Social activities in multidomain dementia prevention interventions: insights from practice and a blueprint for the future.

Introduction: Social activities are important for health and act as a driver of cognitive reserve during aging. In this perspective paper, we describe challenges and outline future (research) endeavors to establish better operationalization of social activities in multidomain interventions to prevent dementia. Body: We first address the lack of conceptual clarity, which makes it difficult to measure engagement in social activities. Second, drawing from our experience with the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), we discuss social activities in multidomain dementia prevention interventions. Using qualitative data from the Multimodal Preventive Trial for Alzheimer's Disease (MIND-ADmini), we reflect on participant experiences with social activities. Third, we address the potential and challenges of digital solutions in promoting social activities in interventions for dementia prevention. Finally, we share insights from a workshop on digital technology, where we consulted with individuals with and without cognitive impairment who have been involved in three European projects (i.e., EU-FINGERS, Multi-MeMo, and LETHE). Discussion: Based on these insights, we advocate for research that strengthens and accelerates the integration of social activities into multidomain interventions for dementia prevention. We propose several ways to achieve this: (a) by conducting mixed methods research to formulate a broadly accepted definition and instructions to measure social activities; (b) by focusing on promoting engagement in social activities beyond the intervention setting; and (c) by exploring the needs and preferences of older adults towards digitally-supported interventions and co-design of new technologies that enrich in-person social activities.

Socio-Cognitive Determinants of Lifestyle Behavior in the Context of Dementia Risk Reduction: A Population-Based Study in the Netherlands.

Background: Unhealthy behavior increases the risk of dementia. Various socio-cognitive determinants influence whether individuals persist in or alter these unhealthy behaviors. Objective: This study identifies relevant determinants of behavior associated to dementia risk. Methods: 4,104 Dutch individuals (40-79 years) completed a screening questionnaire exploring lifestyle behaviors associated with dementia risk. Subsequently, 3,065 respondents who engaged in one or more unhealthy behaviors completed a follow-up questionnaire investigating socio-cognitive determinants of these behaviors. Cross-tables were used to assess the accuracy of participants' perceptions regarding their behavior compared to recommendations. Confidence Interval-Based Estimation of Relevance (CIBER) was used to identify the most relevant determinants of behavior based on visual inspection and interpretation. Results: Among the respondents, 91.3% reported at least one, while 65% reported two or more unhealthy lifestyle behaviors associated to dementia risk. Many of them were not aware they did not adhere to lifestyle recommendations. The most relevant determinants identified include attitudes (i.e., lacking a passion for cooking and finding pleasure in drinking alcohol or smoking), misperceptions on social comparisons (i.e., overestimating healthy diet intake and underestimating alcohol intake), and low perceived behavioral control (i.e., regarding changing physical inactivity, altering diet patterns, and smoking cessation). Conclusions: Individual-level interventions that encourage lifestyle change should focus on enhancing accurate perceptions of behaviors compared to recommendations, while strengthening perceived control towards behavior change. Given the high prevalence of dementia risk factors, combining interventions at both individual and environmental levels are likely to be the most effective strategy to reduce dementia on a population scale.

Enhancing Dementia Risk Mitigation: Standardized Digital Monitoring in Clinical Trials and Platform Design for At-Risk Elderly Individuals.

BACKGROUND: Dementia is becoming a significant public health concern, affecting approximately 130,000 individuals in Austria, whereby nearly 40% of the cases are attributed to modifiable risk factors. Multidomain lifestyle interventions have thereby demonstrated significant effects in reducing the risk of dementia. OBJECTIVES: The goal was to define an interoperability framework to conduct standardized monitoring in clinical trials for enhancing dementia risk mitigation. In addition, the identified standards should be integrated into the components of the project. METHODS: A step-by-step approach was used, where initially data collection, aggregation and harmonization was carried out with retrospective data from various clinical centers. Afterwards, the interoperability framework was defined including the prospective data that is gathered during a clinical trial. RESULTS: A guideline for integrating healthcare standards was developed and incorporated into the technical components for the clinical trial. CONCLUSION: The interoperability framework was designed in a scalable way and will be regularly updated for future needs.

Instance Selection Algorithms for Predictive Modelling in Telehealth Applications.

Telehealth services are becoming more and more popular, leading to an increasing amount of data to be monitored by health professionals. Machine learning can support them in managing these data. Therefore, the right machine learning algorithms need to be applied to the right data. We have implemented and validated different algorithms for selecting optimal time instances from time series data derived from a diabetes telehealth service. Intrinsic, supervised, and unsupervised instance selection algorithms were analysed. Instance selection had a huge impact on the accuracy of our random forest model for dropout prediction. The best results were achieved with a One Class Support Vector Machine, which improved the area under the receiver operating curve of the original algorithm from 69.91 to 75.88 %. We conclude that, although hardly mentioned in telehealth literature so far, instance selection has the potential to significantly improve the accuracy of machine learning algorithms.

LETHE: A Digital Intervention for Cognitive Decline.

