Search

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.

Las tecnologías han penetrado en todos los quehaceres del ser humano. Se han desarrollado numerosos impulsos tecnológicos en el campo de la neuropsicología, pero éstos no suelen ser de uso cotidiano. Al respecto se plantean algunas reflexiones respecto a las dificultades y al proceso de implementación de soluciones tecnológicas en el campo de la neuropsicología.

Abstract Background Social robots have demonstrated promising outcomes in terms of increasing the social health and well-being of people with dementia and mild cognitive impairment. According to the World Health Organization’s Monitoring and assessing digital health interventions framework, usability and feasibility studies are crucial before implementing prototype social robots and proving their efficacy and effectiveness. This protocol paper aims to detail the plan for conducting the usability and feasibility study of the MINI robot based on evidence-based recommended methodology. Methods In this study, an experimental design and a mixed method of data collection will be applied. Twenty participants aged 65 and over with dementia or mild cognitive impairment will be recruited. Eight sessions of interaction with the robot, as well as qualitative and quantitative assessments, will be accomplished. The research will take place in a laboratory. Ethical approvals have been acquired. This research will be valuable in the development of the MINI robot and its practical deployment in the actual world, as well as the methodological evidence base in the sector of social robots. Discussion By the winter of 2022–2023, the findings of this study will be accessible for dissemination. This study will aid to improve the evidence-based methodology used to study the feasibility and usability of social robots in people with dementia and mild cognitive impairment as well as what can be learned to advance such study designs in the future.

BackgroundNew research fields to design social robots for older people are emerging. By providing support with communication and social interaction, these robots aim to increase quality of life. Because of the decline in functioning due to cognitive impairment in older people, social robots are regarded as promising, especially for people with dementia. Although study outcomes are hopeful, the quality of studies on the effectiveness of social robots for the elderly is still low due to many methodological limitations. ObjectiveWe aimed to review the methodologies used thus far in studies evaluating the feasibility, usability, efficacy, and effectiveness of social robots in clinical and social settings for elderly people, including persons with dementia. MethodsDedicated search strings were developed. Searches in MEDLINE (PubMed), Web of Science, PsycInfo, and CINAHL were performed on August 13, 2020. ResultsIn the 33 included papers, 23 different social robots were investigated for their feasibility, usability, efficacy, and effectiveness. A total of 8 (24.2%) studies included elderly persons in the community, 9 (27.3%) included long-term care facility residents, and 16 (48.5%) included people with dementia. Most of the studies had a single aim, of which 7 (21.2%) focused on efficacy and 7 (21.2%) focused on effectiveness. Moreover, forms of randomized controlled trials were the most applied designs. Feasibility and usability were often studied together in mixed methods or experimental designs and were most often studied in individual interventions. Feasibility was often assessed with the Unified Theory of the Acceptance and Use of Technology model. Efficacy and effectiveness studies used a range of psychosocial and cognitive outcome measures. However, the included studies failed to find significant improvements in quality of life, depression, and cognition. ConclusionsThis study identified several shortcomings in methodologies used to evaluate social robots, resulting in ambivalent study findings. To improve the quality of these types of studies, efficacy/effectiveness studies will benefit from appropriate randomized controlled trial designs with large sample sizes and individual intervention sessions. Experimental designs might work best for feasibility and usability studies. For each of the 3 goals (efficacy/effectiveness, feasibility, and usability) we also recommend a mixed method of data collection. Multiple interaction sessions running for at least 1 month might aid researchers in drawing significant results and prove the real long-term impact of social robots.

Due to the significant increase in the percentage of older adults, as well as degenerative diseases, there is growing interest in the determination of effective psychosocial approaches aimed to subjects with mild cognitive impairment (MCI). Recent experimental studies indicate positive effects of cognitive interventions in population with DCL in both, traditional methods and computer based interventions. The present review provides a systematic analysis of the literature in order to assess the effect and scope of actual non-pharmacological cognitive interventions, aimed to older adults with mild cognitive impairment.

Technologies have penetrated all walks of human beings. Numerous technological advances have been developed in the field of neuropsychology, but they are not usually used in daily care. In this regard, it are developed some reflections on the difficulties and of the process of implementing technology solutions in the field of neuropsychology.

Readmissions are an indicator of hospital care quality; a high readmission rate is associated with adverse outcomes. This leads to an increase in healthcare costs and quality of life for patients. Developing predictive models for hospital readmissions provides opportunities to select treatments and implement preventive measures. The aim of this study is to develop predictive models for the readmission risk of patients with schizophrenia, combining the particle swarm optimization (PSO) algorithm with machine learning classification algorithms. The database used in the study includes a total of 6089 readmission records of patients with schizophrenia. These records were collected from 11 public hospitals in Castilla and León, Spain, in the period 2005–2015. The results of the study show that the Random Forest algorithm combined with PSO achieved the best results across the evaluated performance metrics: AUC = 0.860, recall = 0.959, accuracy = 0.844, and F1-score = 0.907. The development of these new models contributes to -improving patient care. Additionally, they enable preventive measures to reduce costs in healthcare systems.

