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Prostate cancer (PCa) is the most prevalent among men, and psychological symptoms may affect many patients. This study aims to describe the prevalence of probable anxiety and depression before PCa treatments and after one year and to identify sociodemographic and clinical factors associated with these outcomes. Between February 2018 and March 2020, 292 patients recently diagnosed with PCa were recruited at the Instituto Português de Oncologia—Porto. The Hospital Anxiety and Depression Scale (HADS) was used to define probable anxiety and depression (cutoff = 11). The prevalence of probable anxiety remained stable from baseline to one year (7.8% vs. 8.5%, p = 0.866) while there was an increase in probable depression (3.1% vs. 6.8%, p = 0.012). After one year, probable depression persisted in 55.6% of patients with probable depression at baseline and 47.8% of those with probable anxiety at the first assessment had normal anxiety scores. At baseline, anxiety was more frequent among dwellers in rural areas (adjusted odds ratio—aOR, 95%CI: 2.80, 0.91–8.58) and less frequent in patients with body mass index 25–29.9 kg/m2 (aOR, 95%CI: 0.33, 0.12–0.91) compared to 18.5–24.9 Kg/m2, while those living alone had higher odds of depression (aOR, 95%CI: 6.35, 1.43–28.30). The frequency of anxiety and depression fluctuated during the course of treatment. Monitoring these symptoms would identify the most affected patients, contributing for a better use of mental health services.

A typical workflow for a distributed application involves a large number of resources that can fail, including network, hardware and software components. Even when monitoring information from all these components is accessible, it is hard to determine how anomalies and failures during the application execution are related to a given workflow component. However the capability of receiving and interpreting intermediate results and interacting with applications plays a significant role for developing scientific experiments. Considering the complexity of implementation of distributed systems and the large scope of issues the monitoring system should cover, what analysis and planning is required to implement effective scientific Grid workflow monitoring? We propose a multi-layer approach which focuses on a clear identification of the workflow-level monitoring abstractions. Through a clear separation between higher and lower level mechanisms, this approach will allow the specification of application monitoring requirements at workflow level, and their implementation upon distinct monitoring technologies, including the ones supported by existing Grid middleware.

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