Metric Measure Research Articles

COMPREHENSION OF THE DESCRIPTIVE QUALITATIVE RESEARCH METHOD: A CRITICAL ASSESSMENT OF THE LITERATURE

When a researcher comprehends the research, they can develop research methods because the research needs to adhere to scientific principles. Because of this matter, a methodical scientific approach is required to guarantee that the study can be reaffirmed by other researchers. Moreover, the descriptive qualitative method has become popular due to its less reliance on numerical values or metrics for variable measurement. The aim of this research is to provide a general review of the literature on descriptive qualitative research methods. The researcher can determine that qualitative descriptive research is a descriptively described research method that employs qualitative data based on the findings of the research. Analyzing events, phenomena, or social conditions is a common use for this kind of descriptive qualitative research. Furthermore, qualitative research does not rely on numerical measurements for its research object, nor does it employ statistical methods to test elaborative characteristics. Its application allows for the combination of different data collection techniques, data analysis, instrument creation, and other research stages commonly used in qualitative and descriptive research methodologies.

A novel ensemble learning method for crop leaf disease recognition.

Deep learning models have been widely applied in the field of crop disease recognition. There are various types of crops and diseases, each potentially possessing distinct and effective features. This brings a great challenge to the generalization performance of recognition models and makes it very difficult to build a unified model capable of achieving optimal recognition performance on all kinds of crops and diseases. In order to solve this problem, we have proposed a novel ensemble learning method for crop leaf disease recognition (named ELCDR). Unlike the traditional voting strategy of ensemble learning, ELCDR assigns different weights to the models based on their feature extraction performance during ensemble learning. In ELCDR, the models' feature extraction performance is measured by the distribution of the feature vectors of the training set. If a model could distinguish more feature differences between different categories, then it receives a higher weight during ensemble learning. We conducted experiments on the disease images of four kinds of crops. The experimental results show that in comparison to the optimal single model recognition method, ELCDR improves by as much as 1.5 (apple), 0.88 (corn), 2.25 (grape), and 1.5 (rice) percentage points in accuracy. Compared with the voting strategy of ensemble learning, ELCDR improves by as much as 1.75 (apple), 1.25 (corn), 0.75 (grape), and 7 (rice) percentage points in accuracy in each case. Additionally, ELCDR also has improvements on precision, recall, and F1 measure metrics. These experiments provide evidence of the effectiveness of ELCDR in the realm of crop leaf disease recognition.

Human-AI Collaboration: Understanding User Trust in ChatGPT Conversations

This research paper delves into the critical dimension of Human-AI Collaboration, with a specific focus on unraveling the intricacies of user trust in ChatGPT conversations. In an era marked by increasing AI integration into various aspects of human life, understanding and fostering user trust in conversational AI systems like ChatGPT is essential for effective collaboration. The study employs a comprehensive approach, investigating metrics for trust measurement, analyzing user experiences, and exploring the factors that influence trust. By examining the evolving impact of trust on collaboration and conducting comparative analyses with other conversational AI models, the research aims to provide valuable insights. Ultimately, the paper not only contributes to a nuanced understanding of user trust in ChatGPT conversations but also offers practical recommendations for developers and stakeholders to enhance the collaborative potential of AI systems in real-world applications. Keywords: Human-AI Collaboration, ChatGPT Conversations, Conversational AI, Trust Metrics, User Trust.

