ActiveUP is at the intersection of medical informatics, computer science, psychology, and clinical research, making the coordination of two subprojects essential. The team is composed of researchers from the Polytechnic University of Madrid (UPM) and the Biomedical Research Foundation of the University Hospital of Getafe (FIBHUG), including biomedical engineers, computer engineers, geriatricians, a psychologist, a user experience researcher, an epidemiologist, and a clinical pharmacologist. The UPM researchers belong to the Ageing Lab1, which is part of the Biomedical Technology Center (CTB), and the Human-Computer Interaction and Advanced Interactive Systems research group at the School of Informatics. The FIBHUG researchers are also part of the Ageing Lab, since its establishment as a joint laboratory in 2015. It is worth highlighting the relevance of both entities in terms of research and knowledge transfer. According to the latest research and knowledge transfer report of Spanish universities by CRUE, UPM is among the top five Spanish universities in practically all indicators related to competitive funding and knowledge transfer, being the technical university that has obtained the most funding for research projects, both national and European. Additionally, the CTB is a leading research center in biomedical technology. On the other hand, the University Hospital of Getafe and its FIBHUG are a leading research center in geriatrics and frailty. Together, both institutions cover all the necessary areas to fulfill the objectives of ActiveUP.
BACKGROUND AND STATE OF THE ART
The aging population poses a threat to both public and private health systems as well as social systems. According to data from the CSIC (Spanish National Research Council), the population aged 65 and over in Spain reached more than 8.7 million in 2017 (18.8%) and is expected to grow to 14 million by 2066, which is equivalent to 34.6% of the Spanish population. This aging of the population poses a major challenge in terms of how to provide the healthcare services required by the elderly, while maintaining the sustainability of the system. The WHO has identified numerous actions to promote healthy aging, but they all have a common goal: to foster functional capacity. In this way, preventing disability and dependence is a public health priority. It is important to note that the underlying process of the disability pathway is the loss of functional capacity and functional reserve. Consequently, the later we intervene along the disability pathway, the less likely we are to successfully reverse or improve the condition. In other words, the main focus is on the prevention, intervention, and reversal of frailty and its effects on health in the later stages of life.
Frailty does not require the prevalence of diseases to be present. It is theoretically defined as a clinically recognizable state of increased vulnerability resulting from the age-related decline in reserve and function across multiple physiological systems, such that the ability to cope with everyday or acute stressors is compromised. When a person is frail, it does not mean they are dependent, however, they have a higher risk of becoming dependent.
THE CHALLENGE
OUR PROJECT
The present proposal builds up on the work, the experience (Alberto et al, 2017), and the results from two previous projects: FACET and POSITIVE. Both projects have been funded by the European Institute of Technology via the EIT-Health network, and they have involved both the technical and clinical teams in the present proposal. A home care system for frailty monitoring and management was developed in FACET. It included a frailty monitoring kit able to detect changes in the two most informative functional variables, namely, gait speed and lower-limb strength. The kit consists of a gait speed sensor, a lower-limb strength sensor, and a mobile app to control both of them. Additionally, the mobile app supports self-administration of usual Comprehensive Geriatric Assessment (CGA) questionnaires. POSITIVE has extended the home care system to formal caregivers as primary care, and informal caregivers. However, these projects and solutions lack personalisation and adaptation, including motivation to the patient, putting at risk adoption and adherence levels. ActiveUP will incorporate not only this personalisation and adaptation but also considering additional data from the patient context such as ambient information.
OBJECTIVES AND STATUS
Tailoring multicomponent interventions to improve frailty condition: This objective focuses on the definition of multicomponent interventions taking into account differences in personal characteristics, functional and cognitive capacity, and psychological traits, particularly those related to motivation, in order to adapt to different individuals allowing for personalized prescription. This includes interventions such as physical exercise, nutrition, and medication adjustment to avoid polypharmacy.
