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The dual task experimental paradigm is used to probe the attentional requirements of postural control. However, findings of dual task postural studies have been inconsistent with many studies even reporting improvement in postural stability during dual tasking and thus raising questions about cognitive involvement in postural control. A U-shaped non-linear relationship has been hypothesized between cognitive task complexity and dual task postural stability suggesting that the inconsistent results might have arisen from the use of cognitive tasks of varying complexities. To systematically review experimental studies that compared the effect of simple and complex cognitive tasks on postural stability during dual tasking, we searched seven electronic databases for relevant studies published between 1980 to September 2020. 33 studies involving a total of 1068 participants met the review’s inclusion criteria, 17 of which were included in meta-analysis (healthy young adults: 15 studies, 281 participants; Stroke patients: 2 studies, 52 participants). Narrative synthesis of the findings in studies involving healthy old adults was carried out. Our result suggests that in healthy population, cognitive task complexity may not determine whether postural stability increases or decreases during dual tasking (effect of cognitive task complexity was not statistically significant; P > 0.1), and thus the U-shaped non-linear hypothesis is not supported. Rather, differential effect of dual tasking on postural stability was observed mainly based on the age of the participants and postural task challenge, implying that the involvement of cognitive resources or higher cortical functions in the control of postural stability may largely depends on these two factors.

The Emergency Department (ED) serves as a vital gateway to acute care, where timely and accurate triage decisions are essential to ensure appropriate patient prioritisation and efficient use of limited resources. Triage nurses operate in high-pressure environments and must make rapid decisions, often under conditions of uncertainty, relying on a blend of analytical reasoning and intuitive judgement. However, this complex decision-making process is susceptible to a range of challenges, including cognitive biases, communication breakdowns, procedural inconsistencies, fatigue, and stress, all of which can compromise patient safety and care quality. This study explores the multifaceted nature of triage decision-making, focusing on the influencing factors, cognitive processes, and real-world challenges experienced by nurses. By deepening our understanding of these elements, the paper lays the groundwork for the development of effective Clinical Decision Support Systems (CDSS) that can enhance clinical judgement and support nurses in delivering safe, timely, and efficient emergency care. The study used cognitive task analysis through interviews and observations to capture the cognitive strategies used by nurses during triage. This approach provided detailed insights into how nurses assess patient acuity, handle uncertainty, verify decisions, and manage challenges. This study identified 26 themes from interviews and observations, illustrating how nurses use experience and protocols such as the Emergency Severity Index to manage patient flow. Key challenges encountered in triage included overcrowding, staff shortages, high patient acuity, communication barriers, frequent interruptions, and multitasking demands. Despite these hurdles, nurses adapted through prioritization and collaboration. The findings highlight significant implications for emergency health care, mainly the need for improvements in triage decision making, resource utilization, and patient safety. Data-driven clinical decision support systems can enhance decision making, streamline assessments, reduce delays, and improve safety and equity in triage, particularly in high-stress, resource-constrained environments. This study has significant implications for clinical practice, particularly in emergency care settings where effective triage is critical for patient outcomes. By exploring the cognitive processes and challenges faced by triage nurses, the research provides valuable insights into the complexities of decision making under pressure. The findings emphasize the importance of clinical decision support systems to enhance decision accuracy, reduce cognitive load, and mitigate the risk of errors. Implementing data-driven technologies and refining triage protocols can lead to more efficient resource allocation, more streamlined workflows, reduced waiting times, and improved patient safety. By aligning clinical decision support system design with the cognitive processes of triage nurses, this study supports the development of tools that enhance decision accuracy, reduce cognitive load, and improve patient prioritization, ultimately promoting safer, faster, and more consistent triage in high-pressure emergency settings.