Using Genetic Algorithm to Solve Puzzle Games: A Review

Puzzles have been recognized for their development as a popular form of entertainment due to their ability to intricately challenge the mind while engendering creativity in the player. The development of puzzle games has given rise to a new generation of puzzle games characterized by diverse sequences and different image variations. With the rapid development of puzzle games, we looked at solving approaches using Genetic Algorithms (GA). In this paper, we try to analyze several puzzle games such as Sliding Blocks, Sudoku, Tic-Tac-Toe, and Jigsaw that can be solved using GA. We found that 120 papers have examined the use of GA for puzzle games, and eliminated into 14 papers. We evaluated these 14 papers for each puzzle game we selected by comparing the chromosome representation, GA operator, GA parameters, and the results. Based on the discussion, the application of GA to solve puzzle games can be effectively executed with a high degree of accuracy. Puzzle games that use measurement methods such as Sliding Block, Sudoku, and Jigsaw run in a similar pattern. What is common to all of them is that the chromosomes are represented as matrices or arrays in all cases, and standard genetic operators such as selection, crossover, and mutation are used. The population size is large, often 1000 chromosomes, and parameters such as mutation rate are kept low, around 5%. On the other hand, the performance of GA for solving Tetris and Tic-Tac-Toe from each publication cannot be compared due to different measurement methods and metrics.

An End-to-End Benchmarking Tool for Analyzing the Hardware-Software Implications of Multi-modal DNNs

Abstract-Multi-modal deep neural networks (DNNs) have become increasingly pervasive in many machine learning application domains due to their superior accuracy by fusing various modalities together. However, multi-modal DNNs present many unique characteristics such as multi-stage execution, frequent synchronization and high heterogeneity, which are not well understood in the system and architecture community. In this article, we first present and characterize a set of multi-modal DNN workloads of different sizes from five domains and measure metrics like accuracy to ensure the availability of these applications from the algorithm perspective. We then explore their important hardwaresoftware implications from system and architecture aspects by conducting an in-depth analysis on the unique hardware-software characteristics of multimodal DNNs. We hope that our work can help guide future hardware-software design and optimization for efficient inference of multi-modal DNN applications on both cloud and edge computing platforms.

Predictive Modelling of Customer Sustainable Jewelry Purchases Using Machine Learning Algorithms

In order to give businesses useful information, the main goal of this study is to forecast future consumer purchases in the sustainable jewelry sector. Companies launching new goods, services, or modifications to their current offers must anticipate client behaviours. Businesses can learn a lot about how current customers behave by studying their behaviour in order to develop marketing and sales techniques that work. The study uses a variety of statistical and machine learning methods to forecast the most important elements influencing the purchasing of sustainable jewelry in order to achieve this. These variables span a wide range of characteristics, including gender, age, educational attainment, occupation, gross monthly income, marital status, previous jewelry purchase history, purchase intention, preferred shopping locations, online purchase influencers, consumer buying patterns, preferences for branded and non-branded jewelry, and participation in sustainable jewelry practices. In this study, supervised classification algorithms such Naive Bayes, Support Vector Machine, Linear Discriminant Analysis, K-Nearest Neighbor, and Decision Tree are compared over a wide range of machine learning approaches. To increase the predictive model's effectiveness and resilience, ensemble learning techniques including Random Forest, AdaBoost, and Bagging Classifiers are also introduced. A confusion matrix and classification report are the performance indicators used to assess these models. When used on a test dataset, these measures evaluate the model's precision, recall, accuracy, and F1 scores. These measurable metrics are essential for evaluating the models' reliability and efficacy. Notably, the study shows that the approaches of Random Forest, Decision Tree, Bagging Classifier, and Linear Discriminant Analysis continuously produce the highest accuracy rates, all of which stand at a remarkable 100% accuracy. Other used machine learning classifiers, such as Naive Bayes, K-Nearest Neighbor, Support Vector Machine, and AdaBoost algorithms, which provide significantly lower accuracy scores, fall short of this degree of accuracy. The Random Forest Classifier, which boasts a remarkable accuracy score of 100% for the training dataset and a reasonable 53% for the testing dataset, is particularly noteworthy. This classifier surpasses its competitors, demonstrating its potency in forecasting purchases of sustainable jewelry. In order to forecast sustainable jewelry purchases, this study uses a rigorous methodology that draws on a variety of statistical and machine learning models. The Random Forest Classifier is the most accurate and significant model for this predictive task, with Precision, Recall, and F1 scores of 100, according to the results, which were assessed using multiple performance measures like Precision, Recall, and F1 score.

