Relevant Predictors Research Articles

FoMO and Socio-Emotional e-Competencies as Predictors of Media Multitasking, Phubbing and Cybergossip in University Students: Transnational Differences Between Mexico and Spain by Gender.

There are clear signs of the growing use of the internet across all cultures, which generate new behaviors in the virtual environment such as media multitasking, phubbing, and cybergossip, all associated with online risks and less positive modes of socialization. FoMO (Fear of Missing Out) along with virtual emotional experiences could be relevant predictors, where literature suggests that FoMO is a trigger for problematic social media use, and socio-emotional e-competencies facilitate adaptive behaviors in virtual environments. Hence, understanding which variables predict these phenomena is crucial and whether they can be generalized across different countries. The objective of this study is to analyze whether two dimensions of socio-personal development used to interact in virtual environments, FoMO (Fear of Missing Out) and socio-emotional e-competencies (e-COM), are predictors of different cyberbehaviors (cybergossip, phubbing and media multitasking) in university students from two different countries. It also aims to verify if socio-emotional e-competencies act as a moderator of this relationship. In order to achieve this, we used a sample of 1524 university students from Mexico and Spain (19.74 years old). The results of the path analysis models show that FoMO is the strongest predictor of the three online behaviors regardless of country and gender, especially in the case of phubbing. In addition, socio-emotional e-competencies help to explain the behaviors in a differential way for each country, and the dimension of e-self-control of impulsivity plays a moderating role in FoMO in the case of phubbing and media multitasking. Some differences between countries and genders are discussed.

Social and health disparities associated with healthy brain ageing in Brazil and in other Latin American countries.

Latin American countries present major health-related inequities due to historical, cultural, and social aspects. Recent evidence highlights that factors related to social and health disparities outweigh classic demographic factors in determining healthy brain aging in these populations. However, these analyses have not been conducted with the Brazilian population, the largest and most ethnically diverse population in Latin America. Here, we evaluated demographic, social, and health factors for healthy brain ageing using a machine learning model in a Brazilian population-based cohort (n=9412) and in additional cohorts from other Latin American countries, including Colombia (n=23 694), Chile (n=1301), Ecuador (n=5235), and Uruguay (n=1450). In the Brazilian population and other Latin American countries, social and health disparities were more influential than demographic factors for cognition and functional ability. Uniquely in Brazil, education emerged as the primary risk factor impacting cognitive outcomes, diverging from other Latin American countries where mental health symptoms played more prominent roles. In terms of functional ability, Brazil displayed a distinct pattern, with mental health symptoms identified as the primary contributing factor. Our findings indicate that Brazil converges with other Latin American countries to show that heterogeneous factors impacted more than demographic factors, but also showed a unique set of health factors when compared with other Latin American countries. Therefore, our study emphasises that social and health disparity factors are relevant predictors of healthy brain ageing in Latin America, but population-specific analyses are necessary to identify the specific risk profiles of each country. None. For the Portuguese and Spanish translations of the abstract see Supplementary Materials section.

Machine Learning to Predict Mortality in Older Patients With Cancer: Development and External Validation of the Geriatric Cancer Scoring System Using Two Large French Cohorts.

