Health Indicators Research Articles

Efficacy of yoga package on cognitive flexibility and attention in university girls: A single group pilot study

Executive functions and attention are key indicator of cognitive health. Cognitive flexibility, the ability to flexibly switch between tasks, is a core dimension of executive functions. Yoga helps to improve mental wellness, which consequently provides cognitive flexibility.This study aimed to determine the effect of yoga on cognitive flexibility and attention in university girls.Thirty healthy university girls (group mean ± SD; 18.27 ± 1.44) were enrolled as participants in the study. Trail Making Test (TMT) parts A and B were used to assess their cognitive flexibility whereas sustained attention was determined using Six Letter Cancellation Test (SLCT). The assessments were taken at baseline and after three months of yogic practices. Statistical analysis was performed by paired t-test to examine the effect of yoga on cognitive flexibility and attention in university girls.There was a significant decrease in time taken to complete TMT part A (t= 4.33; P<0.05) and a significant increase in total attempts (t= -2.75; P<0.05) and net attempts (t=-2.80; P<0.01) in six letter cancellation test after three months of yoga practice.The results suggest that yoga improves visual search ability and motor speed skills and hence cognitive flexibility and attention in university girls.

Incivility is systematically associated with indicators of health, stress, well-being, and the psychosocial work environment

Incivility is systematically associated with indicators of health, stress, well-being, and the psychosocial work environment

Effects of pulmonary rehabilitation on functional and psychological parameters in post-acute sequelae of SARS-CoV-2 infection (PASC) patients

BackgroundCOVID-19 survivors may develop long-term symptoms of fatigue, dyspnea, mental health issues, and functional limitations: a condition termed post-acute sequelae of COVID-19 (PASC). Pulmonary rehabilitation (PR) is a recommended treatment for PASC; however, there is a lack of data regarding PR’s effect on multiple health indices and the factors that influence patient outcomes. The aim of our study is to evaluate the impact of pulmonary rehabilitation on functional and psychological parameters in patients diagnosed with Post-Acute Sequelae of SARS-CoV-2 Infection (PASC), thereby offering insights into the efficacy of such interventions in improving the quality of life and clinical outcomes for these individuals.MethodsWe extracted patient demographic, comorbidity, and outcome data from Allegheny Health Network’s electronic medical records. Functionality test results were compared before and after PR, including 6-minute walk test (6MWT), chair rise repetitions (CR reps), timed up and go test (TUG), gait speed (Rehab gait), modified medical research council scale (MMRC), shortness of breath questionnaire (SOBQ), hospital anxiety and depression scale (HADS) and chronic obstructive pulmonary disease assessment test (CAT) scores. Multiple regression analysis was done to evaluate the effect of comorbidities and patient factors on patient responses to PR.ResultsThe 55 patients included in this study had a mean time of 4 months between the initial COVID-19 diagnosis and the subsequent PASC diagnosis. Following pulmonary rehabilitation (PR), significant improvements were observed across various metrics. The distance covered in the 6-minute walk test (6MWT) increased markedly from a pre-rehabilitation average of 895 feet (SD 290) to 1,300 feet (SD 335) post-rehabilitation, with a mean change of 405 feet (95% CI [333, 477]). Chair rise repetitions (CR reps) saw an increase from 9 (SD 3) reps to 13 (SD 3) reps, with a change of 4 reps (95% CI [3.7, 4.9]). The timed up and go test (TUG) time decreased significantly from 13 s (SD 5) to 10 s (SD 2), reflecting a mean reduction of 3 s (95% CI [-4.5, -2.5]). Rehabilitation gait speed improved from 1.0 m/s to 1.3 m/s, changing by 0.3 m/s (95% CI [0.2, 0.3]). The Modified Medical Research Council (MMRC) dyspnea scale showed a notable decrease from a mean of 2 (SD 1) to 1 (SD 1), a change of -1 (95% CI [-1.5, -1]). The Shortness of Breath Questionnaire (SOBQ) scores reduced significantly from 51 (SD 21) to 22 (SD 18), with a change of -29 (95% CI [-34, -23]). The Hospital Anxiety and Depression Scale (HADS) scores decreased from 11 (SD 7) to 8 (SD 7), a reduction of -4 (95% CI [-5, -2]). Lastly, the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test (CAT) scores significantly dropped from 18 (SD 7) to 9 (SD 7), changing by -10 (95% CI [-11, -8]). However, the presence of hypertension, diabetes, chronic lung diseases, outpatient status, and receipt of specific pharmacologic treatments (decadron, decadron + remdesivir, and decadron + remdesivir + tocilizumab) were identified as factors associated with a poor response to PR.ConclusionOur study supports PR as an integrated model of care for PASC patients to improve several physical and mental health indices. The long-term effects of PR on patients’ functional status should be investigated in the future.