Individuals in occupational settings are often exposed to moderate levels (e.g., < 85 dBA) of background noise (i.e., broadband noise) while performing cognitive-based tasks. Exposure to such noise may affect individuals’ cognitive performance and thus affect desired performance level (e.g., safety performance). The present review outlines the effect of moderate broadband noise on indices of cognition such as reaction time, attention, short-term and long-term memory, and high(er)-order cognitive tasks such as decision-making, mental arithmetic, grammatic reasoning, or reading comprehension. The present review highlights limitations associated with moderators, and their effect on broadband noise. 43 studies ranging from 1980 to February 2023 were selected and analysed to investigate this issue. Reviewed results offered minimal evidence for a negative effect of moderate broadband noise on cognitive performance. Negative effects that were found, were often evident only under specific circumstances. The roles of cognitive load, repeated session, and individual differences (e.g., sex) were found to differ from theoretical predictions based on evidence from broader noise(s), which highlights the distinctive characteristics of the mild stressor; hence an area for future research.

This paper describes how an interdisciplinary design team used the Four-Component Instructional Design (4C/ID) model and its accompanying Ten Steps design approach to systematically design a professional development program for teaching differentiation skills to primary school teachers. This description illustrates how insights from a cognitive task analysis into classroom differentiation skills were combined with literature-based instructional design principles to arrive at the training blueprint for workplace-based learning. It demonstrates the decision-making processes involved in the systematic design of each of the four components: learning tasks, supportive information, procedural information, and part-task practice. While the design process was time and resource-intensive, it resulted in a detailed blueprint of a five-month professional development program that strategically combines learning activities to stimulate learning processes that are essential for developing the complex skill providing differentiated instruction in a mathematics lesson.

Introduction : Dual-task studies have demonstrated that walking is attention-demanding for younger adults. However, numerous studies have attributed this to task type rather than the amount of required to accomplish the task. This study examined four tasks: two discrete (i.e., short intervals of attention) and two continuous (i.e., sustained attention) to determine whether greater attentional demands result in greater dual-task costs due to an overloaded processing capacity. Methods : Nineteen young adults (21.5 ± 3.6 years, 13 females) completed simple reaction time (SRT) and go/no-go (GNG) discrete cognitive tasks and n-back (NBK) and double number sequence (DNS) continuous cognitive tasks with or without self-paced walking. Prefrontal cerebral hemodynamics were measured using functional near-infrared spectroscopy (fNIRS) and performance was measured using response time, accuracy, and gait speed. Results : Repeated measures ANOVAs revealed decreased accuracy with increasing cognitive demands ( p = 0.001) and increased dual-task accuracy costs ( p < 0.001). Response times were faster during the single compared to dual-tasks during the SRT ( p = 0.005) and NBK ( p = 0.004). DNS gait speed was also slower in the dual compared to single task ( p < 0.001). Neural findings revealed marginally significant interactions between dual-task walking and walking alone in the DNS ( p = 0.06) and dual -task walking compared to the NBK cognitive task alone ( p = 0.05). Conclusion : Neural findings suggest a trend towards increased PFC activation during continuous tasks. Cognitive and motor measures revealed worse performance during the discrete compared to continuous tasks. Future studies should consider examining different attentional demands of motor tasks.

Consumer eHealth interventions are of a growing importance in the individual management of health and health behaviors. However, a range of access, resources, and skills barriers prevent health care consumers from fully engaging in and benefiting from the spectrum of eHealth interventions. Consumers may engage in a range of eHealth tasks, such as participating in health discussion forums and entering information into a personal health record. eHealth literacy names a set of skills and knowledge that are essential for productive interactions with technology-based health tools, such as proficiency in information retrieval strategies, and communicating health concepts effectively. We propose a theoretical and methodological framework for characterizing complexity of eHealth tasks, which can be used to diagnose and describe literacy barriers and inform the development of solution strategies. We adapted and integrated two existing theoretical models relevant to the analysis of eHealth literacy into a single framework to systematically categorize and describe task demands and user performance on tasks needed by health care consumers in the information age. The method derived from the framework is applied to (1) code task demands using a cognitive task analysis, and (2) code user performance on tasks. The framework and method are applied to the analysis of a Web-based consumer eHealth task with information-seeking and decision-making demands. We present the results from the in-depth analysis of the task performance of a single user as well as of 20 users on the same task to illustrate both the detailed analysis and the aggregate measures obtained and potential analyses that can be performed using this method. The analysis shows that the framework can be used to classify task demands as well as the barriers encountered in user performance of the tasks. Our approach can be used to (1) characterize the challenges confronted by participants in performing the tasks, (2) determine the extent to which application of the framework to the cognitive task analysis can predict and explain the problems encountered by participants, and (3) inform revisions to the framework to increase accuracy of predictions. The results of this illustrative application suggest that the framework is useful for characterizing task complexity and for diagnosing and explaining barriers encountered in task completion. The framework and analytic approach can be a potentially powerful generative research platform to inform development of rigorous eHealth examination and design instruments, such as to assess eHealth competence, to design and evaluate consumer eHealth tools, and to develop an eHealth curriculum.

