Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Knowledge attainment, learning approaches, and self-perceived study burnout among European veterinary students.

Introduction: This study investigates the relationship between approaches to learning, self-perceived study burnout, and the level of knowledge among veterinary students. Veterinary educational programs are under regular development and would benefit greatly from detailed feedback on students' knowledge, proficiency, influencing factors, and coping mechanisms. Methods: The VetRepos consortium developed and calibrated an item repository testing knowledge across the entire veterinary curriculum. Two hundred forty-eight students from seven European veterinary institutions took the VetRepos test, comprising a subset of the repository. They also responded to a questionnaire assessing deep and unreflective learning approaches and self-perceived study burnout, represented by exhaustion and cynicism. Structural equation modeling analyzed the relationship between these latent traits and the VetRepos test score. Results: The model failed the exact-fit test but was retained based on global fit indices, inter-item residual correlations, and standardized residual covariances. Root Mean Square Error of Approximation with robust standard errors and scaled test statistic was 0.049 (95% confidence interval 0.033-0.071), scaled and robust Comparative Fit Index 0.95 (0.90-0.98), and scaled Standardized Root Mean Square Residual 0.056 (0.049-0.071). Measurement invariance across study years was not violated (ΔCFI = 0.00, χ2 = 3.78, Δdf = 4, p = 0.44), but it could not be confirmed between genders or universities. The VetRepos test score regressed on the study year [standardized regression coefficient = 0.68 (0.62-0.73)], showed a negative regression on the unreflective learning approach [-0.25 (-0.47 to -0.03)], and a positive regression on the deep approach [0.16 (0.03-0.28)]. No direct association with perceived burnout was observed; however, a significant, medium-sized association was found between the unreflective approach and self-perceived study burnout. No significant differences in learning approaches or perceived burnout were found between study years. Discussion: The most important source of variance in VetRepos test scores, unrelated to the study year, was the learning approach. The association between the VetRepos test score and self-perceived burnout was indirect. Future research should complement this cross-sectional approach with longitudinal and person-oriented studies, further investigating the relationship between study burnout and learning approaches.

Analysis of Factors Influencing Hospitalization Expenses of Patients with Gastric Cancer in Shanghai, 2014-2021: Based on Grey Relational Analysis and Structural Equation Modeling.

OBJECTIVES: This study analyzed the basic condition and the influencing factors of hospitalization costs of patients with gastric cancer in Shanghai from 2014 to 2021, so as to provide a scientific reference for promoting the reform of the medical and healthcare system. METHODS: The study data were obtained from the electronic medical record system of Shanghai Hospital. The grey relational analysis was applied to analyze the correlation strength of various expenses with hospitalization costs. The structural equation modeling was constructed to analyze the influences of factors on the hospitalization expenses, as well as the relationship between each factor. RESULTS: A total of 23 335 study subjects were included. The results of grey relational analysis showed that the total cost of drugs had the strongest correlation with hospitalization expenses, followed by material expenses and surgery cost, whereas those of others were lower. The results of the structural equation modeling showed that age had the greatest influence on hospitalization expenses with a path coefficient of 0.618. Other influencing factors included surgery history, length of stay, hospital level, gender, and medical insurance. CONCLUSIONS: The total cost of drugs had the strongest correlation with hospitalization expenses. Factors such as gender, age, and hospital level all affect the hospitalization expenses. In the future, it is necessary to take further measures to control the cost of drugs and constantly optimize the structure of hospitalization costs. Meanwhile, the reform of the medical and healthcare system should be deepened to reasonably regulate the medical behaviors and reduce the financial burden of patients.

Latent Reciprocal Engagement and Accuracy Variables in Social Relations Structural Equation Modeling.

The social relations model (SRM) is the standard approach for analyzing dyadic data stemming from round-robin designs. The model can be used to estimate correlation-coefficients that reflect the overall reciprocity or accuracy of judgements for individual and dyads on the sample- or population level. Within the social relations structural equation modeling framework and on the statistical grounding of stochastic measurement and classical test theory, we show how the multiple indicator SRM can be modified to capture inter-individual and inter-dyadic differences in reciprocal engagement or inter-individual differences in reciprocal accuracy. All models are illustrated on an open-access round-robin data set containing measures of mimicry, liking, and meta-liking (the belief to be liked). Results suggest that people who engage more strongly in reciprocal mimicry are liked more after an interaction with someone and that overestimating one's own popularity is strongly associated with being liked less. Further applications, advantages and limitations of the models are discussed.

Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model.

BACKGROUND: The implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems. METHODS: A quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (ß), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05. RESULTS: The proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (ß = 0.215, p-value < 0.018), performance expectancy (ß = 0.245, p-value < 0.001), and attitude (ß = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention. CONCLUSION: The extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.

Social isolation, depression, and anxiety among young adult cancer survivors: The mediating role of social connectedness.

BACKGROUND: Social isolation and social connectedness are health determinants and aspects of social well-being with strong associations with psychological distress. This study evaluated relationships among social isolation, social connectedness, and psychological distress (i.e., depression, anxiety) over 1 year in young adult (YA) cancer survivors 18-39 years old. METHODS: Participants were YAs in a large cohort study that completed questionnaires every 2 months for 1 year. Social isolation, aspects of social connectedness (i.e., companionship, emotional support, instrumental support, and informational support), depression, and anxiety were assessed with Patient-Reported Outcomes Measurement Information System short form measures. Mixed-effect models were used to evaluate changes over time. Confirmatory factor analysis and multilevel structural equation modeling were used to define social connectedness as a latent construct and determine whether relationships between social isolation and psychological distress were mediated by social connectedness. RESULTS: Participants (N = 304) were mean (M) = 33.5 years old (SD = 4.7) and M = 4.5 years (SD = 3.5) post-initial cancer diagnosis. Most participants were female (67.4%) and non-Hispanic White (68.4%). Average scores for social well-being and psychological distress were within normative ranges and did not change (p values >.05). However, large proportions of participants reported at least mild social isolation (27%-30%), depressive symptoms (36%-37%), and symptoms of anxiety (49%-51%) at each time point. Across participants, more social isolation was related to less social connectedness (p values <.001), more depressive symptoms (p < .001), and more symptoms of anxiety (p < .001). Social connectedness mediated the relationship between social isolation and depression (p = .004), but not anxiety (p > .05). CONCLUSIONS: Social isolation and connectedness could be intervention targets for reducing depression among YA cancer survivors.

Exploring adolescent participation in football: a gender-differentiated structural equation model based on the theory of planned behavior.

This paper explores the intersection of gender and sports participation in adolescence, focusing on traditionally male-dominated sports like football. We aim to develop a structural model based on the Theory of Planned Behavior (TPB) to examine gender-differentiated patterns and factors influencing adolescent participation in football. We analyzes data from 1,147 adolescents using Structural Equation Modeling (SEM). The results indicate that a blend of attitudes, subjective norms, perceived behavioral control, and past behavior effectively predicts adolescents' willingness to engage in football. Moreover, the study investigates the roles of perceived behavioral control, past behavior, and behavioral intentions in actual football participation, uncovering significant gender disparities in the progression from past behavior to intentions and subsequent behavior. This research highlights the complex role of gender in football participation and provides strategic insights for increasing girls' involvement in the sport. Our study sets the stage for future research on enhancing girls' participation in football.

Structural equation modeling analysis of health belief model-based determinants of COVID-19 preventive behavior of academic staff: a cross-sectional study.

