Individual Characteristics Research Articles

The Impact of Post-Activation Performance Enhancement Protocols on Vertical Jumps: Systematic Review

Background: The post-activation performance enhancement (PAPE) protocol is frequently utilized to enhance athletic performance through high-intensity stimuli. Despite its popularity, the effectiveness and mechanisms of PAPE remain unclear, particularly in the context of improving jump performance in sports. Methods: A systematic review was conducted following the PRISMA guidelines. A comprehensive search was conducted across databases including Scopus, Web of Science, PubMed, and EBSCO up to 31 March 2024. Results: Initially, 481 studies were identified, of which 8 were finally included, involving a varied number of participants, each focusing on the impact of PAPE on jump performance. These studies predominantly indicated that PAPE protocols can lead to short-term improvements in jump performance. Two publications demonstrated a positive effect on jump height, and one of them reported a questionable effect on jump height in the initial time period, while the remaining five publications showed a positive impact on jump performance. The average increase in height achieved based on the intervention from the publications was 4.03% (p < 0.05). Variability in effectiveness was noted depending on the type of exercises used in the activation phase, timing of performance post activation, and individual physiological characteristics. Conclusions: PAPE protocols can effectively enhance jump performance in athletic populations, although outcomes vary based on protocol specifics and individual differences. These findings support the need for individualized approaches in the application of PAPE protocols to optimize athletic performance.

The adoption of Best Management Practices: empirical evidence from oil palm farming in rural Ghana

ABSTRACT Purpose We examine how farmer characteristics, farm, institutional, perception and shock factors influence the adoption of the Best Management Practices (BMPs). A thorough understanding of how relevant drivers affect the adoption decision of diverse BMPs can contribute to the development of evidence-based policies. Approach We use a household survey of 1031 smallholder oil palm farmers in the forest zones of Ghana to assess the determinants of farmers’ adoption decisions. We employ a seemingly unrelated regression (SUR) model after a principal component analysis is used to extract five latent components. Findings Our findings indicate that different farmer characteristics, farm, institutional, perception and shock factors have differential associations with the adoption of diverse BMPs. Key among these are the positive perception of BMPs, access to mills, perception of soil fertility and pests and diseases. Practical implications Specific BMPs must be tailored to meet the prevailing individual socio-economic characteristics and challenges of farmers to facilitate their adoption and scale-up. Educational programmes and dissemination of information by extension agents and research institutions are needed in the promotion of BMPs to enhance effective uptake. Theoretical implication The paper contributes to the growing literature especially on the uptake of diverse management practices, utilizing both behavioural and socio-economic theories underpinning adoption, thus providing empirical evidence that adoption is influenced by both behavioural and socio-economic factors. Originality/value In the bid to increase agricultural productivity, while preserving ecosystem services, the adoption of sustainable management practices remains key for all stakeholders and policy makers. This paper presents credible information for policy makers in Ghana and Africa in designing polices to influence their adoptions.

MÜASİR TƏHSİL SİSTEMİNDƏ YENİYETMƏLƏRİN İNTELLEKTUAL VƏ FƏRDİ İNKİŞAFININ NƏZƏRƏ ALINMASI

The article talks about taking into account the intellectual and individual characteristics of teenagers in the modern education system. Education not only reflects the state of society and its development direction, but also uses opportunities to exert a strong influence on it. However, education as a part of science and culture is the main tool that promotes its development and influences the formation of personality. It is possible to get more effective results in the education system by taking into account the intellectual and individual characteristics of children in the period of adolescence.

Sex differences in LDL-C reduction response to evolocumab: A propensity score matching analysis.

Proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) have been shown to improve cardiovascular outcomes by reducing low-density lipoprotein cholesterol (LDL-C). However, sex differences in the efficacy of evolocumab remain unclear. This study aimed to investigate sex differences in the efficacy of evolocumab using real-world data. Data were collected from the First Affiliated Hospital of Guangxi Medical University. A total of 416 eligible patients were selected from 1463 patients treated with evolocumab for secondary prevention. Clinical data, including individual characteristics and lipids profiles, were recorded. Propensity score matching (PSM) was used to control for potential confounders, with covariates including age, body mass index, smoking status, and diabetes. All eligible participants were propensity-matched 1:1 for female versus male with a match tolerance of 0.02. The efficacy of evolocumab in females and males was compared by PSM-adjusted analysis. In the PSM analysis, a significant difference was found in the relative percentage reduction of LDL-C between females and males (-42.7% vs. -54.4%, p < 0.001). In addition, the absolute LDL-C reduction was lower in females compared to males (interquartile range: -1.5 [-2.2, -0.8] mmol/L vs. -1.9 [-2.5, -1.0] mmol/L, p = 0.018). The rate of target LDL-C attainment was lower in females than in males after treatment with evolocumab (21.6% vs. 39.8%, p = 0.009). These results suggest that males have a better response to evolocumab in term of LDL-C reduction compared to females.

The dynamic effects of becoming disabled on work, wages and wellbeing in the UK from 1991 to 2018

Abstract Over recent decades it has consistently been shown that disabled adults in the UK fare worse in the labour market and have lower levels of wellbeing than non-disabled adults. However, this is in part due to the selection into dis-ability of those with existing socio-economic disadvantages. In this article, we use panel data from the combined British Household Panel Survey and Understanding Society, covering the 27 years from 1991 to 2018, to distinguish between the effect of selection, the effect of dis-ability onset and the effect of dis-ability duration on a range of labour market and wellbeing outcomes. We show that there is important selection both into dis-ability and into longer experience of dis-ability on the basis of observable characteristics. We also show the importance of controlling for time-invariant unobservable individual characteristics that similarly affect selection into dis-ability and duration of dis-ability. Even after controlling for both forms of selection, we find significant negative effects of dis-ability onset and duration, and offer policy solutions to address them.

Ensemble approach for predicting the diagnosis of osteoarthritis using physical activity factors.

Osteoarthritis (OA) is a common degenerative disease of the joints. Risk factors for OA include non-modifiable factors such as age and sex, as well as modifiable factors like physical activity. this study aimed to construct a soft voting ensemble model to predict OA diagnosis using variables related to individual characteristics and physical activity and identify important variables in constructing the model through permutation importance. By using the recursive feature elimination, cross-validated technique, the variables with the best predictive performance were selected among variables, and an ensemble model combining RandomForest, XGBoost, and LightGBM algorithms was constructed. The predictive performance and permutation importance of each variable were evaluated. The variables selected to construct the model were age, sex, grip strength, and quality of life, and the accuracy of the ensemble model was 0.828. The most important variable in constructing the model was age (0.199), followed by grip strength (0.053), quality of life (0.043), and sex (0.034). The performance of the model for predicting OA was relatively good. If this model is continuously used and updated, it could be used to predict OA diagnosis, and the predictive performance of the OA model may be further improved.

Development and validation of a nomogram for sleep disorders among stroke patients

BackgroundPrecisely identifying high-risk sleep disorder patients and implementing suitable measures are important for decreasing the incidence of sleep disorders. In this study, a nomogram method was adopted to construct a tool to predict sleep disorders in stroke based on four factors: individual characteristics, treatment-related factors, psychological factors, and family-related factors. MethodsA total of 450 stroke patients were continuously diagnosed at the Affiliated Hospital of Nantong University, and the data on participants were randomly distributed into a training set (n = 315) and a validation set (n = 135). Within the training set, using LASSO regression and random forest methods, five optimal predictors of sleep disorders were identified. Five optimal predictors were used to develop a model. The calibration, discrimination, generalization, and clinical applicability of the model were evaluated using calibration curves, receiver operating characteristic (ROC) curves, internal validation, and decision curve analysis (DCA). ResultsWe found that the place of residence, average daily infusion time, the Hospital Anxiety and Depression Scale (HADS), the Type D Personality Scale-14 (DS14), and the Fatigue Severity Scale (FSS) were crucial factors associated with sleep disorders. The validation data showed an area under the curve (AUC) of 0.903 compared to 0.899 in the training set. There was an approach to the diagonal in the calibration curve of this model, and the results of DCA noted that it is clinically beneficial across a range of thresholds from 5 % to 99 %. ConclusionA model was developed to predict sleep disorders among stroke patients to help hospital staff evaluate the risk among patients and screen high-risk patients.