Establishing an accurate prognosis remains challenging in older patients with cancer because of the population's heterogeneity and the current predictive models' reduced ability to capture the complex interactions between oncologic and geriatric predictors. We aim to develop and externally validate a new predictive score (the Geriatric Cancer Scoring System [GCSS]) to refine individualized prognosis for older patients with cancer during the first year after a geriatric assessment (GA). Data were collected from two French prospective multicenter cohorts of patients with cancer 70 years and older, referred for GA: ELCAPA (training set January 2007-March 2016) and ONCODAGE (validation set August 2008-March 2010). Candidate predictors included baseline oncologic and geriatric factors and routine biomarkers. We built predictive models using Cox regression, single decision tree (DT), and random survival forest (RSF) methods, comparing their predictive performance for 3-, 6-, and 12-month mortalities by computing time-dependent area under the receiver operator curve (tAUC). A total of 2,012 and 1,397 patients were included in the training and validation set, respectively (mean age: 81 ± 6 years/78 ± 5 years; women: 47%/70%; metastatic cancer: 50%/34%; 12-month mortality: 43%/16%). Tumor site/metastatic status, cancer treatment, weight loss, ≥five prescription drugs, impaired functional status and mobility, abnormal G-8 score, low creatinine clearance, and elevated C-reactive protein (CRP)/albumin were identified as relevant predictors in the Cox model. DT and RSF identified more complex combinations of features, with G-8 score, tumor site/metastatic status, and CRP/albumin ratio contributing most to the predictions. The RSF approach gave the highest tAUC (12 months: 0.87 [RSF], 0.82 [Cox], 0.82 [DT]) and was retained as the final model. The GCSS on the basis of a machine learning approach applied to two large French cohorts gave an accurate externally validated mortality prediction. The GCSS might improve decision making and counseling in older patients with cancer referred for pretherapeutic GA. GCSS's generalizability must now be confirmed in an international setting.

Outcomes after surgical repair of primary parastomal hernia

PurposeParastomal hernia is a frequent complication after stoma construction, with increasing incidence over time. Surgical repair is reported with a high recurrence rate and the evidence on the topic is limited. We conducted a retrospective study to evaluate the incidence of recurrence after parastomal hernia repair and assessed the risk factors and predictors for recurrence at the Regional Hernia Center at Horsens Regional Hospital, Denmark.Methods119 patients underwent primary parastomal hernia repair from January 2017 until April 2021. Mean follow-up period was 72 months. Information including demographic data, non-modifiable risk factors and modifiable risk factors were assessed and analyzed using LASSO to select relevant predictors and GLM was employed hereafter.ResultsMultivariate analysis showed that age, diabetes, IBD, constipation, and fecal incontinence were strong pre-operative predictors, with age, IBD, ileostomy, and colorectal cancer also reaching significance in univariate analyses. Post-operatively, EHS classification 1, and Clavien Dindo Grade 3b were identified as strong predictors in univariate analyses.ConclusionRecurrence after parastomal hernia repair was 17.64% during a follow-up period of minimum 3.5 years.

P0290 Predicting disease remission in Ulcerative Colitis patients - early results from the PRESAGER study

Abstract Background The disease course of Ulcerative Colitis (UC) remains unpredictable. While some patients experience years of remission, others experience frequent flares, hospitalization, and surgery. The PRESAGER study aims to develop decision models to predict UC disease activity. In this preliminary analysis, we created a logistic regression model to identify predictors of remission. Methods UC patients with disease activity (Mayo endoscopic score [MES] ≥ 1) were recruited into the study. Demographic data, disease history, IBD Disability Index (IBD-DI), biopsies, and blood samples were collected at inclusion. Eight weeks later, patients were followed up with IBD-DI, blood samples, and an optional sigmoidoscopy. Medications taken before inclusion and during the follow-up period were recorded. Symptomatic or endoscopic remission was noted depending on if patients opted for follow-up endoscopy. Using multivariate logistic regression, we examined the association between inclusion data and disease remission. Stepwise model selection by Akiake information criterion (AIC) was used to create a logistic regression model with the most relevant predictors. Results were reported as odds ratios (ORs), 95% confidence intervals (CIs) and p-values. AI was used for writing inspiration and help with R coding, and no work was copied directly from an AI source. Results As of September 2024, 250 patients were included in the study. 182 (73%) patients had completed eight-week follow-up with IBD-DI and blood samples, and 72 (29%) patients had completed the eight-week follow-up sigmoidoscopy. At inclusion, left-sided colitis and moderate disease activity were most common. When looking at medications taken within 14 days of inclusion, 66% of patients used mesalazine suppositories and 61% used oral mesalazine or sulfasalazine (Figure 1). Other treatments included mesalazine foam/enemas (30%), oral corticosteroids (26%), immunomodulators (18%), and biologics (14%). A multivariate logistic regression model showed that older age and higher IBD-DI scores significantly reduced the likelihood of remission, whereas a longer disease duration was linked to a greater likelihood (Table 1). Surprisingly, calprotectin levels above 500 was associated with increased likelihood of remission compared to levels below 250. Additionally, patients with moderate disease activity had a greater likelihood of remission than those with mild activity, likely due to the increased use of corticosteroids and biologics in these cases. Conclusion Older age and higher disability scores significantly reduced the likelihood of remission for UC patients, while longer disease duration, moderate disease activity, and high calprotectin increased the likelihood.

