Decision trees for detection of activity intensity in youth with cerebral palsy

PURPOSE To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP); and 2) compare the classification accuracy of the new DT models to that achieved by previously published cut-points for youth with CP. METHODS Youth with CP (GMFCS Levels I - III) (N=51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (<1.5 METs), light PA (LPA) (>/=1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (>/=3 METs). Models were trained and cross-validated using the 'rpart' and 'caret' packages within R. RESULTS For the VA (VA_DT) and VM decision trees (VM_DT), a single threshold differentiated LPA from SED, while the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracy for the VC_DT was 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III, respectively. The corresponding cross-validation accuracy for the VM_DT was 80.5%, 75.6%, and 84.2%, respectively. Within each GMFCS level, the decision tree models achieved better PA intensity recognition than previously published cut-points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut-points misclassified 40% of the MVPA activity trials. CONCLUSION GMFCS-specific cut-points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.

The lived experience of diagnosis delivery in motor neurone disease: A sociological-phenomenological study

Relatively few previous studies of individuals receiving a diagnosis of Motor Neurone Disease within the UK health care system have employed qualitative approaches to examine the diagnostic journey from a patient perspective. A qualitative sociological study was undertaken, involving interviews with 42 participants diagnosed with MND, to provide insight into their experiences of undergoing testing and receiving a diagnosis. Adopting a sociological-phenomenological perspective, this article examines key themes that emerged from participant accounts surrounding the lived experience of the diagnostic journey. The key themes that emerged were: The diagnostic quest; living with uncertainty; hearing bad news; communication difficulties; and a reified body of medical interest. In general, doctor-patient communication both at pre and post diagnosis was experienced as highly stressful, distressing and profoundly upsetting. Participants reported such distress as being due to the mode of delivery and communication strategies used by health professionals. We therefore suggest that professional training needs to emphasize the importance to health professionals of fostering greater levels of tact, sensitivity and empathy towards patients diagnosed with devastating, life-limiting illnesses such as MND.

A Bayesian latent class safety performance function for identifying motor vehicle crash blackspots

The current state of the practice in Blackspot Identification (BSI) utilizes safety performance functions based on total crash counts to identify transport system sites with potentially high crash risk. This paper postulates that total crash count variation over a transport network is a result of multiple distinct crash generating processes including geometric characteristics of the road, spatial features of the surrounding environment, and driver behaviour factors. However, these multiple sources are ignored in current modelling methodologies in both trying to explain or predict crash frequencies across sites. Instead, current practice employs models that imply that a single underlying crash generating process exists. The model mis-specification may lead to correlating crashes with the incorrect sources of contributing factors (e.g. concluding a crash is predominately caused by a geometric feature when it is a behavioural issue), which may ultimately lead to inefficient use of public funds and misidentification of true blackspots. This study aims to propose a latent class model consistent with a multiple crash process theory, and to investigate the influence this model has on correctly identifying crash blackspots. We first present the theoretical and corresponding methodological approach in which a Bayesian Latent Class (BLC) model is estimated assuming that crashes arise from two distinct risk generating processes including engineering and unobserved spatial factors. The Bayesian model is used to incorporate prior information about the contribution of each underlying process to the total crash count. The methodology is applied to the state-controlled roads in Queensland, Australia and the results are compared to an Empirical Bayesian Negative Binomial (EB-NB) model. A comparison of goodness of fit measures illustrates significantly improved performance of the proposed model compared to the NB model. The detection of blackspots was also improved when compared to the EB-NB model. In addition, modelling crashes as the result of two fundamentally separate underlying processes reveals more detailed information about unobserved crash causes.