INTRODUCTION: Despite COVID-19 being highly contagious and spreading to several countries, the university community has overlooked prevention measures. For more than five decades, the Health Belief Model (HBM) has been a widely used conceptual framework in health behavior. structural equation modeling(SEM) analysis is an advanced statistical method capable of rectifying failures of the basic models and showing complex relations Thus this study aimed to determine the magnitude of COVID-19 prevention behavior and identify its associated factors using HBM and SEM analysis. METHOD: An institutional-based cross-sectional study was conducted among academic staff of the University of Gondar in Ethiopia from April 10 to May 10/2021. Daniel Soper's sample size calculator was used to determine the sample size. Proportional allocation to each campus followed by a simple random sampling technique was employed to select study subjects. A pre-tested, structured questionnaire was used to collect the data. Structural equation modeling analysis was employed to show the relationship between health belief model constructs and their effect on preventive behavior. RESULT: A total of 602 academic staff participated. The magnitude of good COVID-19 preventive behavior was 24.8%. The HBM explained 55% of the variance in preventive behavior. Perceived barriers (ß = -0.37, p < 0.05), self-efficacy (ß = 0.32, p < 0.05), perceived susceptibility (ß = 0.23, p < 0.05), and perceived benefit (ß = 0.16, p < 0.05) were the direct significant predictors of COVID 19 prevention behavior. CONCLUSION: only a quarter of the academic staff have good COVID-19 preventive behavior. The HBM explained a great amount of variance in preventive behavior and Perceived barriers, benefits, susceptibility, and self-efficacy significantly associated with prevention behavior. Carefully planned intervention that considers those significant perceptions should be designed and implemented to raise COVID-19 prevention behavior.

Statistical analysis of the effect of socio-political factors on individual life satisfaction.

Life satisfaction refers to an individual's cognitive evaluation of the quality of their life. The aim of the present study is to develop the current understanding of how perceived corruption, attitudes toward migration, perceived security, and strength of national identity influence life satisfaction. Additionally, the study examines how demographic variables of relationship status, social class, sex, religious affiliation, and country impact life satisfaction in the provided cultural context. Ordinal logistic regression analysis, Confirmatory Factor Analysis, and Structural Equation Modeling are used to analyze data from the World Values Survey. Findings from the analyses indicate that perceived corruption, perceived security, and strength of national identity have a significant impact on life satisfaction, whereas migration has an indirect effect on life satisfaction through perceived security. The present research can develop our current understanding of life satisfaction from a socio-political perspective.

Beliefs negatively mediate adolescents' and adults' knowledge about sickle cell disease prevention and relationship choices; a one-center exploratory study in Central Region, Ghana.

BACKGROUND: Sickle cell disease (SCD) is a common monogenic inheritable condition in sub-Saharan Africa. 30% of Ghanaians are estimated to be carriers creating a condition for stable SCD penetrance. Being inheritable, SCD is scientifically preventable through intentional spousal selection. This study therefore explored young adults' knowledge, beliefs and prevention strategies regarding SCD. METHODS: This exploratory study employed a mixed-methods approach (semi-structured questionnaires and focus group discussion [FGD]) to explore the SCD knowledge and beliefs of participants (15-49 years). The data collection was intentionally sequential; initial administration of 386 questionnaires and then followed by the FGD (16 participants). FGD was thematically analysed whereas quantitative data was explored using structural equation modeling (SEM); p < 0.05 was considered statistically significant under two-tailed assumptions. RESULTS: Whereas 98.7% reported having heard about SCD, < 50% got the information through formal education. Overall, 49.7% knew their respective SCD status; the proportion increased with age (48.8% in < 30 years vs 64.4% in ≥ 30 years old), or higher degree status (48.8% in undergraduates vs 67.4% in postgraduates). Moreover, whereas nine-in-ten correctly identified that SCD is hereditable, three-in-ten believed that having a SCD child was a matter of fate. Our FGD revealed that whereas curses, and spiritual attack were misconceived as potential causes of SCD, stigmatization associated with SCD was a major concern. The SEM demonstrated that one's knowledge about SCD prevention is significantly positively associated with relationship choices (b = 0.757, p < 0.05). Also, a participant's knowledge about SCD preventive strategies was significantly associated with the individual's beliefs about SCD (b = 0.335; p < 0.05). However, a participant's SCD beliefs negatively mediated SCD preventive strategies-relationship choices association. CONCLUSIONS: SCD beliefs likely transform linear quantitative associations into a complex non-linear interaction; public health campaigns ought to unearth and address SCD beliefs to maximize achieving the intended targets.

Exploring social media determinants in fostering pro-environmental behavior: insights from social impact theory and the theory of planned behavior.

