Neuropsychological profile of executive functions in autism spectrum disorder and schizophrenia spectrum disorders: a comparative group study in adults.

As assessed by numerous neuropsychological tasks, individuals with autism spectrum disorder (ASD) and schizophrenia spectrum disorders (SSDs) have similar impairments related to executive functions (EFs). The neuropsychological profile of these two conditions was examined using the three-component EFs' framework of Miyake and Friedman (Cogn Psychol 41(1):49-100, 2000). This approach assesses Inhibition (suppression of unwanted and irrelevant information/responses), Updating (use and control of contents of working memory), and Shifting (disengagement between activities or mental tasks) using nine different tasks. In line with previous research, we expected greater performance deficits in ASD in all three components compared to SSD, as well as faster responses for the SSD group. A self-paced task format allowed us to examine whether unlimited time given for a task would lead to better performance. The sample was constituted by the control group (N = 25), ASD group (N = 24), and SSD group (N = 12). Groups did not differ on Inhibition performance. In Updating, individuals with SSD performed poorer than the other groups. As for Shifting, both groups demonstrated poorer performance compared to controls, with the SSD group presenting the greatest difficulties. In terms of reaction time (RT), SSD participants' RT were the slowest on Inhibition and Shifting tasks. There was a positive correlation between performance and time spent on Inhibition and Shifting only for the SSD group, which demonstrates that their performance improves when there are no time constraints. Our work provides a better understanding of spared and impaired EFs, which could be useful for designing strategies aimed at improving specific EFs in each group.

Potential Facilitators of and Barriers to Implementing the MINI Robot in Community-Based Meeting Centers for People With Dementia and Their Carers in the Netherlands and Spain: Explorative Qualitative Study.

BACKGROUND: Social robots, as a form of digital health technologies, are used to support emotional, cognitive, and physical care and have shown promising outcomes in enhancing social well-being in people with dementia (PwD) by boosting emotions, social interactions, and activity participation. OBJECTIVE: The goal is to investigate the attitude of stakeholders and potential facilitators and the barriers to implementing the social robot MINI in community-based meeting centers (MCs) for PwD and carers in the Netherlands and Spain. METHODS: Based on the British Medical Research Council guidance for process evaluation of the implementation of complex interventions and the model for tracing the facilitators of and barriers to the adaptive implementation of innovations in dementia care, an explorative qualitative study was conducted. Following the introduction of the MINI robot, 11 stakeholders were interviewed in 3 MCs in the Netherlands and 1 in Spain, as well as stakeholders in health and welfare organizations in both countries. In addition, 12 adults with dementia participated in focus groups. The data were thematically analyzed and narratively described. RESULTS: Overall, the stakeholder opinion and interest in the MINI robot were positive. The most important (expected) facilitating factors mentioned by stakeholders appeared to be human resources, funding, the impact of the MINI robot on the users and programs of the MCs, characteristics of the innovation, and collaboration with other care and welfare organizations. However, the (expected) barriers mentioned concerned the physical context and functionalities of the MINI robot, the user context, and MC activity policies. CONCLUSIONS: The findings will inform professional stakeholders, such as MC directors and managers, as well as care and welfare organizations, on the practicality of using the MINI robot in MCs. Furthermore, our research will aid MINI robot developers in tailoring its features to PwD's preferences and demands and MC policies, which will contribute to the MINI robot's effective adoption and deployment.

Methodologies Used to Study the Feasibility, Usability, Efficacy, and Effectiveness of Social Robots For Elderly Adults: Scoping Review.

BACKGROUND: New 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. OBJECTIVE: We 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. METHODS: Dedicated search strings were developed. Searches in MEDLINE (PubMed), Web of Science, PsycInfo, and CINAHL were performed on August 13, 2020. RESULTS: In 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. CONCLUSIONS: This 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.

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.

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.

Online Mindfulness Experience for Emotional Support to Healthcare staff in times of Covid-19.

