Objective The primary aim was to review and synthesise the current evidence of how older adults are involved in codesign approaches to develop electronic healthcare tools (EHTs). The secondary aim was to identify how the codesign approaches used mutual learning techniques to benefit older adult participants. Design Systematic review following the Preferred Reporting Items for Systematic Reviews 2020 checklist. Data sources PubMed, Embase and Scopus databases were searched for studies from January 2010 to March 2021. Eligibility criteria Inclusion criteria were studies employing codesign approaches to develop an EHTs, and the study population was aged 60 years and older. Data extraction and synthesis Data were extracted for analysis and risk of bias. We evaluated the quality of studies using the Agency for Healthcare Research and Quality Evidence-based Practice Center approach. Results Twenty-five studies met the inclusion criteria for this review. All studies used at least two involvement processes, with interviews and prototypes used most frequently. Through cross-classification, we found an increased utilisation of functional prototypes in studies reaching the ‘empower’ level of participation and found that studies which benefitted from mutual learning had a higher utilisation of specific involvement processes such as focus groups and functional prototyping. Conclusions We found gaps to support which involvement processes, participation levels and learning models should be employed when codesigning with older adults. This is important because higher levels of participation may increase the user’s knowledge of technology, enhance learning and empower participants. To ensure studies optimise participation and learning of older adults when developing EHTs, there is a need to place more emphasis on the approaches promoting mutual learning. PROSPERO registration number CRD42021240013.
BACKGROUND: Burnout is prevalent among surgical residents. Neurofeedback is a technique to train the brain in self-regulation skills. We aimed to assess the impact of neurofeedback on the cognitive workload and personal growth areas of surgery residents with burnout and depression. STUDY DESIGN: Fifteen surgical residents with burnout (Maslach Burnout Inventory [MBI] score > 27) and depression (Patient Health Questionnaire-9 Depression Screen [PHQ-9] score >10), from 1 academic institution, were enrolled and participated in this institutional review board-approved prospective study. Ten residents with more severe burnout and depression scores were assigned to receive 8 weeks of neurofeedback treatments, and 5 others with less severe symptoms were treated as controls. Each participant's cognitive workload (or mental effort) was assessed initially, and again after treatment via electroencephalogram (EEG) while the subjects performed n-back working memory tasks. Analysis of variance (ANOVA) tested for significance between the degree of change in the treatment and control groups. Each subject was also asked to rate changes in growth areas, such as sleep and stress. RESULTS: Both groups showed high cognitive workload in the pre-assessment. After the neurofeedback intervention, the treatment group showed a significant (p < 0.01) improvement in cognitive workload via EEG during the working memory task. These differences were not noted in the control group. There was significant correlation between time (NFB sessions) and average improvement in all growth areas (r = 0.98) CONCLUSIONS: Residents demonstrated high levels of burnout, correlating with EEG patterns indicative of post-traumatic stress disorder. There was a notable change in cognitive workload after the neurofeedback treatment, suggesting a return to a more efficient neural network.
In convolutional neural networks, pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models. These methods reduce the computational amount of convolutional neural networks, making the neural network more efficient. Maximum pooling, average pooling, and minimum pooling methods are generally used in convolutional neural networks. However, these pooling methods are not suitable for all datasets used in neural network applications. In this study, a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks. This method, which we call MAM (Maximum Average Minimum) pooling, is more interactive than other traditional maximum pooling, average pooling, and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value. The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks. To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods, training was carried out on the LeNet-5 model using CIFAR-10, CIFAR-100, and MNIST datasets. According to the results obtained, the proposed MAM pooling method performed better than the maximum pooling, average pooling, and minimum pooling methods in all pool sizes on three different datasets.
In today's world, making millions of data understandable has become important. To take faster steps in criminal matters, especially by using these data, data analysis should be done quickly. In this context, sentiment analysis performed with the natural language processing (NLP) method of artificial intelligence enables the elimination of possible loss of life and property. In addition, by listening to all radio frequencies at the same time in possible terror areas, the attacks of terror organizations can be analyzed with natural language processing methods, so that the attack can be prevented before it takes place. In this study, natural language processing methods of artificial intelligence were used in the analysis of text, audio, and image data in the virtual environment for the detection of terror threat elements. In this way, it is aimed to ensure the healthy intervention of law enforcement officers and the security of life by analyzing the talks of terror elements in terror zones. For this purpose, an 85% accuracy rate was reached with the word/sentence vector creation method GloVe in the first model created with the Spark NLP library on textual data. In addition, a 74% accuracy rate was achieved with the LSTM method on audio data, while a 71% accuracy rate was achieved with the GRU method on visual data.
