The provision of high-quality food data presents challenges for developers of health apps. There are no standardized data sources with information on all food products available in Europe. Commercial data sources are expensive and do not allow long-term storage, whereas open data sources from communities often contain inconsistent, duplicate, and incomplete data. In this thesis, methods are presented to load data from multiple sources via extract, transform, and load process into a central food data warehouse and to improve the data quality. Data profiling is used to detect inconsistencies and duplicates. With the help of machine learning methods and ontologies, data is completed and checked for plausibility using similar datasets. Via a specific API, an usage context can send to the central food data warehouse together with the search word to be queried. The API send a response with the food data results which were checked based on the context and provides further information as to whether the quality of the result data is sufficient in the respective context. All developed methods are tested using linear sampled test data.

Due to the physical, psychological, or socioeconomic changes that accompany aging, many people will be affected by geriatric frailty syndrome, which can lead to multimorbidity and premature death. Nutrition counseling is often used to prevent and intervene in frailty syndrome, especially in geriatric rehabilitation. To this end, the consumption behavior of geriatric patients is recorded using paper-based, as well as retrospective memory logs in face-to-face interviews between patients and nutritionists. To simplify this procedure, a digital nutrition diary was developed that is specially adapted to the needs of geriatric patients (>=70 years), enabling them to record their consumption behavior themselves. In an initial study (Study 1), conducted in a geriatric rehabilitation division with twelve subjects (ten male, two female, mean age 79.2 ±5.9 years), feedback about the usability of the digital nutrition diary, and how to improve it, was surveyed. In addition, the usability of an activity tracker and a body composition scale was surveyed to determine whether geriatric patients are generally able to use these devices. In a second study (Study 2), also conducted in the geriatric rehabilitation division, this time with sixteen subjects (ten male, six female, mean age 79.3 ±3.9 years), the usability of the digital nutrition diary was surveyed again to evaluate its modifications based on the feedback from Study 1. In Study 1, the usability rating of the system (0–100) was 82.5 for the activity tracker, 29.71 for the body composition scale, and 51.66 initially for the digital nutrition diary, which increased to 76.41 in Study 2.

Medical Apps are increasingly gaining in importance. But comprehensive testing of the app is difficult and there is no single method. For example, compliance has been ignored so far in most cases. A general remote method has been developed to test the compliance behavior when using medical apps, as well as to take them into account in later treatment. With the help of timestamps at appropriate locations, frequency, times and periods of use can be measured and (automatically) compared with predefined values. Furthermore, this method provides information about the efficiency and the learning phase of the users, as well as the usability, which is of great importance for the evaluation of compliance. With the help of different smartphone sensors, it is possible to evaluate the data quality. By taking other factors into consideration, such as the quality of experience and external circumstances, as well as the determination of data quality, the medical app can be evaluated comprehensively. This allows user profiles to be created . This resulting information can be entered into the treatment and the quality of the treatment can be increased .

Although digitization and artificial intelligence are already being used in many areas, nutritional counseling for frailty patients is largely retrospective and based on analog questionnaires. In order to enable a broad spectrum of frailty patients to independently keep a digital nutrition diary, this paper presents a hybrid input method based on object detection using artificial intelligence in combination with a dynamic and interactive interview mode. The interview mode dynamically queries for possible missing inputs based on the objects detected by the artificial intelligence. The artificial intelligence was trained with open source and specifically for this use case generated data. A study with 21 subjects compared the hybrid approach with four other approaches and mobile applications based on usability, time effort, and detection accuracy. Especially in the area of usability, the hybrid approach came out on top.

Usability tests play an important role in any kind of software, as they limit errors and misunderstandings. Especially in the growing market of medical applications it is indispensable, but time-consuming and expensive. In order to improve the quality of medical applications and remove obstacles for developers, a method has been developed that simplifies testing the usability of mobile medical applications and provides additional data on compliance and effectiveness. Because this test method is remote-controlled and asynchronous, finding examiners is simplified. It also allows more subjects to be found and more data to be collected. This increases user experience and achieves more natural results as study participants act in their natural environment. In order to decide whether the app developed is suitable for this remote testing method, a questionnaire was developed to assist in the decision-making process. The described method will be tested in a study.