Health-related quality of life among lower limb amputees using prostheses in Nepal: a cross-sectional study

IntroductionLower limb amputation significantly impacts individuals’ quality of life, with prostheses playing a crucial role in rehabilitation and mobility restoration. In Nepal, where access to advanced medical care is limited, understanding the factors affecting the well-being of prosthesis users is essential for optimizing rehabilitation services. This study investigates the sociodemographic and health-related variables influencing the quality of life among lower-limb amputees using prostheses in Nepal.MethodsA cross-sectional study was conducted at a super-specialized disability care center within a tertiary care hospital. The study included 43 amputees who had received prostheses at least five years prior and were regular users. The sample was drawn from patients at the same hospital. The study collected data on sociodemographic characteristics, health components, and pain experiences. Health-related quality of life (HrQoL) was assessed using the SF-12 tool, with scores above 50 indicating high HrQoL on the Mental Component Summary (MCS) and Physical Component Summary (PCS). Scores below 50 indicated low HrQoL. Factors associated with these variables were analyzed using Chi-square (χ²) at a 95% confidence interval.ResultsOur study assessed the health-related quality of life among lower limb amputees, finding that 76.74% of participants scored high in mental health and 81.40% in physical health. Significant associations were identified between age and marital status with the Mental Component Summary (MCS) and Physical Component Summary (PCS). Additionally, the reason for amputation and the experience of the Phantom pain were linked to the MCS. Factors like treatment for stump pain, perceived effectiveness of stump pain treatment, and residual stump pain were associated with both the PCS and MCS, while actions taken for stump pain and extremity dominance were associated significantly with the PCS. The level of amputation and treatment for phantom pain also showed significant association. All associations were significant (p > 0.05) at a 95% confidence interval.ConclusionThese findings indicate that age, marital status, extremity dominance, reason for amputation, stump pain treatment, and pain management are critical factors influencing the quality of life among lower limb amputees. The study underscores the need for comprehensive rehabilitation programs incorporating effective pain management, spouse support, and tailored interventions based on individual demographic and clinical characteristics.

Effect of Visual Images on Digital Communication: Empirical Research Review

As mobile messengers gain popularity, visual images affect digital communication, leading to new interaction patterns. This review sums up scientific experience in describing digital communication facilitated by emoji and memes. The review covered scientific articles that reported empirical research on visual communication in digital environment in such open research databases as CyberLeninka, PubMed, Google Scholar, eLibrary, and ResearchGate in 2019–2024. The current rise in visual communication tools seems to reflect the general visual shift in modern culture, associated with the changes in human psychology brought about by total digitalization. Visual communication tools have a high information capacity, which hinders their interpretation, thus modifying the complex of individual and socio-psychological characteristics of interlocutors in an attempt to ensure mutual understanding. The review can be used to improve digital communication that employs alternative communication tools.

Rare but relevant: Methamphetamine and Parkinson's disease.

Evidence from a number of paradigms suggests that methamphetamine use is associated with increased risk for the development of Parkinson's disease and parkinsonism, and that it may be associated with the premature development of Parkinson's disease. Prevalence of Parkinson's disease and parkinsonism is greater in both methamphetamine users and people who previously used methamphetamine, and evidence from animal studies provides a plausible mechanism for this observation. Despite this increased risk, Parkinson's disease is rarely diagnosed in methamphetamine users. Reasons for this may include under-detection, premature mortality, and individual and substance use characteristics which moderate the risk, including higher rates of smoking. Clinicians should be vigilant to signs and symptoms of Parkinson's disease and parkinsonism in methamphetamine users.

