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Research has shown that diverse activity engagement has positive effects on cognitive functioning in older age. However, it is unknown whether the positive effect holds within persons across days and across people. We examined daily within-person association between activity diversity and working memory in older age and effects of potential moderators therein. We examined 16-day smartphone-based ambulatory assessment data from 150 older adults (aged 65+). Participants reported their present activities and completed working memory tasks seven times per day. Within persons, higher daily activity diversity was positively associated with higher daily working memory. Moreover, the prior day’s activity diversity led to that day’s higher working memory, but not vice versa. We did not find any moderating effects of age, education, or fluid and crystallized intelligence. Our results strengthen the evidence on the beneficial effect of activity diversity on cognitive performance. Results are discussed in the context of cognitive reserve theory.

Ageing is a multi-factorial physiological process and the development of novel IoT systems, tools and devices, specifically targeted to older people, must be based on a holistic framework built on robust scientific knowledge in different health domains. Furthermore, interoperability must be guaranteed using standardized frameworks or approaches. These aspects still largely lack in the specific literature. The main aim of the paper is to develop a new ontology (the NESTORE ontology) to extend the available ontologies provided by universAAL-IoT (uAAL-IoT). The ontology is based on a multidomain healthy ageing holistic model, structuring well-assessed scientific knowledge, specifically targeted to healthy older adults aged between 65 and 75. The tool is intended to support, and standardize heterogeneous data about ageing in compliance with the uAAL-IoT framework. The NESTORE ontology covers all the relevant concepts to represent 3 significant domains of ageing: (1) Physiological Status and Physical Activity Behaviour; (2) Nutrition; and (3) Cognitive and Mental Status and Social Behaviour. In total, 12 sub-ontologies were modelled with more than 60 classes and sub-classes referenced among them by using more than 100 relations and around 20 enumerations. The proposed ontology increases the uAAL collection by 40%. NESTORE ontology provides innovation both in terms of semantic content and technological approach. The thorough use of this ontology can support the development of a decision support system, to promote healthy ageing, with the capacity to do dynamic multi-scale modelling of user-specific data based on the semantic annotations of users’ profile.

With growing use of machine learning algorithms and big data in health applications, digital measures, such as digital biomarkers, have become highly relevant in digital health. In this paper, we focus on one important use case, the long-term continuous monitoring of cognitive ability in older adults. Cognitive ability is a factor both for long-term monitoring of people living alone as well as a relevant outcome in clinical studies. In this work, we propose a new potential digital biomarker for cognitive abilities based on location eigenbehaviour obtained from contactless ambient sensors. Indoor location information obtained from passive infrared sensors is used to build a location matrix covering several weeks of measurement. Based on the eigenvectors of this matrix, the reconstruction error is calculated for various numbers of used eigenvectors. The reconstruction error in turn is used to predict cognitive ability scores collected at baseline, using linear regression. Additionally, classification of normal versus pathological cognition level is performed using a support-vector machine. Prediction performance is strong for high levels of cognitive ability but grows weaker for low levels of cognitive ability. Classification into normal and older adults with mild cognitive impairment, using age and the reconstruction error, shows high discriminative performance with an ROC AUC of 0.94. This is an improvement of 0.08 as compared with a classification with age only. Due to the unobtrusive method of measurement, this potential digital biomarker of cognitive ability can be obtained entirely unobtrusively-it does not impose any patient burden. In conclusion, the usage of the reconstruction error is a strong potential digital biomarker for binary classification and, to a lesser extent, for more detailed prediction of inter-individual differences in cognition.

Social integration is a key predictor of health in later life, as is cognitive functioning. This chapter describes the evidence related to levels of and interventions on social integration and cognitive functioning for older adults and outlines how this evidence was translated into the personalized coaching approach in NESTORE in both of these domains. From the technological side, social beacons are used to obtain objective contact measures for users' local social networks and thus complement self-report information on social interactions beyond face-to-face contacts. In the cognitive domain, a serious game involving a multidomain cognitive training was developed on the basis of evidence-based game and training principles.