During the first confinement in Spain, between the months of March to June 2020, Information and Communication Technologies strategies were implemented in order to support health workers in the Wellbeing of Mental Health. Faced with so much uncertainty about the pandemic, an Online Mindfulness course. The objective of the course was to support healthcare professionals in Castilla y León in managing stress, anxiety and other emotional disturbances generated by coping with a situation as uncertain and unexpected as a pandemic, in order to manage emotions and thoughts that can lead to suicidal ideation. The motivations for the demand, reasons or motivations in which the health professionals of Castilla y León decided to participate in the mindfulness course in the first wave of Covid-19 in Spain are described. The descriptive and inferential statistical analysis of the customer satisfaction survey applied at the end of the mindfulness course, to the health professionals who participated in a satisfaction survey (CSQ-8: Client Satisfaction Questionnaire). Professional were asked to complete a survey based on (CSQ-8: Client Satisfaction Questionnaire) whose Cronbach's alpha = 0.917 is why the instrument used with N = 130 participants has high reliability. The 66% answered with a highly satisfied that they would return to the mindfulness online course. The 93% of the people who answered the satisfaction survey were women, of which they are professionals in the nursing area, with a participation of around 62%. In relation to the online system used in the Mindfulness intervention, 74% expressed that they fully agreed that it has been easy to use the online system for the mindfulness intervention. Health Professionals responded with 58% high satisfaction and 36% satisfaction, making a total of 94% on the help received in the online mindfulness courses to solve their problems. There is no difference between the age groups of the professionals who have preferred the Mindfulness online course (p = 0.672).

Trends in psychiatric hospitalization for alcohol and drugs in Castilla y León between 2005 and 2015. / Tendencias en la hospitalización psiquiátrica por alcohol y drogas en Castilla y León entre 2005 y 2015.

It has been estimated that alcohol, tobacco, and illicit drugs were responsible for more than 10 million deaths worldwide in 2016, and there are many opportunities for improvement. Regarding innovative data analysis, advances have been made in the extraction of information from administrative databases for analytics purposes. We studied trends in hospitalization rates for alcohol and drug abuse over eleven years with Joinpoint Trend Analysis software. This is a descriptive study of cross-associations in 3,758 hospital admissions of patients admitted with a main diagnosis of alcohol and drug abuse or dependence in psychiatry units of public health centres of Castilla y León (Spain) between 2005 and 2015. Hospitalization trends for alcohol and drug related conditions declined over the eleven-year period. Separately, there was a statistically significant decrease in alcohol and cocaine related conditions, but a strong upward trend in cannabis related conditions between 2013 and 2015. Alcohol was the main cause of admission to psychiatric units with a diagnosis of addiction. In the 11 years researched, there was a progressive and constant reduction in admissions for substance use except for cannabis. The innovative statistical methodology has already proven to be useful for identifying trends and changes in different pathologies over time.

A nivel mundial, se ha estimado que el alcohol, el tabaco y las drogas han sido responsables de más de 10 millones de muertes en 2016, y que existe mucho margen para reducir la mortalidad. Se han realizado avances en la extracción de información de bases de datos administrativas con el fin de analizar grandes volúmenes de datos sanitarios. Hemos estudiado las tendencias en las tasas de hospitalización con diagnóstico de adicción a alcohol y drogas durante once años con el software Joinpoint Trend Analysis. Se trata de un estudio descriptivo de asociación cruzada de 3.758 ingresos hospitalarios de pacientes con diagnóstico principal de abuso o dependencia de alcohol y drogas en unidades de Psiquiatría de centros públicos de Castilla y León entre 2005 y 2015. Las tendencias en la hospitalización por adicción al alcohol y/o drogas disminuyeron a lo largo de los once años. Además de una reducción estadísticamente significativa de los ingresos por alcohol y cocaína, se apreció una fuerte tendencia al alza en los ingresos por cannabis entre 2013 y 2015. El alcohol fue durante todo el periodo de estudio la principal causa de ingreso y el que más días de hospitalización ha generado. No obstante, en los 11 años se observó una reducción progresiva y constante en los ingresos por todas las sustancias a excepción del cannabis. La metodología utilizada ya ha demostrado ser muy útil para identificar cambios de tendencias en diferentes patologías.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Computer-based cognitive rehabilitation program GRADIOR for mild dementia and mild cognitive impairment: new features.

