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Athanasiou A, Mitsopoulos K, Praftsiotis A, Astaras A, Antoniou P, Pandria N, Petronikolou V, Kasimis K, Lyssas G, Terzopoulos N, Fiska V, Kartsidis P, Savvidis T, Arvanitidis A, Chasapis K, Moraitopoulos A, Nizamis K, Kalfas A, Iakovidis P, Apostolou T, Magras I, Bamidis P. Neurorehabilitation Through Synergistic Man-Machine Interfaces Promoting Dormant Neuroplasticity in Spinal Cord Injury: Protocol for a Nonrandomized Controlled Trial. JMIR Res Protoc 2022; 11:e41152. [PMID: 36099009 PMCID: PMC9516361 DOI: 10.2196/41152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 07/21/2022] [Accepted: 07/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background Spinal cord injury (SCI) constitutes a major sociomedical problem, impacting approximately 0.32-0.64 million people each year worldwide; particularly, it impacts young individuals, causing long-term, often irreversible disability. While effective rehabilitation of patients with SCI remains a significant challenge, novel neural engineering technologies have emerged to target and promote dormant neuroplasticity in the central nervous system. Objective This study aims to develop, pilot test, and optimize a platform based on multiple immersive man-machine interfaces offering rich feedback, including (1) visual motor imagery training under high-density electroencephalographic recording, (2) mountable robotic arms controlled with a wireless brain-computer interface (BCI), (3) a body-machine interface (BMI) consisting of wearable robotics jacket and gloves in combination with a serious game (SG) application, and (4) an augmented reality module. The platform will be used to validate a self-paced neurorehabilitation intervention and to study cortical activity in chronic complete and incomplete SCI at the cervical spine. Methods A 3-phase pilot study (clinical trial) was designed to evaluate the NeuroSuitUp platform, including patients with chronic cervical SCI with complete and incomplete injury aged over 14 years and age-/sex-matched healthy participants. Outcome measures include BCI control and performance in the BMI-SG module, as well as improvement of functional independence, while also monitoring neuropsychological parameters such as kinesthetic imagery, motivation, self-esteem, depression and anxiety, mental effort, discomfort, and perception of robotics. Participant enrollment into the main clinical trial is estimated to begin in January 2023 and end by December 2023. Results A preliminary analysis of collected data during pilot testing of BMI-SG by healthy participants showed that the platform was easy to use, caused no discomfort, and the robotics were perceived positively by the participants. Analysis of results from the main clinical trial will begin as recruitment progresses and findings from the complete analysis of results are expected in early 2024. Conclusions Chronic SCI is characterized by irreversible disability impacting functional independence. NeuroSuitUp could provide a valuable complementary platform for training in immersive rehabilitation methods to promote dormant neural plasticity. Trial Registration ClinicalTrials.gov NCT05465486; https://clinicaltrials.gov/ct2/show/NCT05465486 International Registered Report Identifier (IRRID) PRR1-10.2196/41152
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Affiliation(s)
- Alkinoos Athanasiou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Mitsopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Apostolos Praftsiotis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexander Astaras
- Computer Science Department, Division of Science and Technology, American College of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Antoniou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Niki Pandria
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileia Petronikolou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Kasimis
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - George Lyssas
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikos Terzopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasilki Fiska
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Kartsidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Theodoros Savvidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Athanasios Arvanitidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Chasapis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Alexandros Moraitopoulos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Kostas Nizamis
- Department of Design, Production and Management, University of Twente, Enschede, Netherlands
| | - Anestis Kalfas
- Laboratory of Fluid Mechanics and Turbo-machinery, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Paris Iakovidis
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - Thomas Apostolou
- Department of Physiotherapy, International Hellenic University, Thessaloniki, Greece
| | - Ioannis Magras
- Second Department of Neurosurgery, Ippokrateio General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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2