Profiles of innovative behavior and associated predictors among clinical nurses: a multicenter study using latent profile analysis

BackgroundInnovative Behavior (IB) is a key prerequisite for nurses in solving clinical problems. However, existing research on IB among clinical nurses is relatively limited.ObjectiveTo identify profiles and characteristics of IB among clinical nurses and explore the associated predictors, as well as the relationships with research outputs.MethodsA multicenter cross-sectional study was conducted on 354 clinical nurses in Shanghai from April 2023 to May 2023 (response rate 98.06%). IB was measured by the Innovative Behavior Scale for Nurses (IBSN), future time perspective was measured by the Future Time Perspective Scale (FTPS), and work engagement was measured by the Utrecht Work Engagement Scale-9 (UWES-9). Socio-demographic and professional data and research output indicators were measured by a self-designed questionnaire. We used latent profile analysis (LPA) by Mplus 7.0 to identify latent classes of IB. Ordinal logistic regression analysis was used to analyze the relevant predictors on the different profiles. And then Pearson’s chi-squared was used to analyze the association between IB level and research output.ResultsAmong the respondents, individuals aged 25 to 35 accounted for 55.9%, and females comprised 94.6%. IB of clinical nurses can be identified into 3 groups: low-level (n = 108, 30.51%), moderate-level (n = 149, 42.09%), and high-level (n = 97, 27.40%) groups. Based on the results of LPA, marital status, education level, work experience, monthly income, night shifts, future time perspective scores, and work engagement scores can be the predictors of IB among different profiles. Statistically significant associations were found between IB level and research productivity, including publishing academic papers (χ2 = 15.307, p < 0.001), registering patents (χ2 = 17.163, p < 0.001), and winning Sci. & Tech awards (χ2 = 27.814, p < 0.001).ConclusionAccording to our research, clinical nurses have three unique IB profiles. The current level is predominantly at a moderate level, with less than 30% demonstrating a high level of innovation. It revealed that better socio-demographic status and professional characteristics, future time perspective, and work engagement positively influenced innovative behavior among clinical nurses. The findings also highlight the potentially important role of IB in contributing to nurses’ research output.Practical implicationsAs far as we know, it might be the first study to employ LPA to clarify the heterogeneity in the levels of IB and their specific distribution among nurses. Our findings may provide a new viewpoint for promoting IB among clinical nurses. Nursing administrators should pay attention to IB of clinical nurses and develop targeted interventions to enhance their IB levels.

Differences between Recidivists and Non-recidivists Attending a community-based Domestic Violence Perpetrator Program in the Dominican Republic: A Brief Report

Abstract Purpose Previous research in Western samples has found differences between recidivists and non- recidivists among individuals who attended intervention programs designed to reduce intimate partner violence (IPV) offending. However, the extent to which similar characteristics differentiate these groups in a non-Western sample is unknown. Methods This brief report includes data collected by psychologists from men referred to a cognitive behavioral program (n = 1,380) in the Dominican Republic. Bivariate and multivariate logistic regression analyses were conducted on the data. Results Although specific IPV-related factors such as attitudes towards women and jealousy were significantly associated with recidivism at the bivariate level, these effects disappeared when they were controlled by other relevant predictors in the multivariate model. Instead, general criminogenic factors such as employment status, alcohol consumption, relationships with parents and violent offending towards others were the key factors explaining IPV recidivism. Conclusions IPV recidivism is best explained by accounting for those factors which are common to violent re-offending in general. The implications of these findings are discussed in terms of theory and program intervention practices.