BACKGROUND: The growing number of older people and, with it, the increase of neurological impairments such as dementia has led to the implementation of the use of computer programs for cognitive rehabilitation in people with dementia. For 20 years, we have been developing the GRADIOR cognitive rehabilitation program and conducted several studies associated with its usability and effectiveness. This paper describes the development of the latest version of the GRADIOR computer-based cognitive rehabilitation program for people with different neurological etiologies, especially mild cognitive impairment and mild dementia. RESULTS: GRADIOR is a program that allows cognitive evaluation and rehabilitation of people affected by cognitive impairment. The new version of GRADIOR is characterized by a structure that is dynamic and flexible for both user and therapist, consisting of: Clinical Manager, Clinical History Manager, Treatment Manager and Report Manager. As a structure based on specific requirements, GRADIOR includes a series of modalities and sub-modalities, each modality comprising a series of exercises with different difficulty levels. DISCUSSION: Previous studies associated with earlier versions of GRADIOR have allowed the development of a new version of GRADIOR. Taking into account aspects associated with user experience, usability and effectiveness. Aspects that have made it possible to achieve a program that can meet the needs of older people with dementia.

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review.

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. Consequently, the objectives, data collection techniques, development process and the validation metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using the methodology proposed by Arksey and O'Malley et al. and the PRISMA protocol was adopted to select the relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75% of the analyzed studies implement machine learning-based models using Python.

Treatment of Depression in Primary Care with Computerized Psychological Therapies: Systematic Reviews.

Depression is one of the most important causes of disability due to illness in our environment. The primary care health system receives a high percentage of this consultation about psychological distress. Often this end in a pharmacological overtreatment in patients with mild depression, due to a lack of access to alternative tools for management. To analyze the evidence that exists by now about the effectiveness of computerized psychological therapies, in people with depression in primary care setting. The search process was mainly done through MEDLINE and Cochrane using keywords such as: "depression", "treatment", "primary care", "online", "internet", "computerized", "Cognitive Behavioral Therapy" and delimiting the search by years and types of studies. The Oxman quality scale was used to analyze quality of Systematic Reviews (SR). 11 previous SR were analyzed. Almost all research is experimental and has not been implemented in the public health network except in the United Kingdom, where there is a tradition in the use of the Beating the Blues program. It requires research in our country and development of programs in Spanish, or adaptation of those of other countries, to test the effectiveness in our health system and to study, in turn, the cost-efficiency. But it is proven to be effective in reducing depressive symptoms and must be study as a possible tool to be introduced in the management of depression in non-specialized care.

Study on the Acceptability of an ICT Platform for Older Adults with Mild Cognitive Impairment.

EhcoBUTLER is an Information and Communication Technology (ICT) solution funded by the European Union (H2020; ID: 643566) and intended especially for elderly people with mild cognitive impairment (MCI) to improve their health, independence and quality of life, particularly at the social level. The purpose of this study is to assess the acceptability of ehcoBUTLER based on a survey delivered to potential users and actors involved in their care, exploring their expectations and preferences, while anticipating the system's functional requirements. The survey was delivered online to 313 participants (11% end users, 25% informal caregivers, 48% formal caregivers and 16% administration/management staff) from eight countries. Participants rated the different functionalities of ehcoBUTLER positively, 86.1% perceiving it as an interesting and useful system. Likewise, they assessed it as a commercially attractive product (75.1%). End users expressed a stronger preference for the social module. Nevertheless, they would be ready to pay a low monthly price for ehcoBUTLER. Professionals would be willing to pay choosing its functionalities modularly, but they would also expect it to be funded by the National Health System, centres or businesses. The conclusion is that all participants found ehcoBUTLER interesting, useful and ergonomic. However, to effectively implement it, it is necessary to bridge the digital gap and address the issue of insufficient investment in products aimed at older adults with cognitive impairment. To supplement cognitive training systems with social, emotional or entertainment functionalities could improve adherence to their use.

Usability study and pilot validation of a computer-based emotion recognition test for older adults with Alzheimer's disease and amnestic mild cognitive impairment.