Read moreTransformer models have demonstrated superior capability in capturing long-range temporal dependencies crucial for Sensor-Based Human Activity Recognition (HAR). However, the quadratic computational complexity inherent to the Softmax-Attention mechanism significantly impedes their deployment on resource-constrained wearable devices and real-time streaming tasks. To address this, we propose a novel Linear Transformer with Taylor Series Attention specifically tailored for the HAR domain, named TSA-Former. It leverages the first-order Taylor expansion to approximate the Softmax-Attention and utilizes the norm-preserving mapping to approximate the high-order non-linear information, resulting in a linear computational complexity. In addition, TSA-Former integrates a multi-branch architecture featuring multi-scale patch embedding, which enables the model to dynamically capture multi-scale temporal features while minimizing overhead. Experimental results across four public HAR benchmarks, namely UniMiB-SHAR, UCI-HAR, WISDM, and OPPORTUNITY, demonstrate that TSA-Former achieves state-of-the-art (SOTA) accuracy and efficiency, outperforming conventional Transformers and existing linear-attention models. Deployment experiments conducted on the Raspberry Pi 5 platform further validate the model’s superior low-latency and minimal power consumption profile, confirming its robust suitability for real-world embedded HAR applications. Code will be released.
Read moreA majority of healthcare workers (HCWs) experience workplace violence (WPV) but most WPV events go unreported. Underreporting of WPV is well documented in the literature as a barrier to identifying underlying causes and to evaluating the effectiveness of WPV interventions. Previous studies suggest that WPV reporting data is fragmentary, unreliable, and inconsistent. Also, WPV reporting systems are suboptimally designed making it difficult for healthcare workers to report WPV incidents. This study aims to assess the usability of an electronic WPV report in a large academic medical center and the perceived cognitive workload (CWL) and performance of HCWs associated with reporting WPV events. Findings from this study suggest that our institutional WPV report has suboptimal perceived usability and suboptimal perceived cognitive workload. Further, participants with training reported lower error rates in comparison to participants without training on performance.
Read moreUsability and cognitive workload (CWL) are multidimensional constructs that describe user experience, predict performance, and inform system design. The relationship between the subjective measures of these constructs has not been adequately explored, especially in healthcare delivery settings where suboptimal usability of electronic health records and CWL of healthcare professionals are among the major contributing factors to medical errors. This study quantifies the perceived usability of a dosimetry quality assurance (QA) checklist and the perceived CWL of dosimetrists in radiation oncology clinical settings of an academic medical center and investigates the association between perceived usability and perceived CWL. Findings suggest that our institutional dosimetry QA checklist has suboptimal usability, but the associated CWL is acceptable. Further, the correlation analysis reveals that perceived usability and perceived CWL are non-overlapping constructs and may be jointly employed to reduce the risk of healthcare professionals committing medical errors.
Read more256 Background: Numerous studies have demonstrated that physician perceptions of patient priorities and patients’ stated preferences differ substantially. Use of preference clarification using discrete choice experiments (DCEs) has been shown to improve preference-concordance for patients with solid tumors. Previously, we developed a DCE to describe preferences of survivors of acute myeloid leukemia (AML). We sought to evaluate this DCE among newly diagnosed patients with AML to inform shared decision-making. Methods: We used a sequential explanatory (quantitative to qualitative) mixed methods design to assess acceptability, feasibility, and content validity of the DCE to elicit individual preferences. Newly diagnosed older (≥ 60 years) adults with AML completed the DCE at the time of treatment decision. Patients and caregivers then completed semi-structured interviews to expand on quantitative findings. Based on initial results, healthy volunteers completed think-aloud sessions while completing the DCE and semi-structured interviews to further evaluate comprehensibility. Results: 47 participants were enrolled (18 patients [8 female, 10 male; aged 60-87], 16 caregivers, 15 healthy volunteers). Patients received best supportive care (n = 1), hypomethylating agents (n = 9), and high-intensity chemotherapy (n = 8). Feasibility/Acceptability: All patients completed the DCE (100% feasibility), and all reported that they answered according to their preferences (100% acceptability). Content Validity: 1) Relevance: 17/18 (94%) felt the DCE was relevant. 2) Comprehensiveness: In addition to the attributes in the DCE, provider recommendations and family considerations were noted to be critical in treatment decision-making. 3) Comprehensibility: Only 13/18 (72%) felt the DCE was easy to understand, and 14/18 (78%) felt it was easy to answer. Explanatory Interviews: Patients reported being in “shock,” “devastated” from receiving the diagnosis, feeling overwhelmed (“I was floored”) and not being able to reliably concentrate on the DCE due to these factors. Caregivers corroborated patients’ reports. Healthy volunteers also reported “information overload” and that the DCE was “technical” and the included outcome levels were “confusing.”. Conclusions: This DCE designed for older adults with AML was relevant, feasible, and acceptable. However, nearly 30% of patients reported difficulty understanding the DCE. Some reported not being able to attend to the complex tasks in the DCE, thereby compromising content validity of the measure. We suspect this was due to the distress caused by the diagnosis. This study demonstrates the challenge of developing valid preference elicitation instruments for newly diagnosed patients and highlights the need for extensive evaluation prior to clinical implementation.