Cognitive‐relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face‐recognition task performed in patients with drug‐resistant epilepsy and monitored with intracranial electroencephalography (EEG). Based on our observations, we developed a novel analytical framework (named “local–global” framework) to statistically correlate the brain activity in every recorded gray‐matter region with the widespread connectivity fluctuations as proxy to identify concurrent local activations and global brain phenomena that may plausibly reflect a common functional network during cognition. The application of the local–global framework to the data from three subjects showed that similar connectivity fluctuations found across patients were mainly coupled to the local activity of brain areas involved in face information processing. In particular, our findings provide preliminary evidence that the reported global measures might be a novel signature of functional brain activity reorganization when a stimulus is processed in a task context regardless of the specific recorded areas. Cognitive‐relevant information is processed by different brain areas that cooperate to eventually produce a response. Here, we develop a novel analytical framework (named “local–global” framework) to statistically correlate the signal power in every recorded gray‐matter region (intracranial electroencephalography) with the widespread connectivity fluctuations as proxy to identify concurrent local and global activity that may plausibly reflect a common functional network during cognition. The application of the local–global framework to the data from three subjects with different recorded areas showed similar connectivity fluctuations, which were mainly coupled to local activity of brain areas involved in the task.

Lack of attention to obstacles on the floor or walking path may cause trip and fall accidents. The preparatory activity in the motor cortex to the perturbation associated with obstacle avoidance movements with cognitive task is still unclear. The purpose of this study was to investigate the motor cortical activity involved in the preparation and execution of concurrent obstacle avoidance movement and cognitive task. Twenty young adults were required to step over obstacles that were projected on the floor while performing a cognitive task. The electroencephalogram was recorded, and the movement-related cortical potentials (MRCP) aligned by foot dorsiflexion were evaluated. There was no significant difference in the number of contacts between the toe and the obstacle between the obstacle avoidance task and obstacle avoidance with cognitive task; however, the distance between the toe and the obstacle just before obstacle avoidance movement was significantly extended in the latter task. The amplitude and the onset of MRCP during the dual task were decreased and delayed, respectively, compared with the simple obstacle avoidance movement task. These results suggest that the young participants changed their clearance strategy to stepping over the obstacle during the concurrent motor and cognitive dual task to reduce motor cortical activity.

A social service robot in elderly care is capable of improving the quality of life of the elderly and simultaneously reducing the workload of healthcare workers. It can have positive and negative effects in terms of the social and technical contexts of healthcare services. This study aims to implement a social service robot in elderly care programs and investigate user acceptance at daycare facilities. A robot was installed at a facility for use in two types of care programs: exercise with cognitive tasks and brain training with arithmetic operations. A questionnaire survey was conducted to estimate the overall impressions and attitudes of elderly users and healthcare workers toward the installation of the robot in elderly care. The results indicated that the care program using the robot was effective, but its efficiency was not relatively high at this facility. The effectiveness of the care program depended considerably on the different capabilities of the elderly users. This issue arose from the diverse capabilities and varying attitudes of acceptance of the elderly users toward the technology. Technical improvements can solve the former issue so that the healthcare worker can more easily adjust robot functions, such as voice control. The latter issue can be improved by establishing better human–robot interaction in the context of daily programs at daycare facilities. Adoption of such robotic services should be considered in the social contexts of elderly care.

Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age: 25 ± 5 years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods: support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.