Tag: nevin manimala

NCHS Data Brief. 2026 Mar;(552). doi: 10.15620/cdc/174648.

ABSTRACT

INTRODUCTION: This report provides the most recent prevalence estimates of abnormal blood cholesterol in children and adolescents ages 6-19 and describes changes in prevalences over time.

METHODS: Cholesterol, anthropometry, and demographics data from the National Health and Nutrition Examination Survey (NHANES) for 2013-2014 through August 2021-August 2023 were used for these analyses. Phlebotomy sample weights were used to estimate prevalence, and confidence intervals were estimated using Taylor series linearization. Statistically significant differences in prevalence estimates by age, sex, and weight status were tested using a t statistic at the p < 0.05 level, and trends were evaluated using linear regression models.

KEY FINDINGS: During August 2021-August 2023, 16.5% of children and adolescents had at least one measure of abnormal cholesterol (high total cholesterol, low high-density lipoprotein cholesterol [HDL-C], or high non-HDL-C). The prevalence of at least one abnormal cholesterol measure was lower in girls (13.6%) than in boys (19.2%) and lower in those with underweight or normal weight (10.3%) or overweight (11.5%) than in those with obesity (35.8%). The prevalence of at least one abnormal cholesterol measure decreased between 2013-2014 (21.3%) and August 2021-August 2023 (16.5%).

PMID:41875385 | DOI:10.15620/cdc/174648

Genetics. 2026 Mar 24:iyag081. doi: 10.1093/genetics/iyag081. Online ahead of print.

ABSTRACT

In many taxa, levels of genetic diversity vary along their genomes. The framework of background selection models this variation in terms of linkage to constrained sites, and recent applications have been able to explain a large portion of the variation in human genomes. However, these studies have also yielded conflicting results, stemming from two key limitations. First, existing models are inaccurate in a critical region of parameter space (Nes ∼ -1), where the local reduction in diversity is sharpest. Second, they assume a constant population size over time. Here, we develop predictions for diversity under background selection based on the Hill-Robertson system of two-locus statistics, which allows for population size changes. We treat the joint effect of multiple selected loci independently, but we show that interference among them is well captured through local rescaling of mutation, recombination and selection in an iterative procedure that converges quickly. We further accommodate existing background selection theory to non-equilibrium demography, bridging the gap between weak and strong selection. Simulations show that our predictions are accurate across the entire range of selection coefficients. We characterize the temporal dynamics of linked selection under population size changes and demonstrate that patterns of diversity can be misinterpreted by other models. Specifically, biases due to the incorrect assumption of equilibrium carry over to downstream inferences of the distribution of fitness effects and deleterious mutation rate. Jointly modeling demography and linked selection therefore improves our understanding of the genomic landscape of diversity, which will help refine inferences of linked selection in humans and other species.

PMID:41875378 | DOI:10.1093/genetics/iyag081

JMIR Form Res. 2026 Mar 24;10:e83430. doi: 10.2196/83430.

ABSTRACT

BACKGROUND: Compared with other mental health problems, self-directed interventions for gambling problems lack in quantity, accessibility, and in some cases, evidence base. Moreover, engagement with these interventions remains modest. Mobile apps may be a viable format to deliver self-directed interventions that enhance user engagement.

OBJECTIVE: The aims of this study were to develop a self-directed mobile app intervention for gambling problems and to conduct initial feasibility and acceptability testing with a small sample of Canadian adults with past or present gambling problems (n=30).

METHODS: Participants were invited via email from a list of people who had previously volunteered in similar research in our laboratory. Theory and content of the mobile app intervention were primarily based on a self-directed workbook that has been evaluated in paperback and static web-based formats. The current app prototype included daily gambling diaries, recommended activities based on diary responses, and psychoeducation. It was available for 2 weeks, after which users provided feedback via surveys (n=30) and a virtual focus group (n=8). Quantitative and qualitative feedback, as well as app usage data, were analyzed to provide descriptive statistics and summaries.

RESULTS: Regarding feasibility, median completion time for activities ranged from 48 (IQR 35-90) to 137 (IQR 93-328) seconds. Daily diary completion rate was 51%. One-third of activities were accessed via prompt, and two-thirds on demand. Many participants repeated at least 1 activity, and all activities were repeated by at least 1 participant. Results also indicated favorable user reviews, particularly regarding the app’s credibility, ease of use, and potential impact. The feedback on some app features was highly variable, such as the perceived utility of daily diaries. Specific recommendations for improvement were provided, such as the inclusion of information on concurrent substance use and more interactive psychoeducation.