Video-Based Vehicle Speed Estimation Using Speed Measurement Metrics

Video-Based Vehicle Speed Estimation Using Speed Measurement Metrics

Measurement of DDD and DOT metrics for optimizing antimicrobial surveillance in two tertiary hospitals in Viet Nam: A four-year retrospective study

Measurement of DDD and DOT metrics for optimizing antimicrobial surveillance in two tertiary hospitals in Viet Nam: A four-year retrospective study

Study of Glycemic Variability in Well-controlled Type 2 Diabetic Patients Using Continuous Glucose Monitoring System.

The world has changed tremendously for patients suffering from diabetes mellitus with the development of cutting-edge technologies like continuous glucose monitoring and flash glucose monitoring systems. Now, the details of constant fluctuations of glucose in their blood can be monitored not only by medical professionals but also by patients, and this is called glycemic variability (GV). Traditional metrics of glycemic control measurement, such as glycated hemoglobin (HbA1c), fail to reflect various short-term glycemic changes like postprandial hyperglycemia and hypoglycemic episodes, paving the way to the occurrence of various diabetic complications even in asymptomatic, well-controlled diabetic patients. This need for advanced management of diabetes and effective monitoring of these swings in blood glucose can be met by using a continuous glucose monitoring system (CGMS). To evaluate the extent of GV in well-controlled type 2 diabetes mellitus (T2DM) patients using a flash CGMS and to assess the correlation between GV and HbA1c. A hospital-based prospective observational study was carried out from May 2020 to Oct 2021 at the Department of Medicine, SMS Hospital, Jaipur, Rajasthan (India), after approval from the Ethics Committee of the institution. A total of 30 patients with well-controlled T2DM (HbA1c was ≥6.5, but ≤7.5) were included in the study using simple random techniques after written informed consent from patients. Patients were studied for glycemic excursions over a period of 7 days by using FreeStyle® Libre Pro™, which is a flash glucose monitoring system. The CGM sensor was attached to the left upper arm of the patient on day 0 and removed on day 7. The data recorded in the sensor was then retrieved using pre-installed computer software and analyzed using standard CGM metrics like standard deviation (SD), percentage coefficient of variation (%CV), time above range (TAR), time below range (TBR), and time in range (TIR), out of which %CV was used to quantify GV. %CV has been used to cluster patients into four cohorts from best to worst, namely: best/low CV ≤ 10%, intermediate CV from 10 to 20%, high CV from 20 to 30%, and very high CV of >30%. Scatterplots are used to establish correlations between various parameters. Data from a total of 30 patients were analyzed using CGMS and thus used for calculating standard CGM metrics; glucose readings every 15 minutes were recorded consecutively for 7-day periods, making it a total of 672 readings for each patient. Interpreting the CGM data of all 30 patients, the following results were found: the mean blood glucose of all cases is 134.925 ± 22.323 mg/dL, the mean SD of blood glucose of all cases is 35.348 ± 9.388 mg/dL, the mean of %CV of all cases is 26.376 ± 6.193%. CGM parameters of time are used in the form of percentages, and the following results were found: the mean of TAR, TBR, and TIR is 14.425 ± 13.211, 5.771 ± 6.808, and 82.594 ± 12.888%, respectively. Clustering the patients into cohorts, the proportion of patients exhibiting best/low %CV (10%) is 0, intermediate %CV (10-20%) is 16.67% (five out of 30 patients), high %CV (20-30%) is 50% (15 out of 30 patients) and very high %CV (>30%) is 33.33% (10 out of 30 patients). Also, there is no significant correlation found between HbA1c and %CV (ρ = 0.076, p-value = 0.690); a significant negative correlation was found between %CV and TIR (ρ = -0.604, p < 0.001S); a positive correlation of %CV with TAR and TBR is significant (ρ = 0.816, p-value of <0.001). Using a flash CGMS device and considering %CV as the parameter and primary measure of GV, the study demonstrated the overall instability of a person's glycemic control, making note of unrecognized events of hypoglycemia and hyperglycemia in asymptomatic well-controlled T2DM patients, revealing the overall volatile glycemic control. The most important finding of this study is that even those diabetics who are considered well-controlled experience a great degree of GV as assessed by CGM-derived metrics. This study also demonstrated that there is no significant correlation between HbA1c and GV, suggesting that patients may not have optimal control of their diabetes despite having "normal HbA1c" values; hence, GV can be considered an HbA1c-independent danger factor, having more harmful effects than sustained hyperglycemia in the growth of diabetic complications. So, by using CGM-derived metrics, the measurement of GV has the potential to complement HbA1c data. In this manner, a more comprehensive assessment of glycemic excursions can be provided for better treatment decisions, thereby facilitating optimal glycemic control, which is essential for reducing overall complications and promoting good quality of life.