The Real-World Usability, Feasibility, and Performance Distributions of Deploying a Digital Toolbox of Computerized Assessments to Remotely Evaluate Brain Health: Development and Usability Study.

An ongoing global challenge is managing brain health and understanding how performance changes across the lifespan. We developed and deployed a set of self-administrable, computerized assessments designed to measure key indexes of brain health across the visual and auditory sensory modalities. In this pilot study, we evaluated the usability, feasibility, and performance distributions of the assessments in a home-based, real-world setting without supervision. Potential participants were untrained users who self-registered on an existing brain training app called BrainHQ. Participants were contacted via a recruitment email and registered remotely to complete a demographics questionnaire and 29 unique assessments on their personal devices. We examined participant engagement, descriptive and psychometric properties of the assessments, associations between performance and self-reported demographic variables, cognitive profiles, and factor loadings. Of the 365,782 potential participants contacted via a recruitment email, 414 (0.11%) registered, of whom 367 (88.6%) completed at least one assessment and 104 (25.1%) completed all 29 assessments. Registered participants were, on average, aged 63.6 (SD 14.8; range 13-107) years, mostly female (265/414, 64%), educated (329/414, 79.5% with a degree), and White (349/414, 84.3% White and 48/414, 11.6% people of color). A total of 72% (21/29) of the assessments showed no ceiling or floor effects or had easily modifiable score bounds to eliminate these effects. When correlating performance with self-reported demographic variables, 72% (21/29) of the assessments were sensitive to age, 72% (21/29) of the assessments were insensitive to gender, 93% (27/29) of the assessments were insensitive to race and ethnicity, and 93% (27/29) of the assessments were insensitive to education-based differences. Assessments were brief, with a mean duration of 3 (SD 1.0) minutes per task. The pattern of performance across the assessments revealed distinctive cognitive profiles and loaded onto 4 independent factors. The assessments were both usable and feasible and warrant a full normative study. A digital toolbox of scalable and self-administrable assessments that can evaluate brain health at a glance (and longitudinally) may lead to novel future applications across clinical trials, diagnostics, and performance optimization.

Health inequality of rural-to-urban migrant workers in eastern China and its decomposition: a comparative cross-sectional study

ObjectivesAs a specific group with high health inequality, it is crucial to improve the health status and health inequalities of rural-to-urban migrant workers. This study aimed to evaluate the health inequality of migrant and urban workers in China and decompose it.MethodsA cross-sectional study was carried out, using a standardized questionnaire to obtain basic information, self-rated health to evaluate health status, concentration index to measure health inequalities, and WDW decomposition to analyze the causes of health inequalities.ResultsThe concentration index of health for migrants was 0.021 and 0.009 for urban workers. The main factors contributing to health inequality among rural-to-urban migrant workers included income, exercise, and age. In contrast, the main factors of health inequality among urban workers included income, the number of chronic diseases, social support, and education.ConclusionThere were health inequalities in both rural-to-urban migrant and urban workers. The government and relevant authorities should formulate timely policies and take targeted measures to reduce income disparities among workers, thereby improving health inequality.

