Random Subsample Research Articles

The Objectification of Others Scale: Confirmatory and Exploratory Factor Analysis in a Sample of Emerging Adults

The objectification of others is an important phenomenon in the cycle of objectification. However, few instruments measure the construct of other-objectification. This study examines the psychometric properties of Objectification of Others Scale using a sample of emerging adults (N = 1,584). Two random subsamples (n = 792) of the dataset were used to conduct exploratory and confirmatory factor analyses, resulting in a two-factor model that explained 72.37% of the variance. We address limitations of the instrument and make recommendations for future research.

Inference from biased polls

People often attempt to present a positive image by overstating virtuous behaviors when responding to unincentivized “polls.” We examine whether others account for this “socially desirable responding” (SDR) when drawing inferences from such unincentivized responses. In an experiment, we incentivize “predictors” to guess others' choice behaviors across actions with varying social desirability. Predictors observe random subsamples of either (i) incentivized choices or (ii) hypothetical claims. The hypothetical claims exhibit systematic SDR and predictors are reasonably skeptical of them. However, their skepticism is not tailored to the direction or magnitude of SDR. This under-correction occurs even though subjects' stated sentiment toward the actions can predict SDR.

Loneliness and the prevalence of injuries, falls and fractures in older people

Abstract The feeling of being alone (loneliness) experienced by older people contributes to deterioration of health, reduced physical and mental fitness, or inability to continue life roles. Loneliness may lead to behaviors that increase the risk of accidents, falls, and fractures, which are particularly dangerous in old age. The aim of this study was to assess the relationship between loneliness and the prevalence of injuries, falls, and fractures in older people. The studied group consisted of 464 people (48% men), aged 62-86 years (mean: 71.6, SD: 6.3), who participated in the Polish part of the HAPIEE Study (Health Alcohol and Psychosocial Factors in Eastern Europe); random subsample examined in 2019-2020. Trained nurses interviewed respondents in their homes. The 3-item UCLA scale (range 3-9, cut-off point≥6) was used to assess loneliness. Data on injuries or accidents in previous 10 years, falls in previous 12 months, history of wrist, hip fracture or undergoing joint surgery/alloplastic was recorded. Multivariable logistic regression analysis was used. There were 460 participants included in the analysis. In 34 participants (7.3%) UCLA score was ≥6. In the previous 10 years, 95 respondents (20.5%) reported an injury or accident, 77 (16.6%) fell in previous 12 months. There were 6 (1.3%) respondents with history of hip fracture, 48 (10.3%) with history of wrist fracture, and 35 (7.5%) with history of joint surgery/alloplastic. No statistically significant relationship between loneliness and the prevalence of injuries, falls and fractures in older residents was found neither in the crude models nor after adjustment for covariates (age, sex, BMI, marital status, having offspring). Loneliness did not predict injuries, falls, and fractures in older urban residents. It seems that perception of being alone is weaker determinant of health as a measure not fully reflecting objective social isolation, which may actually have a greater impact on falls and injuries. Key messages • 1/5 of older people have experienced any injury or accident and 17% reported to have fallen in the previous year. • Loneliness did not predict injuries, falls, and fractures in older urban residents.

Factor Structure, Construct Validity, and Measurement Invariance of the Neuroception of Psychological Safety Scale (NPSS).

Psychological safety has recently emerged as a central construct, strictly implicated in mental health and emotional well-being. The Neuroception of Psychological Safety Scale (NPSS) is the first scale designed to assess feelings of psychological safety from a multidimensional perspective. However, the robustness of its factorial structure requires further examination in large community samples, and evidence of construct validity along with measurement invariance across genders is scarce. The present study aimed to address these gaps through a comprehensive cross-validation approach. A community sample of 660 Italian adults, aged 18-65, completed self-report questionnaires including the NPSS, the Compassionate Engagement and Action Scale (CEAS), the Sussex-Oxford Compassion for Others (SOCS), and the Body Perception Questionnaire (BPQ). A three-factor model, i.e., Social Engagement, Compassion, and Bodily Sensations, demonstrated a good fit to the data in two random subsamples. Moreover, the measurement model was factorially invariant across genders. Model-based omega coefficients supported the internal consistency of the NPSS scores (ω ranged between 0.879 and 0.918). Zero-order correlations between NPSS subscales and CEAS, SOCS, and BPQ provided construct validity evidence. Additionally, inter-correlations between latent factors and Harman's single-factor test supported the discriminant validity of the NPSS dimensions. Overall, this study provided compelling evidence regarding the psychometric properties of the NPSS, demonstrating for the first time the invariance of its factorial structure across gender.

