Mobile Health interventions to enhance physical activity. Overview, methodological considerations, and just-in-time adaptive interventions

Physical activity has far-reaching health benefits and contributes to the prevention of noncommunicable diseases like cardiovascular disease, cancer, and diabetes. Today\u27s level of physical activity; however, is below the recommendations of e.g. the World Health Organization for all age groups. This amount of physical inactivity (i.e. not meeting physical activity guidelines) contributes to the rising cases of noncommunicable diseases and is responsible for over 7% of all-cause deaths along with a huge economic toll on the society. Recently, the COVID-19 crisis aggravated matters as many opportunities to be physically active were limited and sports clubs were temporarily closed. Today, effective interventions with a large reach are required to facilitate health behavior change towards more physical activity in the population. Here, even minor changes towards a more physically active lifestyle e.g. going for a daily ten-minute walk or interrupting prolonged physical inactivity can accumulate valuable health benefits over time. There are a variety of evidence-based interventions for different settings which range from individual or group-based face-to-face interventions to digital interventions. While the former is well established in today\u27s physical activity promotion, especially for rehabilitation, the latter is especially promising to promote physical activity on a broad scale due to the availability, fast-evolving technological progress, and ease of use of digital devices in modern society. Digital interventions for health behavior change can be delivered on desktop personal computers (e.g. via DVD), over the internet (e.g. on websites), or on mobile devices (e.g. via text message or mobile application). As nearly every household worldwide has access to and experience with at least one of those devices, the potential reach and cost-efficiency of such interventions are promising. Here, the use of information and communication technologies for health, in general, is defined as electronic health while every health practice supported by mobile devices is defined as mobile health. Recently, technological advances lead to the development of smaller, more convenient, and accurate devices to continuously measure physical activity (e.g. energy expenditure, step count, and classification of physical exertion), physiological (e.g. heart rate, blood sugar, and cortisol), and report psychological (e.g. valence, energetic arousal, and calmness) parameters. This opens up new perspectives using multilevel modeling in longitudinal designs to distinguish between within- and between-person effects and allows for a higher grade of individualization of interventions. One intervention type which greatly benefits from these continuous measurements and the technological advances is just-in-time adaptive interventions. These interventions aim to deliver interventional content (e.g. motivation to be physically active) during the most promising time for the desired health behavior (i.e. physical activity) or during the most vulnerable time for unhealthy behavior (i.e. inactivity) and aim to maximize the usefulness of the intervention while minimizing participant burden. To do so, they rely on high-resolution data to depict opportune moments to deliver the intervention content. Recent progress with machine learning processes also benefits just-in-time adaptive interventions by offering sophisticated decision-making algorithms which can be guided by participants\u27 behavior and preferences. Previous studies on electronic and mobile interventions found heterogenic results for the effectiveness of digital health interventions for physical activity promotion. Here, evidence- and theory-based interventions which are guided by behavior change techniques (e.g. goal-setting or demonstration of behavior) were associated with higher intervention effectiveness. Furthermore, including the social context (e.g. peers, school, work, or family) in the interventions can be beneficial but it is important to distinguish between e.g. collaborative vs competitive settings based on participants\u27 preferences. Finally, a high degree of individualization delivered by e.g. just-in-time adaptive interventions can enhance the effectiveness of mobile health interventions. However, the importance of the different interventional and contextual facets along with additional influences on the evaluation of the effectiveness remains unclear in the fast-developing field of electronic and mobile health behavior change interventions for children, adolescents, and adults. To help close the gap between technological advances and the state of the research in electronic and mobile health interventions for physical activity promotion, this thesis aimed to 1) provide an overview of the effectiveness of electronic and mobile health interventions regarding physical activity promotion and 2) delve into important considerations and research gaps depicted by the overview (i.e. the choice of a measurement tool for physical activity and just-in-time adaptive interventions). In our first paper, we conducted an umbrella review to summarize the evidence on the overall effectiveness of electronic and mobile health interventions along with the association of the key facets of theoretical foundation, behavior change techniques, social context, and just-in-time adaptive interventions with effectiveness. Derived from the eleven included reviews (182 original studies) we found significant benefits in favor of the intervention group (vs. control or over time) in the majority of interventions (59%). Here, the use of theoretical foundations and behavior change techniques were associated with higher effectiveness, the social context was often reported but not evaluated and just-in-time adaptive interventions were not included in any of the studies. One frequently reported shortcoming was the difficulty do compare self-reported and device-based measured results between studies. These findings suggest the potential effectiveness of digital interventions which is very likely facilitated by the key facets. Moreover, these findings helped us to determine promising but understudied facets of intervention effectiveness (i.e. just-in-time adaptive interventions) and depict frequently reported