Frontal Plane Knee Kinematics and Kinetics During Gait in Children and Youth with Achondroplasia-Correspondence with Static X-Ray Images and Relevance to Symptoms.

Background: Frontal knee malalignments are hallmarks of Achondroplasia (ACH), along with disproportional short stature. Typically, X-rays are used to assess them, but 3D gait analysis (3DGA) may additionally be used to evaluate dynamic knee function. The research questions were as follows: (1) What is the relationship between X-rays and 3DGA in ACH? (2) Do children with ACH have abnormal frontal knee kinematics and kinetics? (3) Are there aspects of 3DGA that relate to knee symptoms? Methods: A total of 62 knees of 31 children with ACH (age: 11.1 ± 4.3 years, 34 symptomatic knees) underwent 3DGA and X-ray as part of their standard clinical care. X-rays were analyzed for mechanical tibiofemoral angle (mTFA). Relationships between X-rays and 3DGA were determined. Sixty-two knees of 31 age-matched typically developing (TD) children served as references for 3DGA. Frontal knee kinematics (including thrust RoM) and adduction moments (KAMs) were compared. Multiple regression was performed for measurements associated with KAM, and ANOVA was used to compare TD and ACH knees with and without pain. Results: There was a high correlation between static frontal knee angles and mTFA (r = 0.93, p < 0.001, mean difference = -2.9°). ACH knees with a regular mTFA also showed significantly increased KAM. Multiple regression analysis showed that mTFA was the most relevant predictor of KAM (R2 = 0.41-0.75). Symptomatic knees (n = 34/62) experienced significantly more knee RoM in early stance than asymptomatic knees. Conclusions: Three-dimensional gait analysis may be an objective screening method for dynamic knee alignment and stability and may complement radiography in monitoring ACH. Symptoms may depend on knee thrust, while the impact of altered KAM needs further study.

Prediction of Aneurysm Sac Shrinkage After Endovascular Aortic Repair Using Machine Learning-Based Decision Tree Analysis.

A simple risk stratification model to predict aneurysm sac shrinkagein patients undergoing endovascular aortic repair (EVAR) for abdominal aortic aneurysms (AAA) was developed using machine learning-based decision tree analysis. One hundred nineteen patients with AAA who underwent elective EVAR at Tokyo Medical University Hospital between November 2013 and July 2019 were included in the study. Predictors of aneurysm sac shrinkage identified in univariable analysis (P<0.05) were entered into the decision tree analysis. Univariable analysis revealed significant differences between patients with and without aneurysm sac shrinkage in the variables of age (<75 y or ≥75y), current smoking, operative type II endoleak, and preoperative pulse wave velocity (PWV) (<1800cm/s or ≥1800cm/s). The decision tree showed that preoperative PWV was the most relevant predictor, followed by operative type II endoleak and current smoking, and identified 6 terminal nodes with likelihoods of aneurysm sac shrinkage ranging from 5.6% to 63.6%. We established a decision tree model with 3 variables (preoperative PWV, operative type II endoleak, and current smoking) to predict aneurysm sac shrinkage in patients undergoing EVAR for AAA. This classification model may help identify patients with a high or low likelihood of aneurysm sac shrinkage.

High-dimensional partially linear functional Cox models.

As a commonly employed method for analyzing time-to-event data involving functional predictors, the functional Cox model assumes a linear relationship between the functional principal component (FPC) scores of the functional predictors and the hazard rates. However, in practical scenarios, such as our study on the survival time of kidney transplant recipients, this assumption often fails to hold. To address this limitation, we introduce a class of high-dimensional partially linear functional Cox models, which accommodates the non-linear effects of functional predictors on the response and allows for diverging numbers of scalar predictors and FPCs as the sample size increases. We employ the group smoothly clipped absolute deviation method to select relevant scalar predictors and FPCs, and use B-splines to obtain a smoothed estimate of the non-linear effect. The finite sample performance of the estimates is evaluated through simulation studies. The model is also applied to a kidney transplant dataset, allowing us to make inferences about the non-linear effects of functional predictors on patients' hazard rates, as well as to identify significant scalar predictors for long-term survival time.