Subsampling Suffices for Adaptive Data Analysis

Ensuring that analyses performed on a dataset are representative of the entire population is one of the central problems in statistics. Most classical techniques assume that the dataset is independent of the analyst’s query and break down in the common setting where a dataset is reused for multiple, adaptively chosen, queries. This problem of adaptive data analysis was formalized in the seminal works of Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014). We identify a remarkably simple set of assumptions under which the queries will continue to be representative even when chosen adaptively: The only requirements are that each query takes as input a random subsample and outputs few bits. This result shows that the noise inherent in subsampling is sufficient to guarantee that query responses generalize. The simplicity of this subsampling-based framework allows it to model a variety of real-world scenarios not covered by prior work. In addition to its simplicity, we demonstrate the utility of this framework by designing mechanisms for two foundational tasks, statistical queries and median finding. In particular, our mechanism for answering the broadly applicable class of statistical queries is both extremely simple and state of the art in many parameter regimes.

The Add Health Parent Study: A Biosocial Resource for the Study of Multigenerational Racial/Ethnic Disparities in Alzheimer's Disease and Alzheimer's Disease-Related Dementias.

Alzheimer's disease and Alzheimer's disease related dementias (AD/ADRD) have increased in prevalence. This article describes the Add Health Parent Study (AHPS) Phase 2, a study of social, behavioral, and biological factors influencing healthy aging and risk for AD/ADRD, in a national sample of adults aged 58-90. Sample members are parents of the National Longitudinal Study of Adolescent to Adult Health (Add Health) cohort, initially interviewed in Add Health in midlife (1994-95). AHPS Phase 1 (2015-17) collected longitudinal data on a random subsample of parents and their spouse/partners, who were mostly Non-Hispanic (NH) White. AHPS Phase 2 will collect the same longitudinal socio-behavioral, and health survey data on all remaining NH Black and Hispanic parents (Black and Hispanic Supplement, BHS). Additionally, Phase 2 will collect cognitive and DNA data from AHPS Phase 1 and BHS sample parents and their current spouse/partners. Funded by the National Institute on Aging, recruitment will occur between June 2025 and May 2026, producing an expected total AHPS sample of 5506 parents and their spouse/partners. The AHPS will be the first longitudinal cohort study powered to address multigenerational racial/ethnic disparities in AD/ADRD risk and protective factors across race/ethnic groups and socioeconomic strata.

SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data

This study focuses on advancing individual tree crown (ITC) segmentation in lidar data, developing a sensor- and platform-agnostic deep learning model transferable across a spectrum of dense laser scanning datasets from drone (ULS), to terrestrial (TLS), and mobile (MLS) laser scanning data. In a field where transferability across different data characteristics has been a longstanding challenge, this research marks a step towards versatile, efficient, and comprehensive 3D forest scene analysis.Central to this study is model performance evaluation based on platform type (ULS vs. MLS) and data density. This involved five distinct scenarios, each integrating different combinations of input training data, including ULS, MLS, and their augmented versions through random subsampling, to assess the model's transferability to varying resolutions and efficacy across different canopy layers. The core of the model, inspired by the PointGroup architecture, is a 3D convolutional neural network (CNN) with dedicated prediction heads for semantic and instance segmentation. The model underwent comprehensive validation on publicly available, machine learning-ready point cloud datasets. Additional analyses assessed model adaptability to different resolutions and performance across canopy layers.Our results reveal that point cloud random subsampling is an effective augmentation strategy and improves model performance and transferability. The model trained using the most aggressive augmentation, including point clouds as sparse as 10 points m−2, showed best performance and was found to be transferable to sparse lidar data and boosts detection and segmentation of codominant and dominated trees. Notably, the model showed consistent performance for point clouds with densities >50 points m−2 but exhibited a drop in performance at the sparsest level (10 points m−2), mainly due to increased omission rates. Benchmarking against current state-of-the-art methods revealed boosts of up to 20% in the detection rates, indicating the model's superior performance on multiple open benchmark datasets. Further, our experiments also set new performance baselines for the other public datasets. The comparison highlights the model's superior segmentation skill, mainly due to better detection and segmentation of understory trees below the canopy, with reduced computational demands compared to other recent methods.In conclusion, the present study demonstrates that it is indeed feasible to train a sensor-agnostic model that can handle diverse laser scanning data, going beyond current sensor-specific methodologies. Further, our study sets a new baseline for tree segmentation, especially in complex forest structures. By advancing the state-of-the-art in forest lidar analysis, our work also lays the foundation for future innovations in ecological modeling and forest management.

Improvements in stress among Latinas participating in a randomized controlled trial of technology-supported physical activity interventions.

High levels of stress and inactivity likely contribute to chronic disease disparities among Latinas in the U.S. and call for intervention. To inform such efforts, the current study examined the relationships among changes (over time) in physical activity, stress, and related cardiometabolic biomarkers among sedentary (mostly) first generation Latinas. Data are taken from a randomized controlled trial (N=199 Latinas) of two home-based physical activity interventions (Original vs. theory- and technology- Enhanced versions). Physical activity and perceived stress were assessed at baseline and 6 and 12months. Blood draws occurred at baseline and 6months in a random subsample (N=153). The participants were underactive (<60/min week of moderate-to-vigorous physical activity) Mexican American (89%) women with moderate perceived stress scores (M=21.49, SD=8.55, range=0-40) and excess weight (M BMI=30.6) at baseline. Overall, participants reported decreases in stress after 6months enroled in the physical activity programs. The odds of a reduction in perceived stress were 11% higher among Enhanced versus Original Intervention participants (OR=1.11, 95% CI: 1.05-2.27). Those who met physical activity guidelines were significantly more likely to report reductions in perceived stress over 6months (OR=1.92, 95% CI: 1.08-4.16). Furthermore, those who reported reductions in perceived stress over 6months reported significantly more moderate-to-vigorous physical activity at 12months (+69min/week, SE=27.98, p=0.01) compared to those who did not. Greater reductions in perceived stress over 6months were associated with greater improvements in cardiometabolic biomarkers (HbA1c, triglycerides, p's<0.05). These results support a bidirectional relationship between improvements in stress and physical activity among Latinas. Future implications include using physical activity to address stress management and health disparities in this community.

Unsupervised outlier detection using random subspace and subsampling ensembles of Dirichlet process mixtures

Probabilistic mixture models are recognized as effective tools for unsupervised outlier detection owing to their interpretability and global characteristics. Among these, Dirichlet process mixture models stand out as a strong alternative to conventional finite mixture models for both clustering and outlier detection tasks. Unlike finite mixture models, Dirichlet process mixtures are infinite mixture models that automatically determine the number of mixture components based on the data. Despite their advantages, the adoption of Dirichlet process mixture models for unsupervised outlier detection has been limited by challenges related to computational inefficiency and sensitivity to outliers in the construction of outlier detectors. Additionally, Dirichlet process Gaussian mixtures struggle to effectively model non-Gaussian data with discrete or binary features. To address these challenges, we propose a novel outlier detection method that utilizes ensembles of Dirichlet process Gaussian mixtures. This unsupervised algorithm employs random subspace and subsampling ensembles to ensure efficient computation and improve the robustness of the outlier detector. The ensemble approach further improves the suitability of the proposed method for detecting outliers in non-Gaussian data. Furthermore, our method uses variational inference for Dirichlet process mixtures, which ensures both efficient and rapid computation. Empirical analyses using benchmark datasets demonstrate that our method outperforms existing approaches in unsupervised outlier detection.