Read moreWe investigated the association of mean daily macronutrient intake with migraine and non-migraine headaches. This cross-sectional study included 8042 men and 23,728 women from the ongoing population-based NutriNet-Santé e-cohort. Headache status was assessed via an online self-report questionnaire (2013⁻2016). Migraine was defined using established criteria and dietary macronutrient intake was estimated via ≥3 24 h dietary records. Mean daily intake (g/day) of carbohydrates (simple, complex, and total), protein, and fat (saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, and total) were the main exposure variables. Adjusted gender-specific analysis of variance (ANOVA) models were fit. Presence of migraines was noted in 9.2% of men (mean age = 54.3 ± 13.3 years) and 25.7% of women (mean age = 49.6 ± 12.8 years). In adjusted models, we observed (1) somewhat lower protein (<i>p</i> < 0.02) and higher total fat (<i>p</i> < 0.01) intake among male migraineurs compared with males without headaches and those with non-migraine headaches; (2) somewhat higher total fat (<i>p</i> < 0.0001) and total carbohydrate intake (<i>p</i> < 0.05) among female migraineurs compared with females without headaches and those with non-migraine headaches. The findings, which provide preliminary support for modest gender-specific differences in macronutrient intake by migraine status, merit confirmation in different population-based settings, as well as longitudinally, and could help to inform future dietary interventions in headache prevention.
Read moreSkin cancer is one of the most common type of cancer in humans. This type of cancer is produced by skin cells called melanocytes and occurs as a result of division and multiplication of the mentioned cells. The most important symptom of skin cancer is the formation of spots on the skin or the observation of changes in the shape, color, or size of the existing spot. It is necessary to consult a specialist to distinguish the difference between a normal spot and skin cancer. Expert physicians examine and follow up the spots on the skin using skin surface microscopy, called dermatoscopy, or take a sample from the suspicious area and request it to be examined in laboratory environment. This situation increases the cost of the procedure for the diagnosis of skin cancer and also causes it to be treated at a later stage. Therefore, there is a need for a metod that can detect skin cancer early. Thanks to machine learning, become popular in recent years, many diseases can be diagnosed with software that helps expert physicians. In this study, VGGNet model structures (VGG-11, VGG-13, VGG-16, VGG-19) that quickly classify skin cancer and become a traditional convolutional neural network architecture using deep learning method, a subfield of machine learning, were used. It has been observed that the VGG-11 architecture, which is one of the VGGNet model structures, detects skin cancer with superior success accuracy (83%) compared to other model structures.
Read moreImproving food choices represents a major goal for the environment and public health. Consideration of future consequences (CFC) is a psychological construct that distinguishes individuals who adopt behaviors based on immediate needs and concerns from individuals who consider the future implications and consequences of their behavior. The objective of this study was to assess the association between CFC and indicators of dietary behaviors such as food choice motives, food intake, diet quality, and snacking. A sample of 50, 955 participants from the NutriNet-Santé study completed the CFC-12 questionnaire. Food choice motives were assessed using a validated questionnaire regrouping 9 food choice motives. Food intake and diet quality (mPNNS-GS) were evaluated with of 24-h dietary records, and snacking frequency by using an ad-hoc question. Linear and logistic regressions adjusted for socio-demographic factors were performed. CFC was associated with all food choice motives (p
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