Health Outcome Comparison Based on Dietary Inflammatory Levels among Sample of Korean Elderly

The aim of this study was to investigate the effect of a senior-friendly diet based on the dietary inflammatory index (DII) on the nutritional status and health indicators of older people. A total of 256 participants were classified into tertiles based on their DII values and split into intervention (n = 201) and control (n = 55) groups. The intervention group was provided with a senior-friendly diet, and the control group was allowed to eat their usual diet. Before and after the trial, anthropometric measurements, blood analyses, and questionnaires were completed for both groups. The mean age of the participants was 82.49 years, and 66.4% were female. On average, participants had 2.5 medical conditions, with a notable prevalence of cardiovascular disease. Following the intervention, the energy, carbohydrate, protein, and fat intakes significantly increased in the intervention group compared to pre-intervention levels. Tertile 3 exhibited substantial improvements in total nutrition score, NQ-E balance, and DII total score, as well as in triglycerides and blood glucose, attributed to this dietary intervention compared to other groups. In comparison with Tertile 1, nutrient intake and nutritional status in Tertile 3 were closely associated with significant influencing factors of the dietary intervention. For the group with the worst DII (Tertile 3), this dietary intervention greatly improved nutritional status, nutrient intake, and clinical indicators; thus, this senior-friendly diet appears to be beneficial for elderly people with nutritional vulnerabilities.

Corneal Nerve Assessment by Aesthesiometry: History, Advancements, and Future Directions

The measurement of corneal sensation allows clinicians to assess the status of corneal innervation and serves as a crucial indicator of corneal disease and eye health. Many devices are available to assess corneal sensation, including the Cochet–Bonnet aesthesiometer, the Belmonte Aesthesiometer, the Swiss Liquid Jet Aesthesiometer, and the newly introduced Corneal Esthesiometer Brill. Increasing the clinical use of in vivo confocal microscopy and optical coherence tomography will allow for greater insight into the diagnosis, classification, and monitoring of ocular surface diseases such as neurotrophic keratopathy; however, formal esthesiometric measurement remains necessary to assess the functional status of corneal nerves. These aesthesiometers vary widely in their mode of corneal stimulus generation and their relative accessibility, precision, and ease of clinical use. The development of future devices to optimize these characteristics, as well as further comparative studies between device types should enable more accurate and precise diagnosis and treatment of corneal innervation deficits. The purpose of this narrative review is to describe the advancements in the use of aesthesiometers since their introduction to clinical practice, compare currently available devices for assessing corneal innervation and their relative limitations, and discuss how the assessment of corneal innervation is crucial to understanding and treating pathologies of the ocular surface.

Mental Health, Suicidality, Health, and Social Indicators Among College Students Across Nine States in India

Background: The prevalence of mental health issues among youth is significantly high globally. This article presents findings from a survey that examined psychological distress, suicidal thoughts and behaviors, and health and social indicators among college students in India. Methods: The study recruited 8,542 students from 30 universities spanning nine Indian states, utilizing a questionnaire that included the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) to measure depression and anxiety symptoms. It also examined the prevalence of suicidal thoughts, attempts, non-suicidal self-injury, and other health and social factors. Results: Findings indicate that 18.8% and 12.4% of students had considered suicide over their lifetime and in the past year, respectively, with 6.7% having attempted it at some point in their lives. Among those with lifetime suicidal thoughts, more than one-third (38.1%) reported having previously disclosed these thoughts to someone, with friends being the most common confidants. Furthermore, one-third (33.6%) of participants reported moderate to severe symptoms of depression, and one-quarter (23.2%) reported moderate to severe symptoms of anxiety. Conclusions: The study revealed a significant prevalence of depressive and anxiety symptoms, as well as suicidal thoughts and attempts among college students in India. The study underscores the critical need for interventions aimed at improving mental health and supporting this demographic.