CONCLUSIONS: Overall, the app met or exceeded heuristic thresholds for feasibility and acceptability testing. These results will inform improvements and subsequent effectiveness testing. The variability in user feedback underscores the demand for further personalization.

PMID:41875357 | DOI:10.2196/83430

J Public Health Manag Pract. 2026 May-Jun 01;32(3):414-419. doi: 10.1097/PHH.0000000000002285. Epub 2025 Nov 24.

ABSTRACT

The 2019-2020 and 2023-2024 flu seasons had unusually active influenza activity late in the season. As vaccines can be a cost-effective way of avoiding severe illness with the flu resulting in hospitalizations or mortality, the Louisiana Department of Health mailed reminder-recall postcards to adults aged 65 to 70 years to raise awareness about respiratory vaccines, including those for flu. In the 2 weeks following the distribution of reminder-recall mailers (n = 47 587 in February 2020 and n = 42 364 in January 2024), an additional 1017 and 1093 flu shots were administered in 2020 and 2024, respectively. These doses comprised 1% of all flu shots administered in their respective seasons and 2% of people who received the mailers. Together, the results suggest potentially limited value of flu reminder-recall mailers for this population without further research on timing, frequency, and cost-effectiveness of the same.

PMID:41875352 | DOI:10.1097/PHH.0000000000002285

Gastroenterol Nurs. 2026 Mar-Apr 01;49(2):74-85. doi: 10.1097/SGA.0000000000000948. Epub 2026 Mar 23.

ABSTRACT

Self-efficacy in coping with cancer surgery is a key concept for planning and organizing nursing care. However, there is no validated measurement tool that can be used to assess self-efficacy in patients with colorectal cancer surgery. This cross-sectional study, with a multi-phase design, was conducted with 170 patients to describe the development and validation of the Self-Efficacy Scale for Patients with Colorectal Cancer Surgery (SES-CRCS). Phase 1 addressed the development procedures of the scale. In Phase 2, a 3-step validation process was conducted: (a) assessing the content validity, (b) evaluating construct validity with exploratory and confirmatory factor analysis, and (c) assessing internal consistency reliability with Cronbach’s α coefficient, test-retest, and item-total correlation methods. Exploratory factor analysis suggested a 16-item single-factor structure. The factor loadings of the 16 items were above .40, and various indices used to examine the consistency of the scale indicated a good model fit. The Cronbach’s α value was .838, the item-total correlations for all items were positive, and there was a strong correlation between the test-retest measurements (r = .933; p < .05). As a result, the SES-CRCS was found to be a valid and reliable measurement tool in assessing self-efficacy among patients with colorectal cancer surgery.

PMID:41875333 | DOI:10.1097/SGA.0000000000000948

J Med Internet Res. 2026 Mar 24;28:e72083. doi: 10.2196/72083.

ABSTRACT

BACKGROUND: The prediction of the onset of sepsis, a life-threatening condition resulting from a dysregulated response to an infection, is one of the most common prediction tasks in intensive care-related machine learning research. To assess the performance of such models, different evaluation strategies, including fixed horizon (a single prediction at a set time before onset), peak score (a single prediction using the maximum predicted risk across time), and continuous evaluation (multiple predictions assessed continuously across time), are commonly implemented, but there is no clear consensus on which approach should be used in order to provide clinically meaningful performance evaluation.

OBJECTIVE: This study aimed to assess different evaluation approaches of sepsis prediction models trained on a public intensive care dataset applied to German intensive care data.

METHODS: In this retrospective, observational cohort study, we assessed the efficacy of machine learning models, pretrained on the Medical Information Mart for Intensive Care IV dataset, when applied to BerlinICU, a multisite German intensive care dataset. To understand the real-world impact of implementing these models, we examined the performance variability across various evaluation strategies.