"Short, Modern, Smart": Humanizing Healthcare Experiences Through Modernized Feedback.

Humanizing healthcare experiences is imperative for global healthcare organizations, emphasizing connections, empathy, and trust between patients, providers, and the broader community. Patients seek personalized, compassionate care, while employees prioritize supportive workplaces. Modernized feedback programs recognize consumer preferences for convenience and accessibility, leveraging technology like artificial intelligence (AI) for real-time insights. Northwestern Medicine's "Short, Modern, Smart" feedback program exemplifies this, focusing on efficiency, contemporary needs, and technology for action. Key considerations include (1) Short: Streamlining surveys for efficiency, (2) Modern: Prioritizing open-ended comments for personalized insights, and (3) Smart: Leveraging AI for proactive understanding. Recommendations for organizations include implementing formal change management, adopting unified metrics for comprehensive experience measurement, and leverage AI to connect feedback back to humans. In conclusion, humanizing healthcare starts with effective feedback collection and actioning. Northwestern Medicine's approach offers insights, emphasizing streamlined processes, personalized engagement, and technological empowerment, which organizations should prioritize for growth and continuous improvement.

An End-to-End Video Coding Method via Adaptive Vision Transformer

Deep learning-based video coding methods have demonstrated superior performance compared to classical video coding standards in recent years. The vast majority of the existing deep video coding (DVC) networks are based on convolutional neural networks (CNNs), and their main drawback is that since CNNs are affected by the size of the receptive field, they cannot effectively handle long-range dependencies and local detail recovery. Therefore, how to better capture and process the overall structure as well as local texture information in the video coding task is the core issue. Notably, the transformer employs a self-attention mechanism that captures dependencies between any two positions in the input sequence without being constrained by distance limitations. This is an effective solution to the problem described above. In this paper, we propose end-to-end transformer-based adaptive video coding (TAVC). First, we compress the motion vector and residuals through a compression network built on the vision transformer (ViT) and design the motion compensation network based on ViT. Second, based on the requirement of video coding to adapt to different resolution inputs, we introduce a position encoding generator (PEG) as adaptive position encoding (APE) to maintain its translation invariance across different resolution video coding tasks. The experiment shows that for multiscale structural similarity index measurement (MS-SSIM) metrics, this method exhibits significant performance gaps compared to conventional engineering codecs, such as [Formula: see text], [Formula: see text], and VTM-15.2. We also achieved a good performance improvement compared to the CNN-based DVC methods. In the case of peak signal-to-noise ratio (PSNR) evaluation metrics, TAVC also achieves good performance.

