profiles based on Allinson and Hayes scale in order to gain further insights into their learning styles and explore any relation between these and performance. We found participants’ cognitive style preferences to be predominantly in the adaptive range, i.e. with combined analytical and intuitive traits, compared to normative data for software engineering, psychology and design professionals. We further identified significant relations between students’ cognitive styles and performance in analytical and creative tasks of a HCI professional individual. We discuss the findings in the context of the distinct backgrounds of the students and universities that participated in this study and the value of research that explores and promotes diversity in HCI education

Speech Graphs Provide a Quantitative Measure of Thought Disorder in Psychosis

Background: Psychosis has various causes, including mania and schizophrenia. Since the differential diagnosis of psychosis is exclusively based on subjective assessments of oral interviews with patients, an objective quantification of the speech disturbances that characterize mania and schizophrenia is in order. In principle, such quantification could be achieved by the analysis of speech graphs. A graph represents a network with nodes connected by edges; in speech graphs, nodes correspond to words and edges correspond to semantic and grammatical relationships. Methodology/Principal Findings: To quantify speech differences related to psychosis, interviews with schizophrenics, manics and normal subjects were recorded and represented as graphs. Manics scored significantly higher than schizophrenics in ten graph measures. Psychopathological symptoms such as logorrhea, poor speech, and flight of thoughts were grasped by the analysis even when verbosity differences were discounted. Binary classifiers based on speech graph measures sorted schizophrenics from manics with up to 93.8% of sensitivity and 93.7% of specificity. In contrast, sorting based on the scores of two standard psychiatric scales (BPRS and PANSS) reached only 62.5% of sensitivity and specificity. Conclusions/Significance: The results demonstrate that alterations of the thought process manifested in the speech of psychotic patients can be objectively measured using graph-theoretical tools, developed to capture specific features of the normal and dysfunctional flow of thought, such as divergence and recurrence. The quantitative analysis of speech graphs is not redundant with standard psychometric scales but rather complementary, as it yields a very accurate sorting of schizophrenics and manics. Overall, the results point to automated psychiatric diagnosis based not on what is said, but on how it is said.FINEP [01.06.1092.00]FINEPCNPq Universal [481506/2007-1]CNPq UniversalCNPqCNPqCapesCAPESad Associacao Alberto Santos Dumont para Apoio a Pesquisa (AASDAP)a'd Associacao Alberto Santos Dumont para Apoio a Pesquisa (AASDAP

Speaker Sex Perception from Spontaneous and Volitional Nonverbal Vocalizations.

In two experiments, we explore how speaker sex recognition is affected by vocal flexibility, introduced by volitional and spontaneous vocalizations. In Experiment 1, participants judged speaker sex from two spontaneous vocalizations, laughter and crying, and volitionally produced vowels. Striking effects of speaker sex emerged: For male vocalizations, listeners' performance was significantly impaired for spontaneous vocalizations (laughter and crying) compared to a volitional baseline (repeated vowels), a pattern that was also reflected in longer reaction times for spontaneous vocalizations. Further, performance was less accurate for laughter than crying. For female vocalizations, a different pattern emerged. In Experiment 2, we largely replicated the findings of Experiment 1 using spontaneous laughter, volitional laughter and (volitional) vowels: here, performance for male vocalizations was impaired for spontaneous laughter compared to both volitional laughter and vowels, providing further evidence that differences in volitional control over vocal production may modulate our ability to accurately perceive speaker sex from vocal signals. For both experiments, acoustic analyses showed relationships between stimulus fundamental frequency (F0) and the participants' responses. The higher the F0 of a vocal signal, the more likely listeners were to perceive a vocalization as being produced by a female speaker, an effect that was more pronounced for vocalizations produced by males. We discuss the results in terms of the availability of salient acoustic cues across different vocalizations

Clustering daily patterns of human activities in the city

Data mining and statistical learning techniques are powerful analysis tools yet to be incorporated in the domain of urban studies and transportation research. In this work, we analyze an activity-based travel survey conducted in the Chicago metropolitan area over a demographic representative sample of its population. Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities of different time of day. The generated clusters combined with social demographic information provide a new perspective for urban and transportation planning as well as for emergency response and spreading dynamics, by addressing when, where, and how individuals interact with places in metropolitan areas.Massachusetts Institute of Technology. Dept. of Urban Studies and PlanningUnited States. Dept. of Transportation (Region One University Transportation Center)Singapore-MIT Alliance for Research and Technolog

Subspace Projection Approaches to Classification and Visualization of Neural Network-Level Encoding Patterns

Recent advances in large-scale ensemble recordings allow monitoring of activity patterns of several hundreds of neurons in freely behaving animals. The emergence of such high-dimensional datasets poses challenges for the identification and analysis of dynamical network patterns. While several types of multivariate statistical methods have been used for integrating responses from multiple neurons, their effectiveness in pattern classification and predictive power has not been compared in a direct and systematic manner. Here we systematically employed a series of projection methods, such as Multiple Discriminant Analysis (MDA), Principal Components Analysis (PCA) and Artificial Neural Networks (ANN), and compared them with non-projection multivariate statistical methods such as Multivariate Gaussian Distributions (MGD). Our analyses of hippocampal data recorded during episodic memory events and cortical data simulated during face perception or arm movements illustrate how low-dimensional encoding subspaces can reveal the existence of network-level ensemble representations. We show how the use of regularization methods can prevent these statistical methods from over-fitting of training data sets when the trial numbers are much smaller than the number of recorded units. Moreover, we investigated the extent to which the computations implemented by the projection methods reflect the underlying hierarchical properties of the neural populations. Based on their ability to extract the essential features for pattern classification, we conclude that the typical performance ranking of these methods on under-sampled neural data of large dimension is MDA>PCA>ANN>MGD

Initial validation of the mindful eating scale

Published Mindfulness, 2013, 5(6), pp. 719-729. The final publication is available at Springer via http://dx.doi.org/10.1007/s12671-013-0227-5Self-report scales for mindfulness are now widely used in applied settings, and have made a contribution to research, for instance in demonstrating mediation effects. To date there are no convincing data as to whether mindfulness skills generalise fully across life domains, and so some researchers have developed mindfulness scales for particular domains of behaviour. We present the development of a self-report scale to measure mindfulness with respect to eating behaviours