Identification of the new molecular subtypes related to inflammation in breast cancer.

Breast cancer is a prevalent ailment among women, and the inflammatory response plays a crucial role in the management and prediction of breast cancer (BRCA). However, the new subtypes based on inflammation in BRCA research are still undefined. The databases including The Cancer Genome Atlas and gene expression omnibus were utilized to gather clinical data and somatic mutation information for approximately 1069 BRCA patients. Through Consensus Clustering, novel subtypes linked to inflammation were identified. A comparative analysis was conducted on the prognosis, and immune cell infiltration, and somatic mutation of the new subtypes. Additionally, an investigation into drug therapy and immunotherapy was conducted to distinguish high-risk individuals from low-risk ones. The findings of this investigation proposed the categorization of BRCA into innovative subtypes predicated on the inflammatory response and 6 key genes were a meaningful approach. Specifically, the low-, medium-, and high-inflammation subtypes exhibited varying degrees of association with clinicopathological features, tumor microenvironment, and prognosis. Notably, the high-inflammation subtype was characterized by a strong correlation with immunosuppressive microenvironments and a higher frequency of somatic mutations, which was an indication of poorer health. This study revealed that a brand-new classification could throw new light on the effective prognosis. The integration of multiple key genes was a new characterization that could promote more immunotherapy strategies and contribute to predicting the efficacy of the chemotherapeutic drugs.

Further examination of the dual-factor model: characteristics of emerging adults with non-traditional mental health profiles

Traditionally, the mental health field has focused on psychological symptoms and distress, and health is considered the absence of illness. An alternative view suggests that optimal mental health must include the presence of positive characteristics in addition to the absence of illness. Accordingly, a dual-factor model of mental health includes measures of both psychopathology and positive subjective well-being to determine an individual’s mental health status. The current study investigated this dual-factor model by examining whether subjective well-being and psychopathology combine to produce distinct mental health profiles in emerging adults. A sample of 559 university students completed self-report surveys indicating their life satisfaction and internalizing and externalizing symptoms. Cluster analysis yielded four unique mental health groups. The well-adjusted cluster included individuals with high life satisfaction and low internalizing and externalizing symptoms. The dissatisfied cluster consisted of individuals with low externalizing, but also low life satisfaction and slightly above-average levels of internalizing. The externalizing cluster had high life satisfaction but also high externalizing psychopathology, and the troubled cluster exhibited low life satisfaction and high levels of both psychopathology symptoms. The clusters also differed in their personality traits, coping responses, and automatic thoughts, with the most favorable pattern across all three dimensions exhibited by the well-adjusted cluster. These findings highlight the importance of considering positive indicators of mental health in addition to measures of psychopathology and support the use of an empirical classification procedure in determining the mental health status of emerging adults.

Participatory action research to develop and implement multicomponent, multilevel strategies for implementing colorectal cancer screening interventions in American Indian communities in New Mexico