OBJECTIVES: This study aimed to carry out a pilot validation of Affect-GRADIOR, a computer-based emotion recognition test, with older adults. The study evaluated its usability, reliability and validity for the screening of people with Alzheimer´s disease (AD) and amnestic mild cognitive impairment (aMCI). METHODS: The test was administered to 212 participants (76.37 ± 6.20 years) classified into three groups (healthy controls, n = 69; AD, n = 84; and aMCI, n = 59) on the basis of detailed neurological, neuropsychological, laboratory and neuro-imaging evidence. Data on usability were collected by means of a questionnaire and automated evaluation. RESULTS: The validated test comprised 53 stimuli and 7 practice items (one per emotion). Participants reported that Affect-GRADIOR was accessible and user-friendly. It had high internal consistency (ordinal Cronbach's α = 0.96). Test-retest reliability correlations were significant and robust (r = 0.840, p < 0.001). Exploratory factor analysis supported a seven-factor model of the emotions assessed (neutral expression, happiness, surprise, disgust, sadness, anger and fear). Receiver operating characteristic curve analyses suggested that the test discriminated healthy older adults from AD and aMCI cases. Correct answer score improved MMSE predictive power from 0.547 to 0.560 (Cox & Snell R2, p = 0.012), and Affect-GRADIOR speed of processing score improved MMSE predictive power from 0.547 to 0.563 (Cox & Snell R2, p = 0.010). CONCLUSIONS: Affect-GRADIOR is a valid instrument for the assessment of the facial recognition of emotions in older adults with and without cognitive impairment.

Sensor Technologies for Caring People with Disabilities.

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.

Efficacy and cost-effectiveness of a blended cognitive behavioral therapy for depression in Spanish primary health care: study protocol for a randomised non-inferiority trial.

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.

A Systematic Literature Review of Technologies for Suicidal Behavior Prevention.

Suicide is the second cause of death in young people. The use of technologies as tools facilitates the detection of individuals at risk of suicide thus allowing early intervention and efficacy. Suicide can be prevented in many cases. Technology can help people at risk of suicide and their families. It could prevent situations of risk of suicide with the technological evolution that is increasing. This work is a systematic review of research papers published in the last ten years on technology for suicide prevention. In September 2017, the consultation was carried out in the scientific databases PubMed, ScienceDirect, PsycINFO, The Cochrane Library and Google Scholar. A general search was conducted with the terms "prevention" AND "suicide" AND "technology. More specific searches included technologies such as "Web", "mobile", "social networks", and others terms related to technologies. The number of articles found following the methodology proposed was 90, but only 30 are focused on the objective of this work. Most of them were Web technologies (51.61%), mobile solutions (22.58%), social networks (12.90%), machine learning (3.23%) and other technologies (9.68%). According to the results obtained, although there are technological solutions that help the prevention of suicide, much remains to be done in this field. Collaboration among technologists, psychiatrists, patients, and family members is key to advancing the development of new technology-based solutions that can help save lives.

ELECTRONIC DEVICES FOR COGNITIVE IMPAIRMENT SCREENING: A SYSTEMATIC LITERATURE REVIEW.

OBJECTIVES: The reduction in cognitive decline depends on timely diagnosis. The aim of this systematic review was to analyze the current available information and communication technologies-based instruments for cognitive decline early screening and detection in terms of usability, validity, and reliability. METHODS: Electronic searches identified 1,785 articles of which thirty-four met the inclusion criteria and were grouped according to their main purpose into test batteries, measures of isolated tasks, behavioral measures, and diagnostic tools. RESULTS: Thirty one instruments were analyzed. Fifty-two percent were personal computer based, 26 percent tablet, 13 percent laptop, and 1 was mobile phone based. The most common input method was touchscreen (48 percent). The instruments were validated with a total of 4,307 participants: 2,146 were healthy older adults (M = 73.59; SD = 5.12), 1,104 had dementia (M = 74.65; SD = 3.98) and 1,057 mild cognitive impairment (M = 74.84; SD = 4.46). Only 6 percent were administered at home, 19 percent reported outcomes about usability, and 22 percent about understandability. The methodological quality of the studies was good, the weakest methodological area being usability. Most of the instruments obtained acceptable values of specificity and sensitivity. CONCLUSIONS: It is necessary to create home delivered instruments and to include usability studies in their design. Involvement of people with cognitive decline in all phases of the development process is of great importance to obtain valuable and user-friendly products. It would be advisable for researchers to make an effort to provide cutoff points for their instruments.

Computer-based cognitive interventions for people living with dementia: a systematic literature review and meta-analysis.

