A Decision Support System to Propose Coaching Plans for Seniors

Subías-Beltrán, Paula; Orte, Silvia; Vargiu, Eloisa; Palumbo, Filippo; Angelini, Leonardo; Khaled, Omar Abou; Mugellini, Elena; Caon, Maurizio

doi:10.1109/cbms.2019.00123

Cited by 10 publications

(8 citation statements)

References 8 publications

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“…Recommender systems based on rules designed by experts may still serve a better job. We believe in the necessity of building in any healthcare virtual coach system a Decision Support System [77] that can analyze the data collected through automated monitoring or self-reporting, and also based on both user preferences and experts' recommendations, in order to provide personalized coaching plans for each of the different domains tackled by the system. In terms of intervention delivery, conversational agents have been mainly proposed in the recent studies issued from 2016 [17,20,27,30,31,32,35,39,41].…”

Section: Q4 How Are Different Systems Implemented In Terms Of Monitomentioning

confidence: 99%

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

et al. 2020

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Virtual Coaches, also known as e-coaches, are a disruptive technology in healthcare. Indeed, among other usages, they might provide cost-effective solutions for increasing human wellbeing in different domains, such as physical, nutritional, cognitive, social, and emotional. This paper presents a systematic review of virtual coaches specifically aimed at improving or maintaining older adults' health in the aforementioned domains. Such digital systems assume various forms, from classic apps, to more advanced conversational agents or robots. Fifty-six articles describing a virtual coach for older adults and aimed at improving their wellbeing were identified and further analyzed. In particular, we presented how previous studies defined their virtual coaches, which behavioral change models and techniques they adopted and the overall system architecture, in terms of monitoring solutions, processing methods and modalities for intervention delivery. Our results show that few thorough evaluations of e-coaching systems have been conducted, especially regarding multi-domain coaching approaches. Through our analysis, we identified the wellbeing domains that should be addressed in future studies as well as the most promising behavior change models and techniques and coaching interfaces. Previous work illustrates that older adults often appreciate conversational agents and robots. However, the lack of a multidomain intervention approach in the current literature motivates us to seek to define future solutions.

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Section: Q4 How Are Different Systems Implemented In Terms Of Monitomentioning

confidence: 99%

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

et al. 2020

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“…All of the recommendations for the week are repeated using this process. Depending on the use case, two methods are used to penalize the already sent recommendations: either they are subjected to a penalty factor [29], or after being sent, they are programmed with a latency time that prevents them from being reprogrammed for a predetermined period of time.…”

Section: Reinforcement Learning Processmentioning

confidence: 99%

“…In NESTORE, we added many kinds of tags to BECOME: domainspecific (e.g., to create the grocery list of nutritional CEs), time-related (to indicate the most appropriate time slots to send a specific CE), language (to describe if the CE is language-specific and to define the communication language of a user), and preferences (to describe the likings of users), among others. The selection of the recommendations was done through a tagging system composed of four modules: a constraint-based system combined with a hybrid recommendation system [29], which employs collaborative (CF), content-based filtering (CBF), and log filtering.…”

Section: Nestore: Personalized Coaching Plans For Seniorsmentioning

confidence: 99%

BECOME: A Modular Recommender System for Coaching and Promoting Empowerment in Healthcare

Orte¹,

Migliorelli²,

Sistach-Bosch³

et al. 2023

Recommender Systems [Working Title]

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In this chapter, we present BECOME (Behavior Change recOMender systEm), a modular Recommender System built to cope with issues like personalization, adaptation, and delivery of contents pertinently designed to solve idiosyncrasies of various topics in the healthcare field. The main objective is to empower citizens or patients to make informed decisions to improve their health condition. It deals with a double-edged personalization process as one of the key aspects to fostering self-empowerment: content dynamically personalized and adapted as new information is gathered and flexibility in the strategies and timings of the delivery. Thus, we take personalization one step further by not only tailoring the content, which is the standard customization strategy, but also adapting its timings and complexity in a dynamic manner while dealing with the feeling of having an entity (the coach) behind, ready to help. To show the modularity of the system and the diverse ways of interaction, different studies representing various use cases are presented.

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“…The potential of mobile and e-health services to improve the quality of life in long-term care settings [12] underlined the importance of technology in supporting older adults, especially those with mild cognitive impairments (MCI). These systems can exploit the presence of Decision Support Systems (DSS) for personalizing coaching, aimed at promoting active ageing [13,14]. We proposed innovative approaches for designing coaching plans tailored to the individual needs and preferences of older adults.…”

Section: Context Awareness and Behavioral Shift Detectionmentioning

confidence: 99%

Intelligent Environments for Longevity and Active Aging. The Role of Technology Solutions in Revolutionizing Healthcare

Palumbo

2023

Workshop Proceedings of the 19th International Conference on Intelligent Environments (IE2023)

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The rapid increase in the aging population has brought about the need for new solutions in healthcare. Intelligent environments offer a promising approach for promoting longevity and active aging through the use of technology solutions. In this paper, following the author’s journey in the field, we explore the role of intelligent environments in revolutionizing healthcare, and we discuss their potential to enhance the quality of life for older adults. We also provide an overview of some of the key technological solutions that are being developed to support intelligent environments for longevity and active aging.

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A Decision Support System to Propose Coaching Plans for Seniors

Cited by 10 publications

References 8 publications

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

BECOME: A Modular Recommender System for Coaching and Promoting Empowerment in Healthcare

Intelligent Environments for Longevity and Active Aging. The Role of Technology Solutions in Revolutionizing Healthcare

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