BackgroundDespite the effectiveness of colorectal cancer (CRC) screening, American Indians (AIs) have low screening rates in the US. Many AIs receive care at Indian Health Services, Tribal, and Urban Indian (I/T/U) healthcare facilities, where published evidence regarding the implementation of CRC screening interventions is lacking. To address this gap, the University of New Mexico Comprehensive Cancer Center and the Albuquerque Area Southwest Tribal Epidemiology Center collaborated with two tribally-operated healthcare facilities in New Mexico with the goal of improving CRC screening rates among New Mexico’s AI communities.MethodsGuided by the principles of Community Based Participatory Research, we engaged providers from the two tribal healthcare facilities and tribal community members through focus group (two focus groups with providers (n = 15) and four focus group and listening sessions with community members (n = 65)), to elicit perspectives on the feasibility and appropriateness of implementing The Guide to Community Preventive Services (The Community Guide) recommended evidence-based interventions (EBIs) and strategies for increasing CRC screening. Within each tribal healthcare facility, we engaged a Multisector Action Team (MAT) that participated in an implementation survey to document the extent to which their healthcare facilities were implementing EBIs and strategies, and an organizational readiness survey that queried whether their healthcare facilities could implement additional strategies to improve uptake of CRC screening.ResultsThe Community Guide recommended EBIs and strategies that received the most support as feasible and appropriate from community members included: one-on-one education from providers, reminders, small media, and interventions that reduced structural barriers. From the providers’ perspective, feasible and acceptable strategies included one-on-one education, patient and provider reminders, and provider assessment and feedback. Universally, providers mentioned the need for patient navigators who could provide culturally appropriate education about CRC and assist with transportation, and improved support for coordinating clinical follow-up after screening. The readiness survey highlighted overall readiness of the tribal facility, while the implementation survey highlighted that few strategies were being implemented.ConclusionsFindings from this study contribute to the limited literature around implementation research at tribal healthcare facilities and informed the selection of specific implementation strategies to promote the uptake of CRC screening in AI communities.

Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction

Predicting the course of neurodegenerative disorders early has potential to greatly improve clinical management and patient outcomes. A key challenge for early prediction in real-world clinical settings is the lack of labeled data (i.e., clinical diagnosis). In contrast to supervised classification approaches that require labeled data, we propose an unsupervised multimodal trajectory modeling (MTM) approach based on a mixture of state space models that captures changes in longitudinal data (i.e., trajectories) and stratifies individuals without using clinical diagnosis for model training. MTM learns the relationship between states comprising expensive, invasive biomarkers (β-amyloid, grey matter density) and readily obtainable cognitive observations. MTM training on trajectories stratifies individuals into clinically meaningful clusters more reliably than MTM training on baseline data alone and is robust to missing data (i.e., cognitive data alone or single assessments). Extracting an individualized cognitive health index (i.e., MTM-derived cluster membership index) allows us to predict progression to AD more precisely than standard clinical assessments (i.e., cognitive tests or MRI scans alone). Importantly, MTM generalizes successfully from research cohort to real-world clinical data from memory clinic patients with missing data, enhancing the clinical utility of our approach. Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled data (i.e., clinical diagnosis) with strong potential for translation to clinical practice.

A laser-Engraved Wearable Electrochemical Sensing Patch for Heat Stress Precise Individual Management of Horse.

In point-of-care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high-value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser-engraved graphene (LEG)sensing electrode array, a non-invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real-time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high-value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

Impact of COVID-19 pandemic on physical health amongst children: Difference-in-differences analyses of nationwide school health checkup database.

The COVID-19 pandemic posed tremendous challenges for children. However, the long-term effects of the pandemic on various aspects of physical health at a national level remain unclear. In this retrospective cohort study, we analysed data from nationwide health checkup records amongst children aged 7-15 years. The dataset comprised 3 544 146 records from 393 794 individuals who graduated from junior high school during fiscal years 2007 to 2022. Difference-in-differences (DID) analyses with multiple time periods were used to examine the impact of COVID-19 on physical health outcomes. Compared with the pre-pandemic period, the COVID-19 pandemic was associated with excess increases in obesity for boys and girls, persisting over the 3 years (+0.42%; [95% CI, 0.23-0.61]). Also, it was associated with excess increases in underweight (+0.28% [0.25-0.32]) and poor visual acuity amongst boys in the 3rd year (+1.80% [1.30-2.30]). There were excess reductions in dental caries (-1.48% [-2.01 to -0.95]), glucosuria (-0.55 [-0.88 to -0.23]) and hematuria (-0.43% [-0.73 to -0.13]) during the 3rd year of the pandemic. These findings underscore the multifaceted impact of the pandemic on various health indicators for school-aged children. This information could be valuable for public health policy and paediatric healthcare planning in the post-pandemic era.