OBJECTIVES: To estimate the efficacy of computer-based cognitive interventions for improving cognition in people with dementia (PWD). METHOD: Online literature databases were searched for relevant studies. Interventions were categorised as follows: cognitive recreation, cognitive rehabilitation, cognitive stimulation or cognitive training. A systematic review, quality assessment and meta-analyses were conducted. RESULTS: Twelve studies were identified. Their methodological quality was acceptable according to Downs & Black criteria, the weakest methodological area being the external validity. The meta-analyses indicated cognitive interventions lead to beneficial effects on cognition in PWD (SMD 0.69; 95% CI = 1.02-0.37; P < 0.0001; I(2) = 29%), [corrected] depression (SMD 0.47; 95% CI = 0.16-0.78; P = 0.003; I(2) = 0%) and anxiety (SMD 0.55; 95% CI = 0.07-1.04; P < 0.03; I(2) = 42%). [corrected]. They benefited significantly more from the computer-based cognitive interventions than from the non-computer-based interventions in cognition (SMD 0.48; 95% CI = 0.09-0.87; [corrected] P = 0.02; I(2) = 2%). CONCLUSION: Computer-based cognitive interventions have moderate effects in cognition and [corrected] anxiety and small effects in depression in PWD. No significant effects were found on activities of daily living. They led to superior results compared to non-computer-based interventions in cognition. Further research is needed on cognitive recreation and cognitive stimulation. There is also a need for longer term [corrected] follow-up to examine the potential retention of treatment effects, and for the design of specific outcome measures.

ICT-based applications to improve social health and social participation in older adults with dementia. A systematic literature review.

OBJECTIVES: Information and communication technologies (ICT) developers, together with dementia experts have created several technological solutions to improve and facilitate social health and social participation and quality of life of older adults living with dementia. However, there is a need to carry out a systematic literature review that focuses on the validity and efficacy of these new technologies assessing their utility to promote 'social health' and 'active ageing' in people with dementia. METHOD: Searches in electronic databases identified 3824 articles of which 6 met the inclusion criteria and were coded according to their methodological approach, sample sizes, type of outcomes and results. RESULTS: Six papers were identified reporting the use of 10 different interventions with people with dementia. Qualitative studies (four) showed a benefit of the use of technologies to foster social participation in people with dementia. At the same time, barriers to a widespread use of these technologies in this population were identified. A quantitative study and a mixed-method study with quantitative outcomes showed that ICT-based interventions promote more social behaviours than non-technology-based interventions. CONCLUSIONS: In the last years, several technological devices for living independently and fostering social health and social participation in people with dementia have been developed. However, specific outcome measures to assess social health and social participation are needed. Even though the analysed studies provided some evidence-base for the use of technology in this field, there is an urge to develop high quality studies and specific outcome measures.

Usability Evaluation of a Private Social Network on Mental Health for Relatives.

Usability is one of the most prominent criteria that must be fulfilled by a software product. This study aims to evaluate the usability of SocialNet, a private social network for monitoring the daily progress of patients by their relatives, using a mixed usability approach: heuristic evaluation conducted by experts and user testing. A double heuristic evaluation with one expert evaluator identified the issues related to consistency, design, and privacy. User testing was conducted on 20 users and one evaluator using observation techniques and questionnaires. The main usability problems were found to be related to the structure of SocialNet, and the users presented some difficulties in locating the buttons or links. The results show a high level of usability and satisfaction with the product. This evaluation provides data on the usability of SocialNet based on the difficulties experienced by the users and the expert. The results help in redesigning the tool to resolve the identified problems as part of an iterative process.

Online interventions for caregivers of people with dementia: a systematic review.

INTRODUCTION: The elderly population is growing driven by the increase in life expectancy, which in turn entails an increase in the number of people with chronic diseases such as dementia. The vast majority of people suffering from this illness is assisted by informal caregivers, who play a key role in fulfilling the patients’ needs, promoting the possibility for people with dementia to live in their home environment. The Internet as a support tool in psychoeducational programs can significantly improve accessibility of them, becoming a currently consolidated interactive resource for the training of patients with acute and chronic diseases, and their caregivers. POPULATION AND METHOD: A literature search of the Pubmed, PsyINFO, Scopus, SciELO and Psicodoc databases was performed to systematically review those studies related to web-based interventions for informal caregivers of people with dementia or cognitive impairment. RESULTS: On balance, the studies show a significant improvement after the psychoeducational intervention. Specifically, the improvement in caregivers’ wellbeing can be observed in the measures for self-efficacy, anxiety and depression. CONCLUSIONS: Online interventions are a promising strategy for the care of people with dementia. It would be advisable to perform further randomized trials to assess the reasons for lack of adherence to intervention, as well as usability studies to test the different software programs used.