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Background: Imprecise carbohydrate counting as a measure to guide the treatment of diabetes may be a source of errors resulting in problems in glycemic control. Exact measurements can be tedious, leading most patients to estimate their carbohydrate intake. In the presented pilot study a smartphone application (BEAR), that guides the estimation of the amounts of carbohydrates, was used by a group of diabetic patients. Methods: Eight adult patients with diabetes mellitus type 1 were recruited for the study. At the beginning of the study patients were introduced to BEAR in sessions lasting 45 minutes per patient. Patients redraw the real food in 3D on the smartphone screen. Based on a selected food type and the 3D form created using BEAR an estimation of carbohydrate content is calculated. Patients were supplied with the application on their personal smartphone or a loaner device and were instructed to use the application in real-world context during the study period. For evaluation purpose a test measuring carbohydrate estimation quality was designed and performed at the beginning and the end of the study. Results: In 44% of the estimations performed at the end of the study the error reduced by at least 6 grams of carbohydrate. This improvement occurred albeit several problems with the usage of BEAR were reported. Conclusions: Despite user interaction problems in this group of patients the provided intervention resulted in a reduction in the absolute error of carbohydrate estimation. Intervention with smartphone applications to assist carbohydrate counting apparently results in more accurate estimations.

Patients with Frozen Shoulder suffer from a decreased mobility and pain. Exercise-based physiotherapy is a common treatment and patients mostly perform the exercises at home. Correct exercise performance and compliance are the main issues in home-based therapy of Frozen Shoulder patients. To support patients diagnosed with Frozen Shoulder, a multimodal 3D smartphone app was designed, developed and evaluated. Additional to ten potential users, one physician, five physiotherapists, three computer scientists, two 3D artists and one HCI specialist were involved in the co-creation process. The app was evaluated by five patients during a three-week pilot study, which showed the feasibility of our approach. Exercise correctness, usage of multimodal instructions and user satisfaction were analyzed. Exercise correctness was nearly perfect and the interactive 3D animation was used for exercise instructions. Satisfaction using the app was rated very high according to SUS score. The results confirm that the co-creation process led to an effective, highly satisfactory and actually used system.

The accurate assessment of nutrition information is a challenging task, but crucial for people with certain diseases, such as diabetes. An important part of the assessment of nutrition information is portion estimation, i.e. volume estimation. Given the volume and the food type, the nutrition information can be computed on the basis of the food type specific nutrition density. Recently mobile devices with depth sensors have been made available for the public (Google's project tango platform). In this work, an app for mobile devices with a depth sensor is presented which assists users in portion estimation. Furthermore, we present the design of a user study for the app and preliminary results.

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. In an earlier study we developed CheckShift, a program for performing this task automatically. Now we spent substantial effort in improving the running time of the CheckShift algorithm, which resulted in an running time decrease of 90%, thereby achieving equivalent quality to the former version of CheckShift. The reason for the running time decrease is twofold. Firstly we improved the search for the optimal re-referencing offset considerably. Secondly, as CheckShift is based on a secondary structure prediction from the amino acid sequence (formally PsiPred was used), we evaluated a wide range of available secondary structure prediction programs focusing on the special needs of the CheckShift algorithm. The results of this evaluation prove empirically that we can use faster secondary structure prediction programs than PsiPred without sacrificing CheckShift's accuracy. Very recently Wang and Markley (2009) gave a small list of extreme outliers of the former version of the CheckShift web-server. Those were due to the empirical reduction of the search space implemented in the old version. The new version of CheckShift now gives very similar results to RefDB and LACS for all outliers mentioned in Table 1 of Wang and Markley (2009).

We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods.

An important quantity that is measured in NMR spectroscopy is the chemical shift. The interpretation of these data is mostly done by human experts.We present a method, named SimShiftDB, which identifies structural similarities between a protein of unknown structure and a database of resolved proteins based on chemical shift data. To evaluate the performance of our approach, we use a small but very reliable test set and compare our results to those of 123D and TALOS. The evaluation shows that SimShiftDB outperforms 123D in the majority of cases. For a significant part of the predictions made by TALOS, our method strongly reduces the error. SimShiftDB also assesses the statistical significance of each similarity identified.

The construction of a consistent protein chemical shift database is an important step toward making more extensive use of this data in structural studies. Unfortunately, progress in this direction has been hampered by the quality of the available data, particularly with respect to chemical shift referencing, which is often either inaccurate or inconsistently annotated. Preprocessing of the data is therefore required to detect and correct referencing errors. We have developed a program for performing this task, based on the comparison of reported and expected chemical shift distributions. This program, named CheckShift, does not require additional data and is therefore applicable to data sets where structures are not available. Therefore CheckShift provides the possibility to re-reference chemical shifts prior to their use as structural constraints.