methodological issues (i.e. comparability of different measurement tools) which we could address within our thesis. In our second paper, we explored the reliability, comparability, and stability of self-reported (i.e. questionnaire and physical activity diary) vs. device-based measured physical activity (i.e. analyzed using 10-second and 60-second epochs) in adults and children. We included two independent measurement weeks from 32 adults and 32 children in the control group of the SMARTFAMILY trial to investigate if the differences between measurement tools were systematic over time. Here, participants wore an accelerometer on the right hip during daily life and completed a daily physical activity diary for seven consecutive days. Additionally, the international physical activity questionnaire was completed by participants at the end of each week. Results indicated non-systematic differences between the measurement tools (up to four-fold). Higher associations between the measurement tools were found for moderate than for vigorous physical activity and the results differed between children and adults. These results confirm the importance of carefully considering the measurement tool to be suitable for the research question and target group and the very limited comparability between different measurement tools. Additionally, the differences within accelerometer-derived results (10-second epochs vs. 60-second epochs) point to the need for comprehensive reporting for each measurement tool to compare and replicate the results. In our third paper, we summarized previous frameworks of just-in-time adaptive interventions and pointed out opportunities and challenges within this research field. We combined recommendations of three previous frameworks and refined that just-in-time adaptive interventions should 1) correspond to real-time needs; 2) adapt to input data; 3) be system-triggered. This can be enhanced by 4) be goal-oriented; and 5) be customized to user preferences. By doing so, just-in-time adaptive interventions can achieve a high degree of individualization which is closely fitted to each individual. The main challenge hereby remains the opportune moment identification (i.e. the exact moment when participants are either likely to engage in unhealthy behavior or when they face opportunities to perform healthy behaviors) to timely deliver intervention content. This can be explored using ambulatory assessments and assessing the context of the behavior. The decision-making process can be enhanced by machine learning algorithms. These results guided the reporting and design of the examinations included in our fourth and fifth papers. In our fourth paper, we evaluated the importance of engaging with a just-in-time adaptive intervention triggered after a period of physical inactivity. For this secondary data analysis, 47 adults and 33 children were included in the analysis who wore an accelerometer on the right hip and used our SMARTFAMILY2.0 application during the three-week intervention period of the SMARTFAMILY2.0 trial. Here, we analyzed 907 just-in-time adaptive intervention triggers and compared step and metabolic equivalent count in the hour after occasions when participants answered the trigger (i.e. responded to the question regarding their previous physical inactivity) within 60 minutes ("engaged" condition) with the hour after occasions when they did not answer the trigger within 60 minutes ("not engaged" condition) in the mobile application. Results indicated significantly higher metabolic equivalent and step count for the "engaged" condition within-persons. This shows that if a person engaged with a trigger within 60 minutes, he or she showed significantly higher physical activity in the following hour compared to when the same person did not engage with the trigger. This expands previous research about participants\u27 engagement with the intervention and the importance of an opportune moment identification to enhance this engagement. In our fifth paper, we explored the association of sleep quality and core affect with physical activity during a mobile health intervention period. Based on the same intervention period reported in the fourth paper, but with different inclusion criteria for the data (e.g. minimum wear time of the accelerometer for 8 hours per day instead of 80% of the hour of interest), daily accumulated self-rated mental state was compared to step count and minutes of moderate-to-vigorous physical activity for 49 adults and 40 children in a secondary data analysis. Overall, 996 measurement days of the participants were included in this analysis. Our results showed that higher reported valence and energetic arousal values were associated with more physical activity, while higher reported calmness values were associated with less physical activity within-persons on the same day. No distinct association was found between sleep quality and physical activity. Our results confirm previous ambulatory assessment studies and we suggest that within-person associations of core affect should be considered when designing physical activity interventions for both children and adults. Additionally, core affect might be a promising consideration for opportune moment identifications in just-in-time adaptive interventions to evaluate the feasibility and causality of targeting changes in e.g. valence to improve subsequent and daily physical activity of participants using micro-randomized trials. Based on the current state of knowledge, our results above address important research gaps depicted by our overview in the field of digital interventions for physical activity promotion. One example is the understudied area of just-in-time adaptive interventions for which we provided a framework, evaluated the effect of engaging with such interventions on subsequent physical activity, and explored core affect and sleep quality as facilitators of physical activity behavior. With these findings in mind, we discussed important considerations to progress future mobile health studies for physical activity promotion in general, and just-in-time adaptive interventions in particular at the end of this work. Finally, we aimed to transfer this knowledge into a proposal for designing a just-in-time adaptive intervention in the special group of participants at risk for or with knee osteoporosis who could specifically benefit from this highly individualized approach