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Pandria N, Petronikolou V, Lazaridis A, Karapiperis C, Kouloumpris E, Spachos D, Fachantidis A, Vasiliou D, Vlahavas I, Bamidis P. An Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: The ADHD360 Project (Preprint). JMIR Res Protoc 2022; 11:e40189. [PMID: 36169998 PMCID: PMC9557982 DOI: 10.2196/40189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior. Objective To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management. Methods A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed. Results A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform. Conclusions ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection. Trial Registration ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982 International Registered Report Identifier (IRRID) RR1-10.2196/40189
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Affiliation(s)
- Niki Pandria
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vasileia Petronikolou
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Aristotelis Lazaridis
- Intelligent Systems Lab, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Eleftherios Kouloumpris
- Intelligent Systems Lab, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Dimitris Spachos
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anestis Fachantidis
- Intelligent Systems Lab, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Ioannis Vlahavas
- Intelligent Systems Lab, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- Medical Physics and Digital Innovation Lab, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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3
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Petsani D, Ahmed S, Petronikolou V, Kehayia E, Alastalo M, Santonen T, Merino-Barbancho B, Cea G, Segkouli S, Stavropoulos TG, Billis A, Doumas M, Almeida R, Nagy E, Broeckx L, Bamidis P, Konstantinidis E. Digital Biomarkers for Supporting Transitional Care Decisions: Protocol for a Transnational Feasibility Study. JMIR Res Protoc 2022; 11:e34573. [PMID: 35044303 PMCID: PMC8811685 DOI: 10.2196/34573] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background Virtual Health and Wellbeing Living Lab Infrastructure is a Horizon 2020 project that aims to harmonize Living Lab procedures and facilitate access to European health and well-being research infrastructures. In this context, this study presents a joint research activity that will be conducted within Virtual Health and Wellbeing Living Lab Infrastructure in the transitional care domain to test and validate the harmonized Living Lab procedures and infrastructures. The collection of data from various sources (information and communications technology and clinical and patient-reported outcome measures) demonstrated the capacity to assess risk and support decisions during care transitions, but there is no harmonized way of combining this information. Objective This study primarily aims to evaluate the feasibility and benefit of collecting multichannel data across Living Labs on the topic of transitional care and to harmonize data processes and collection. In addition, the authors aim to investigate the collection and use of digital biomarkers and explore initial patterns in the data that demonstrate the potential to predict transition outcomes, such as readmissions and adverse events. Methods The current research protocol presents a multicenter, prospective, observational cohort study that will consist of three phases, running consecutively in multiple sites: a cocreation phase, a testing and simulation phase, and a transnational pilot phase. The cocreation phase aims to build a common understanding among different sites, investigate the differences in hospitalization discharge management among countries, and the willingness of different stakeholders to use technological solutions in the transitional care process. The testing and simulation phase aims to explore ways of integrating observation of a patient’s clinical condition, patient involvement, and discharge education in transitional care. The objective of the simulation phase is to evaluate the feasibility and the barriers faced by health care professionals in assessing transition readiness. Results The cocreation phase will be completed by April 2022. The testing and simulation phase will begin in September 2022 and will partially overlap with the deployment of the transnational pilot phase that will start in the same month. The data collection of the transnational pilots will be finalized by the end of June 2023. Data processing is expected to be completed by March 2024. The results will consist of guidelines and implementation pathways for large-scale studies and an analysis for identifying initial patterns in the acquired data. Conclusions The knowledge acquired through this research will lead to harmonized procedures and data collection for Living Labs that support transitions in care. International Registered Report Identifier (IRRID) PRR1-10.2196/34573
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Affiliation(s)
- Despoina Petsani
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Sara Ahmed
- Faculty of Medicine, School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.,Centre de Recherche Interdisciplinaire en Réadaptation, Constance-Lethbridge Rehabilitation Center du CIUSSS du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada.,Clinical Epidemiology, Centre for Outcomes Research and Evaluation (CORE), McGill University Health Center Research Institute, Montreal, QC, Canada
| | - Vasileia Petronikolou
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eva Kehayia