The necessity of using food data in mobile health applications is often linked with difficulties. In Europe no standardized and quality-controlled food product databases are accessible. Data from third party sources are often incomplete and have to be checked carefully before use for errors and inconsistencies. The purpose of this approach is to improve data quality and to increase information density by developing a dedicated food data warehouse. By using the extract, transform and load processes known from data warehouse technologies, multiple data sources will be combined, inserted and evaluated. The data is cleaned up by using data profiling techniques. Data mining methods are used to merge the datasets from food composition databases and food product databases to increase information density. The aim is to analyze, if and how Big Data technologies can increase performance of data processing significantly.

As there is no standardized food database with all products available in Europe, many developers of health apps fall back on databases of communities whose quality is often insufficient. In health apps, the quality of the data sets is critical, as poor quality lowers the user's confidence. This paper examines the plausibility of nutrient data from such data sources using similarity analysis, decision support methods and Big Data technology. During a special developed process, the plausibility of the data is to be increased. Finally, the methods used will be evaluated on the basis of test data.

To simplify the evaluation and certification process for mobile medical applications, a methodology for simple testing has been developed. For this purpose, certain actions of the apps are provided with timestamps in order to be able to reproduce the entire behavior of the testers within the scope of the test study. Therefore, the tester does not have to come to a test laboratory to fulfill certain tasks that are set by the test leader. Nevertheless, all main and secondary functions are tested naturally, as is the entire app.With the help of these asynchronous remote tests, the testers can test the medical app in their familiar environment. Thus the test environment is not present and the testers act naturally. The timestamps are not noticed by the testers and require only little additional programming effort. The results are automatically transferred to a database of the developer and evaluated in terms of usability, compliance, risk factors, learning curve and validity. It is possible to automatically compare the timestamp with a previously defined Happy Path and other previously defined reference values.

This paper focuses on integrating food data sources into a central database using extract, transform and load processing and the subsequent data quality enhancement. The obtained data will be transmitted by a food data web service to certain health apps for further use. Furthermore, it is planned to identify inconsistent, incorrect, duplicate and incomplete data using methods of data profiling so that they can be corrected. In order to quantify the data quality purposefully and appropriately, certain quality metrics were used. These metrics were calculated and evaluated using random test data selected from the food data.

The goal of the DiDiER project is to verifiably improve services in the field of dietary counselling. This will be achieved by digitising information to increase counselling intensity and to improve workflows for the service provider. The project will develop an IT-based support system for dietary counselling, covering two use cases, facilitation and support of the work of nutritionists in ambulatory allergological nutrition counselling and of nutritionists involved in the care of geriatric patients, especially of those with frailty. One of the project's significant features is that the user's sensitive data remain under his or her personal control at all times.

One of the most effective current approaches to preventing stroke events is the reduction of lifestyle risk factors, such as unhealthy diet, physical inactivity and smoking. In this study, we assessed the efficacy and usability of the phone-based Computer-aided Prevention System (CAPSYS) in supporting the reduction of lifestyle-related risk factors.

Little is known about the loss of productive workforce or healthy life time due to food allergy or food intolerance. One aim of the BELANA trial (Burdens and Expenses of Living as Adult with Nutrition based Allergy or Intolerance) was to investigate the healthy time lost for work or spare time due to corresponding disease. 314 study participants (≥18) with self-reported food allergies or intolerances were recruited in 2009 by the German Allergy and Asthma Association (DAAB). 247 completed the BELANA questionnaire four times within four-month intervals. The participants had to state whether they were unable to fulfil their daily tasks at work, at home, or at school due to problems with their food allergy or intolerance. Furthermore, they had to indicate the number of affected days. About 43% (average of all single surveys) of all self-reported food allergy and food intolerance suffers reported that they have missed at least one day of work or every day life during the past four months. About 37% of the participants stated to not have experienced such losses. Comparing the two subgroups of food allergic and food intolerant persons, no significant difference could be determined. Similarly, reviewing the actual number of days lost, people from both subgroups accounted for a noticeably high number of lost days: 15.68 days on average during the past four months. Figure 1 exemplarily shows the density distribution of lost workdays for the participants of the fourth iteration of the questionnaire. The group of self-reported food allergy and food intolerance sufferers that complain about the loss of healthy time due to their illness is larger as compared to the group that does not experience this kind of burden. The high number of lost healthy days indicates that the data might be biased towards severe cases of food allergy and intolerance. This will need further investigation through cross-validation with other questionnaire items.