Endothelium-mediated vasodilation: associations with cardiometabolic diseases and their risk factors in aging

Aim. To evaluate the association of flow-mediated vasodilation (FMD) with cardiometabolic diseases (CMDs) and their risk factors (RF) in old age.Material and methods. The study was conducted in the Russian population cohort of the HAPIEE project (Novosibirsk, 2015-2018). Endothelial function was assessed in a random subsample (788 men and women aged 55-84 years) using ultrasound measurements of the brachial artery FMD. Endothelial dysfunction (ED) was recorded with FMD &lt;10%. Cardiovascular diseases (CVD) and risk factors were assessed using standard epidemiological methods.Results. The incidence of hypertension (HTN) was 78,8% (the same in men and women), major CVDs — 21,7% and 17,1%, type 2 diabetes (T2D) — 18,7% and 19,1% in men and women, respectively. The mean FMD values were 2,7% (SD 7,32) and 3,2% (7,19), while the prevalence of ED was 88,2% and 85,8% in men and women, respectively, and did not differ significantly by sex. Men with ED had higher levels of blood triglycerides (125,1 (71,23) vs 102,7 (45,79) mg/dl; p=0,033), waist/hip circumference indices (0,94 (0,050) vs 0,92 (0,076); p=0,009) and body weight (27,7 (45,79) vs 26,5 (4,36) kg/m2; p=0,077). In women, a tendency towards an association of ED with smoking was revealed (p=0,067). There were no associations of ED with HTN, T2D and CVD in men and women.Conclusion. The PMD test demonstrated a high incidence of ED in a population sample of 55-84 years old. ED is associated with metabolic risk factors in men and smoking in women. No associations of PMD with CMDs were found, presumably due to the high incidence of ED and cumulative disease in the elderly. Modification of metabolic risk factors and smoking cessation are relevant at any age to prevent the progression of ED.

Sample size in multistakeholder Delphi surveys: at what minimum sample size do replicability of results stabilize?

Background and ObjectiveThe minimum sample size for multistakeholder Delphi surveys remains understudied. Drawing from three large international multistakeholder Delphi surveys, this study aimed to: 1) investigate the effect of increasing sample size on replicability of results; 2) assess whether the level of replicability of results differed with participant characteristics: for example, gender, age, and profession. MethodsWe used data from Delphi surveys to develop guidance for improved reporting of health-care intervention trials: SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) and CONSORT (Consolidated Standards of Reporting Trials) extension for surrogate end points (n = 175, 22 items rated); CONSORT-SPI [CONSORT extension for Social and Psychological Interventions] (n = 333, 77 items rated); and core outcome set for burn care (n = 553, 88 items rated). Resampling with replacement was used to draw random subsamples from the participant data set in each of the three surveys. For each subsample, the median value of all rated survey items was calculated and compared to the medians from the full participant data set. The median number (and interquartile range) of medians replicated was used to calculate the percentage replicability (and variability). High replicability was defined as ≥80% and moderate as 60% and <80% ResultsThe average median replicability (variability) as a percentage of total number of items rated from the three datasets was 81% (10%) at a sample size of 60. In one of the datasets (CONSORT-SPI), a ≥80% replicability was reached at a sample size of 80. On average, increasing the sample size from 80 to 160 increased the replicability of results by a further 3% and reduced variability by 1%. For subgroup analysis based on participant characteristics (eg, gender, age, professional role), using resampled samples of 20 to 100 showed that a sample size of 20 to 30 resulted to moderate replicability levels of 64% to 77%. ConclusionWe found that a minimum sample size of 60–80 participants in multistakeholder Delphi surveys provides a high level of replicability (≥80%) in the results. For Delphi studies limited to individual stakeholder groups (such as researchers, clinicians, patients), a sample size of 20 to 30 per group may be sufficient.