RESULTS: The BerlinICU dataset includes 40,132 intensive care admissions spanning 10 years (2012-2021). Using the latest Sepsis-3 definition, we identified 4134 septic admissions (10.3% prevalence). Application of a temporal convolutional network model to BerlinICU yielded an area under the receiver operating characteristic curve (AUROC) of 0.67 (95% CI 0.66-0.68) for continuous evaluation with a 6-hour prediction horizon, compared with 0.84 (95% CI 0.83-0.85) on the test set of Medical Information Mart for Intensive Care IV. On BerlinICU, peak score evaluation showed a similar AUROC compared with continuous evaluation, while fixed horizon evaluation showed a reduced AUROC of 0.61 (95% CI 0.60-0.62). Onset matching had minimal impact on performance estimates using continuous evaluation or fixed horizon evaluation, but increased estimates for peak score evaluation. Performance metrics improved with shorter prediction horizons across all strategies.

CONCLUSIONS: Our results demonstrate that the choice of evaluation strategy has a significant impact on the performance metrics of intensive care prediction models. The same model applied to the same dataset yields markedly different performance metrics depending on the evaluation approach. Therefore, careful selection of the evaluation approach is essential to ensure that the interpretation of performance metrics aligns with clinical intentions and enables meaningful comparisons between studies. In our view, the continuous evaluation approach best reflects the continual monitoring of patients that is performed in real-world clinical practice. In contrast, fixed-horizon and peak score evaluation approaches may produce skewed results when not properly matching the length of stay distributions between sepsis cases and control cases. Especially for peak score evaluation, longer visits tend to produce higher maximum scores because sampling from more values increases the likelihood of capturing higher values purely by chance.

PMID:41874553 | DOI:10.2196/72083

J Acoust Soc Am. 2026 Mar 1;159(3):2754-2778. doi: 10.1121/10.0043155.

ABSTRACT

Road traffic noise represents a major source of environmental pollution, and its prediction remains a critical task. This challenge particularly emerges when traffic data are not available, such as during the design phases of new infrastructures, where it becomes necessary to predict the noise exposure affecting nearby residents, even in the absence of measurement data. To address this issue, this work augments a previously developed microscopic and stochastic-core traffic noise model, integrating it with forecasting time series models for traffic flows and average vehicle speeds. This integration produces a hybrid model that enables the estimation of hourly traffic noise levels based solely on historical traffic patterns, even in the absence of direct traffic observations for the period under investigation. The methodology has been evaluated through a statistical analysis of simulated noise levels, with a focus on error distribution and conventional error metrics. The mean error of 0.43 dBA and the mean absolute error of 1.30 dBA confirm the accuracy of the proposed approach for estimating road traffic noise in data-scarce scenarios. A comparison with the CNOSSOS-EU model’s performance highlights the possibility of using such methodology in early-stage infrastructure design and planning.

PMID:41874545 | DOI:10.1121/10.0043155

J Med Internet Res. 2026 Mar 24;28:e89102. doi: 10.2196/89102.

ABSTRACT

BACKGROUND: With expansion of technology-enabled care, digital health literacy (DHL) has become integral to effective cardiovascular disease (CVD) management. However, quantitative evidence regarding determinants and health outcomes of DHL in CVD remains limited and heterogeneous, necessitating comprehensive evidence synthesis.

OBJECTIVE: This study aimed to (1) estimate DHL levels, (2) synthesize DHL-associated factors, and (3) examine DHL-related health outcomes in CVD.

METHODS: A systematic review and meta-analysis of DHL in adults with CVD was conducted per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. PubMed, Embase, Cochrane CENTRAL, CINAHL, Scopus, Web of Science, and Google Scholar were searched for peer-reviewed studies published between 2006 and January 31, 2026. Quantitative studies enrolling adults with CVD, which reported a measure of DHL were included. Studies focusing exclusively on primary cerebrovascular disease and non-peer-reviewed articles were excluded. Risk of bias (ROB) was assessed using the Appraisal Tool for Cross-Sectional Studies tool, the Newcastle-Ottawa Scale, the Revised Cochrane Risk-of-Bias Tool for Randomized Trials, and the Risk of Bias in Nonrandomized Studies of Interventions. Certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Pooled mean eHealth Literacy Scale (eHEALS) scores were synthesized using a random-effects meta-analysis. Heterogeneity was quantified using the I² statistic and 95% prediction intervals.