Predicting suicidality with small sets of interpretable reward behavior and survey variables

AbstractThe prediction of suicidal thought and behavior has met with mixed results. This study of 3,476 de-identified participants (4,019 before data exclusion) quantified the prediction of four suicidal thought and behavior (STB) variables using a short reward/aversion judgment task and a limited set of demographic and mental health surveys. The focus was to produce a simple, quick and objective framework for assessing STB that might be automatable, without the use of big data. A balanced random forest classifier performed better than a Gaussian mixture model and four standard machine learning classifiers for predicting passive suicide ideation, active suicide ideation, suicide planning and planning for safety. Accuracies ranged from 78% to 92% (optimal area under the curve between 0.80 and 0.95) without overfitting, and peak performance was observed for predicting suicide planning. The relative importance of features for prediction showed distinct weighting across judgment variables, contributing between 40% and 64% to prediction per Gini scores. Mediation/moderation analyses showed that depression, anxiety, loneliness and age variables moderated the judgment variables, indicating that the interaction of judgment with mental health and demographic indices is fundamental for the high-accuracy prediction of STB. These findings suggest the feasibility of an efficient and highly scalable system for suicide assessment, without requiring psychiatric records or neural measures. The findings suggest that STB might be understood within a cognitive framework for judgment with quantitative variables whose unique constellation separates passive and active suicidal thought (ideation) from suicide planning and planning for safety.

Colorectal cancer health and care quality indicators in a federated setting using the Personal Health Train

ObjectiveHospitals and healthcare providers should assess and compare the quality of care given to patients and based on this improve the care. In the Netherlands, hospitals provide data to national quality registries, which in return provide annual quality indicators. However, this process is time-consuming, resource intensive and risks patient privacy and confidentiality. In this paper, we presented a multicentric ‘Proof of Principle’ study for federated calculation of quality indicators in patients with colorectal cancer. The findings suggest that the proposed approach is highly time-efficient and consume significantly lesser resources.Materials and methodsTwo quality indicators are calculated in an efficient and privacy presevering federated manner, by i) applying the Findable Accessible Interoperable and Reusable (FAIR) data principles and ii) using the Personal Health Train (PHT) infrastructure. Instead of sharing data to a centralized registry, PHT enables analysis by sending algorithms and sharing only insights from the data.ResultsETL process extracted data from the Electronic Health Record systems of the hospitals, converted them to FAIR data and hosted in RDF endpoints within each hospital. Finally, quality indicators from each center are calculated using PHT and the mean result along with the individual results plotted.Discussion and conclusionPHT and FAIR data principles can efficiently calculate quality indicators in a privacy-preserving federated approach and the work can be scaled up both nationally and internationally. Despite this, application of the methodology was largely hampered by ELSI issues. However, the lessons learned from this study can provide other hospitals and researchers to adapt to the process easily and take effective measures in building quality of care infrastructures.

Exposure to synthesized tribromobisphenol A and critical effects: Metabolic pathways, disease signature, and benchmark dose derivation

2,2′,6-Tribromobisphenol A (Tri-BBPA), the main debrominated congener of tetrabromobisphenol A (TBBPA), is ubiquitous in the environment and human body but with unknown toxicity. Tri-BBPA was synthesized and applied to investigate its sub-chronic exposure effects on 28 organ coefficients and clinical health indicators related to liver function, kidney function, and cardiovascular system function in female mice. Results showed that the liver was the targeted organ of Tri-BBPA exposure. Compared to the control group, the changes in liver coefficient, cholinesterase, total protein, albumin, γ-glutamyl transpeptidase, lactate dehydrogenase, and creatine kinase levels ranged from −61.2 % to 35.5 % in the high-exposed group. Creatine kinase was identified as a critical effect indicator of Tri-BBPA exposure. Using the Bayesian benchmark dose derivation method, a lower reference dose than TBBPA was established for Tri-BBPA (10.6 μg/kg-day). Serum metabolomics revealed that Tri-BBPA exposure may primarily damage the liver by disrupting tryptophan metabolism related to L-alanine, tryptamine, 5-hydroxyindoleacetic acid, and 5-methoxyindoleacetate in liver cells and leading to liver dysfunction. Notably, epilepsy, schizophrenia, early preeclampsia, and late-onset preeclampsia were the top six enriched diseases, suggesting that the nervous system may be particularly affected by Tri-BBPA exposure. Our findings hinted a non-negligible health risk of exposure to debrominated products of TBBPA.