- Faculty of Medicine, School of Physical & Occupational Therapy, McGill University, Montreal, QC, Canada.,Centre de Recherche Interdisciplinaire en Réadaptation, Constance-Lethbridge Rehabilitation Center du CIUSSS du Centre-Ouest-de-l'Île-de-Montréal, Montreal, QC, Canada
| | - Mika Alastalo
- Laurea University of Applied Sciences, Vantaa, Finland
| | | | | | - Gloria Cea
- Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain
| | - Sofia Segkouli
- Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece
| | - Thanos G Stavropoulos
- Centre for Research & Technology Hellas, Information Technologies Institute, Thessaloniki, Greece
| | - Antonis Billis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Michael Doumas
- Second Propedeutic Department of Internal Medicine, General Hospital "Hippokration", Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Rosa Almeida
- Fundación INTRAS, RDi Projects Department, Valladolid, Spain
| | - Enikő Nagy
- Nagykovácsi Wellbeing Living Lab, Nagykovácsi, Hungary
| | - Leen Broeckx
- Thomas More University of Applied Sciences - LiCalab, Antwerp, Belgium
| | - Panagiotis Bamidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Evdokimos Konstantinidis
- Medical Physics and Digital Innovation Laboratory, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.,European Network of Living Labs, Brussels, Belgium
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Santonen T, Petsani D, Julin M, Garschall M, Kropf J, Van der Auwera V, Bernaerts S, Losada R, Almeida R, Garatea J, Muñoz I, Nagy E, Kehayia E, de Guise E, Nadeau S, Azevedo N, Segkouli S, Lazarou I, Petronikolou V, Bamidis P, Konstantinidis E. Cocreating a Harmonized Living Lab for Big Data-Driven Hybrid Persona Development: Protocol for Cocreating, Testing, and Seeking Consensus. JMIR Res Protoc 2022; 11:e34567. [PMID: 34989697 PMCID: PMC8778542 DOI: 10.2196/34567] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/04/2021] [Indexed: 11/15/2022] Open
Abstract
Background Living Labs are user-centered, open innovation ecosystems based on a systematic user cocreation approach, which integrates research and innovation processes in real-life communities and settings. The Horizon 2020 Project VITALISE (Virtual Health and Wellbeing Living Lab Infrastructure) unites 19 partners across 11 countries. The project aims to harmonize Living Lab procedures and enable effective and convenient transnational and virtual access to key European health and well-being research infrastructures, which are governed by Living Labs. The VITALISE consortium will conduct joint research activities in the fields included in the care pathway of patients: rehabilitation, transitional care, and everyday living environments for older adults. This protocol focuses on health and well-being research in everyday living environments. Objective The main aim of this study is to cocreate and test a harmonized research protocol for developing big data–driven hybrid persona, which are hypothetical user archetypes created to represent a user community. In addition, the use and applicability of innovative technologies will be investigated in the context of various everyday living and Living Lab environments. Methods In phase 1, surveys and structured interviews will be used to identify the most suitable Living Lab methods, tools, and instruments for health-related research among VITALISE project Living Labs (N=10). A series of web-based cocreation workshops and iterative cowriting processes will be applied to define the initial protocols. In phase 2, five small-scale case studies will be conducted to test the cocreated research protocols in various real-life everyday living settings and Living Lab infrastructures. In phase 3, a cross-case analysis grounded on semistructured interviews will be conducted to identify the challenges and benefits of using the proposed research protocols. Furthermore, a series of cocreation workshops and the consensus seeking Delphi study process will be conducted in parallel to cocreate and validate the acceptance of the defined harmonized research protocols among wider Living Lab communities. Results As of September 30, 2021, project deliverables Ethics and safety manual and Living lab standard version 1 have been submitted to the European Commission review process. The study will be finished by March 2024. Conclusions The outcome of this research will lead to harmonized procedures and protocols in the context of big data–driven hybrid persona development among health and well-being Living Labs in Europe and beyond. Harmonized protocols enable Living Labs to exploit similar research protocols, devices, hardware, and software for interventions and complex data collection purposes. Economies of scale and improved use of resources will speed up and improve research quality and offer novel possibilities for open data sharing, multidisciplinary research, and comparative studies beyond current practices. Case studies will also provide novel insights for implementing innovative technologies in the context of everyday Living Lab research. International Registered Report Identifier (IRRID) DERR1-10.2196/34567
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Affiliation(s)
- Teemu Santonen
- Department of Research, Development, Innovation and Business Development, Laurea University of Applied Sciences, Espoo, Finland
| | - Despoina Petsani