The Convergence of Human and Artificial Intelligence on Clinical Care - Part I

This edited book contains twelve studies, large and pilots, in five main categories: (i) adaptive imputation to increase the density of clinical data for improving downstream modeling; (ii) machine-learning-empowered diagnosis models; (iii) machine learning models for outcome prediction; (iv) innovative use of AI to improve our understanding of the public view; and (v) understanding of the attitude of providers in trusting insights from AI for complex cases. This collection is an excellent example of how technology can add value in healthcare settings and hints at some of the pressing challenges in the field. Artificial intelligence is gradually becoming a go-to technology in clinical care; therefore, it is important to work collaboratively and to shift from performance-driven outcomes to risk-sensitive model optimization, improved transparency, and better patient representation, to ensure more equitable healthcare for all

New Indicators for the Assessment and Prevention of Noise Nuisance

This Special Issue was launched to promote a subject that is deserving of more attention: the study of new metrics, indicators or evaluation methods for noise exposure, and the relationship of noise with annoyance or other health effects, thus not relying only on an average noise exposure measure. This Special Issue on the theme of the New Indicators for the Assessment and Prevention of Noise Nuisance has attracted the interest of authors from all over the world, with the publication of two reviews and two communications, as well as original research papers. Progress has been made in the investigated topic; however, it is still necessary to increase the awareness of the population, both in geographical terms and for workers in specific sectors, such as the marine industry. It emerged that it is essential to carry out future studies that distinguish better between different sound sources with respect to their sound quality in terms of frequency, time pattern (fluctuation, emergence), and psychoacoustic indices, because a differential human reaction to sound sources is increasingly evident. More longitudinal studies are required. However, cross-sectional studies employing a more detailed soundscape description (including background) by competing sound indices are also useful to further the required knowledge to understand the human response in terms of the broad spectrum of potential adverse effects on health and quality of life