Comparative Machine Learning Approaches to Analyzing the Illnesses of the Chronic Renal and Heart Diseases

The considerable increase in the risk of clinical events associated with chronic renal disease makes it a severe global public health issue. Chronic kidney disease (CKD) is a severe global public health issue, increasing the risk of clinical events and being associated with renal failure, cardiovascular disease, and early mortality. An accurate and timely diagnosis is essential. This research paper focuses on the global public health issue of chronic kidney disease (CKD) and its association with cardiovascular disease. It emphasizes the importance of accurate diagnosis and timely intervention for CKD, which poses significant risks to patients’ health. The study proposes a machine learning (ML) approach using deep neural networks and feature selection methods to diagnose CKD and heart attack disease. The ensemble learning algorithms used in this study are decision tree (DT), logistic regression (LR), Naive Bayes (NB), random forest (RF), support vector machine (SVM), and gradient boosted trees (GBT) classifier, as well as one deep learning technique called recurrent neural network (RNN). Feature selection techniques like correlation coefficient methods are used to identify critical characteristics. The evaluation of the proposed approach was conducted using accuracy, precision, recall, and F1 measure metrics. The study employed all features for grid search and testing in each approach.

Comprehensive Standardized Assessment for Information Continuity: What Does the Workforce Need

Introduction: Older adults living with frailty and multimorbidity interact with multiple care providers across different health settings increasing the risk for fragmented care and information discontinuity. Information discontinuity results in workforce inefficiencies and adverse health events, including duplication of assessment and diagnostics, medication errors and increased health service use. Standardized assessments potentiate integrated care by communicating consistent measures of health information between health care sectors and providers. InterRAI assessments facilitate integration through promoting a common language and aligning successive assessments across the care continuum.&#x0D; Description: We used a pragmatic case example of a theoretical medically-complex older adult to illustrate effective use of interRAI standardized assessments throughout the health care journey. The interRAI suite of instruments spans across the age continuum, from pediatrics to geriatrics, and is designed to be used across diverse care settings, including community services, primary care, home care, long-term care, acute care, inpatient and community mental health, and palliative services. The case example represents one patient’s assessment findings, derived from standardized assessment instruments, such as the contact assessment, home care assessment and long-term care facility assessment. Automated and embedded risk algorithms are generated as outputs from the assessment, acting as decision support tools to inform care planning for clinical, functional, and social support needs. Process schematics depict potential workflows, where instruments can guide care strategies and facilitate the flow of information between the care team members.&#x0D; Discussion: Integrating elements such as using a common language, standardized assessment items, and embedded decision support algorithms, can support effective communication and collaboration in the care of older adults between clinical settings. Risk algorithms and scales support real-time identification of care issues, with standardized assessment items allowing for changes in health status to be easily recognized. Operationalizing a suite of standardized assessment instruments across the health system offers advantages for the individual including improved continuity of care, as well as for organizations and the system through use of a consistent measurement of health metrics between health providers and sectors, and evaluating health system performance. Successful adoption of comprehensive assessment tools to support integration requires training, stakeholder engagement and time to embed work and care processes into practice.&#x0D; Conclusion: Standardized language and algorithms used in interRAI comprehensive assessments can increase capacity for integration and continuity of care across the full spectrum of health sectors and settings. Findings from this pragmatic case example demonstrate real-world application and utility of standardized assessments to support an integrated workforce.

Financial development and natural resource efficiency: Unlocking green growth potential

The study aims to test the nexus between financial development and natural resource efficiency to determine the green growth potential. The correlation between financial development and natural resource efficiency has been a topic of much scholarly and policy discussion. This research seeks to investigate the complex connection within the specific setting of China between the years 2000 and 2019. The empirical analysis uses the Autoregressive Distributed Lag (ARDL) model. The study examines how progress in the finance industry might facilitate green economic expansion by improving the effective exploitation of natural resources. The results of our study demonstrate a strong and lasting connection between measures of financial progress and metrics of natural resource efficiency. More specifically, enhanced availability of finance improved financial literacy, and higher investment in sustainable initiatives have been shown to have a favorable effect on the effective use of natural resources.

