Background To assess levels of kinesiophobia (fear of movement) in patients hospitalized for acute cardiovascular disease. Hypothesis Increased levels of kinesiophobia can be found in subjects hospitalized for acute cardiovascular disease. Methods Seventy‐four consecutive patients admitted for acute coronary syndrome and 58 for acute heart failure were enrolled in the study and assessed by the Tampa Scale for the evaluation of kinesiophobia. Subjects were compared with a reference population with stable coronary artery disease and healthy controls. Results No significant differences were found between acute coronary syndrome and acute heart failure in terms of kinesiophobia, even considering the rates of high kinesiophobia (Tampa score >37) and the 4 groups of questionnaire items (danger, fear, avoidance, dysfunction). Differences, however, were significant comparing our population with an historical population of subjects with stable coronary artery disease and controls (43 ± 5 vs 35 ± 7 vs 33 ± 6, P < 0.0001 in both cases). A significant correlation was found between the grade of kinesiophobia in the Tampa Scale and the age of subjects (r = 0.27, P = 0.001) and inversely with level of education (r = −0.33, P < 0.0001). Conclusions Increased levels of kinesiophobia can be found in subjects hospitalized for acute cardiovascular disease. Kinesiophobia is related to age and education. Kinesiophobia should be carefully considered in subjects hospitalized in acute cardiac care units.

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Abstract-Historically, medical imaging repositories have been supported by indoor infrastructures. However, the amount of diagnostic imaging procedures has continuously increased over the last decades, imposing several challenges associated with the storage volume, data redundancy and availability. Cloud platforms are focused on delivering hardware and software services over the Internet, becoming an appealing solution for repository outsourcing. Although this option may bring financial and technological benefits, it also presents new challenges. In medical imaging scenarios, communication latency is a critical issue that still hinders the adoption of this paradigm.This paper proposes an intelligent Cloud storage gateway that optimizes data access times. This is achieved through a new cache architecture that combines static rules and pattern recognition for eviction and prefetching.The evaluation results, obtained through simulations over a real-world dataset, show that cache hit ratios can reach around 80%, leading reductions of image retrieval times by over 60%.The combined use of eviction and prefetching policies proposed can significantly reduce communication latency, even when using a small cache in comparison to the total size of the repository. Apart from the performance gains, the proposed system is capable of adjusting to specific workflows of different institutions.

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