Factors associated with exercise participation in adults with arthritis

Most people with arthritis are not regularly active. Understanding what factors influence exercise is essential for designing programs to increase participation. The objective of this study was to examine the correlates of exercise in people with arthritis. Using a cross-sectional design, sociodemographic, health-related, and psychosocial variables were collected from community-dwelling individuals with arthritis (N = 141). Associations with exercise level were examined with bivariate statistics (ANOVAs, chi-squares) and logistic-regression analyses. Exercisers were less likely than nonexercisers and insufficiently active people to report that arthritis negatively affected their physical and social functioning, and they reported more positive affect and greater self-efficacy (p < .05). Exercisers also reported less pain than nonexercisers (p < .05). In multiple logistic-regression analyses, self-efficacy and physical limitations remained independent predictors of exercise. The results suggest the need to target exercise self-efficacy when designing exercise interventions. Results also suggest the need to tailor exercise programs to individuals' physical limitations.     

Moving from ‘what we know works’ to ‘what we do in practice’: An evidence overview of implementation and diffusion of innovation in transition to adulthood for care experienced young people

Global research has shown that most young people who are care experienced are not prepared to transition to independent living at 18 years of age and require support into early adulthood. We used rigorous systematic methods to identify English-based peer reviewed and grey literature describing innovations relevant to care experienced young people as they transition into adulthood, with a focus upon lessons for their implementation and diffusion. We synthesised the evidence narratively and organise data linked to seven key areas important to the transition to adulthood: (1) Health and well-being; (2) relationships; (3) education and training; (4) employment; (5) participation in society; (6) accommodation; (7) other. Twenty-five papers met our inclusion criteria. This review has found that, whilst there are a broad spectrum of innovations taking place within the social care environment for care experienced young people to support their transition into adulthood, there exists limited insight into how best to support implementation and diffusion of evidence-based innovation. We drew upon the ‘Consolidated Framework for Implementation Research’, developed in the setting of clinical service delivery, to highlight challenges in implementing and diffusing evidence-based innovation for care experienced young people transitioning into adulthood.     

Further results on simultaneous confidence bounds in normal models with restricted alternatives

Consider a setup where one-sided simultaneous confidence bounds for linear parametric functions are desired. Here we improve the Bohrer and Francis (1972) bounds for situations where apriori information on the parameters is available in form of some restrictions on the parameter space. Application is made essentially to ordered ANOVA models and simple-tree ANOVA models.     

Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges

With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry among biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyse individual animal movement data. Specifically, we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology towards what are arguably overly complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based on mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Lévy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.     

A combined p-value test for multiple hypothesis testing

Tests that combine p-values, such as Fisher's product test, are popular to test the global null hypothesis H₀ that each of n component null hypotheses, H₁,…,H_n, is true versus the alternative that at least one of H₁,…,H_n is false, since they are more powerful than classical multiple tests such as the Bonferroni test and the Simes tests. Recent modifications of Fisher's product test, popular in the analysis of large scale genetic studies include the truncated product method (TPM) of Zaykin et al. (2002), the rank truncated product (RTP) test of Dudbridge and Koeleman (2003) and more recently, a permutation based test—the adaptive rank truncated product (ARTP) method of Yu et al. (2009). The TPM and RTP methods require users' specification of a truncation point. The ARTP method improves the performance of the RTP method by optimizing selection of the truncation point over a set of pre-specified candidate points. In this paper we extend the ARTP by proposing to use all the possible truncation points {1,…,n} as the candidate truncation points. Furthermore, we derive the theoretical probability distribution of the test statistic under the global null hypothesis H₀. Simulations are conducted to compare the performance of the proposed test with the Bonferroni test, the Simes test, the RTP test, and Fisher's product test. The simulation results show that the proposed test has higher power than the Bonferroni test and the Simes test, as well as the RTP method. It is also significantly more powerful than Fisher's product test when the number of truly false hypotheses is small relative to the total number of hypotheses, and has comparable power to Fisher's product test otherwise.     

A New Multiperiod Multiple Traveling Salesman Problem with Heuristic and Application to a Scheduling Problem

A new multiperiod variation of the M-traveling salesman problem is introduced. The problem arises in efficient scheduling of optimal interviews among tour brokers and vendors at conventions of the tourism and travel industry. In classical traveling salesman problem vocabulary, a salesman is a tour broker at the convention and a city is a vendor's booth. In this problem, more than one salesman may be required to visit a city, but at most one salesman per time period can visit each city. The heuristic solution method presented is polynomial and is guaranteed to produce a nonconflicting set of salesmen's tours. The results of an implementation of the method for a recent convention are also reported.     

An Empirical Estimation of the Investor Loss Function Associated with the Use of Analysts' Forecasts of Earnings

The aggregate investor loss function associated with the use of analysts' forecasts of earnings expresses the relation between loss and prediction error. Prior research suggested that the shape of this loss function should provide guidance in decision rules for investors. This study empirically estimates the investor loss function and finds an asymmetry in the relation between loss and analysts' prediction error. For overestimates of earnings, loss is a quadratic function of error; while for underestimates of earnings, loss is a linear function of error.