The Digital Walls of the University: Translating New Metrics of Labour, Education, and Mobility in the COVID-19 Crisis

The COVID-19 crisis has drastically accelerated the reconfiguration of the borders between the university and the outside world. In this scenario, the walls of the university, whose function is the discrimination of the university’s external and internal elements, are satisfied via units of artificial measure (metrics) that organise the social division of labour and continuously retrace the boundary between the “foreign” and the “internal.” If earlier reforms in higher education aimed at widening access and improving academic success, on the one hand, and adapting to the demand of the labour market for a more “flexible” workforce, on the other, the exponential use of digital platforms in education strongly focuses on establishing a real-time translatability among different demands within a single tool of governance. While educational institutions exponentially rely on indexes, which are intended to both measure the fluctuations and the demands of the labour market in real time, it is urgent to rethink how we translate technology across social, economic, and cultural fields and to formulate teaching and learning machines capable of responding to the complexities and the challenges of the present.

Using Knowledge Construction Theory to Evaluate Learning Processes: A Randomized Controlled Trial on Showing Gradually Built-up Concept Maps Alongside a Scientific Text

Scientific texts convey new and complex information relevant not only to researchers but also to lay audiences. We present a randomized controlled trial that investigated whether showing a concept map, gradually building up as a user reads a scientific text, supports the cognitive processes of knowledge construction. The intervention group (n = 44) exhibited significantly more knowledge construction [t(89) = 1.94, p = .029, d = 0.41] than the control group (n = 47). The intervention supported basic knowledge construction, i.e., simple processing of information, but not medium and complex levels. This study makes two contributions: (1) showing interactive concept maps and building them up gradually alongside reading helps make scientific texts cognitively more accessible to a lay audience, e.g., to inform public debate, and (2) knowledge construction theory can be used as a theoretical foundation for measuring specific aspects of learning processes—complementing other metrics for comparative design examinations, e.g., usability, user experience, or performance-based measures.

Reflection-enabled HDOP-weighted underwater acoustic passive localization with a small aperture array

This paper addresses the localization problem of a noncooperative source in the multipath-rich environment. Unlike conventional schemes that only use the line-of-sight (LOS) and the once-surface-reflected (S1B0) path, the proposed scheme utilizes the complete spatial diversity of all multipaths by quantifying their positioning confidence. In the proposed scheme, all the measurement metrics (angle, relative delay and amplitude) extracted from multipath arrival structure, are combined to achieve localization with a small aperture array.Specifically, we first propose an algorithm for extracting multipath arrival structure based on spatial time-frequency analysis of underwater multipath signals. In this algorithm, each path is iteratively extracted, and the extraction error is close to the Cramér–Rao lower bound (CRLB). Then, the obtained CRLB is used to derive horizontal dilution of precision (HDOP) to quantify the positioning confidence of each path. Finally, the source location is estimated from an optimal HDOP-weighted multipath localization algorithm that assigns different weights to the LOS path and non-line-of-sight (NLOS) paths.Statistical-channel-model-based simulations and lake trial corroborate that the proposed scheme reduces the localization error by at least 128% compared with angle-only, relative-delay-only, and conventional hybrid metrics localization schemes.Index Terms--Underwater acoustic passive localization; Multipath arrival structure; Horizontal dilution of precision; Hybrid metrics.

Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics. Ninety-six high-speed 9 mm 31-line volume scans obtained from 48 patients with RPGR-associated XLRP were selected based on the following criteria: the presence of an EZ band within the scan limit and a detectable EZ in at least three B-scans in a volume scan. All the B-scan images in each volume scan were manually segmented for the EZ and proximal retinal pigment epithelium (pRPE) by two experienced human graders to serve as the ground truth for comparison. The test volume scans were also segmented by a DLM and then manually corrected for EZ and pRPE by the same two graders to obtain DLM-MC segmentation. The EZ area and OS volume were determined by interpolating the discrete two-dimensional B-scan EZ-pRPE layer over the scan area. Dice similarity, Bland-Altman analysis, correlation, and linear regression analyses were conducted to assess the agreement between DLM-MC and MG for the EZ area and OS volume measurements. For the EZ area, the overall mean dice score (SD) between DLM-MC and MG was 0.8524 (0.0821), which was comparable to 0.8417 (0.1111) between two MGs. For the EZ area > 1 mm2, the average dice score increased to 0.8799 (0.0614). When comparing DLM-MC to MG, the Bland-Altman plots revealed a mean difference (SE) of 0.0132 (0.0953) mm2 and a coefficient of repeatability (CoR) of 1.8303 mm2 for the EZ area and a mean difference (SE) of 0.0080 (0.0020) mm3 and a CoR of 0.0381 mm3 for the OS volume. The correlation coefficients (95% CI) were 0.9928 (0.9892-0.9952) and 0.9938 (0.9906-0.9958) for the EZ area and OS volume, respectively. The linear regression slopes (95% CI) were 0.9598 (0.9399-0.9797) and 1.0104 (0.9909-1.0298), respectively. The results from this study suggest that the manual correction of deep learning model segmentation can generate EZ area and OS volume measurements in excellent agreement with those of conventional manual grading in RP. Because DLM-MC is more efficient for retinal layer segmentation from OCT scan images, it has the potential to reduce the burden of human graders in obtaining quantitative measurements of biomarkers for assessing disease progression and treatment outcomes in RP.

Mind versus body: Perceived stress and biological stress are independently related to cognitive decline

Chronic stress may increase risk of age-related cognitive decline. ‘Stress’, however, is a multidimensional construct and few studies have investigated the inter-relationship of subjective stress and biological stress with cognitive decline. In this study, we examine the relationship between perceived stress and two measures of biological stress – allostatic load, indexing stress at the physiological level and leukocyte telomere length, indexing stress at the cellular level – with cognitive decline over a 12-year period in adults aged 50 and older.3,458 participants (aged ≥ 50) from The Irish Longitudinal study on Ageing with measurements of allostatic load, telomere length and perceived stress at baseline and repeated measures of cognitive function were included. Hierarchical linear regression models with adjustment for multiple potential confounders were applied, and repeated stratified by sex in sensitivity analyses.Higher perceived stress at baseline was associated with lower cognitive function (β = −0.10, 95 % CI −0.12, −0.07, p <.001), with similar strength of associations across waves. There were significant interactions between measures of biological stress and wave; higher allostatic load was associated (X2(18) = 64.4; p <.001), and telomere length was borderline (X2(18) = 9.4; p =.09) associated with cognitive decline from 4-year follow-up onward. Sex stratified analyses revealed that the association between telomere length and cognitive decline was present in women only. Mutual adjustment did not attenuate associations in either case. The interactions between allostatic load and telomere length with perceived stress were not significant.Our findings suggest that subjective measures of stress and biological metrics may be independently related to cognitive function over time in older adults, hinting at the potential for different underlying mechanisms.

The Impact of COVID-19 on Psychiatric Acuity in a Community Psychiatric Hospital.

The COVID-19 pandemic has had extensive impacts on mental health care delivery. Anecdotal observations of inpatient care teams at Pennsylvania Psychiatric Institute suggested increased patient acuity during the pandemic. The authors found no consensus definition for measuring psychiatric acuity in the literature. We performed an interrupted time series analysis to identify whether COVID-19 was associated with changes in several hospital parameters that might reflect our patients' access to psychiatric services and acuity. We found increases in inpatient parameters for length of stay, rates of involuntary admissions, and the incidence of restraints, seclusion, and 1:1 observation orders. Observing these increasing trends can inform mitigation efforts to improve the quality of mental health care treatment and care delivery. We suggest the use of these metrics for objective measurements of psychiatric acuity.