Introduction Current cardiac telerehabilitation (CTR) interventions are insufficiently tailored to the preferences and competences of individual patients, which raises the question whether their implementation will increase overall participation and adherence to cardiac rehabilitation. However, research on patient-specific factors that influence participation and adoption of CTR interventions is scarce. Objective The aim of this study was to evaluate which patient-related characteristics influence participation in a novel CTR intervention in patients with coronary artery disease. Methods This prospective observational substudy of the SmartCare-CAD randomised controlled trial evaluated patient characteristics of study participants as proxy for participation in a CTR intervention. We compared demographic, geographic and health-related characteristics between trial participants and non-participants to determine which characteristics influenced trial participation. Results A total of 699 patients (300 participants and 399 non-participants; 84% male, mean age 64.3 ± 10.5 years) were included. Most of the non-participants refused participation because of insufficient technical skills or lack of interest in digital health (26%), or preferred centre-based cardiac rehabilitation (CR) (21%). Variables independently associated with non-participation included: higher age, lower educational level, shorter traveling distance, smoking, positive family history for cardiovascular disease, having undergone coronary artery bypass grafting; and a higher blood pressure, worse exercise capacity and higher risk of depression before the start of CR. Conclusion Participation in CTR is strongly influenced by demographic and health-related factors such as age, educational level, smoking status and both physical and mental functioning. CTR interventions should therefore be redesigned with the involvement of these currently underrepresented patient subgroups.

One-class modelling is a useful approach in metabolomics for the untargeted detection of abnormal metabolite profiles, when information from a set of reference observations is available to model “normal” or baseline metabolite profiles. Such outlying profiles are typically identified by comparing the distance between an observation and the reference class to a critical limit. Often, multivariate distance measures such as the Mahalanobis distance (MD) or principal component-based measures are used. These approaches, however, are either not applicable to untargeted metabolomics data, or their results are unreliable. In this paper, five distance measures for one-class modeling in untargeted metabolites are proposed. They are based on a combination of the MD and five so-called eigenvalue-shrinkage estimators of the covariance matrix of the reference class. A simple cross-validation procedure is proposed to set the critical limit for outlier detection. Simulation studies are used to identify which distance measure provides the best performance for one-class modeling, in terms of type I error and power to identify abnormal metabolite profiles. Empirical evidence demonstrates that this method has better type I error (false positive rate) and improved outlier detection power than the standard (principal component-based) one-class models. The method is illustrated by its application to liquid chromatography coupled to mass spectrometry (LC-MS) and nuclear magnetic response spectroscopy (NMR) untargeted metabolomics data from two studies on food safety assessment and diagnosis of rare diseases, respectively.

The robustness of the t linear mixed model (tLMM) has been proved and exploited in many applications. Various publications emerged with the aim of proving superiority with respect to traditional linear mixed models, extending to more general settings and proposing more efficient estimation methods. However, little attention has been paid to the mathematical properties of the model itself and to the evaluation of the proposed estimation methods. In this paper we perform an indepth analysis of the tLMM, evaluating a direct maximum likelihood estimation method via an intensive simulation study and investigating some identifiability properties. The theoretical findings are illustrated through an application to a dataset collected from a sleep trial.

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Introduction: Italy delayed limiting access to the Faculty of Medicine and the reform of schools of specialization was not accompanied by programming the number of scholarships, so employment expectations are often disappointing. The aim of this study is to analyze the employment prospects of specialists in urology through the development of possible scenarios for the 5-year period 2000–2004. Materials and Methods: We recorded data received from Italian Schools of Specialization in Urology on specialists in the 5-year period 1994–1998. We also tried to obtain a picture of the national distribution of urologists and urological units. Statistical processing was done with SPSS for Windows 5.0. Results: In the last 5 years, 501 urologists were licensed at an average age of 37 years; 535 urological units exist; 2,332 doctors practice urology (2,235 males and 97 females) for a ratio of 1 urologist to 24,500 inhabitants. By comparing the ‘entrance’ forecast with the potential ‘exit’, we can hypothesize an annual excess of 80 units. There is no significant correlation between the number of urologists in each structure and the number of inhabitants for each urologist. Conclusions: The present government programme does not take into account continual changes in employment and many other variables when defining the actual need for specialists. For valid predictions, the data we obtain must be updated for at least 5 years.