Understanding urban rail travel for improved patronage forecasting

Improved forecasting of urban rail patronage is essential for effective policy development and efficient planning for new rail infrastructure. Past modelling and forecasting of urban rail patronage has been based on legacy modelling approaches and often conducted at the general level of public transport demand, rather than being specific to urban rail. This project canvassed current Australian practice and international best practice to develop and estimate time series and cross-sectional models of rail patronage for Australian mainland state capital cities. This involved the implementation of a large online survey of rail riders and non-riders for each of the state capital cities, thereby resulting in a comprehensive database of respondent socio-economic profiles, travel experience, attitudes to rail and other modes of travel, together with stated preference responses to a wide range of urban travel scenarios. Estimation of the models provided a demonstration of their ability to provide information on the major influences on the urban rail travel decision. Rail fares, congestion and rail service supply all have a strong influence on rail patronage, while a number of less significant factors such as fuel price and access to a motor vehicle are also influential. Of note, too, is the relative homogeneity of rail user profiles across the state capitals. Rail users tended to have higher incomes and education levels. They are also younger and more likely to be in full-time employment than non-rail users. The project analysis reported here represents only a small proportion of what could be accomplished utilising the survey database. More comprehensive investigation was beyond the scope of the project and has been left for future work.

Evaluating the effectiveness of a self-management exercise intervention on wound healing, functional ability and health-related quality of life outcomes in adults with venous leg ulcers: a randomised controlled trial

Exercise that targets ankle joint mobility may lead to improvement in calf muscle pump function and subsequent healing. The objectives of this research were to assess the impact of an exercise intervention in addition to routine evidence-based care on the healing rates, functional ability and health-related quality of life for adults with venous leg ulcers (VLUs). This study included 63 patients with VLUs. Patients were randomised to receive either a 12-week exercise intervention with a telephone coaching component or usual care plus telephone calls at the same timepoints. The primary outcome evaluated the effectiveness of the intervention in relation to wound healing. The secondary outcomes evaluated physical activity, functional ability and health-related quality of life measures between groups at the end of the 12 weeks. A per protocol analysis complemented the effectiveness (intention-to-treat) analysis to highlight the importance of adherence to an exercise intervention. Intention-to-treat analyses for the primary outcome showed 77% of those in the intervention group healed by 12 weeks compared to 53% of those in the usual care group. Although this difference was not statistically significant due to a smaller than expected sample size, a 24% difference in healing rates could be considered clinically significant. The per protocol analysis for wound healing, however, showed that those in the intervention group who adhered to the exercise protocol 75% or more of the time were significantly more likely to heal and showed higher rates for wound healing than the control group (P = 0·01), that is, 95% of those who adhered in the intervention group healed in 12 weeks. The secondary outcomes of physical activity, functional ability and health-related quality of life were not significantly altered by the intervention. Among the secondary outcomes (physical activity, functional ability and health-related quality of life), intention-to-treat analyses did not support the effectiveness of the intervention. However, per protocol analyses revealed encouraging results with those participants who adhered more than 75% of the time (n = 19) showing significantly improved Range of Ankle Motion from the self-management exercise programme (P = 0·045). This study has shown that those participants who adhere to the exercise programme as an adjunctive treatment to standard care are more likely to heal and have better functional outcomes than those who do not adhere to the exercises in conjunction with usual care.

The usefulness of fair values in improving the predictive ability of earnings: Evidence from international banks

One of the objectives of general-purpose financial reporting is to provide information about the financial position, financial performance and cash flows of an entity that is useful to a wide range of users in making economic decisions. The current focus on potentially increased relevance of fair value accounting weighed against issues of reliability has failed to consider the potential impact on the predictive ability of accounting. Based on a sample of international (non-U.S.) banks from 24 countries during 2009-2012, we test the usefulness of fair values in improving the predictive ability of earnings. First, we find that the increasing use of fair values on balance-sheet financial instruments enhances the ability of current earnings to predict future earnings and cash flows. Second, we provide evidence that the fair value hierarchy classification choices affect the ability of earnings to predict future cash flows and future earnings. More precisely, we find that the non-discretionary fair value component (Level 1 assets) improves the predictability of current earnings whereas the discretionary fair value components (Level 2 and Level 3 assets) weaken the predictive power of earnings. Third, we find a consistent and strong association between factors reflecting country-wide institutional structures and predictive power of fair values based on discretionary measurement inputs (Level 2 and Level 3 assets and liabilities). Our study is timely and relevant. The findings have important implications for standard setters and contribute to the debate on the use of fair value accounting.