This chapter discusses the theoretical behaviour change framework and its integration and implementation of behaviour change techniques that form the conceptual psychological basis for innovative but efficient coaching approach. We review the current state of e-coaching solutions for older adults that can be found in the literature and identify gaps. Finally, we review and discuss the technological implementation of behaviour change techniques applied in NESTORE to support healthy older adults to adopt a healthy lifestyle in all NESTORE well-being domains as reference case study.

This article describes the coaching strategies of the NESTORE e-coach, a virtual coach for promoting healthier lifestyles in older age. The novelty of the NESTORE project is the definition of a multi-domain personalized pathway where the e-coach accompanies the user throughout different structured and non-structured coaching activities and recommendations. The article also presents the design process of the coaching strategies, carried out including older adults from four European countries and experts from the different health domains, and the results of the tests carried out with 60 older adults in Italy, Spain and The Netherlands.

In recent years, due to the rise in availability of unstructured big data and technological affordances, there has been a proverbial “explosion” of research within the field of sentiment analysis and affective computing, facilitating the study of in-situ communicated emotion and its accurate detection at an unprecedented scale. Increasingly the emotional dimension and its key important role is being recognized in e-coaching, with attempts at its integration within such systems. This chapter reviews the predominant extant models for representing affect, as well as emotion related states relevant to older persons’ experiences, including the state-of-art for their automated detection and integration within e-coaching systems. Given the approaches available, we describe how the NESTORE system addresses emotion recognition, self-tracking and subsequent feedback intended to help older adults make sense of their affective experiences across their daily life, as well as the role it can play in holistic e-coaching systems. Finally, from the NESTORE experience, we draw recommendations about how future e-coaching system could be implemented in the future to include the emotional dimension.

Age-related differences in white matter (WM) microstructure have been linked to lower performance in tasks of processing speed in healthy older individuals. However, only few studies have examined this link in a longitudinal setting. These investigations have been limited to the correlation of simultaneous changes in WM microstructure and processing speed. Still little is known about the nature of age-related changes in WM microstructure, i.e., regionally distinct vs. global changes. In the present study, we addressed these open questions by exploring whether previous changes in WM microstructure were related to subsequent changes in processing speed: (a) 1 year later; or (b) 2 years later. Furthermore, we investigated whether age-related changes in WM microstructure were regionally specific or global. We used data from four occasions (covering 4 years) of the Longitudinal Healthy Aging Brain (LHAB) database project (N = 232; age range at baseline = 64–86). As a measure of WM microstructure, we used mean fractional anisotropy (FA) in 10 major WM tracts averaged across hemispheres. Processing speed was measured with four cognitive tasks. Statistical analyses were conducted with bivariate latent change score (LCS) models. We found, for the first time, evidence for lagged couplings between preceding changes in FA and subsequent changes in processing speed 2 years, but not 1 year later in some of the WM tracts (anterior thalamic radiation, superior longitudinal fasciculus). Our results supported the notion that FA changes were different between regional WM tracts rather than globally shared, with some tracts showing mean declines in FA, and others remaining relatively stable across 4 years.

Objectives and Methods. Using 5 measurement bursts spanning 1 year, this study examined correspondences between retrospective end-of-year ratings and momentary ratings of positive affect (PA) and negative affect (NA) in 53 older adult participants from the Berlin Aging Study. Results. Average momentary assessments were moderately positively correlated with retrospective ratings of PA and NA. Hierarchical regression analyses further indicate that mean momentary PA best predicted retrospective PA over and above peak or recent momentary PA, whereas no such pattern emerged for NA. No evidence for age differences in these patterns was found. Discussion. Our discussion focuses on the implications of affective memory biases for examining affective experiences as they occur in peoples’ daily lives and points to implications concerning methods and theory development.