Category: Statistics

Eur J Neurosci. 2026 Apr;63(7):e70486. doi: 10.1111/ejn.70486.

ABSTRACT

How the brain signals prediction errors for non-rewarding, yet significant, sensory events remains a central question. Although the cortical mismatch negativity provides a well-known signature for deviance detection, the contribution of subcortical dopamine remains unclear. This study tested the hypothesis that phasic dopamine in the nucleus accumbens encodes the salience associated with the violation of an ongoing statistical regularity. Using fiber photometry in freely moving rats, we contrasted an auditory oddball paradigm with a many-standards control. Deviant stimuli elicited a significantly amplified dopamine response compared with standard stimuli. Crucially, this dopamine response enhancement was absent in the control condition, demonstrating that the nucleus accumbens dopamine responds specifically to rule violation rather than mere stimulus rarity. The long latency of this signal (~500 ms) relative to the cortical mismatch negativity argues against a direct role in the initial detection of deviance. Instead, our findings support a model in which subcortical dopamine acts as a distinct salience signal, operating in parallel with cortical deviance detection, to evaluate unexpected events and guide subsequent behavioral adjustments.

PMID:41924943 | DOI:10.1111/ejn.70486

Cancer Rep (Hoboken). 2026 Apr;9(4):e70536. doi: 10.1002/cnr2.70536.

ABSTRACT

BACKGROUND: Physical activity (PA) is associated with improved health outcomes among cancer survivors (CS), yet PA participation and access to PA programs in cancer care are low. Mobile health (mHealth) technologies such as wearable activity trackers and smartphone lifestyle applications are promising strategies to promote PA among CS. However, CS’s adoption patterns and willingness to share resulting mHealth data with healthcare providers are underexplored.

AIMS: This study examined mHealth technology ownership and usage as well as willingness to share wearable data with healthcare providers among CS and identified demographic and health-related correlates.

METHODS: Self-reported data were collected from post treatment CS (n = 518; M_age = 56.5 (SD = 14.5); 54.6% female) from a large healthcare system. Univariate logistic regression models examined associations between demographic (age, sex, race/ethnicity, education, income, marital status, employment status, health status, BMI) and disease (time since diagnosis, treatment received, disease stage) characteristics and meeting PA guidelines (i.e., 150 min/week of moderate to vigorous PA) and activity tracker ownership, lifestyle app usage, and willingness to share wearable data with healthcare providers.

RESULTS: Nearly all CS (97.5%) owned a smartphone. Over half (52.9%) owned an activity tracker, and one-third (32.4%) used a lifestyle app. Most (64.3%) were willing to share wearable data with healthcare providers. Participants with a college degree or higher income, those who met PA guidelines, and those who were obese were more likely to own a wearable activity tracker. Along with those factors, younger age (< 65) and full-time employment were also associated with a higher likelihood of using a lifestyle app (p < 0.05). Being employed full-time was significantly associated with willingness to share data with a healthcare provider. No other relationships were significant.

CONCLUSIONS: Many CS use or are open to using mHealth technologies. However, differences in adoption by demographic characteristics and unclear demographic and disease correlates of willingness to share data highlight the need for targeted, inclusive, and evidence-based strategies to integrate these tools into survivorship care. Understanding who adopts mHealth technologies is essential to optimizing their potential to improve long-term cancer outcomes.

PMID:41924934 | DOI:10.1002/cnr2.70536

Health Promot Int. 2026 Mar 2;41(2):daag045. doi: 10.1093/heapro/daag045.

ABSTRACT

In 2020, Health Canada approved the INSTI human immunodeficiency virus (HIV) self-test. Adoption and distribution of alternative HIV testing interventions, like self-testing, are essential in meeting the United Nations 95-95-95 goals. We explored the acceptability of HIV self-testing among sexual health service providers and Two-Spirit, gay, bisexual, and queer men (2SGBQM). Between 2020 and 2021, peer researchers conducted virtual focus groups (13) and interviews (18) with providers (n = 18) and 2SGBQM (n = 38) across Ontario, Canada, and analysed data using community-based participatory research approach and reflexive thematic analysis. HIV self-testing was highly acceptable among both providers and 2SGBQM. Both groups identified ‘increased access to HIV testing’ as a benefit. Providers identified ‘client empowerment’ and ‘reduced workload for providers’ as other perceived benefits. 2SGBQM highlighted ‘convenience’ as a key benefit and rationale for self-testing, though some expressed concerns and hesitance due to ‘fear of needles/blood’ and ‘perceptions of lower accuracy and reliability’ of self-test results. Providers and 2SGBQM referred to the ‘potential for missed connections to care’, and ‘self-harm’ with positive test results as additional concerns for self-testing. Both groups suggested that first-time or inexperienced testers should be tested in-clinic, compared with experienced or regular testers who may benefit from self-testing. Participants expressed that HIV self-testing should be widely available for free, or a modest fee up to $20 CAD. Providers and 2SGBQM both found HIV self-testing highly acceptable, particularly when self-administered by experienced testers. Clinic-based testing remains important, especially for first-time testers and 2SGBQM who have concerns or hesitance regarding self-testing.