Privacy-preserving data analytics in cloud computing

The evolution of digital content and rapid expansion of data sources has raised the need for streamlined monitoring, collection, storage and analysis of massive, heterogeneous data to extract useful knowledge and support decision-making mechanisms. In this context, cloud computing o↵ers extensive, cost-e↵ective and on demand computing resources that improve the quality of services for users and also help service providers (enterprises, governments and individuals). Service providers can avoid the expense of acquiring and maintaining IT resources while migrating data and remotely managing processes including aggregation, monitoring and analysis in cloud servers. However, privacy and security concerns of cloud computing services, especially in storing sensitive data (e.g. personal, healthcare and financial) are major challenges to the adoption of these services. To overcome such barriers, several privacy-preserving techniques have been developed to protect outsourced data in the cloud. Cryptography is a well-known mechanism that can ensure data confidentiality in the cloud. Traditional cryptography techniques have the ability to protect the data through encryption in cloud servers and data owners can retrieve and decrypt data for their processing purposes. However, in this case, cloud users can use the cloud resources for data storage but they cannot take full advantage of cloud-based processing services. This raises the need to develop advanced cryptosystems that can protect data privacy, both while in storage and in processing in the cloud. Homomorphic Encryption (HE) has gained attention recently because it can preserve the privacy of data while it is stored and processed in the cloud servers and data owners can retrieve and decrypt their processed data to their own secure side. Therefore, HE o↵ers an end-to-end security mechanism that is a preferable feature in cloud-based applications. In this thesis, we developed innovative privacy-preserving cloud-based models based on HE cryptosystems. This allowed us to build secure and advanced analytic models in various fields. We began by designing and implementing a secure analytic cloud-based model based on a lightweight HE cryptosystem. We used a private resident cloud entity, called ”privacy manager”, as an intermediate communication server between data owners and public cloud servers. The privacy manager handles analytical tasks that cannot be accomplished by the lightweight HE cryptosystem. This model is convenient for several application domains that require real-time responses. Data owners delegate their processing tasks to the privacy manager, which then helps to automate analysis tasks without the need to interact with data owners. We then developed a comprehensive, secure analytical model based on a Fully Homomorphic Encryption (FHE), that has more computational capability than the lightweight HE. Although FHE can automate analysis tasks and avoid the use of the privacy manager entity, it also leads to massive computational overhead. To overcome this issue, we took the advantage of the massive cloud resources by designing a MapReduce model that massively parallelises HE analytical tasks. Our parallelisation approach significantly speeds up the performance of analysis computations based on FHE. We then considered distributed analytic models where the data is generated from distributed heterogeneous sources such as healthcare and industrial sensors that are attached to people or installed in a distributed-based manner. We developed a secure distributed analytic model by re-designing several analytic algorithms (centroid-based and distribution-based clustering) to adapt them into a secure distributed-based models based on FHE. Our distributed analytic model was developed not only for distributed-based applications, but also it eliminates FHE overhead obstacle by achieving high efficiency in FHE computations. Furthermore, the distributed approach is scalable across three factors: analysis accuracy, execution time and the amount of resources used. This scalability feature enables users to consider the requirements of their analysis tasks based on these factors (e.g. users may have limited resources or time constrains to accomplish their analysis tasks). Finally, we designed and implemented two privacy-preserving real-time cloud-based applications to demonstrate the capabilities of HE cryptosystems, in terms of both efficiency and computational capabilities for applications that require timely and reliable delivery of services. First, we developed a secure cloud-based billing model for a sensor-enabled smart grid infrastructure by using lightweight HE. This model handled billing analysis tasks for individual users in a secure manner without the need to interact with any trusted parties. Second, we built a real-time secure health surveillance model for smarter health communities in the cloud. We developed a secure change detection model based on an exponential smoothing technique to predict future changes in health vital signs based on FHE. Moreover, we built an innovative technique to parallelise FHE computations which significantly reduces computational overhead

Modern Telemetry

Telemetry is based on knowledge of various disciplines like Electronics, Measurement, Control and Communication along with their combination. This fact leads to a need of studying and understanding of these principles before the usage of Telemetry on selected problem solving. Spending time is however many times returned in form of obtained data or knowledge which telemetry system can provide. Usage of telemetry can be found in many areas from military through biomedical to real medical applications. Modern way to create a wireless sensors remotely connected to central system with artificial intelligence provide many new, sometimes unusual ways to get a knowledge about remote objects behaviour. This book is intended to present some new up to date accesses to telemetry problems solving by use of new sensors conceptions, new wireless transfer or communication techniques, data collection or processing techniques as well as several real use case scenarios describing model examples. Most of book chapters deals with many real cases of telemetry issues which can be used as a cookbooks for your own telemetry related problems

Detection, Modelling and Visualisation of Georeferenced Emotions from User-Generated Content