Integrating Quantitative Data and Qualitative Insights to Understand 30-Day Readmission Rates: A Mixed-Methods Study.

The rate of patients readmitted to hospitals within 30 days of discharge is a critical indicator of healthcare quality. This study explored the factors contributing to 30-day hospital readmission rates nationally and at Arrowhead Regional Medical Center (ARMC) through a mixed-methods research design. Quantitative analysis utilized data from the Centers for Medicare & Medicaid Services (CMS) database, focusing on patient demographics, principal diagnoses, length of stay, and hospital characteristics. Multivariate regression and descriptive statistics were employed to identify predictors of 30-day readmission. The qualitative analysis sought to understand the specific medical conditions and patient profiles linked to higher readmission rates. The findings revealed that older age, specific principal diagnoses (e.g., heart failure, pneumonia, chronic obstructive pulmonary disease (COPD)), and longer initial hospital stays were associated with an increased likelihood of 30-day readmission. Gender disparities and hospital size/type also influenced readmission rates. These results provide valuable insights into the complex interplay of individual patient characteristics and hospital attributes in driving readmissions. The study's mixed-methods approach yielded a comprehensive understanding of the quantitative patterns and qualitative factors contributing to 30-day hospital readmission rates, offering important implications for healthcare quality improvement initiatives.

Detecting event-related driving anger with facial features captured by smartphones

Driving anger is a serious global issue that poses risks to road safety, thus necessitating the development of effective detection and intervention methods. This study investigated the feasibility of using smartphones to capture facial expressions to detect event-related driving anger. Sixty drivers completed the driving tasks in scenarios with and without multi-stage road events and were induced to angry and neutral states, respectively. Their physiological signals, facial expressions, and subjective data were collected. Four feature combinations and six machine learning algorithms were used to construct driving anger detection models. The model combining facial features and the XGBoost algorithm outperformed models using physiological features or other algorithms, achieving an accuracy of 87.04% and an F1-score of 85.06%. Eyes, mouth, and brows were identified as anger-sensitive facial areas. Additionally, incorporating individual characteristics into models further improved classification performance. This study provides a contactless and highly accessible approach for event-related driving anger detection. Practitioner Summary: This study proposed a cost-effective and contactless approach for event-related and real-time driving anger detection and could potentially provide insights into the design of emotional interactions in intelligent vehicles.

Legal Constraints or Aggregate Individual Characteristics? Examining Factors of State R&amp;D Intensity and Spending in the United States

ABSTRACTThis study empirically examines the impacts of legal constraints and aggregate individual characteristics on the funding and expenditure related to states' research and development (R&amp;D). A system generalized method of moments (GMM) analysis is performed over a 20‐year period (2000–2019). The budgetary institutions, including rainy‐day funds, TELs, and debt limitations, significantly affect states' overall innovative capacity and R&amp;D spending. They can hinder improvements in states' innovative capacity and R&amp;D expenditure. On the other hand, states can circumvent these institutions and increase their R&amp;D intensity and spending. States' development and human capacity foster their innovative capacity and R&amp;D spending. Catholic work ethic is also crucial for enhancing state governments' innovative capacity and R&amp;D expenditure. Legislative term limits reduce states' R&amp;D intensity and expenditure. Meanwhile, economic downturns may motivate additional spending on R&amp;D activities to promote states' innovative capacity and R&amp;D spending. Fiscal burdens can prevent states from investing in R&amp;D.

