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Introduction: Mobile health, or mHealth, is based on mobile information and communication technologies and provides solutions for empowering individuals to participate in healthcare. Personalisation techniques have been used to increase user engagement and adherence to interventions delivered as mHealth solutions. This study aims to explore the current state of personalisation in mHealth, including its current trends and implementation. Materials and Methods: We conducted a review following PRISMA guidelines. Four databases (PubMed, ACM Digital Library, IEEE Xplore, and APA PsycInfo) were searched for studies on mHealth solutions that integrate personalisation. The retrieved papers were assessed for eligibility and useful information regarding integrated personalisation techniques. Results: Out of the 1,139 retrieved studies, 62 were included in the narrative synthesis. Research interest in the personalisation of mHealth solutions has increased since 2020. mHealth solutions were mainly applied to endocrine, nutritional, and metabolic diseases; mental, behavioural, or neurodevelopmental diseases; or the promotion of healthy lifestyle behaviours. Its main purposes are to support disease self- management and promote healthy lifestyle behaviours. Mobile applications are the most prevalent technological solution. Although several design models, such as user-centred and patient-centred designs, were used, no specific frameworks or models for personalisation were followed. These solutions rely on behaviour change theories, use gamification or motivational messages, and personalise the content rather than functionality. A broad range of data is used for personalisation purposes. There is a lack of studies assessing the efficacy of these solutions; therefore, further evidence is needed. Discussion: Personalisation in mHealth has not been well researched. Although several techniques have been integrated, the effects of using a combination of personalisation techniques remain

The lifespan of older people is growing together with their proportion in the population Growth in the older population. This leads to an increasing need for support services for older adults who will inevitably experience a significant decrease in their cognitive capabilities and mental health conditions. Cognitive function, including memory, attention, sleeping, problem-solving activities, or speed processing, are playing a crucial role in everyone’s independent daily life. Technologies can help maintain their independence and improve quality of life, reducing the care costs. In this regards, smart home applications (SMAs) offer a solution to the complex needs of older adults and their families, monitoring physiological and functional issues, as well as aiding in emergency detection and response. This chapter provides an overview of current applications reported in the scientific literature, identifies the frameworks proposed for designing these types of applications, and defines evidence based recommendations for designing SMAs for cognitive health of older adults.

The lifespan of older people is growing together with their proportion in the population growth in the older population. This leads to an increasing need for support services for older adults who will inevitably experience a significant decrease in their cognitive capabilities and mental health conditions. Cognitive functions, including memory, attention, sleeping, problem-solving activities, or speed processing, are playing a crucial role in everyone’s independent daily life. Technologies can help maintain their independence and improve quality of life, reducing the care costs. In this regard, Smart home applications (SMAs) offer a solution to the complex needs of older adults and their families, monitoring physiological and functional issues, as well as aiding in emergency detection and response. This chapter provides an overview of current applications reported in the scientific literature, identifies the frameworks proposed for designing these types of applications, and defines evidence-based recommendations for designing SMAs for cognitive health of older adults., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Applied Ergonomics and Design

Background: Recommender systems are information retrieval systems that provide users with relevant items (e.g., through messages). Despite their extensive use in the e-commerce and leisure domains, their application in healthcare is still in its infancy. These systems may be used to create tailored health interventions, thus reducing the cost of healthcare and fostering a healthier lifestyle in the population. Objective: This paper identifies, categorizes, and analyzes the existing knowledge in terms of the literature published over the past 10 years on the use of health recommender systems for patient interventions. The aim of this study is to understand the scientific evidence generated about health recommender systems, to identify any gaps in this field to achieve the United Nations Sustainable Development Goal 3 (SDG3) (namely, “Ensure healthy lives and promote well-being for all at all ages”), and to suggest possible reasons for these gaps as well as to propose some solutions. Methods: We conducted a scoping review, which consisted of a keyword search of the literature related to health recommender systems for patients in the following databases: ScienceDirect, PsycInfo, Association for Computing Machinery, IEEExplore, and Pubmed. Further, we limited our search to consider only English-lan-guage journal articles published in the last 10 years. The reviewing process comprised three researchers who filtered the results simultaneously. The quantitative synthesis was conducted in parallel by two researchers, who classified each paper in terms of four aspects—the domain, the methodological and procedural aspects, the health promotion theoretical factors and behavior change theories, and the technical aspects—using a new multidisciplinary taxonomy. Results: Nineteen papers met the inclusion criteria and were included in the data analysis, for which thirty-three features were assessed. The nine features associated with the health promotion theoretical factors and beha