Preferences of Integrated Care and the Influencing Factors Among Chinese Community-Dwelling Older Adults.

Population aging and epidemiological transition have prompted requests for integrating health and social care. The goal of meeting complex care needs necessitates the understanding of preferred patterns among older adults. The study aimed to elicit the preferred care patterns and the influencing factors of integrated health and social care among community-dwelling older adults in multiple regions of China. From a national survey, 1184 community-dwelling older adults in three cities across China were included in the study. Individual characteristics and preferred care patterns were measured. Five preferred care patterns were classified, including Home-based health and social care, Community-based health and social care, Home-based health care but community-based social care, Community-based health care but home-based social care, and Institution-based health and social care. Multivariate logistic regression and random forest model were applied to obtain reliable results. Overall, approximately half of the participants (47.7%) in the survey preferred Home-based health and social care and more than a quarter of participants (25.6%) preferred Community-based health and social care, followed by Institution-based health and social care (14.4%). A relatively small fraction of participants reported preferences for Home-based health care but community-based social care (8.0%) and Community-based health care but home-based social care (4.2%). Gender, age, education, living arrangement, resident city, income per month, access to medication, and self-care capability were significantly associated with older adults' preferences (p<0.05 each). Education, living arrangement, age, resident city, and income per month were the most relevant predictors, followed by gender, number of chronic diseases, self-care capability, and access to medication. To effectively meet care needs, efforts should focus on home- and community-based care. Since the preferred care patterns were characterized by obvious variability, policymakers and service providers should carefully consider the differences in making tailored strategies.

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.

Prediction of Snatch and Clean and Jerk Performance From Physical Performance Measures in Elite Male Weightlifters.

Sandau, I and Kipp, K. Prediction of snatch and clean and jerk performance from physical performance measures in elite male weightlifters. J Strength Cond Res 39(1): 33-40, 2025-This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors.

The predictive value of emotion regulation difficulties and negative mood on short and long-term treatment outcomes in an online guided self-help programme for adults with binge-eating disorder.

Cognitive behaviour therapy (CBT) online guided self-help programs represent efficacious and accessible treatment options for adults with binge-eating disorder (BED), but research on predictors of treatment outcome is scarce. This study aimed to investigate the predictive value of emotion regulation difficulties relative to that of negative mood on short- and longer-term treatment outcomes in an online guided self-help programme for BED above and beyond other predictors (age, sex, baseline BED severity). Participants were 63 adults (87% female, mean age 37.2years) with BED. Data was analysed using a hierarchical model approach. Emotion regulation difficulties better predicted both weekly binge-eating frequency and eating disorder (ED) pathology at posttreatment than negative mood, after controlling for the effect of age, sex and baseline BED severity. At 6-month follow-up, neither emotion regulation difficulties nor negative mood further added to the prediction of weekly binge-eating frequency, whereas negative mood, but not emotion regulation difficulties, did so for ED pathology. These findings indicate that emotion regulation difficulties at treatment begin might be a relevant predictor of immediate treatment outcome in online guided self-help for adults with BED but might have lower impact on longer-term treatment outcome than negative mood. The clinical trial register number is not given in this version of the manuscript due to anonymisation.