The Socio-Educational Model: An Evidence-Based Re-evaluation

Abstract The socio-educational model suggests that social milieu influences individual characteristics, which in turn affect engagement in learning contexts, ultimately impacting linguistic and non-linguistic outcomes. Operationalized representations of the model tend to focus on relations among integrativeness, attitudes to the learning situation, motivation, language anxiety, and language achievement. While objections to the model exist, recent work has challenged their persuasiveness, and as such, it seems appropriate to re-evaluate the model using new data to elucidate its continuing utility in understanding second language outcomes. The objective of this study was therefore to measure the levels of the aforementioned constructs in an international sample and to determine the degree to which they covaried as predicted by the model. A structural equation modelling study of 278 Malaysian high school students studying English compulsorily in national secondary schools in Malaysia found substantial levels of model constructs and statistically significant relationships between them, implying the model’s potential ongoing utility. Implications for second language learning theorization, educational policy, curriculum design, teaching strategies, global language learning contexts, and further research are explored.

Mediating Role of Psychological Capital in Korean Teachers’ Grit and Burnout

This study aimed to investigate whether psychological capital mediates the relationship between grit and teacher burnout. Data from 574 Korean elementary and secondary school teachers were analyzed using correlation and regression analyses to determine whether psychological capital negatively mediates the link between grit and burnout. The results revealed that grit significantly reduces teacher burnout and confirmed the negative mediating role of psychological capital in this relationship. Given the limited research in this area, these findings contribute to the theoretical and empirical understanding of how grit, psychological capital, and burnout are interconnected in the teaching profession. For future research, the author suggests exploring group differences in burnout, grit, and psychological capital based on teachers' demographic factors, using multilevel or hierarchical linear models to identify distinct strategies and the impact of individual and organizational characteristics.

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Who engages in well-being interventions? An analysis of a global digital intervention study

ABSTRACT Despite growing interest in interventions aimed at enhancing emotional well-being, little research has addressed the question of engagement. This study explored engagement in a 7-day online well-being intervention study involving 24,180 participants from 195 countries/territories (78% female, M age = 49, 62% White). Following an onboarding survey, participants completed a morning well-being practice and an evening follow-up survey for the week. Overall, 76% of the participants initiated the intervention (i.e. returned to the platform after enrollment to start the intervention), completing an average of four daily practices. Several demographic (e.g. being older, White) and psychological variables (e.g. lower financial strain, higher life satisfaction) emerged as common predictors of initiating the intervention and completing more practices. Age was a particularly important predictor across outcomes. These findings offer novel insights into how individual characteristics relate to engagement in online well-being interventions and have implications for both designing the interventions and interpreting findings.

Hospital Network Centralization of Primary Total Joint Arthroplasty Is Associated With Reduced Early Complication Rates But Not Reduced Readmission or Reoperation Rates: A Retrospective Database Study

Background: Hospital networks centralize primary total joint arthroplasty (TJA) within their existing systems to develop specialized service lines with higher surgical volumes to reduce adverse events. It is not known what role hospital network centralization has had on primary TJA outcomes. Purpose: We sought to determine whether the degree of hospital network centralization for primary TJA is associated with (1) 90-day postoperative complication rates, (2) 90-day hospital readmission rates, or (3) 1-year revision rates. Methods: We conducted a retrospective database study of Medicare Part A beneficiaries who underwent inpatient primary TJA for osteoarthritis in 2016 and 2017 ( n = 523,142 patients); individual hospital-level characteristics and hospital networks were also identified ( n = 360 unique networks, n = 3339 hospitals). Patients having surgery at a hospital that was not a member of a health care network were excluded ( n = 163,998 patients) because we wanted to examine only the role of network structures on outcomes; this resulted in a cohort of 359,144 patients. Hospital network centralization, which was defined as the percentage of total network cases performed at the highest volume hospital and categorized into quartiles (eg, lowest 25% of networks by concentration, 26%–50% of networks by concentration, etc). Primary outcomes included postoperative 90-day complications, 90-day readmissions, and 1-year revisions. Multivariable logistic and linear regressions evaluated associations of hospital network centralization with outcomes and controlled for relevant patient-level and hospital-level covariates, including hospital network volumes. Results: Odds of 90-day complications were lower in the most centralized hospital networks than in least centralized networks (odds ratio [OR] = 0.85; 95% confidence interval [CI]: 0.75, 0.95). Degree of centralization was not associated with readmissions or 1-year revision rates. Non-modifiable patient and individual hospital characteristics appeared to have a greater association with complications, readmissions, and early revision rates than hospital network centralization or volume. Conclusion: This retrospective database study found that increased centralization of primary TJA within a hospital network was associated with lower 90-day complication rates but not with 90-day readmission or 1-year revision rates. This suggests that structural changes within hospital networks may be beneficial to reduce early complications in this patient population. In addition, our findings suggest that risk adjustment in assessing non-modifiable patient and hospital risk factors may be important when assessing TJA outcomes.

