RESUMO
While the term task load (TL) refers to external task demands, the amount of work, or the number of tasks to be performed, mental workload (MWL) refers to the individual's effort, mental capacity, or cognitive resources utilized while performing a task. MWL in multitasking scenarios is often closely linked with the quantity of tasks a person is handling within a given timeframe. In this study, we challenge this hypothesis from the perspective of electroencephalography (EEG) using a deep learning approach. We conducted an EEG experiment with 50 participants performing NASA Multi-Attribute Task Battery II (MATB-II) under 4 different task load levels. We designed a convolutional neural network (CNN) to help with two distinct classification tasks. In one setting, the CNN was used to classify EEG segments based on their task load level. In another setting, the same CNN architecture was trained again to detect the presence of individual MATB-II subtasks. Results show that, while the model successfully learns to detect whether a particular subtask is active in a given segment (i.e., to differentiate between different subtasks-related EEG patterns), it struggles to differentiate between the two highest levels of task load (i.e., to distinguish MWL-related EEG patterns). We speculate that the challenge comes from two factors: first, the experiment was designed in a way that these two highest levels differed only in the quantity of work within a given timeframe; and second, the participants' effective adaptation to increased task demands, as evidenced by low error rates. Consequently, this indicates that under such conditions in multitasking, EEG may not reflect distinct enough patterns to differentiate higher levels of task load.
RESUMO
PURPOSE: Cardiovascular diseases are the leading cause of death and disability worldwide. Among these diseases, heart failure (HF) and acute myocardial infarction (AMI) are the most common causes of hospitalization. Therefore, readmission for HF and AMI is receiving increasing attention. Several socioeconomic factors could affect readmissions in this target group, and thus, a systematic review was conducted to identify the effect of socioeconomic factors on the risk for readmission in people aged 65 years and older with HF or AMI. METHODS: The search was carried out by querying an electronic database and hand searching. Studies with an association between the risk for readmission and at least one socioeconomic factor in patients aged 65 years or older who are affected by HF or AMI were included. A quality assessment was conducted independently by two reviewers. The agreement was quantified by Cohen's Kappa statistic. The outcomes of studies were categorized in the short-term and the long-term, according to the follow-up period of readmission. A positive association was reported if an increase in the risk for readmission among disadvantaged patients was found. A cumulative effect of socioeconomic factors was computed by considering the association for each study and the number of available studies. RESULTS: A total of eleven articles were included in the review. They were mainly published in the United States. All the articles analyzed patients who were hospitalized for HF, and four of them also analyzed patients with AMI. Seven studies (63.6%) were found for the short-term outcome, and four studies (36.4%) were found for the long-term outcome. For the short-term outcome, race/ethnicity and marital status showed a positive cumulative effect on the risk for readmission. Regarding the educational level of a patient, no effect was found. CONCLUSION: Among the socioeconomic factors, mainly race/ethnicity and marital status affect the risk for readmission in elderly people with HF or AMI. Multidisciplinary hospital-based quality initiatives, disease management, and care transition programs are a priority for health care systems to achieve better coordination.