Dementia is the main cause of disability in elderly populations. It has been shown that the risk factors of dementia are a mixture of pathological, lifestyle and heritable factors, with some of those being provably modifiable. Early diagnosis of dementia and approaches to slow down its evolution are currently the most prominent management methodologies due to lack of a cure. For that reason, a plethora of home-based assistive technologies for dementia management do exist, with most of them focusing on the improvement of memory and thinking. The main objective of LETHE is prevention in the whole spectrum of cognitive decline in the elderly population at risk reaching from asymptomatic to subjective or mild cognitive impairment to prodromal Dementia. LETHE will provide a Big Data collection platform and analysis system, that will allow prevention, personalized risk detection and intervention on cognitive decline. Through the subsequent 2-year clinical trial, the LETHE system, as well as the respective knowledge gained will be evaluated and validated. The scope of the current paper is to introduce the LETHE study and its respective novel platform as a holistic approach to multidomain lifestyle intervention trial studies. The present work depicts the architectural perspective and extends beyond state-of-the-art guidelines and approaches to health management systems and cloud platform development.Clinical Relevance - Patient Management Systems as well as lifestyle management platforms have significant clinical relevance as they allow for remote and continuous monitoring of patients' health status. LETHE aims to improve patient outcomes by providing predictive models for cognitive decline and patient adherence to the multimodal lifestyle intervention, enabling prompt and appropriate medical decisions.

Public Perspectives on Lifestyle-Related Behavior Change for Dementia Risk Reduction: An Exploratory Qualitative Study in The Netherlands.

BACKGROUND: There is accumulating evidence that addressing modifiable risk and protective factors has an impact on dementia rates. Insight into the public's perspectives on dementia risk reduction is needed to inform future individual-level interventions and public health approaches. OBJECTIVE: This study explores the publics' openness towards dementia risk reduction and willingness towards changing lifestyle behavior to reduce the future risk for dementia. METHODS: Using a screening questionnaire, participants were purposively selected based on lifestyle behaviors that are associated with dementia risk. One-on-one interviews were used to explore their openness towards dementia risk reduction and willingness towards behavior change. Independently, two researchers performed an inductive content analysis. RESULTS: Interviews were conducted with 23 participants aged from 40 to 79 years. Main themes that were identified from the data were: 1) abstractness of dementia risk reduction, 2) ambivalence towards changing behavior, 3) negative self-image and low behavioral control, and 4) all-or-nothing thinking about lifestyle change. CONCLUSIONS: The concept of dementia risk reduction seems difficult to translate to the personal context, particularly if individuals perceive that dementia would occur decades in the future. This is problematic because a large proportion of the public needs a healthier lifestyle to reduce the incidence of dementia. Translating healthy intentions into behavior is complex and involves overcoming a variety of barriers that complicate dementia risk reduction initiatives. Support is needed for individuals who experience additional obstacles that obstruct commencing to a healthier lifestyle (e.g., negative self-image, engaging in multiple unhealthy behaviors, unrealistic perceptions about lifestyle change).

Wearable Device Health Data Mapping to Open mHealth and FHIR Data Formats.

Health data collected by wearables and apps can be useful as part of patient-generated health data (PGHD) or personal health data for medical diagnosis or general health monitoring. Mobile health apps are more and more accepted, generate evidence and might be increasingly used in personal medicine. Data retrieved from wearables and apps are mostly not following a medical data standard and cannot be retrieved from the vendors in a straightforward way. The present work started the implementation of a Digital Health Convener and described the process to collect data from several wearables - starting with Fitbit data - and transforms this data to standardized JSON files following the Open mHealth (OmH) IEEE and the HL7 FHIR standard. The project achieved is provided as open source and can be extended and used in future projects to generate OmH and FHIR conform PGHD.

User experience and acceptance of a device assisting persons with dementia in daily life: a multicenter field study.

BACKGROUND: Assistive technologies have the potential to facilitate everyday life of people with dementia and their families. Close collaboration with affected people and interdisciplinary research are essential to understand and address the needs of prospective users. In this study, we present the results of the evaluation of such an assistive system prototype. AIMS: Challenges from the patient and caregiver side, technical and design problems and acceptance and usability with regard to our special target group were evaluated. METHODS: MEMENTO, a system of two e-ink tablets and a smartwatch, was tested in the domestic environment of dementia patients. Thirty participants from Italy, Spain and Austria took part in a 3-month field trial and compared the MEMENTO system to traditional strategies in everyday life. Quantitative and qualitative data were collected and frequency of use of the system was monitored. RESULTS: There were no significant changes in quantitative measurements, such as activities of daily living and caregiver burden over the duration of the 3-month field trial. More frequent usage was significantly correlated with positive attitude towards technology (r = 0.723, p < 0.05), but not with age. The design of the system was positively emphasized, reducing fear of the technology on the one hand and stigmatization on the other. CONCLUSION: We show that a positive attitude towards technology is the essential variable for successful implementation of such systems, regardless of age. Participants showed great interest in digital solutions and agreed that technological systems will help in maintaining independency of persons with cognitive dysfunction in the future.

A Systematic Approach to Quality Requirement Management in Medical Software.