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessalonki, Greece
| | - Mikko Julin
- Department of Research, Development, Innovation and Business Development, Laurea University of Applied Sciences, Espoo, Finland
| | - Markus Garschall
- Center for Technology Experience, AIT Austrian Institute of Technology, Vienna, Austria
| | | | | | - Sylvie Bernaerts
- LiCalab, Thomas More University of Applied Sciences, Geel, Belgium.,Expertise Unit Psychology, Technology & Society, Thomas More University of Applied Sciences, Antwerp, Belgium
| | - Raquel Losada
- Fundación INTRAS, Research, Development and Innovation Projects Department, Spain, Valladolid, Spain
| | - Rosa Almeida
- Fundación INTRAS, Research, Development and Innovation Projects Department, Spain, Valladolid, Spain
| | - Jokin Garatea
- GAIA, Asociación de Industrias de Conocimiento y Tecnologías Aplicadas, Basque Country, Spain
| | - Idoia Muñoz
- GAIA, Asociación de Industrias de Conocimiento y Tecnologías Aplicadas, Basque Country, Spain
| | - Eniko Nagy
- Nagykovácsi Wellbeing Living Lab, Nagykovácsi, Hungary
| | - Eva Kehayia
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montreal, QC, Canada.,School of Physical and Occupational Therapy, McGill University, Montreal, QC, Canada
| | - Elaine de Guise
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montreal, QC, Canada.,Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Sylvie Nadeau
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montreal, QC, Canada.,School of Rehabilitation, Université de Montréal, Montreal, QC, Canada
| | - Nancy Azevedo
- Centre for Interdisciplinary Research in Rehabilitation of Greater Montreal (CRIR), Montreal, QC, Canada
| | - Sofia Segkouli
- Centre for Research and Technology-Hellas (CERTH)/Information Technologies Institute (ITI), Thessaloniki, Greece
| | - Ioulietta Lazarou
- Centre for Research and Technology-Hellas (CERTH)/Information Technologies Institute (ITI), Thessaloniki, Greece
| | - Vasileia Petronikolou
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessalonki, Greece
| | - Panagiotis Bamidis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessalonki, Greece
| | - Evdokimos Konstantinidis
- Laboratory of Medical Physics and Digital Innovation, School of Medicine, Aristotle University of Thessaloniki, Thessalonki, Greece.,European Network of Living Labs, Brussels, Belgium
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Romanopoulou ED, Zilidou VI, Gilou S, Dratsiou I, Varella A, Petronikolou V, Katsouli AM, Karagianni M, Bamidis PD. Technology Enhanced Health and Social Care for Vulnerable People During the COVID-19 Outbreak. Front Hum Neurosci 2021; 15:721065. [PMID: 34566606 PMCID: PMC8461025 DOI: 10.3389/fnhum.2021.721065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 08/25/2021] [Indexed: 01/20/2023] Open
Abstract
The COVID-19 pandemic has spread rapidly worldwide with critical consequences in health, as well as in social, economic, and particularly in psychological conditions of vulnerable people, especially older adults. Therefore, it is necessary the direct attention to their health care needs and related interventions. Information and Communication Technology (ICT) have direct impact on older adults' health and quality of life leading to decreased depression and loneliness, along with empowerment of independent life. Many studies involve cognitive training programs/software based on new technological systems that provide to vulnerable people access to gamified, attractive, cognitive exercises for overall functionality everywhere and at any time. Twenty-four participants (mean age 69.3 years) were assigned to this study. The cognitive training component of LLM Care was used as an interactive software to enhance participants' cognitive functions. The intervention lasted 12 weeks with the frequency of 2-4 times per week in sessions of at least 30 min. Participants used their personal devices (tablets/laptops) in their own residence, while technical and consulting guidance was provided by LLM Care certified trainers. They were informed about the purpose of the study, while consent forms along with psychological assessments were distributed every 2 weeks to periodically evaluate their psychosocial and mental health conditions. The assessments included the World Health Organization-Five Well-Being Index (WHO-5), the Short Anxiety Screening Test (SAST), the System Usability Scale (SUS) and the Impact Factor Event Scale (IES-R). According to the results, the participants with improved well-being tended to report decreased subjective distress caused by COVID-19, and their engagement with new technologies can potentially minimize the negative outcomes occurred by the current stressful situation, mitigating the effect of hyperarousal symptoms, while increasing their overall well-being. Well-being seems to remain relatively stable among older adults and decreases only when adversities occur, while the usability of the software was perceived as marginally acceptable by participants. The exploitation of the LLM Care contributes to the improvement of older adults' well-being and alleviates the negative experience caused by stressful situations like COVID-19.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Panagiotis D. Bamidis
- Laboratory of Medical Physics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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