BackgroundIn the era of big data, networks are becoming a popular factor in the field of data analysis. Networks are part of the main structure of BeGraph software, which is a 3D visualization application dedicated to the analysis of complex networks. ObjectiveThe main objective of this research was to visually analyze tendencies of mental health diseases in a region of Spain, using the BeGraph software, in order to make the most appropriate health-related decisions in each case. MethodsFor the study, a database was used with 13,531 records of patients with mental health disorders in three acute medical units from different health care complexes in a region of Spain. For the analysis, BeGraph software was applied. It is a web-based 3D visualization tool that allows the exploration and analysis of data through complex networks. ResultsThe results obtained with the BeGraph software allowed us to determine the main disease in each of the health care complexes evaluated. We noted 6.50% (463/7118) of admissions involving unspecified paranoid schizophrenia at the University Clinic of Valladolid, 9.62% (397/4128) of admissions involving chronic paranoid schizophrenia with acute exacerbation at the Zamora Hospital, and 8.84% (202/2285) of admissions involving dysthymic disorder at the Rio Hortega Hospital in Valladolid. ConclusionsThe data analysis allowed us to focus on the main diseases detected in the health care complexes evaluated in order to analyze the behavior of disorders and help in diagnosis and treatment.

Los desarrollos tecnológicos pueden ser un complemento a la atención tradicional, por ello primero se revisa el impacto que tiene la tecnología en la alianza de trabajo terapéutico. A continuación se señalan algunos recursos tecnológicos disponibles para la actividad psicológica, así como una clasificación de las tecnologías. Finalmente se revisan las posibles ventajas y riesgos en la aplicación de tratamiento mediados por tecnologías, así como los resultados en cuanto a efectividad.

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

Abstract Background Data from primary health care in Spain show a high prevalence of the major depressive disorder. Blended treatment (combination of face-to-face and online components) seems to be a very promising tool for the optimization and dissemination of psychological treatments in a cost-effective form. Although there is growing data that confirm the advantages of blended therapies, few studies have analyzed their application in regular clinical practice. The objective of the present paper is to describe the protocol for a clinical study aimed at exploring the clinical and cost-effectiveness of a blended cognitive behavioral therapy (b-CBT) for depression, compared to treatment as usual (TAU) in a primary health care setting. Methods A two-arm randomised controlled non-inferiority trial will be carried out, with repeated measures (baseline, 3 months, 6 months, and 12 months) under two conditions: b-CBT and TAU. The b-CBT program will consist in three face-to-face sessions and eight online sessions. The TAU is defined as the routine care delivered by the general practitioner for the treatment of depression in primary care. The primary outcome is a symptomatic change of depressive symptoms on the patient-health questionnaire (PHQ-9). Other secondary outcomes will be considered (e.g., quality of life, treatment preference). All participants must be 18 years of age or older and meet the diagnostic criteria for major depressive disorder according to the Diagnostic and Statistical Manual of Mental disorders 4th edition. 156 participants will be recruited (78 per arm). Discussion It is expected that b-CBT is clinically non-inferior when compared to TAU. This is the first study in Spain to use a b-CBT format in primary and specialized care, and this format could be an efficacious and cost-effective therapeutic strategy for the treatment of depression. Trial registration ClinicalTrials.gov NCT02361684. Registered on 8 January 2015. Currently recruiting participants.

Today, the population uses technology for every daily activity involving business, education, communication, entertainment, etc. Technology may also help us to take care of people who suffer some kind of disability. Complex technological ecosystems with pervasive and intelligent capabilities get along with us, facilitating the vigilance of those who need special attention or assisted living cares due to their health limitations. The advances in sensor research have enriched the powerful of these ecosystems to achieve more sophisticated monitoring and alarm systems, also taking into account the balance between the level of assistance and the people’s privacy. The Special Issue on “Sensor Technologies for Caring People with Disabilities” aims to present recent developments on sensor technologies for caring people with disabilities, focusing on the different configurations that can be used and novel applications in the field.

The use of robotic platforms for neuro-rehabilitation may boost the neural plasticity process and improve motor recovery in patients with upper limb mobility impairment as a consequence of an acquired brain injury. A robotic platform for this aim must provide ergonomic and friendly design, human safety, intensive task-oriented therapy, and assistive forces. Its implementation is a complex process that involves new developments in the mechanical, electronics, and control fields. This article presents the end-effector rehabilitation robot, a 2-degree-of-freedom planar robotic platform for upper limb rehabilitation in patients with neuromotor disability after a stroke. We describe the ergonomic mechanical design, the system control architecture, and the rehabilitation therapies that can be performed. The impedance-based haptic controller implemented in end-effector rehabilitation robot uses the information provided by a JR3 force sensor to achieve an efficient and friendly patient–robot interaction. Two task-oriented therapy modes have been implemented based on the “assist as needed” paradigm. As a result, the amount of support provided by the robot adapts to the patient’s requirements, maintaining the therapy as intensive as possible without compromising the patient’s health and safety and promoting engagement.