Gendered Uptake of Sustainable Intensification Practices among Maize Commercializing Smallholder Farmers in Eastern Districts of Uganda

Commercialization of food crops, such as maize can dynamically shift control over productive resources or accrued income from women to men. This can restrict women&amp;rsquo;s ability to access or buy productive inputs. However, scanty information is available on how changing gender roles and relations can affect smallholder farmers&amp;rsquo; use of sustainable intensification practices. This study described how gender inequalities in access to resources and appropriation of gains affect the uptake of sustainable intensification (SI) practices among maize commercializing farmers in eastern Uganda. Qualitative Survey data collected from a random sub-sample of 72 (36 women and 36 men) maize farmers from 584 maize commercializing smallholder farmers survey participants, was subjected to thematic content analysis. It was intended to follow up four unique profiles of farmers; those who did not use SI practices, the ones who used only intensification (improve maize varieties and fertilizers) or sustainable practices (organic manure and maize-legume intercrop), and those who used joint, one or more sustainable intensification practices. Findings revealed all four profiles to be highly gendered. Only women did not use any SI practice, and only men used intensification practices due to gender roles, responsibilities, and tasks shaped by traditions that give men terminal control over productive assets and gained resources. Both men and women used sustainable and sustainable intensification practices because of the negotiation process and opportunities that gave women more rights over resources. Thus, social-cultural histories and values constrain married women from implementing innovations such as SI practices because these values restrict women&amp;rsquo;s authority over productive resources. It is recommended that for inclusive transformative interventions to be introduced successfully in rigid patriarchal contexts, it is vital to enhance the negotiation skills of women.

Selecting PedsQL items to derive the PedsUtil health state classification system to measure health utilities in children

BackgroundThere is a lack of preference-based health-related quality of life (HRQoL) measures that consistently value health across a full range of child age groups. The PedsQL is a generic HRQoL instrument validated for children 2–18 years, but it is not preference-based. The objective of this study was to derive the PedsUtil health state classification system from the PedsQL as a basis for a preference-based HRQoL measure for children.MethodsA two-step process was used to select PedsQL items to include in the health state classification system: 1) exclude poorly functioning items according to Rasch analysis in each of the previously established seven dimensions of the PedsUtil health state classification system and 2) select a single item to represent each dimension based on Rasch and psychometric analyses, as well as input from child health experts and parents. All secondary analyses were conducted using data from the Longitudinal Study of Australian Children (LSAC). Analyses were stratified by age group (i.e., 2–5 years, 6–13 years, and 14–17 years) to represent the different developmental stages of children and to reflect the study design of the LSAC. Rasch analyses were also performed on five random subsamples for each age group to enhance robustness of results.ResultsTwelve items were excluded from the PedsUtil health state classification system after the first step of the item selection process. An additional four items were excluded in the second step, resulting in seven items that were selected to represent the seven dimensions of the PedsUtil health state classification system: Physical Functioning (“participating in sports activity or exercise”), Pain (“having hurts or aches”), Fatigue (“low energy level”), Emotional Functioning (“worrying about what will happen to them”), Social Functioning (“other kids not wanting to be their friend”), School Functioning (“keeping up with schoolwork”), and School Absence (“missing school because of not feeling well”).ConclusionsThe PedsUtil health state classification system was derived from the PedsQL based on several criteria and was constructed to be applicable to children two years and older. Research is ongoing to elicit preferences for the PedsUtil health state classification system to construct the PedsUtil scoring system.

Study protocol for the development and validation of a questionnaire evaluating predisposition to immunosuppressant medication non-adherence of kidney pre-transplant patients. The KATITA project.

