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Background Cognitive deficits arise with age and can increase the risk for subjective cognitive decline (SCD) and mild cognitive impairment (MCI), which may result in dementia, leading to health problems, care dependency and institutionalization. Computer-based cognitive interventions (CCIs) have the potential to act as important counteraction functions in preserving or improving cognition concomitant to available pharmacological treatment. The aim was to assess the effectiveness of CCIs performed individually with a personal or tablet computer, game console, virtual, augmented, or mixed reality application on cognition in community-dwelling people with SCD, MCI and dementia. Methods A systematic review with meta-analyses of randomized controlled trials (RCTs) was performed. The systematic literature search was conducted in MEDLINE, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore Digital Library, Web of Science, Scopus and PsycINFO. In addition, a search for gray literature and backward citation searching were carried out. To judge on the evidence, two reviewers independently used the Cochrane Risk of Bias Tool. The standardized mean difference (SDM) for pooling comparable studies using the random-effects model was applied. Results Twenty-four RCTs were identified, of which 1 RCT examined CCIs in individuals with SCD, 18 RCTs with MCI, and 6 RCTs with dementia. Most interventions were conducted with personal computers. Meta-analyses with 12 RCTs showed significant effects of computer-based cognitive interventions for people with MCI in the domains memory, working memory, attention/concentration/processing speed and executive functioning, but no significant improvements in global cognition and language. Regarding dementia a meta-analysis pooled with 4 RCTs demonstrated a tendency towards, but no significant increase of memory functions (SMD 0.33, CI 95% [-0.10, 0.77]). One RCT regarding SCD reported significant improvements in memory functions for participants conducting a cognitive training on a personal computer. Conclusions The results demonstrated that CCIs have beneficial effects on domain-specific cognition in people with MCI but no significant effects on people with dementia. In terms of SCD, one study showed significant improvements in memory functions. It seems that the beneficial effect for cognitive preservation or improvement due to CCIs occurs at the earliest intervention state. However, more research on SCD is needed. Trial registration PROSPERO International Prospective Register of Systematic Reviews CDR42020184069.

Many students struggle with statistical concepts such as interaction. In an experimental group, participants took a paper‐and‐pencil test and then were given training to establish equivalent classes containing four different statistical interactions. All participants formed the equivalence classes and showed maintenance when probes contained novel negative exemplars. Thereafter, participants took a second paper‐and‐pencil test. Participants in the control group received two versions of the paper‐and‐pencil test without equivalence‐based instruction. All participants in the experimental group showed increased paper‐and‐pencil test scores after forming the interaction‐indicative equivalence classes. Class‐indicative responding also generalized to novel exemplars and the novel question format used in the paper‐and‐pencil test. Test scores did not change with repetition for control group participants. Implications for behavioral diagnostics and teaching technology are discussed.

Children with language-based learning impairments (LLIs) have major deficits in their recognition of some rapidly successive phonetic elements and nonspeech sound stimuli. In the current study, LLI children were engaged in adaptive training exercises mounted as computer \"games\" designed to drive improvements in their \"temporal processing\" skills. With 8 to 16 hours of training during a 20-day period, LLI children improved markedly in their abilities to recognize brief and fast sequences of nonspeech and speech stimuli.

Background iSupport is an online program developed by the World Health Organization to provide education, skills training, and social support to informal carers of persons with dementia. This pilot study examines the feasibility of the protocol for a main effectiveness trial of iSupport-Portugal and explores how the intervention and control arms compare over time on well-being outcomes. Methods A mixed-methods experimental parallel between-group design with two arms is followed. Participants were recruited nationwide, by referral or advertising, through the National Alzheimer’s Association. Inclusion criteria are being Portuguese adults, providing e-consent, providing unpaid care to someone with dementia for at least 6 months, experiencing relevant scores on burden (≥ 21 on ZBI) or depression or anxiety (≥ 8 on HADS), and using webpages autonomously. Participants were consecutively randomized to receive iSupport-Portugal or an education-only e-book and were not blinded to group assignment. Data were collected online with self-administered instruments, at baseline, 3 and 6 months after. Outcomes comprise caregiver burden, depression, anxiety, QoL, positive aspects of caregiving, and self-efficacy. Generalized estimating equations were used to estimate group, time, and group-by-time effects. Intervention engagement data were extracted from iSupport’s platform. Semi-structured interviews were conducted. Results Forty-two participants were allocated to the intervention ( N  = 21) and control ( N  = 21) arms. Participation (78.1%) and retention rates (73.8%) were fair. More carers in the control arm completed the study ( N  = 20, 95.2%) than in the intervention arm ( N  = 11; 52.4%) ( χ 2  = 9.98, p  = .002). Non-completers were younger, spent less time caring, and scored higher on anxiety. Among carers in the intervention arm, the average attendance rate was of 53.7%. At post-test 38.9% of participants still used iSupport; the remainder participants interrupted use within 2 weeks (Mdn). For per-protocol analyses, significant group-by-time interaction effects favouring the intervention were found for anxiety (Wald χ2 = 6.17, p  = .046) and for environmental QoL (Wald χ 2  = 7.06, p  = .029). Those effects were not observed in intention-to-treat analyses adjusted for age. Interviewees from the intervention arm ( N  = 12) reported positive results of iSupport on knowledge and on experiencing positive feelings. No adverse effects were reported. Conclusions This study provides information for a forthcoming full-scale effectiveness trial, as on the acceptability and potential results of iSupport-Portugal. iSupport is suggested as a relevant resource for Portuguese carers. Trial registration ClinicalTrials.gov, NCT04104568 . 26/09/2019.

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state‐of‐the‐art models in terms of accuracy and computational cost. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre‐trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models.

Crises demand swift and effective decision-making; yet there are many problems in training personnel on the skills necessary to achieve the goals of crisis management. This paper has three objectives concerning training for crisis management. First we integrate diverse literatures and present a framework for an understanding of the unique challenges in crisis management training, and the role of training systems with capabilities for simulation, immersion, and critiquing. Second, we describe an example of a trainer for ship damage control, called DC-Train, which addresses these challenges. This system consists of a first-principles simulator that generates large numbers of realistic scenarios, an immersive multimedia interface that helps elicit psychological processes involved in actual crisis management, and a critiquing expert system that provides real-time and post-session feedback on human decision-making performance. Finally, we present an empirical method for evaluating the effectiveness of such a system for crisis management training. Results of evaluation experiments with participants in a ship damage control training program indicate that the described computer-based trainer has psychological realism and improves decision-making performance.

As the generation of data in the life and health sciences expands rapidly, there is a growing need for professionals and students in these fields to master core bioinformatics skills, particularly those relating to Unix-like systems, most commonly used in bioinformatics. This paper introduces two key contributions to address this need: (1) A Unix curriculum for life scientists with little or no command-line experience, based on progressive Unix skill levels for bioinformatics and (2) An implementation of this curriculum into a series of interactive online tutorials deployed through Sandbox.bio—an open-source platform for learning bioinformatics that embeds a command line in the browser, which removes barriers related to software installation and access. We performed an overall evaluation of this teaching framework in different contexts. This inclusive, sustainable approach provides widespread access to essential bioinformatics skills for life science students and professionals alike.