BACKGROUND: The quality requirements for medical software have become increasingly demanding. Several quality standards and models are already in place, but there is a debate on whether these are specific enough for medical software. Moreover, mapping requirements to quality criteria can be challenging but is required throughout the software development process. OBJECTIVES: We propose a workflow in which we apply proven methods and tools for systematic collection, analysis, and evaluation of software quality criteria based on the ISO/IEC 25010:2011. METHODS: We employ affinity diagrams, Kano analysis and quality function deployment for the systematic requirement development, analysis, and management. RESULTS: We outline a systematic approach on how to use the recommended process when developing medical software. CONCLUSION: The paper proposes a systematic approach for requirements management that could be used for mapping medical software quality criteria and stakeholder requirements, independent from the quality criteria (and the underlying model) itself.

Development of and Adherence to a Computer-Based Gamified Environment Designed to Promote Health and Wellbeing in Older People with Mild Cognitive Impairment.

BACKGROUND: The older population of Europe is increasing and there has been a corresponding increase in long term care costs. This project sought to promote active ageing by delivering tasks via a tablet computer to participants aged 65-80 with mild cognitive impairment. OBJECTIVES: An age-appropriate gamified environment was developed and adherence to this solution was assessed through an intervention. METHODS: The gamified environment was developed through focus groups. Mixed methods were used in the intervention with the time spent engaging with applications recorded supplemented by participant interviews to gauge adherence. There were two groups of participants: one living in a retirement village and the other living separately across a city. RESULTS: The retirement village participants engaged in more than three times the number of game sessions compared to the other group possibly because of different social arrangements between the groups. CONCLUSION: A gamified environment can help older people engage in computer-based applications. However, social community factors influence adherence in a longer term intervention.

Reasoning and Data Representation in a Health and Lifestyle Support System.

Case-based reasoning and data interpretation is an artificial intelligence approach that capitalizes on past experience to solve current problems and this can be used as a method for practical intelligent systems. Case-based data reasoning is able to provide decision support for experts and clinicians in health systems as well as lifestyle systems. In this project we were focusing on developing a solution for healthy ageing considering daily activities, nutrition as well as cognitive activities. The data analysis of the reasoner followed state of the art guidelines from clinical practice. Guidelines provide a general framework to guide clinicians, and require consequent background knowledge to become operational, which is precisely the kind of information recorded in practice cases; cases complement guidelines very well and helps to interpret them. It is expected that the interest in case-based reasoning systems in the health.

Development and evaluation of SOA-based AAL services in real-life environments: a case study and lessons learned.

INTRO: The proper use of ICT services can support seniors in living independently longer. While such services are starting to emerge, current proprietary solutions are often expensive, covering only isolated parts of seniors' needs, and lack support for sharing information between services and between users. For developers, the challenge is that it is complex and time consuming to develop high quality, interoperable services, and new techniques are needed to simplify the development and reduce the development costs. This paper provides the complete view of the experiences gained in the MPOWER project with respect to using model-driven development (MDD) techniques for Service Oriented Architecture (SOA) system development in the Ambient Assisted Living (AAL) domain. METHOD: To address this challenge, the approach of the European research project MPOWER (2006-2009) was to investigate and record the user needs, define a set of reusable software services based on these needs, and then implement pilot systems using these services. Further, a model-driven toolchain covering key development phases was developed to support software developers through this process. Evaluations were conducted both on the technical artefacts (methodology and tools), and on end user experience from using the pilot systems in trial sites. RESULTS: The outcome of the work on the user needs is a knowledge base recorded as a Unified Modeling Language (UML) model. This comprehensive model describes actors, use cases, and features derived from these. The model further includes the design of a set of software services, including full trace information back to the features and use cases motivating their design. Based on the model, the services were implemented for use in Service Oriented Architecture (SOA) systems, and are publicly available as open source software. The services were successfully used in the realization of two pilot applications. There is therefore a direct and traceable link from the user needs of the elderly, through the service design knowledge base, to the service and pilot implementations. The evaluation of the SOA approach on the developers in the project revealed that SOA is useful with respect to job performance and quality. Furthermore, they think SOA is easy to use and support development of AAL applications. An important finding is that the developers clearly report that they intend to use SOA in the future, but not for all type of projects. With respect to using model-driven development in web services design and implementation, the developers reported that it was useful. However, it is important that the code generated from the models is correct if the full potential of MDD should be achieved. The pilots and their evaluation in the trial sites showed that the services of the platform are sufficient to create suitable systems for end users in the domain. CONCLUSIONS: A SOA platform with a set of reusable domain services is a suitable foundation for more rapid development and tailoring of assisted living systems covering reoccurring needs among elderly users. It is feasible to realize a tool-chain for model-driven development of SOA applications in the AAL domain, and such a tool-chain can be accepted and found useful by software developers.

Interoperability services in the MPOWER Ambient Assisted Living platform.

Ambient Assisted Living systems for the ageing and cognitively disabled do not exist in isolation. What characterizes such systems is the cooperation of several different stakeholders in the care process and the service platforms need to address this. This paper reports on our work in the EU IST MPOWER project where we have designed and implemented interoperability services based on patterns, service-oriented architectures, web services and XSDL transformations. The services we present are freely available as open source under the MIT license.