Non-adherence to immunosuppressive medication after kidney transplant is an important cause of graft rejection and loss. Approaches to minimization of non-adherence have focused on the identification of episodes of medication non-adherence, but by then irreparable harm to the graft may already have occurred, and a more effective approach would be to adopt preventive measures in patients who may have difficulty in adhering to medication. The aim of this study protocol is to develop and validate a clinical questionnaire for assessing, in kidney transplant candidate patients in the pre-transplant setting, the predisposition to non-adherence to immunosuppressive medication. In this multicenter, prospective study, a pilot questionnaire in Brazilian Portuguese language, composed of Likert-scaled statements expressing patients' beliefs, behaviors and barriers regarding medication taking will be assembled from a literature review, from focus groups, and an expert panel. The pilot questionnaire will be administered to a minimum of 300 patients in kidney transplant waiting lists and exploratory factor analysis will be used for development of the definitive questionnaire. A random subsample of a minimum of 60 patients will have the scale re-administered after one month for evaluation of test-retest reliability. A multicenter, external validation study will include 364 kidney transplant candidates who will be evaluated immediately before surgery and at months 3, 6 and 12 post-transplant for assessment of concurrent validity, by comparison with two scales that assess medication non-adherence, and for determination of predictive validity using a triangulation method for assessment of medication non-adherence. Structural validity will be assessed with confirmatory factor analysis using structural equation modeling. Cross-cultural generalizability and validity will be assessed by a multicenter study, in which a translation of the scale to another language will be administered to kidney transplant candidate patients from a different culture, with a subsample being selected for test-retest. This study will be conducted in Spain with a Spanish translation of the scale.

An improved neural operator framework for large-scale CO2 storage operations

Geologic carbon storage (GCS) is a practical solution to mitigate the impact of climate change and achieve net-zero carbon emissions. However, due to complex subsurface characteristics and operational constraints, GCS can potentially induce seismic events and leakage to groundwater resources. Moreover, predicting subsurface response in pressure and saturation changes during GCS requires high-fidelity models with high computational times. Machine learning (ML) offers a promising solution to alleviate these challenges. Yet, ML-driven approaches in subsurface physics face significant obstacles, such as the substantial computational resources required for training large-scale models and the accuracy requirement as a reliable surrogate model. To tackle these issues, we propose an improved neural operator (INO) that has improved a DeepONet architecture to handle a realistic reservoir model efficiently, with computational efficiency and high accuracy.Our proposed INO framework has three key benefits: precision, adaptability, and cost-efficiency. For precision, it achieves an average root mean square error (RMSE) of 0.05% (1.5 psi) compared to the average reservoir pressure (3,150 psi) and a RMSE value of 0.016 in void fraction for CO2 saturation among testing cases. Although it is sensitive to high-error areas with steep state variable gradients, proper domain decomposition, ensemble methods, and/or integrating domain-specific knowledge could improve this limitation. For adaptability, the INO framework can be trained with a subset of the computational domain during each backpropagation to achieve better training adaptability, even for problems with high-dimensional inputs (i.e., heterogeneous fields). For example, it can be trained with random subsampling of only 0.2% of the full domain but can predict pressure and saturation in any space and time within the entire domain. For cost efficiency, training procedures with a subset of the whole computational domain significantly improve computational time. For example, for 90 training cases with 1.73 million cells and 50-time steps, the overall training time was about 2.5 h using a single GPU. A trained INO model takes 1 s per evaluation over 1.73 million and 50 time stamps, much faster than a high-fidelity simulator. Overall, the proposed INO approach with these improved benefits will enable us to perform large-scale GCS applications, enhancing the potential of ML models in the subsurface and other multiphysics problems.

Comparison of venous and pooled capillary hemoglobin levels for the detection of anemia among adolescent girls.