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition status during adolescence, in addition to infancy and childhood is very crucial. However, the available studies in Ethiopia have been usually focused in early childhood and they used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia. A total of 3156 weighted samples of adolescent girls aged 15-19 years were used from the 2016 Ethiopian Demographic and Health Survey dataset. The data was pre-processed, and 80% and 20% of the observations were used for training, and testing the model, respectively. Eight machine learning algorithms were included for consideration of model building and comparison. The performance of the predictive model was evaluated using evaluation metrics value through Python software. The synthetic minority oversampling technique was used for data balancing and Boruta algorithm was used to identify best features. Association rule mining using an Apriori algorithm was employed to generate the best rule for the association between the independent feature and the targeted feature using R software. The random forest classifier (sensitivity = 81%, accuracy = 77%, precision = 75%, f1-score = 78%, AUC = 85%) outperformed in predicting stunting compared to other ML algorithms considered in this study. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having one or more children were the top attributes to predict stunting. Association rule mining was identified the top seven best rules that most frequently associated with stunting among adolescent girls in Ethiopia. The random forest classifier outperformed in predicting and identifying the relevant predictors of stunting. Results have shown that machine learning algorithms can accurately predict stunting, making them potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt stunting among adolescent girls.

Make it real with gen Z! The impact AR reality congruence on brand information sharing: Exploring a sequential mediation mechanism

Augmented reality (AR) has garnered considerable interest for its potential to motivate engagement and advance customer-brand interactions. This study explored the impact of reality congruence (RC) of AR-menus on brand information sharing (BIS) in fast-food restaurants, particularly among Generation Z (Gen Z), as well as the mediating effects of usefulness of AR menu (UAR) and brand positivity (BP) on this relationship. Media Richness Theory (MRT) was employed as the theoretical umbrella for developing the study model. To validate the research model, we employed structural equation modeling (PLS-SEM) with a sample of 209 respondents. The results demonstrate that reality congruence of AR menus is a relevant predictor of Gen Z members’ behavior in sharing information about the brand. Furthermore, this relationship was mediated by the UAR and BP. The findings also demonstrated that UAR and BP had a sequential mediating effect on the relationship between the RC of AR-menu and BIS. This revolutionary study revealed that RC of AR-menu in restaurants fosters positive behaviors in fast food settings. By highlighting AR's potential to create engaging dining experiences for Gen Z members, this study offers valuable insights for service businesses, marketing managers, and the hospitality industry. Addressing this gap in existing research emphasizes the importance of adopting innovative technologies to enhance Gen Z's customer experience and engagement in the restaurant industry.

EARLY ADOPTION AND LONG-TERM USE: ANALYZING OLDER ADULTS’ USAGE PATTERNS AND PREDICTORS OF PRISM GERMANY

Abstract This study introduces PRISM-Germany, the German version of the PRISM-App originally developed by North American research consortium CREATE (Czaja et al. 2018), aiming to support social participation among older adults. Addressing the challenge of long-term adherence, we explored changes in app usage over 3 months and identified predictors of use, focusing on socio-demographic and technology-related variables. The analytical sample consisted of 171 community-dwelling adults aged 67–93 (mean age=73.7 years; female=55%) with medium digital competencies as part of the ongoing SMART-AGE complex intervention trial. Usage behavior in PRISM-Germany was assessed through a cloud-based data log procedure, providing real-time recording of individual usage behaviors. A baseline data protocol collected data on relevant predictors via the Redcap online software. Usage analyses indicated that on average 11 out of 32 available categories (e.g. email, video-chat, health information) were accessed in PRISM-Germany per week. A significant decline in average usage over time was observed, ranging from 1st month: m = 15.3 to 2nd month: m = 9.7, and finally to 3rd month: m = 7.5. ANCOVA revealed the decline as robust, controlling for socio-demographic and technology-related variables. At the multivariate level, chronological age, health, general technology acceptance, and previous technology competencies did not predict frequency of use, while female gender, not living alone, and being lower educated did (Adjusted R2 = 0.108). Concluding, 50% reduction in app use across three months as well as the sole predictive role of sociodemographic factors seem remarkable.