In recent years emotion-related applications like smartphone apps that document and analyse the emotions of the user, have become very popular. But research also can deal with human emotions in a very technology-driven approach. Thus space-related emotions are of interest as well which can be visualised cartographically and can be captured in different ways. The research project of this dissertation deals with the extraction of georeferenced emotions from the written language in the metadata of Flickr and Panoramio photos, thus from user-generated content, as well as with their modelling and visualisation. Motivation is the integration of an emotional component into location-based services for tourism since only factual information is considered thus far although places have an emotional impact. The metadata of those user-generated photos contain descriptions of the place that is depicted within the respective picture. The words used have affective connotations which are determined with the help of emotional word lists. The emotion that is associated with the particular word in the word list is described on the basis of the two dimensions ‘valence’ and ‘arousal’. Together with the coordinates of the respective photo, the extracted emotion forms a georeferenced emotion. The algorithm that was developed for the extraction of these emotions applies different approaches from the field of computer linguistics and considers grammatical special cases like the amplification or negation of words. The algorithm was applied to a dataset of Flickr and Panoramio photos of Dresden (Germany). The results are an emotional characterisation of space which makes it possible to assess and investigate specific features of georeferenced emotions. These features are especially related to the temporal dependence and the temporal reference of emotions on one hand; on the other hand collectively and individually perceived emotions have to be distinguished. As a consequence, a place does not necessarily have to be connected with merely one emotion but possibly also with several. The analysis was carried out with the help of different cartographic visualisations. The temporal occurrence of georeferenced emotions was examined detailed. Hence the dissertation focuses on fundamental research into the extraction of space-related emotions from georeferenced user-generated content as well as their visualisation. However as an outlook, further research questions and core themes are identified which arose during the investigations. This shows that this subject is far from being exhausted.:Statement of Authorship I Acknowledgements II Abstract III Zusammenfassung V Table of Contents VII List of Figures XI List of Tables XIV List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Research Questions 3 1.3 Thesis Structure 4 1.4 Underlying Publications 4 2 State of the Art 6 2.1 Emotions 6 2.1.1 Definitions and Terms 6 2.1.2 Emotion Theories 7 2.1.2.1 James-Lange Theory 9 2.1.2.2 Two-Factor Theory 9 2.1.3 Structuring Emotions 9 2.1.3.1 Dimensional Approaches 10 2.1.3.2 Basic Emotions 11 2.1.3.3 Empirical Similarity Categories 12 2.1.4 Acquisition of Emotions 14 2.1.4.1 Verbal Procedures 14 2.1.4.2 Non-Verbal Procedures 14 2.1.5 Relation between Emotions and Places 15 2.1.6 Emotions in Language 17 2.1.7 Affect Analysis and Sentiment Analysis 20 2.2 User-Generated Content 22 2.2.1 Definition and Characterisation 22 2.2.2 Advantages and Disadvantages 23 2.2.3 Tagging 24 2.2.4 Inaccuracies 28 2.2.5 Flickr and Panoramio 29 2.2.5.1 Flickr 30 2.2.5.2 Panoramio 31 2.3 Related Work on Georeferenced Emotions 32 2.3.1 Emotional Data Resulting from Biometric Measurements 33 2.3.1.1 Bio Mapping 33 2.3.1.2 