The main focus of the paper is to propose a smart recommender system based on the methods of hybrid learning for personal well-being services, called a smart recommender system of hybrid learning ...(SRHL). The essential health factor is considered to be personal lifestyle, with the help of a critical examination of various disciplines. Integrating the recommender system effectively contributes to the prevention of disease, and it also leads to a reduction in treatment cost, which contributes to an improvement in the quality of life. At the same time, there exist various challenges within the recommender system, mainly cold start and scalability. To effectively address the inefficiencies, we propose combined hybrid methods in regard to machine learning. The primary aim of this learning method is to integrate the most effective and efficient learning algorithms to examine how combined hybrid filtering can help to improve the cold star problem efficiently in the provision of personalized well-being in regard to health food service. These methods include: (1) switching among content-based and collaborative filtering; (2) identifying the user context with the integration of dynamic filtering; and (3) learning the profiles with the help of processing and screening of reflecting feedback loops. The experimental results were evaluated by using three absolute error measures, providing comparable results with other studies relative to machine learning domains. The effects of using the hybrid learning method are gathered with the help of the experimental results. Our experiments also show that the hybrid method improves accuracy by 14.61% of the average error predicted in the recommender systems in comparison to the collaborative methods, which mainly focus on the computation of similar entities.
In recent years, many Finnish cities and municipalities have aspired to develop services that support older adults’ well‐being and social inclusion. This study focuses on the Social Hub model, a ...local social innovation developed in the city of Tampere. Social hubs operate on a neighbourhood level, providing free‐of‐charge service coordination and counselling, group activities, and meeting places for social gatherings. This study aims to look at whether this kind of local innovation can support older adults’ well‐being and social inclusion. The sociomaterial perspective and multidimensional model of well‐being (the having–doing–loving–being approach) provided theoretical and analytical guidelines to examine older adults’ experiences and perceptions of social hubs. The qualitative interview data was collected among people living in service housing, senior housing, or ordinary housing in the proximity of the social hubs studied. Face‐to‐face and “go‐along” interviews with 19 older adults aged between 57 and 96 were analysed with theory‐driven content analysis. The results showed that the hubs are a valuable local resource for older adults, providing free services, accessible and appealing shared spaces, and activities that promote social well‐being, physical activity, creativity, and autonomy. The hubs serve as important gathering points for older adults in the neighbourhood, fostering community‐building among citizens residing in different types of housing. The results highlight the importance of acknowledging well‐being as a multidimensional phenomenon. The Social Hub model provides one practical tool to support older adults’ well‐being and social inclusion by offering various kinds of resources and social and cultural activities.
In-person traditional approaches to mental health care services are facing difficulties amidst the coronavirus disease (COVID-19) crisis. The recent implementation of social distancing has redirected ...attention to nontraditional mental health care delivery to overcome hindrances to essential services. Telehealth has been established for several decades but has only been able to play a small role in health service delivery. Mobile and teledigital health solutions for mental health are well poised to respond to the upsurge in COVID-19 cases. Screening and tracking with real-time automation and machine learning are useful for both assisting psychological first-aid resources and targeting interventions. However, rigorous evaluation of these new opportunities is needed in terms of quality of interventions, effectiveness, and confidentiality. Service delivery could be broadened to include trained, unlicensed professionals, who may help health care services in delivering evidence-based strategies. Digital mental health services emerged during the pandemic as complementary ways of assisting community members with stress and transitioning to new ways of living and working. As part of a hybrid model of care, technologies (mobile and online platforms) require consolidated and consistent guidelines as well as consensus, expert, and position statements on the screening and tracking (with real-time automation and machine learning) of mental health in general populations as well as considerations and initiatives for underserved and vulnerable subpopulations.
Users are each day more aware of their privacy and data protection. Although this problem is transversal to every digital service, it is especially relevant when critical and personal information is ...managed, as in eHealth and well-being services. During the last years, many different innovative services in this area have been proposed. However, data management challenges are still in need of a solution. In general, data are directly sent to services but no trustworthy instruments to recover these data or remove them from services are available. In this scheme, services become the users’ data owners although users keep the rights to access, modify, and be forgotten. Nevertheless, the adequate implementation of these rights is not guaranteed, as services use the received data with commercial purposes. In order to address and solve this situation, we propose a new trustworthy personal data protection mechanism for well-being services, based on privacy-by-design technologies. This new mechanism is based on Blockchain networks and indirection functions and tokens. Blockchain networks execute transparent smart contracts, where users’ rights are codified, and store the users’ personal data which are never sent or given to external services. Besides, permissions and privacy restrictions designed by users to be applied to their data and services consuming them are also implemented in these smart contracts. Finally, an experimental validation is also described to evaluate the Quality of Experience (in terms of user satisfaction) and Quality of Service (in terms of processing delay) compared to traditional service provision solutions.
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