The greatest wealth is health. It is sometimes said your health is a function of what you are not doing, not what you are currently doing. The degree to which individuals can attain, process, and comprehend the necessary health information and services they need to make proper health decisions is vital for optimal health and well-being. This project documents the analysis, design, development, and implementation of a prototype web-based data-driven health & wellness system targeted for college students. The architecture for this system uses business intelligence to develop a smart online platform for real-time analysis based on inputs entered by its users. The objective is to develop modules that can be used to provide meal plan options that dietitians can recommend to students, while also providing a standard wellness health check. This also promotes constant awareness for students with specialized health diets. User-health and wellness history of each Student is collected and stored for generating progress and wellness reports for end users. The dietitian can monitor the user in real time through the data collected and stored in the data server. Users can monitor their own progress. The system incorporates user context and feedback to personalize each user's lifestyle. Implementation of this system provides a complete and easy to use integrated system that promotes the process of analyzing wellness and improving the user’s overall health. The system is designed to be in a non-clinical setting and hence more lifestyle-oriented compared to other health-oriented systems. It is thus more relevant and convenient to student’s everyday life context.

Background The aging population, particularly in Sweden, has heightened the need for innovative solutions in long-term elder care. This demographic shift results in a wealth of health data, from vital signs to social interactions. Accurate documentation of this data is crucial for providing effective care. However, there is a lack of systematic understanding of the types of health data collected in Swedish elder care. This study aims to bridge the gap between idealized and actual documentation practices. Methods The method combines literature reviews with qualitative techniques such as interviews, aiming to thoroughly understand the documentation of health data in Swedish long-term care facilities for older adults. By coding and categorizing the content into manageable data, researchers can identify patterns, trends, and relationships within the media being studied. Results Current documentation practices in Swedish long-term elder care were examined, revealing a variety of health data types collected, including personal information, assessments, care plans, and incident reports. Legal guidance from the Social Authority is deemed insufficient by managers, particularly concerning the Act on Coherent Documentation (SVOD).Documentation practices vary among municipalities due to operational differences and municipal autonomy. These findings highlight complexities and gaps, necessitating further research and improvement efforts. Conclusion Within the context of long-term elder care in Sweden, effective documentation practices play a crucial role. However, there is vagueness surrounding the specific health data that should be and is documented. This is what has been revealed in my study, drawing from interviews with multiple informants. While digital documentation offers promise, addressing complexity requires coordinated efforts. Streamlining regulations, enhancing interoperability, and improving usability are essential steps toward improving elder care documentation practices. / Bakgrund Den åldrande befolkningen i världen, och särskilt i Sverige, har ökat behovet av innovativa lösningar inom äldreomsorgen då denna demografiska förändring har resulterat i en ackumuleringen av stora mängder hälsodata. För att kunna tillhandahålla effektiv vård så är det viktigt att den samlade hälsodatan dokumenteras korrekt. Det finns dock en brist i den systematiska förståelsen för vilka typer av hälsodata som samlas in inom svensk äldreomsorg. Syftet med denna studie är därför att undersöka klyftan mellan hur hälsodata bör dokumenteras enligt lagar och regler, och hur det faktiskt dokumenteras i praktiken. Metoder Studien kombinerar dokumentanalyser med intervjuer, och har som mål att djupgående förstå dokumentationen av hälsoinformation i svenska vård-och omsorgsboenden. Genom att koda och kategorisera resultatet från dokumentanalyserna och intervjudata kunde mönster, trender och relationer inom det studerade materialet identifieras. Resultat Nuvarande dokumentationspraxis inom svensk äldreomsorg undersöktes. Resultatet visade att en mängd olika typer av hälsoinformation samlas in, däribland personlig information, bedömningar, vårdplaner och incidentrapporter. Juridisk vägledning från Socialstyrelsen bedöms vara otillräcklig av chefer, särskilt när det gäller lagen om sammanhållen dokumentation (SVOD). Dokumentationspraxis varierar mellan kommuner på grund av operativa skillnader samt det kommunala självstyret. Dessa resultat belyser problemet med hur hälsoinformation dokumenteras och de brister som kräver ytterligare forskning och förbättringsåtgärder. Slutsats Inom ramen för äldreomsorgen i Sverige spelar effektiva dokumentationsmetoder en avgörande roll. Det råder dock oklarhet kring vad för hälsoinformation som bör och faktiskt dokumenteras. Detta är vad som avslöjats i denna studie, baserat på intervjuer med flera informanter. Även om digital dokumentation är fördelaktigt så kräver hanteringen av de påvisade problemensamordnade insatser. Förtydligandet av regelverk, förbättrad interoperabilitet och ökadanvändbarheten är viktiga steg mot att förbättra dokumentationspraxis inom äldreomsorgen.

Stress and overload are strongly associated with unsafe behaviour, which motivated various studies to detect them automatically in workplaces. This study aims to advance safety research by developing a data-driven stress and overload detection method. An unsupervised deep learning-based anomaly detection method is developed to detect stress. The proposed method performs with convolutional neural network encoder-decoder and long short-term memory equipped with an attention layer. Data from a field experiment with 18 participants was used to train and test the developed method. The field experiment was designed to include a pre-defined sequence of activities triggering mental and physical stress, while a wristband biosensor was used to collect physiological signals. The collected contextual and physiological data were pre-processed and then resampled into correlation matrices of 14 features. Correlation matrices are used as an input to the unsupervised Deep Learning (DL) based anomaly detection method. The developed method is validated, offering accuracy and F-measures close to 0.98. The technique employed captures the input data attributes correlation, promoting higher interpretability of the DL method for easier comprehension. Over-reliance on uncertain absolute truth, the need for a high number of training samples, and the requirement of a threshold for detecting anomalies are identified as shortcomings of the proposed method. To overcome these shortcomings, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed and developed. While the ANFIS method did not improve the overall accuracy, it outperformed the DL-based method in detecting anomalies precisely. The overall performance of the ANFIS method is better than the DL-based method for the anomalous class, and the method results in lower false alarms. However, the DL-based method is suitable for circumstances where false alarms are tolerated. / Dissertation / Doctor of Philosophy (PhD)

The purpose of this study is to produce new knowledge concerning the perceived user’s value generated using machine learning technologies that activate data network effects factors that create value through various business model themes. The data network effects theory represents a set of factors that increase the user’s perceived value for a platform that uses artificial intelligence capabilities. The study followed an abductive research approach where initially found facts were matched against the data network effects theory to be put in context and understood. The study’s data was gathered through semi-structured interviews with experts who were active within the research area and chosen based on their practical experience and their role in the digitization of the healthcare sector. The results show that three out of six factors were fully realized contributing to value creation while two of the factors showed to be partially realized in order to contribute to value creation and that is justified by the exclusion of users' perspectives in the scope of the research. Lastly, only one factor has limited contribution to the value creation due to the heavy regulations limiting its realization in the health sector. It is concluded that data network effects moderators contributed differently in the activation of various business model themes for value creation in a general manner where further studies should apply the theory in the assessment of one specific AI health offering to take full advantage of the theory potential. The theoretical implications showed that the data network factors may not necessarily be equally activated to contribute to value creation which was not initially highlighted by the theory. Additionally, the practical implications of the study’s results may help managers in their decision-making process on which factors to be activated for which business model theme.