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Acceptance of Technologies for Aging in Place: A Conceptual Model

BACKGROUND: Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs.OBJECTIVE: The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors.METHODS: A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships.RESULTS: Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73).CONCLUSIONS: This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users' needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation.

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Acceptance of Technologies for Aging in Place: A Conceptual Model

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A Conceptual Model for Implementing Explainable AI by Design

Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.

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A Conceptual Model for Implementing Explainable AI by Design

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Conceptual Framework for Autonomous Airside Vehicles Business Models

The adoption of electric autonomous vehicles (EAVs) is set to revolutionize airport ground operations. Airports are increasingly developing new autonomous innovation strategies to meet sustainability goals and address future challenges, such as shifting labor markets, evolving working conditions, and the growing impact of digitalization [1]. The traditional business model, in which manufacturers sell vehicles to operators (ground handlers), may no longer be relevant. The increasing complexity and advancement of EAVs will drive up costs, making the ownership model less appealing and shifting the focus from product-oriented to service-oriented models. This paper aims to provide a conceptual framework for potential business models for the implementation of EAVs in airport airside operations.

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What If the Clinical and Older Adults’ Perspectives about Frailty Converge? A Call for a Mixed Conceptual Model of Frailty

Abstract: Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults’ perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled “by others” as compared to “self-labeling”, from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term “frail”, and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult’s perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.

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What If the Clinical and Older Adults’ Perspectives about Frailty Converge? A Call for a Mixed Conceptual Model of Frailty

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A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator

Background: Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective: In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods: We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results: We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions: Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.

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A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator

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Stargazing live!

We present the Stargazing Live! program comprising a planetarium experience and supporting lesson activities for pre-university physics education. The mobile planetarium aims to inspire and motivate learners using real telescope data during the experience. Learners then consolidate their learning by creating conceptual models in the DynaLearn software. During development of the program, content experts and stakeholders were consulted. Three conceptual model lesson activities have been created: star properties, star states and the fusion-gravity balance. The present paper evaluates the planetarium experience plus the star properties lesson activity in nine grade 11 and 12 classes across three secondary schools in the Netherlands. Learners are very positive about the planetarium experience, but they are less able to link the topics in the planetarium to the curriculum. The conceptual modelling activity improves the learners understanding of the causal relationship between the various stellar properties. Future work includes classroom testing of the star states and fusion-gravity balance lessons.

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Professional youth work as a preventive service: towards an integrated conceptual framework

Knowledge of how professional youth work might prevent individual and social problems in socially vulnerable youngsters is poorly developed. This article presents a conceptual framework that clarifies the implicit methodical process used by professional youth workers and focuses on what stakeholders regard as the potential of professional youth work as a preventive service. A qualitative research synthesis approach was used to combine the findings of six practice-based studies conducted in six European countries. This synthesis revealed that professional youth workers employ a multi-methodic approach in their prevention efforts, strengthening the social skills and self-mastery of youngsters, reinforcing their social network, enhancing their civic participation and helping them find additional social or health services. Twelve methodic principles were identified as contributing to achieving these prevention efforts, shedding light on the process taking place between youngsters and youth workers. This conceptual framework provides essential information for future evaluation research.

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Professional youth work as a preventive service: towards an integrated conceptual framework

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International Business Matters

Phd Thesis Higher professional education aims to prepare graduates for the complexity of professional practices. The development of conceptual understanding is important to deal adequately with this complexity, especially in an unstructured professional domain such as international business. The aim of this dissertation is to investigate the concept conceptual understanding in this professional domain, how it can be measured, what it looks like, how it changes, and in what ways it differs between students. The dissertation comprises five empirical studies for which data collection took place at a university of applied sciences in the Netherlands.

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Modelling employee resilience using wearables and apps:

Occupational stress can cause health problems, productivity loss or absenteeism. Resilience interventions that help employees positively adapt to adversity can help prevent the negative consequences of occupational stress. Due to advances in sensor technology and smartphone applications, relatively unobtrusive self-monitoring of resilience-related outcomes is possible. With models that can recognize intra-individual changes in these outcomes and relate them to causal factors within the employee's context, an automated resilience intervention that gives personalized, just-in-time feedback can be developed. This paper presents the conceptual framework and methods behind the WearMe project, which aims to develop such models. A cyclical conceptual framework based on existing theories of stress and resilience is presented as the basis for the WearMe project. The operationalization of the concepts and the daily measurement cycle are described, including the use of wearable sensor technology (e.g., sleep tracking and heart rate variability measurements) and Ecological Momentary Assessment (mobile app). Analyses target the development of within-subject (n=1) and between-subjects models and include repeated measures correlation, multilevel modelling, time series analysis and Bayesian network statistics. Future work will focus on further developing these models and eventually explore the effectiveness of the envisioned personalized resilience system.

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