Lower vena cava response index as a predictor of hypovolemic shock due to gastrointestinal tract hemorrhage

Aim: to determine the response rate of the inferior vena cava as a predictor of hypovolemic shock due to bleeding from the gastrointestinal tract in patients in the Emergency Area of ​​the General Surgery Service of a General Hospital in Venezuela, during the period of February. to July 2024. Method: a study with a quantitative, non-experimental, predictive and longitudinal approach, with a sample of 15 patients. Results: the average age of the patients was 66.07 years, with a greater proportion of men (60%). Most patients had a medical history, with high blood pressure and diabetes mellitus being the most common. A significant increase in the diameter of the inferior vena cava was observed after the administration of fluids, going from 1.48 cm upon admission to 1.98 cm one hour after treatment (p=0.0000). Furthermore, an improvement was recorded in the degree of hypovolemic shock, with an increase in the percentage of patients with grade I and a decrease in patients with grade III (p=0.001). The inferior vena cava response index showed a sensitivity of 71.43% and a specificity of 75% to predict hypovolemic shock, with an AUC of 0.732, indicating good discriminative capacity.Conclusion: measuring the diameter of the inferior vena cava is a useful and clinically relevant predictor of hypovolemic shock in patients with gastrointestinal bleeding, improving decision-making and the clinical results obtained.

Cognitive abilities predict naturalistic speech length in older adults

Past research has demonstrated the association between social engagement and the maintenance of cognitive abilities. However, inconsistent definitions of social engagement have posed challenges to systematically investigate this association. This paper addresses the role of social relationships in cognitive functioning among older adults, focusing on the real-life communication indicator—length of own speech—as a measure of social activity. Utilizing advanced technology to unobtrusively measure older adults’ real-life speech, this study investigates its association with various cognitive abilities and sociodemographic factors. Differential cognitive measures, and sociodemographic data including factors like age, sex, education, income, persons living in the same household, loneliness, and subjective hearing status were included. Audio data of 83 participants are analyzed with a machine learning speaker identification algorithm. Using Elastic Net regularized regression, results indicate that higher levels of working memory, cognitive speed, and semantic fluency predict own speech in everyday life. While having no partner negatively predicted own speech length, we unexpectedly found that higher hearing status was related to lower speech frequency. Age was neither a relevant predictor in the regression nor correlated with any other variables. We discuss implications and future research applications based on the findings from our novel approach.

Vedolizumab Clearance as a Surrogate Marker for Remission in Inflammatory Bowel Disease Patients: Insights from Real-World Pharmacokinetics.

Background/Objectives: Vedolizumab (VDZ) is approved in the treatment of patients with moderate to severe ulcerative colitis (UC) or Crohn's disease (CD). VDZ exhibits considerable variability in its pharmacokinetic (PK) profile, and its exposure-response relationship is not yet fully understood. The aim was to investigate the variability in VDZ trough levels and PK parameters, to assess the relationship between VDZ PK and biochemical response, as well as clinical and endoscopic outcomes. Methods: We included 61 UC and 45 CD patients. Patients' data and trough VDZ concentrations were retrospectively obtained. Population PK analysis was performed using non-linear mixed-effects modelling with NONMEM (version 7.5). Graphs and statistical analyses were performed using R (version 4.1.3). Results: In total, 116 trough VDZ concentrations from 106 patients were described by a two-compartment model. For a typical patient, clearance (CL) was estimated at 0.159 L/day, while in patients previously treated with anti-TNFα agents, VDZ CL increased by 26.4% on average. In univariate binary logistic regression, VDZ trough concentration was not statistically significant predictor of remission, whereas CL was. Moreover, combined CL and faecal calprotectin (FCP) were a statistically significant predictors of remission. The hazard ratio (HR) for CL above 0.1886 L/day was 0.35 (p = 0.05) and for FCP below 250 µg/g was 2.66 (p = 0.02) in a time-to-event analysis. Conclusions: Our population PK model incorporates the effect of prior anti-TNFα agents on CL, suggesting its association with more severe forms of IBD. VDZ CL emerged as a more robust and clinically relevant predictor of remission in IBD patients than trough concentration.