As soon as telemedicine aims at supporting the prevention of ischemic events (e.g., stroke and myocardial infarction), the mere monitoring of vital parameters is not sufficient. Instead, the patients should be supported in their efforts to actively reduce their individual risk factors and to achieve and maintain a healthier lifestyle. The Luxembourg-based CAPSYS project (Computer-Aided Prevention System) aims at combining the advantages of telephone coaching with those of home telemonitoring and with methods of computer-aided decision support in direct contact with the patients. The suitability and user acceptance of the system is currently being evaluated in a first pilot study.

The development and maintenance of a healthy lifestyle (smoking cessation, healthy nutrition, moderate physical exercises etc.) is a major objective concerning the primary and secondary prevention of CVD. CAPSYS is a computer-based lifestyle coaching system, which aims at supporting CVD patients in performing appropriate behavior changes in order to minimize their individual risk factors. Patients can access CAPSYS by dialing a local-rate telephone number and answer to a set of previously known questions concerning their current nutrition, physical activity, blood pressure, smoking etc. In an interactive voice response approach, questions are issued by the system in natural language using a text-to-speech module, and the patient can provide the required values using the phone keypad (DTMF touchtone). Based on the gathered values for each patient, the system automatically generates personalized verbal feedback at runtime and presents it to the patient during the phone dialog. Depending on the individual development of the patient's risk factors, the system feedback can contain advice for improvement, praise for healthy behavior and motivation to pursue a certain goal.

Background People suffering from food allergies must study food ingredients prior to consumption. While the Internet facilitates this information acquisition, little is known about consumer experiences. One aim of the BELANA trial (Burdens and Expenses of Living as Adult with Nutrition based Allergy or Intolerance) was to study the use of, and satisfaction with, Internet information sources by those strongly affected by food allergies.

The availability of food ingredient information in digital form is a major factor in modern information systems related to diet management and health issues. Although ingredient information is printed on food product labels, corresponding digital data is rarely available for the public. In this demo, we present the Mobile Food Information Scanner (MoFIS), a mobile user interface designed to enable users to semi-automatically extract ingredient lists from food product packaging.

Representing a subarea of eHealth and Telemedicine, mHealth solutions are becoming more and more widespread. Food-related conditions, like food hypersensitivities, diabetes, obesity and associated risk factors, pose a major threat to the public worldwide. This paper investigates the variety of mHealth solutions in the area of food-related health conditions and assesses their potentials for the affected end-users, related health professionals and possible health economic impacts. A wealth of mHealth approaches targeted at the area of food and health exist; however, several areas for improvement are revealed. Keywords-Personalized eHealth; mHealth; food-related diseases; diet; nutritional risk factors

The development and maintenance of a healthy lifestyle (smoking cessation, healthy nutrition, moderate physical exercises etc.) is a major objective concerning the primary and secondary prevention of cerebro-and cardiovascular diseases. CAPSYS is a computer-based lifestyle coaching system, which aims at supporting patients in performing appropriate behavior changes in order to minimize their individual risk factors. Patients can access CAPSYS by dialing a local-rate telephone number and answer to a set of previously known questions concerning their current nutrition, physical activity, blood pressure, smoking etc. In an interactive voice response approach, questions are issued by the system in natural language using text-to-speech, and the patient can provide the required values using the phone keypad. Based on the gathered values for each patient, the system automatically generates personalized verbal feedback at runtime and presents it to the patient during the phone dialog. Depending on the individual development of the patient's risk factors, the system feedback can contain advice for improvement, praise for healthy behavior and motivation to pursue a certain goal.The automated feedback is generated based on rules derived from the guidelines of the Luxembourg Conseil Scientifique for prevention of cerebro-cardiovascular diseases and obesity. CAPSYS has been developed by researchers from the Public Research Centre (CRP) Henri Tudor in Luxembourg in collaboration with neurologists from the Centre Hospitalier de Luxembourg (CHL). Currently, the user acceptance and effectiveness of the system is being evaluated in a six-month randomized controlled study with eligible participants recruited at CHL's neurology department.