To design, develop and validate an ICT solution for assessing the user’s performance when exercising, without external supervision: This objective aims to monitor frailty and function in unsupervised home settings, ensuring technology is non-intrusive for independent older adults. It involves developing a new wearable sensor and transitioning to an Internet of Things architecture. Data from IoT devices and a patient app are sent directly to the internet for monitoring and exercise. An algorithm measures gait speed automatically using signals from the wearable device, undergoing evaluation in an observational study. The objective includes defining and developing the wearable device and UbiHome system. Validation involves an observational study with 15 patients over 6 months, with promising preliminary results indicating near completion of system validation.
Personalization and adaptation to the older user: This objective centers on enhancing patient adherence and preventing dropout by personalizing interventions. It involves designing a motivational component that identifies and models patient motivation, activating tailored motivational strategies. Additionally, it considers the patient’s stage of behavior change, monitored through the Transtheoretical Model of Change, influencing interaction approaches. The objective is fully accomplished, with a designed and implemented motivational component. It includes an objective diagnostic test for patient motivation traits and a motivational assistant adjusting strategies based on the patient’s profile. While experimentation with real patients was planned, it has been transferred to another research project called MOTIVA. However, an internal test has confirmed the effective operation of the motivational component across various scenarios.
Development of the ActiveUP system: This objective aims to develop the complete ActiveUP system, which includes the non-intrusive monitoring environment (see objective 2), the personalized and adaptive patient subsystem (see objective 3), the cloud subsystem, as well as all software and hardware components.
RELATED PUBLICATIONS
Cobo A, Villalba-Mora E, Pérez-Rodríguez,R, Ferre X., & Rodríguez-Mañas L. Unobtrusive Sensors for the Assessment of Older Adult’s Frailty: A Scoping Review. Sensors, 2021 21(9): 2983. doi: link | ||||
Pérez-Rodríguez R, Villalba-Mora E, Valdés-Aragonés M, Ferre X, Moral, C, Mas-Romero M, … Rodríguez-Mañas L. Usability, User Experience, and Acceptance Evaluation of CAPACITY: A Technological Ecosystem for Remote Follow-Up of Frailty. Sensors 2021, 21(19), 6458. doi: link | ||||
Cobo A, Rodríguez-Laso Á, Villalba-Mora E, Pérez-Rodríguez R, Rodríguez-Mañas L. Frailty detection in older adults via fractal analysis of acceleration signals from wrist-worn sensors. Health Inf Sci Syst 2023 11: 29. doi: link | ||||
Villalba-Mora E, Ferre X, Pérez-Rodríguez R, Moral C, Valdés-Aragonés M, Sánchez-Sánchez A, Rodríguez-Mañas L. Home monitoring system for Comprehensive Geriatric Assessment in patient’s dwelling: system design and UX evaluation. Frontiers in Digital Health 2021, 3, 40. doi: link | ||||
Fernández-Avilés, Daniel, Angelica De Antonio, and Elena Villalba-Mora (2020). «A systematic mapping study on the use of motivational theories in the design of motivational software.» IEEE Access 8 (2020): 176840-176863. JCR (Q2) doi: link | ||||
Jiyeon Yu, Angelica de Antonio & Elena Villalba-Mora (2021). «Older Adult Segmentation According to Residentially-Based Lifestyles and Analysis of their Needs for Smart Home Functions». International Journal of Environmental Research and Public Health, Special Issue «Active/Healthy Ageing and Quality of Life», 17(22), 8492; link, ISSN 1661-7827. JCR (Q1) | ||||
Jiyeon Yu, Angelica de Antonio and Elena Villalba-Mora (2022). Influential Factors for Older Users’ Acceptance of eHealth Services: an Integrated Acceptance Framework (IAF). Journal of Medical Internet Research (accepted, in edition). ISSN 1438-8871. JCR (Q1) | ||||
Daniel Fernández-Avilés, Angelica De Antonio and Elena Villalba-Mora (2022). «Motivational Traits: An Objective Behavioral Test Using a Computer Game». Frontiers in Psychology, 13:812918, 2022. doi: link, ISSN 1664-1078. JCR (Q1) | ||||
Jiyeon Yu, Angelica de Antonio and Elena Villalba-Mora (2022). «Deep Neural Network (CNN, RNN) applications for smart homes: A systematic review». Computers 11(2):26. Special Issue «Survey in Deep Learning for IoT Applications». ISSN 2073-431X. JCR (Q3) doi: link |