Blood source is a known preanalytical factor affecting hemoglobin (Hb) concentrations, and there is evidence that capillary and venous blood may yield disparate Hb levels and anemia prevalence. However, data from adolescents are scarce. To compare Hb and anemia prevalence measured by venous and individual pooled capillary blood among a sample of girls aged 10-19 years from 232 schools in four regions of Ghana in 2022. Among girls who had venous blood draws, a random subsample was selected for capillary blood. Hb was measured using HemoCue® Hb-301. We used Lin's concordance correlation coefficient (CCC) to quantify the strength of the bivariate relationship between venous and capillary Hb and a paired t-test for difference in means. We used McNemar's test for discordance in anemia cases by blood source and weighted Kappa to quantify agreement by anemia severity. A multivariate generalized estimating equation was used to quantify adjusted population anemia prevalence and assess the association between blood source and predicted anemia risk. We found strong concordance between Hb measures (CCC = 0.86). The difference between mean venous Hb (12.8 g/dL, ± 1.1) and capillary Hb (12.9 g/dL, ± 1.2) was not significant (p = 0.26). Crude anemia prevalence by venous and capillary blood was 20.6% and 19.5%, respectively. Adjusted population anemia prevalence was 23.5% for venous blood and 22.5% for capillary (p = 0.45). Blood source was not associated with predicted anemia risk (risk ratio: 0.99, 95% CI: 0.96, 1.02). Discordance in anemia cases by blood source was not significant (McNemar p = 0.46). Weighted Kappa demonstrated moderate agreement by severity (ĸ = 0.67). Among those with anemia by either blood source (n = 111), 59% were identified by both sources. In Ghanaian adolescent girls, there was no difference in mean Hb, anemia prevalence, or predicted anemia risk by blood source. However, only 59% of girls with anemia by either blood source were identified as having anemia by both sources. These findings suggest that pooled capillary blood may be useful for estimating Hb and anemia at the population level, but that caution is needed when interpreting individual-level data.

Hepatic steatosis, metabolic dysfunction and risk of mortality: findings from a multinational prospective cohort study

BackgroundNon-alcoholic fatty liver disease (NAFLD) and metabolic syndrome (MetS) are implicated in the aetiology of non-communicable diseases. Our study aimed to evaluate associations between NAFLD and MetS with overall and cause-specific mortality.MethodsWe used dietary, lifestyle, anthropometric and metabolic biomarker data from a random subsample of 15,784 EPIC cohort participants. NAFLD was assessed using the fatty liver index (FLI) and MetS using the revised definition. Indices for metabolic dysfunction–associated fatty liver disease (MAFLD) were calculated. The individual associations of these indices with overall and cause-specific mortality were assessed using multivariable Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (95%CIs). As a subobjective, risk associations with adaptations of new classifications of metabolic dysfunction–associated steatotic liver disease (MASLD) and metabolic and alcohol-related liver disease (MetALD) were also assessed.ResultsAmong the 15,784 sub-cohort participants, a total of 1997 deaths occurred (835 due to cancer, 520 to CVD, 642 to other causes) over a median 15.6 (IQR, 12.3–17.1) years of follow-up. Compared to an FLI < 30, FLI ≥ 60 was associated with increased risks of overall mortality (HR = 1.44, 95%CI = 1.27–1.63), and deaths from cancer (HR = 1.32, 95%CI = 1.09–1.60), CVD (HR = 2.06, 95% CI = 1.61–2.63) or other causes (HR = 1.21, 95%CI = 0.97–1.51). Mortality risk associations were also elevated for individuals with MAFLD compared to those without. Individuals with MetS were at increased risk of all mortality endpoints, except cancer-specific mortality. MASLD and MetALD were associated with higher risk of overall mortality.ConclusionsOur findings based on a prospective cohort suggest that individuals with hepatic steatosis or metabolic dysfunction have a higher overall and cause-specific mortality risk.

The Alliance Negotiation Scale - Therapist Version: Psychometric Properties in a Sample of Portuguese Psychologists.