Introduction: This study investigates the impact of social media on pro-environmental behavior (PEB) through the lenses of the Theory of Planned Behavior (TPB) and Social Impact Theory. The research aims to elucidate how social media influences Environmental Attitude (EA) and Subjective Norms (SN), and how these factors contribute to Behavioral Intentions (BI) that ultimately affect PEB. Additionally, it examines the moderating effect of Perceived Behavioral Control (PBC) on the relationship between BI and PEB. Methods: To explore these relationships, the study employs a dual methodological approach using Variance-Based Structural Equation Modeling (VBSEM) and Artificial Neural Networks (ANN). Data were collected from two distinct samples: 1200 participants from Taiwan for the SEM analysis and 602 respondents for the ANN study. SEM was utilized to explore causal relationships, while ANN was employed to enhance predictive accuracy. Results: The SEM analysis reveals that social media significantly affects both EA and SN, except for Social Networking Site Involvement (SNSI), which does not significantly impact EA. Additionally, the findings indicate that BI mediates the relationship between EA and PEB. However, BI does not mediate the SN-PEB relationship, and the link between SN and BI is found to be non-significant. Empirical evidence also suggests that PBC moderates the BI-PEB relationship, with a stronger influence observed under higher levels of PBC and a weaker influence under lower levels. Discussion: These results underscore the complex dynamics between social media factors and pro-environmental behavior. The study concludes that while social media plays a significant role in shaping EA and SN, its impact on EA is not mediated by SNSI. Furthermore, PBC significantly moderates the BI-PEB relationship, highlighting its critical role in PEB. The discussion addresses the implications of these findings, acknowledges the limitations encountered, and suggests potential avenues for future research.

Checking the inventory: Illustrating different methods for individual participant data meta-analytic structural equation modeling.

Researchers may have at their disposal the raw data of the studies they wish to meta-analyze. The goal of this study is to identify, illustrate, and compare a range of possible analysis options for researchers to whom raw data are available, wanting to fit a structural equation model (SEM) to these data. This study illustrates techniques that directly analyze the raw data, such as multilevel and multigroup SEM, and techniques based on summary statistics, such as correlation-based meta-analytical structural equation modeling (MASEM), discussing differences in procedures, capabilities, and outcomes. This is done by analyzing a previously published collection of datasets using open source software. A path model reflecting the theory of planned behavior is fitted to these datasets using different techniques involving SEM. Apart from differences in handling of missing data, the ability to include study-level moderators, and conceptualization of heterogeneity, results show differences in parameter estimates and standard errors across methods. Further research is needed to properly formulate guidelines for applied researchers looking to conduct individual participant data MASEM.

A new strategy model for reducing industrial pollutants using structural equation modeling (SEM): a case study.

Economic developments over recent decades have led to environmental issues. Industries have been introduced among the main causes of environmental degradation. From this perspective, this research work identifies strategies for reducing industrial pollutants. In the first part, the factors concerned and their impacts on mitigating industrial pollutants are identified. Then, the initial model is developed. The case examined here is Mashhad Industrial Zone, Mashhad, Iran, wherein the questionnaire was distributed. Given the non-normal data in this study, the initial model fit is further measured by structural equation modeling (SEM), using SmartPLS. Upon the model fit confirmation, the research hypotheses, i.e., the factors affecting the reduction of industrial pollutants, are assessed. The results indicated that, among the variables examined, seven components can significantly prevent industrial pollutants. In view of this, green industry status quo with the effect size of 0.3 has the greatest possible impact on diminishing industrial pollutants. The next uppermost strategies for this purpose are government incentives, management commitment, green product marketing, competitive strategies, government oversight, and political issues, with the effect size values of 0.21, 0.20, 0.19, 0.15, 0.12, and 0.09, respectively. Financial issues and government regulations are not directly linked with lowering pollutants emanating from industries.

The Depression Anxiety Stress Scales for Youth (DASS-Y): evidence of validity and cross-national and gender measurement invariance.

