Wearable computing has long been described as the solution to many health challenges because of its ability to be ever present and accessible to patients. The market for these technologies is immature and is still developing, offering more possibilities for health management every year. In fact, benefits for diabetes management were recently cited in an editorial of Journal of Diabetes Science and Technology. 1 Through a feasibility study, our research group aimed to explore the interoperability and usability of a suitable wearable computing device in conjunction with a developed smartphone application, and initiate the evaluation of such systems for use in diabetes self-management. Review of current devices and platforms illustrates the context of this study. While monitoring systems such as continuous glucose monitors (CGMs) and insulin pumps are made widely available through the medical system, wearable computers have not yet reached such common status in the diabetes self-management market. Wearables include smart eyewear, such as Google Glass (Google Inc, Mountain View, CA), as well as wristbands and mobile phone apps, which offer more computing functionalities including sleep pattern analysis, elevation climbed, and goal tracking. Evaluation of Mobile Phones and Applications While all of these are expected to offer useful health applications, mobile phones and applications (apps), in particular, 567708D STXXX10.

A growing number of advanced intensity modulated treatment techniques is becoming available. In this study, the specific strengths and weaknesses of four techniques, static and dynamic multileaf collimator (MLC), conventional linac-based IMRT, helical tomotherapy (HT), and spot-scanning proton therapy (IMPT) are investigated in the framework of biological, EUD-based dose optimization. All techniques were implemented in the same in-house dose optimization tool. Monte Carlo dose computation was used in all cases. All dose-limiting, normal tissue objectives were treated as hard constraints so as to facilitate comparability. Five patient cases were selected to offer each technique a chance to show its strengths: a deep-seated prostate case (for 15 MV linac-based IMRT), a pediatric case (for IMPT), an extensive head-and-neck case (for HT), a lung tumor (for HT), and an optical neurinoma (for noncoplanar linac-based IMRT with a miniMLC). The plans were compared by dose statistics and equivalent uniform dose metrics. All techniques delivered results that were comparable with respect to target coverage and the most dose-limiting normal tissues. Static MLC IMRT struggled to achieve sufficient target coverage at the same level of dose homogeneity in the lung case. IMPT gained the greatest advantage when lung sparing was important, but did not significantly reduce the risk of nearby organs. Tomotherapy and dynamic MLC IMRT showed mostly the same performance. Despite the apparent conceptual differences, all four techniques fare equally well for standard patient cases. The absence of relevant differences is in part due to biological optimization, which offers more freedom to shape the dose than do, e.g., dose volume histogram constraints. Each technique excels for certain classes of highly complex cases, and hence the various modalities should be viewed as complementary, rather than competing.

Recently, several techniques have been developed to improve the quality of computed tomography (CT) images of the thoracic and abdominal region that are degraded by the interference of the scanning process and respiration. Several devices for respiratory-correlated CT are available for clinical usage. They are based on the synchronization of the acquired CT image data with the respiratory motion using a signal from an external respiratory monitoring system. In this work, some practical aspects of clinical implementation of the multi-slice 4D CT scanner Somatom Sensation Open (Siemens Medical Solutions, Erlangen, Germany) equipped with a respiratory gating system (RGS) AZ-733V (Anzai Medical, Tokyo, Japan) are discussed. A new algorithm developed for automatic respiratory phase determination needed for the reconstruction of the 4D CT images is presented.

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