EmBaGIS 34 2.3.1.3 Ein emotionales Kiezportrait 35 2.3.2 Emotional Data Resulting from Empirical Surveys 35 2.3.2.1 EmoMap 35 2.3.2.2 WiMo 36 2.3.2.3 ECDESUP 37 2.3.2.4 Map of World Happiness 38 2.3.2.5 Emotional Study of Yeongsan River Basin 39 2.3.3 Emotional Data Resulting from User-Generated Content 40 2.3.3.1 Emography 40 2.3.3.2 Twittermood 40 2.3.3.3 Tweetbeat 42 2.3.3.4 Beautiful picture of an ugly place 42 2.3.4 Visualisation in the Related Work 43 3 Methods 45 3.1 Approach for Extracting Georeferenced Emotions from the Metadata of Flickr and Panoramio Photos 45 3.2 Implemented Algorithm 45 3.3 Grammatical Special Cases 47 3.3.1 Degree Words 48 3.3.2 Negation 52 3.3.2.1 Syntactic Negation in English Language 55 3.3.2.2 Syntactic Negation in German Language 57 3.3.3 Modification of Words Affected by Grammatical Special Cases 60 4 Visualisation and Analysis of Extracted Georeferenced Emotions 62 4.1 Data Basis 62 4.2 Density Maps 67 4.3 Inverse Distance Weight 71 4.4 3D Visualisation 73 4.5 Choropleth Mapping 74 4.6 Point Symbols 78 4.7 Impact of Considering Grammatical Special Cases 80 5 Investigation in Temporal Aspects 85 5.1 Annually Occurrence of Emotions 85 5.2 Periodic Events 87 5.3 Single Events 91 5.4 Dependence of Georeferenced Emotions on Different Periods of Time 93 5.4.1 Seasons 95 5.4.2 Months 96 5.4.3 Weekdays 98 5.4.4 Times of Day 99 5.5 Potentials and Limits of Temporal Analyses 99 6 Discussion 100 6.1 Evaluation 100 6.2 Weaknesses and Problems 102 7 Conclusions and Outlook 105 7.1 Answers to the Research Questions 105 7.2 Outlook and Future Work 107 8 Bibliography 112 Appendices XVIIn den letzten Jahren sind emotionsbezogene Anwendungen, wie Apps, die die Emotionen des Nutzers dokumentieren und analysieren, sehr populär geworden. Ebenfalls in der Forschung sind Emotionen in einem sehr technologiegetriebenen Ansatz ein Thema. So auch ortsbezogene Emotionen, die sich somit kartographisch darstellen lassen und auf verschiedene Art und Weisen gewonnen werden können. Das Forschungsvorhaben der Dissertation befasst sich mit der Extraktion von georeferenzierten Emotionen aus geschriebener Sprache unter Verwendung von Metadaten verorteter Flickr- und Panoramio-Fotos, d.h. aus nutzergenerierten Inhalten, sowie deren Modellierung und Visualisierung. Motivation hierfür ist die Einbindung einer emotionalen Komponente in ortsbasierte touristische Dienste, da diese bisher nur faktische Informationen berücksichtigen, obwohl Orte durchaus eine emotionale Wirkung haben. Die Metadaten dieser nutzergenerierten Inhalte stellen Beschreibungen des auf dem Foto festgehaltenen Ortes dar. Die dafür verwendeten Wörter besitzen affektive Konnotationen, welche mit Hilfe emotionaler Wortlisten ermittelt werden. Die Emotion, die mit dem jeweiligen Wort in der Wortliste assoziiert wird, wird anhand der zwei Dimensionen Valenz und Erregung beschrieben. Die extrahierten Emotionen bilden zusammen mit der geographischen Koordinate des jeweiligen Fotos eine georeferenzierte Emotion. Der zur Extraktion dieser Emotionen entwickelte Algorithmus bringt verschiedene Ansätze aus dem Bereich der Computerlinguistik zum Einsatz und berücksichtigt ebenso grammatikalische Sonderfälle, wie Intensivierung oder Negation von Wörtern. Der Algorithmus wurde auf einen Datensatz von Flickr- und Panoramio-Fotos von Dresden angewendet. Die Ergebnisse stellen eine emotionale Raumcharakterisierung dar und ermöglichen es, spezifische Eigenschaften verorteter Emotionen festzustellen und zu untersuchen. Diese Eigenschaften beziehen sich sowohl auf die zeitliche Abhängigkeit und den zeitlichen Bezug von Emotionen, als auch darauf, dass zwischen kollektiv und individuell wahrgenommenen Emotionen