The transtheoretical conceptualization of the working alliance and the resultant evaluation tools often overestimate the collaboration between therapist and client, while neglecting the negotiation process. The degree to which therapists and clients can negotiate disagreements regarding goals and tasks is an important indicator in establishing and maintaining the alliance. Even though the negotiation concept is not new, there is still a lack of reliable and parsimonious self-report measures of the construct. The purpose of this study was to translate, execute the cultural adaptation and, also, to perform a preliminary psychometric analysis of the Portuguese form of the therapist version of the Alliance Negotiation Scale (ANS-T_Pt). Data were collected online from 100 Portuguese psychologists. Two random sub-samples were used to conduct both exploratory factorial analysis and confirmatory factorial analysis. Convergent validity was assessed through comparison with the Portuguese version of the Working Alliance Inventory. The ANS-T_Pt showed a one-factorial structure, consistent with previous versions, and demonstrated adequate internal consistency. Evidence supporting criterion-related validity was found based on the correlations between ANS-T_Pt and WAI-T scores. The results showed moderate to large associations between the instruments. These results support the usefulness of the scale, construct's relevance and its transtheoretical nature. These results are a step forward for Portuguese therapists' and researchers' ability to evaluate the bond between client and therapist and to compare results from different countries.

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity

BackgroundFunctional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AimsEvaluate discriminative performance and generalizability of functional connectivity markers for BPD. MethodWhole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain—global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data. ResultsFull-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LimitationsThe accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. ConclusionsSpatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

Dietary sodium intake and atrial myopathy

Abstract Background Diagnostic markers associated with atrial cardiomyopathy, a precursor state to atrial fibrillation (AF), include premature atrial complex (PACs) frequency, left atrial volume index (LAVi) and p-wave indices. Life-style factors associated with atrial myopathy markers are insufficiently studied. Sodium intake is a modifiable risk factor for hypertension, cardiovascular disease and AF. Purpose We aimed to study the association between sodium intake and atrial myopathy markers in the population-based multicenter Swedish CArdioPulmonary bioImage Study. Methods We included 5,309 randomly invited 50–65-year-olds at the Malmö (n=1,125,) and Uppsala sites (n=4,184) without AF who had contributed a 24hECG and a fasting morning urine sample, from which daily sodium intake was estimated using the Kawasaki formula. At the Malmö site resting ECGs from which P-wave indices could be measured were available in 1,066 individuals, and LAVi was available in a random subsample (n=357). The association between sodium intake and atrial myopathy markers was modeled adjusted for age, sex, body mass index, smoking, systolic blood pressure, antihypertensive treatment, coronary artery disease, and heart failure using negative binomial regression for PAC frequency, linear regression for P-wave duration and LAVi and logistic regression for P-wave terminal force in V1, all using sodium intake as a continuous variable and strata at &amp;lt;2, 2.0-2.99g, 3.0-3.99g, 4.0-4.99g and ≥5 g/day, with 3.0-3.99 as the reference category. We also modeled the association between sodium intake and PACs using restricted cubic splines. Results The mean age was 58.1 (±4.1) years, and 52.7% were female. The mean estimated sodium intake was 3.3 (±1.4) g/day. Mean 24h PAC count was 27, 4.7% had ≥500 PACs. High sodium intakes were associated with increased PAC frequency, of 14% (95% confidence interval (CI 1-29% p=0.03)) for 4.0-4.99g/day and 25% (95%CI 8-45%, p=0.002) for intakes ≥5.0g/day, compared to sodium intakes of 3.0-3.99 g/day. There was no significant association between low sodium intakes (&amp;lt;3.0 g/day) and PACs, Figure 1. Sodium intakes of 4.0-4.99 g/day were associated with increased P-wave duration, ß-coefficient 2.4 (95%CI 0.3-4.1, p=0.03), compared to 3.0-3.99g/day, but there was no association between sodium intakes and LAVi, or P-wave terminal force in V1, either as a continuous variable or in strata. Conclusion High dietary sodium intake is associated with PAC frequency, a known prediction of AF and stroke. Whether reduced sodium diets could reduce PAC frequency, and whether that would impact AF and stroke risk needs further study.Sodium intake and PAC frequency