The Depression Anxiety Stress Scales for Youth (DASS-Y) is a recently developed measure of unpleasant emotional experiences for children and adolescents; however, the evidence for its validity is still limited. The present study evaluated the evidence for the structural, convergent, and incremental validity of the DASS-Y on a sample of Serbian adolescents aged 15-19 years (N = 421; 48% female, mean age = 16.86). Measurement invariance across gender and two countries (Serbia and Australia) was also tested. The findings supported the bifactor exploratory structural equation modeling (bifactor-ESEM) representation of the DASS-Y, which includes the General factor of negative affect/emotional distress, and three specific factors of Depression, Anxiety, and Stress. The DASS-Y factors showed evidence of incremental validity in predicting global externalizing symptoms and demonstrated convergent validity in relation to measures of global internalizing and externalizing symptoms as well as subjective well-being. The bifactor-ESEM model of the DASS-Y was found to be invariant across gender and two countries. The present study findings highlight the importance of distinguishing between general and specific components of unpleasant emotional states.

EXPRESS: From task-general towards task-specific cognitive operations in a few minutes? Working memory performance as an adaptive process.

Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilized longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment (N = 296) was successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment (N = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying working memory task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.

Regional brain structural network topology mediates the associations between white matter damage and disease severity in first-episode, Treatment-naïve pubertal children with major depressive disorder.

Puberty is a vulnerable period for the onset of major depressive disorder (MDD) due to considerable neurodevelopmental changes. Prior diffusion tensor imaging (DTI) studies in depressed youth have had heterogeneous participants, making assessment of early pathology challenging due to illness chronicity and medication confounds. This study leveraged whole-brain DTI and graph theory approaches to probe white matter (WM) abnormalities and disturbances in structural network topology related to first-episode, treatment-naïve pediatric MDD. Participants included 36 first-episode, unmedicated adolescents with MDD (mean age 15.8 years) and 29 age- and sex-matched healthy controls (mean age 15.2 years). Compared to controls, the MDD group showed reduced fractional anisotropy in the internal and external capsules, unveiling novel regions of WM disruption in early-onset depression. The right thalamus and superior temporal gyrus were identified as network hubs where betweenness centrality changes mediated links between WM anomalies and depression severity. A diagnostic model incorporating demographics, DTI, and network metrics achieved an AUROC of 0.88 and a F1 score of 0.80 using a neural network algorithm. By examining first-episode, treatment-naïve patients, this work identified novel WM abnormalities and a potential causal pathway linking WM damage to symptom severity via regional structural network alterations in brain hubs.

Equivalence Testing Based Fit Index: Standardized Root Mean Squared Residual.

A popular measure of model fit in structural equation modeling (SEM) is the standardized root mean squared residual (SRMR) fit index. Equivalence testing has been used to evaluate model fit in structural equation modeling (SEM) but has yet to be applied to SRMR. Accordingly, the present study proposed equivalence-testing based fit tests for the SRMR (ESRMR). Several variations of ESRMR were introduced, incorporating different equivalence bounds and methods of computing confidence intervals. A Monte Carlo simulation study compared these novel tests with traditional methods for evaluating model fit. The results demonstrated that certain ESRMR tests based on an analytic computation of the confidence interval correctly reject poor-fitting models and are well-powered for detecting good-fitting models. We also present an illustrative example with real data to demonstrate how ESRMR may be incorporated into model fit evaluation and reporting. Our recommendation is that ESRMR tests be presented in addition to descriptive fit indices for model fit reporting in SEM.

Small airway dysfunction links asthma exacerbations with asthma control and health-related quality of life.

BACKGROUND: Small airway dysfunction not only affects asthma control, but also has adverse effects on the psychological and/or social activities of asthma patients. However, few long-term observational studies have explored the complex relationship between small airway dysfunction and asthma control and health-related quality of life in patients with asthma exacerbations. METHODS: The study recruited 223 patients with exacerbations of asthma (i.e. those with at least one asthma attack over the past year) and 228 patients without exacerbations of asthma (i.e. those without asthma attacks over the past year). We evaluated SAD in patients with asthma exacerbations using impulse oscillometry method. At each evaluation time point within one year of follow-up, the attending physician conducts a case investigation of the patients. We analyzed the correlation between SAD and general characteristics (age, obesity, smoking history), type 2 inflammation (blood eosinophils, exhaled nitric oxide), FEV1, as well as asthma control (ACT) and health-related quality of life (mini-AQLQ) in patients with asthma exacerbations, and constructed a structural equation model to evaluate the causality of these clinical variables. RESULTS: The SAD prevalence in patients with asthma exacerbation is as high as 75%. SAD is connected with poor asthma control and poor health-related quality of life. The structural equation model indicates that age, obesity, FeNO, and FEV1 are independent predictive factors of SAD. SAD is the main determinant factor of asthma control, which in turn affected health-related quality of life. FEV1 and age directly affect asthma control and affect health-related quality of life through asthma control. In addition, there is a bidirectional relationship between FEV1 and small airway dysfunction and between asthma control and health-related quality of life. CONCLUSIONS: Small airways are involved from an early stage in asthma. Abnormal function of the small airways can significantly increase airway resistance in asthma patients, while worsening their clinical symptoms. In addition, aging is also a key risk factor for asthma control. Especially, small airway dysfunction links asthma control with health-related quality of life.