Association between treatment response and dose of blonanserin transdermal patch in patients with acute schizophrenia: A post hoc cluster analysis based on baseline psychiatric symptoms.

To explore the optimal dose of blonanserin transdermal patch (BNS-P) based on baseline psychiatric symptomatic characteristics during acute schizophrenia. A post hoc cluster analysis was conducted using data from a 6-week randomized, double-blind, placebo-controlled study of BNS-P (40 or 80 mg/day) in acute schizophrenia. We classified patients into three clusters based on baseline psychiatric symptoms. Efficacy was assessed using the change from baseline to week 6 in the PANSS total score. Safety was assessed by the incidence of adverse events. Among 577 patients, three clusters were identified, characterized by severe psychiatric (Cluster-S; n = 122), predominant negative (Cluster-N; n = 191), and predominant positive (Cluster-P; n = 264) symptoms. In Cluster-P, both BNS-P 40 and 80 mg/day reduced PANSS total score significantly more than placebo (p = 0.036, effect size = 0.342; p < 0.001, effect size = 0.687, respectively). In Cluster-S and -N, only BNS-P 80 mg/day reduced PANSS total score significantly more than placebo (p = 0.045, effect size = 0.497; p = 0.034, effect size = 0.393, respectively). The effect size was greater at 80 mg/day than at 40 mg/day across all clusters. The most common treatment-emergent adverse events were akathisia and skin-related adverse events in all clusters. BNS-P exhibited a dose-dependent antipsychotic effect in all clusters, particularly highlighting its efficacy in patients with predominant positive symptoms, even at lower doses. These findings provide novel and valuable insights for determining BNS-P dose tailoring to individual symptomatic characteristics in real-world practice.

Exploring the Impact of Digital Literacy and Policy Cognition on Rural Residents' Eco-friendly Behaviors.

The active participation of rural residents in ecological protection is crucial for preserving the rural environment and advancing ecological civilization. However, existing literature often neglects the impact of rural residents' digital literacy and policy cognition on their adoption of eco-friendly behaviors. In the digital age, rural residents can efficiently access and utilize ecological information, deepen their policy cognition, and consequently, become more actively involved in ecological protection through enhancing their digital literacy. This enhancement is crucial for ensuring the smooth implementation of rural ecological policies at the grassroots level, thereby providing a strong impetus for the further development of rural ecological protection. Therefore, based on survey data from 851 respondents in four counties and cities in Hainan Province, this paper uses the Ordered Probit model to analyze the impact of digital literacy on rural residents' adoption of eco-friendly behaviors, supported by theoretical mechanism analysis. The results indicate that: (1) Digital literacy enhances the adoption of eco-friendly behaviors among rural residents; (2) The mechanism analysis shows that digital literacy promotes such adoption through improved policy cognition. Consequently, the government should actively promote digital technology education and training to improve rural residents' digital literacy. Therefore, targeted digital skills training policies should be developed according to the individual characteristics of rural residents. Additionally, promoting digital literacy to improve policy cognition will likely lead to greater engagement in eco-friendly behaviors.