unterschieden werden muss. Das bedeutet, dass ein Ort nicht nur mit einer Emotion verbunden sein muss, sondern möglicherweise auch mit mehreren. Die Auswertung erfolgte mithilfe verschiedener kartographischer Visualisierungen. Eingehender wurde das zeitliche Auftreten der ortsbezogenen Emotionen untersucht. Der Fokus der Dissertation liegt somit auf der Grundlagenforschung zur Extraktion verorteter Emotionen aus georeferenzierten nutzergenerierten Inhalten sowie deren Visualisierung. Im Ausblick werden jedoch weitere Fragestellungen und Schwerpunkte genannt, die sich im Laufe der Untersuchungen ergeben haben, womit gezeigt wird, dass dieses Forschungsgebiet bei Weitem noch nicht ausgeschöpft ist.:Statement of Authorship I Acknowledgements II Abstract III Zusammenfassung V Table of Contents VII List of Figures XI List of Tables XIV List of Abbreviations XV 1 Introduction 1 1.1 Motivation 1 1.2 Research Questions 3 1.3 Thesis Structure 4 1.4 Underlying Publications 4 2 State of the Art 6 2.1 Emotions 6 2.1.1 Definitions and Terms 6 2.1.2 Emotion Theories 7 2.1.2.1 James-Lange Theory 9 2.1.2.2 Two-Factor Theory 9 2.1.3 Structuring Emotions 9 2.1.3.1 Dimensional Approaches 10 2.1.3.2 Basic Emotions 11 2.1.3.3 Empirical Similarity Categories 12 2.1.4 Acquisition of Emotions 14 2.1.4.1 Verbal Procedures 14 2.1.4.2 Non-Verbal Procedures 14 2.1.5 Relation between Emotions and Places 15 2.1.6 Emotions in Language 17 2.1.7 Affect Analysis and Sentiment Analysis 20 2.2 User-Generated Content 22 2.2.1 Definition and Characterisation 22 2.2.2 Advantages and Disadvantages 23 2.2.3 Tagging 24 2.2.4 Inaccuracies 28 2.2.5 Flickr and Panoramio 29 2.2.5.1 Flickr 30 2.2.5.2 Panoramio 31 2.3 Related Work on Georeferenced Emotions 32 2.3.1 Emotional Data Resulting from Biometric Measurements 33 2.3.1.1 Bio Mapping 33 2.3.1.2 EmBaGIS 34 2.3.1.3 Ein emotionales Kiezportrait 35 2.3.2 Emotional Data Resulting from Empirical Surveys 35 2.3.2.1 EmoMap 35 2.3.2.2 WiMo 36 2.3.2.3 ECDESUP 37 2.3.2.4 Map of World Happiness 38 2.3.2.5 Emotional Study of Yeongsan River Basin 39 2.3.3 Emotional Data Resulting from User-Generated Content 40 2.3.3.1 Emography 40 2.3.3.2 Twittermood 40 2.3.3.3 Tweetbeat 42 2.3.3.4 Beautiful picture of an ugly place 42 2.3.4 Visualisation in the Related Work 43 3 Methods 45 3.1 Approach for Extracting Georeferenced Emotions from the Metadata of Flickr and Panoramio Photos 45 3.2 Implemented Algorithm 45 3.3 Grammatical Special Cases 47 3.3.1 Degree Words 48 3.3.2 Negation 52 3.3.2.1 Syntactic Negation in English Language 55 3.3.2.2 Syntactic Negation in German Language 57 3.3.3 Modification of Words Affected by Grammatical Special Cases 60 4 Visualisation and Analysis of Extracted Georeferenced Emotions 62 4.1 Data Basis 62 4.2 Density Maps 67 4.3 Inverse Distance Weight 71 4.4 3D Visualisation 73 4.5 Choropleth Mapping 74 4.6 Point Symbols 78 4.7 Impact of Considering Grammatical Special Cases 80 5 Investigation in Temporal Aspects 85 5.1 Annually Occurrence of Emotions 85 5.2 Periodic Events 87 5.3 Single Events 91 5.4 Dependence of Georeferenced Emotions on Different Periods of Time 93 5.4.1 Seasons 95 5.4.2 Months 96 5.4.3 Weekdays 98 5.4.4 Times of Day 99 5.5 Potentials and Limits of Temporal Analyses 99 6 Discussion 100 6.1 Evaluation 100 6.2 Weaknesses and Problems 102 7 Conclusions and Outlook 105 7.1 Answers to the Research Questions 105 7.2 Outlook and Future Work 107 8 Bibliography 112 Appendices XV

eHealth in Chronic Diseases

This book provides a review of the management of chronic diseases (evaluation and treatment) through eHealth. Studies that examine how eHealth can help to prevent, evaluate, or treat chronic diseases and their outcomes are included