RESULTS: Twenty studies involving 8581 adults with CVD were included. The overall pooled mean eHEALS score was 24.26 (95% CI 21.19-27.32), with substantial heterogeneity (I²=98.4%; τ²=15.55; τ=3.94) and a wide 95% prediction interval (14.66-33.85). Lower DHL was consistently associated with older age, lower educational attainment, female sex, limited social support, and less experience with digital technologies. Higher DHL was associated with more favorable health-related outcomes, including health behaviors, better quality of life, and greater use and acceptance of digital health technologies. Subgroup analyses showed no statistically significant differences in DHL by region, disease type, or age group. The certainty of evidence was rated as low to very low, and substantial heterogeneity persisted across analyses.

CONCLUSIONS: Our findings underscore DHL as a foundational capability for digitally supported self-management in CVD care and reveal disparities associated with age and socioeconomic factors. By integrating evidence on DHL levels, associated factors, and DHL-related health outcomes in CVD populations, this review provides a more comprehensive, clinically relevant understanding of DHL beyond studies relying on a single instrument (eg, eHEALS) or examining isolated domains. DHL appears to be a context-dependent competency shaped by broader structural and social determinants. From a clinical and health system perspective, digital health interventions should be accompanied by structured digital inclusion strategies, including routine assessment of DHL and care delivery to patients’ digital capacities. Further longitudinal and interventional studies are warranted to clarify the causal pathways linking DHL to health outcomes in adults with CVD and to incorporate provider- and system-level perspectives beyond individual-level assessments.

TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD420251068000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251068000.

PMID:41874540 | DOI:10.2196/89102

Elife. 2026 Mar 24;13:RP97848. doi: 10.7554/eLife.97848.

ABSTRACT

Among brain areas, axonal projections carry channels of information that can be mixed to varying degrees. Here, we assess the rules for the network consisting of the primary visual cortex and higher visual areas (V1-HVA) in mice. We use large field-of-view two-photon calcium imaging to measure correlated variability (i.e. noise correlations, NCs) among thousands of neurons, forming over a million unique pairs, distributed across multiple cortical areas simultaneously. The amplitude of NCs is proportional to functional connectivity in the network, and we find that they are robust, reproducible statistical measures and are remarkably similar across stimuli, thus providing effective constraints to network models. We used these NCs to measure the statistics of functional connectivity among tuning classes of neurons in V1 and HVAs. Using a data-driven clustering approach, we identify approximately 60 distinct tuning classes found in V1 and HVAs. We find that NCs are higher between neurons from the same tuning class, both within and across cortical areas. Thus, in the V1-HVA network, mixing of channels is avoided. Instead, distinct channels of visual information are broadcast within and across cortical areas, at both the micron and millimeter length scales. This principle for the functional organization and correlation structure at the individual neuron level across multiple cortical areas can inform and constrain computational theories of neocortical networks.

PMID:41874539 | DOI:10.7554/eLife.97848

J Strength Cond Res. 2026 Apr 1;40(4):439-450. doi: 10.1519/JSC.0000000000005333.

ABSTRACT

Nakamura, H, Yamashita, D, Nishiumi, D, Nakaichi, N, and Hirose, N. The determinants of deceleration and reacceleration abilities in pro-agility test in adolescent soccer players. J Strength Cond Res 40(4): 439-450, 2026-This study investigated how deceleration and reacceleration abilities in a Pro-Agility test are influenced by kinematic, physical, and maturation factors in male adolescent soccer players. Seventy-one soccer players performed jump tests (standing long jump, countermovement jump, squat jump) and a Pro-Agility test. Kinetic variables during countermovement and squat jumps were obtained using dual force plates, and 3-dimensional kinematic data from the Pro-Agility test were obtained using a markerless motion capture system. In the Pro-Agility test, the deceleration and acceleration phases were determined from the center-of-mass (COM) velocity and subdivided into early and late halves. The mean COM deceleration (Dec) and acceleration (Acc) were calculated in each phase and event. A linear mixed model was used to identify the variables predicting Dec and Acc. Statistical significance was set at p < 0.05. Both first late Dec and second late Dec were associated with Dec during penultimate foot contact (β = 0.231 and β = 0.197, respectively) and COM height at the final foot contact (β = 5.431 and β = 2.910, respectively). Both second early Acc and third early Acc were associated with peak propulsive force in squat jump (β = 0.050 and β = 0.086, respectively). Second early Acc was associated with body height (β = 0.086), and third early Acc was associated with chronological age (β = 0.086), but not with deceleration abilities. These findings highlight the importance of tailoring training strategies to enhance overall change-of-direction performance.

PMID:41874530 | DOI:10.1519/JSC.0000000000005333