Exploring college students' resistance to mandatory use of sports apps: a psychological reactance theory perspective.

Introduction: To improve the physical fitness of college students, numerous Chinese universities have mandated students to use sports apps for running on campus. However, this has led to widespread resistance among students. Methods: To gain a deep understanding of the potential reasons for student resistance, we have developed a conceptual model based on psychological reactance theory. Specifically, we conducted a questionnaire survey involving 449 Chinese college students, using partial least squares structural equation modeling to test and analyze the research model and its related hypotheses. Results: Our results reveal that: (1) Psychological reactance poses a potential reason for students' resistance to mandatory use of sports apps, and it has a significant negative impact on students' attitudes and behavioral intentions. (2) Students' perceived threat to freedom and reactance proneness are two important antecedents of psychological reactance, which can account for 51% (R 2 = 0.51) of the variance in psychological reactance. Furthermore, the results indicate that students' reactance proneness has a positive impact on perceived threats to freedom. (3) The mandatory use of sports apps leads students to have two different conditions: mandated-acceptance and mandated-rejection, both of which have a positive impact on the antecedents of psychological reactance. Discussion: These findings provide insights into the psychological processes underlying students' resistance to mandatory use of sports apps, facilitating the application of sports apps in intervention measures that improve health and fitness. Furthermore, this study is the first to apply psychological reactance theory to mandatory exercise behavior, contributing to the reactance literature.

The multiple mediating effects of vision-specific factors and depression on the association between visual impairment severity and fatigue: a path analysis study.

BACKGROUND: Severe fatigue is a common symptom for people with visual impairment, with a detrimental effect on emotional functioning, cognition, work capacity and activities of daily living. A previous study found that depression was one of the most important determinants of fatigue, but less is known about disease-specific factors in this patient population. This study aimed to explore the association between visual impairment severity and fatigue in adults with low vision, both directly and indirectly, with vision-specific factors and depression as potential mediators. METHODS: Cross-sectional data were collected from 220 Dutch low vision service patients by telephone interviews. Fatigue was defined as a latent variable by severity and impact on daily life. Potential mediators included vision-related symptoms, adaptation to vision loss and depression. Hypothesized structural equation models were constructed in Mplus to test (in)direct effects of visual impairment severity (mild/moderate, severe, blindness) on fatigue through above mentioned variables. RESULTS: The final model explained 60% of fatigue variance and revealed a significant total effect of visual impairment severity on fatigue. Patients with severe visual impairment (reference group) had significantly higher fatigue symptoms compared to those with mild/moderate visual impairment (ß = -0.50, 95% bias-corrected confidence interval [BC CI] [-0.86, -0.16]) and those with blindness (ß = -0.44, 95% BC CI [-0.80, -0.07]). Eye strain & light disturbance, depression and vision-related mobility mediated the fatigue difference between the severe and mild/moderate visual impairment categories. The fatigue difference between the severe visual impairment and blindness categories was solely explained by eye strain & light disturbance. Moreover, depressive symptoms (ß = 0.65, p < 0.001) and eye strain & light disturbance (ß = 0.19, p = 0.023) were directly associated with fatigue independent of visual impairment severity. CONCLUSIONS: Our findings indicate an inverted-U shaped relationship between visual impairment severity and fatigue in patients with low vision. The complexity of this relationship is likely explained by the consequences of visual impairment, in particular by strained eyes and depressive mood, rather than by severity of the disability itself.