Heterogeneous trajectories of perceived stress and their associations with active leisure: a longitudinal study during the first year of COVID-19

IntroductionThere is a plethora of literature on the dynamics of mental health indicators throughout the COVID-19 pandemic, yet research is scarce on the potential heterogeneity in the development of perceived stress. Furthermore, there is a paucity of longitudinal research on whether active leisure engagement, which typically is beneficial in reducing stress, might have similar benefits during times of major disruption. Here we aimed to extend previous work by exploring the dynamics of change in stress and coping, and the associations with active leisure engagement over the first year of COVID-19.MethodsData from 439 adults (Mage = 45, SD = 13) in Estonia who participated in a longitudinal online study were analyzed. The participants were assessed at three timepoints: April–May 2020; November–December 2020; and April–May 2021.ResultsMean stress and coping levels were stable over time. However, latent profile analysis identified four distinct trajectories of change in stress and coping, involving resilient, stressed, recovering, and deteriorating trends. Participants belonging to the positively developing stress trajectories reported higher active leisure engagement than those belonging to the negatively developing stress trajectories.DiscussionThese findings highlight the importance of adopting person-centered approaches to understand the diverse experiences of stress, as well as suggest the promotion of active leisure as a potentially beneficial coping resource, in future crises.

Co-located ecological data for exploring top- and subsoil carbon dynamics across grassland-woodland contrasts

Soil organic carbon (SOC) is a soil health indicator and understanding dynamics changing SOC stocks will help achieving net zero goals. Here we present four datasets featuring 11,750 data points covering co-located aboveground and below-ground metrics for exploring ecosystem SOC dynamics. Five sites across England with an established land use contrast, grassland and woodland next to each other, were rigorously sampled for aboveground (n = 109), surface (n = 33 soil water release curves), topsoil, and subsoil metrics. Commonly measured soil metrics were analysed in five soil increments for 0–1 metre (n = 4550). Less commonly measured soil metrics which were assumed to change across the soil profile were measured on a subset of samples only (n = 3762). Additionally, we developed a simple method for soil organic matter fractionation using density fractionation which is part of the less common metrics. Finally, soil metrics which may impact SOC dynamics, but with less confidence as to their importance across the soil profile were only measured on topsoil (~5–15 cm = mineral soil) and subsoil (below 50 cm) samples (n = 2567).

Quantitative condition assessment method for rotating machinery using fuzzy neural network

Health condition assessment of rotating machinery has been a persistent challenge. Traditional condition assessment methods often rely on single features, limiting their application to comprehensively measure the health condition of rotating machinery. This study introduced a quantitative condition assessment method for rotating machinery using fuzzy neural network (FNN). Initially, multi-domain features of signals from rotating machinery are extracted to achieve comprehensive representation of signals in the feature space. To eliminate redundant information of various features, a feature dimensionality reduction method is explored based on variance variation and stacked auto-encoder. Afterward, a normalized health indicator is constructed by integrating the optimized features through FNN, and it can indicate the current conditions of rotating machinery. Furthermore, an early anomaly alarm strategy based on 3σ criterion is designed for rotating machinery. The abnormal signal will be recognized automatically when it exceeds the predetermined thresholds. Last, the effectiveness of the proposed method is validated on IMS bearing dataset and XJTU-SY bearing dataset. The results show that the proposed method can effectively obtain the quantitative indicators that reflect the operation conditions of rotating machinery and can accurately detect the early abnormal signals.