PMID:41924929 | DOI:10.1093/heapro/daag045

Vet Rec. 2026 Apr 2. doi: 10.1002/vetr.70554. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is becoming increasingly prevalent in the modern world, including in veterinary medicine. This cross-sectional study aimed to investigate horse carers’ attitudes towards using AI use in equine care.

METHODS: An online survey was distributed to UK horse owners/carers in 2025, covering participants’ demographics and use of AI and their opinions of AI for equine care. Statistical analysis included descriptive statistics, categorisation of free-text responses and logistic regression to determine factors associated with opinions.

RESULTS: Ninety-seven responses were analysed. Participants had a predominantly positive opinion of AI to automate large datasets for equine care, and a predominantly negative opinion for automating communications and medical decision making. Key categories identified in free-text responses were: AI use in general/equine care, desire for human interaction and AI as a supportive aid only. Positive attitudes towards AI for equine care were significantly associated with participants’ opinions of AI in their own lives (odds ratio [OR]: 3.69, 95% confidence interval [CI]: 3.06‒4.45) and understanding of AI (OR: 1.31, 95% CI 1.03‒1.66).

LIMITATIONS: This is a small exploratory study of horse owners/carers in the UK, and the findings may not be more widely generalisable.

CONCLUSION: Horse owners/carers had mixed opinions on the use of AI in equine care, and their primary concern was around it replacing human decision making.

PMID:41924893 | DOI:10.1002/vetr.70554

Biomed Res Int. 2026;2026(1):e9163340. doi: 10.1155/bmri/9163340.

ABSTRACT

BACKGROUND: World Health Organization reported a lacuna in receiving hepatitis B virus vaccination for a considerable proportion of healthcare workers, with coverage ranging from 18% to 39% in developing countries. In Ethiopia, full immunization coverage ranges from 5.8% to 36.9% among healthcare workers. This study is aimed at assessing the hepatitis B virus vaccination coverage, its determinants and barriers among healthcare workers in Lemi Kura Subcity, Addis Ababa, Ethiopia, 2024.

METHODS: A facility-based convergent parallel mixed design was conducted in a cross-sectional approach among 277 health workers from June 15, 2024 to July 15, 2024 in Health Centers of Lemi Kura Subcity, Addis Ababa. For the quantitative study, the retrospective samples were chosen using simple random sampling. Conversely, purposive sampling was employed for the qualitative study. Multivariable logistic regression was used to identify independent determinants of HBV vaccination. Variables with p value < 0.05 were considered statistically significant.

RESULTS: Hepatitis B vaccination rate was 78.3% (95% CI: 73.6, 83.0). Being female (AOR: 2.50; 95% CI: 1.21, 5.20), having more than 6 years of experience (AOR: 6.35; 95% CI: 2.33, 17.30), history of exposure to a person infected with hepatitis B (AOR: 2.88; 95% CI: 1.33, 6.21), screened for HBV (AOR: 2.72; 95% CI: 1.36, 5.41), and favorable attitude toward hepatitis B (AOR: 7.53; 95% CI: 3.11, 18.23) were statistically significant in multivariable analysis. Moreover, limited resources, misinformation, and low public awareness, workforce challenges, cost and affordability, and stigma and cultural barriers were reported challenges, and improving access, capacity building, and community engagement were reported opportunities of HBV vaccination.

CONCLUSION AND RECOMMENDATIONS: This study found that HBV vaccination coverage among healthcare workers in public health centers of Lemi Kura Subcity, Addis Ababa, Ethiopia, was relatively high compared with several previous reports from similar settings. Vaccination status was independently associated with sex, years of professional experience, history of occupational exposure, prior HBV screening, and attitude toward HBV vaccination.

PMID:41924885 | DOI:10.1155/bmri/9163340

J Sleep Res. 2026 Apr 2:e70336. doi: 10.1111/jsr.70336. Online ahead of print.

ABSTRACT

Previous studies have examined how the COVID-19 pandemic affected dream recall, dream content, and nightmares. However, relatively little attention has been devoted to the individual and situational factors associated with pandemic-induced changes in dreams. The threat simulation theory of dreaming predicts that threatening situations in our waking life (situational factors) influence threatening dream content. At the same time, individual differences predispose some people to be more prone to experiencing threatening dreams more frequently than others. Using a large Finnish sample of prospective dream diaries, we analysed the relative importance of individual (e.g., belonging to a COVID-19 risk group, life satisfaction, depression and anxiety symptoms) and situational factors (e.g., daily COVID-19 worry, COVID-19 media consumption, negative and positive emotions) to determine the best predictors for threatening events and COVID-19-related threatening events in dreams. Random forest analyses revealed that individual factors were consistently better predictors than situational factors for both threatening events and pandemic-related threatening events in dreams. Lower life satisfaction was the only statistically significant predictor of threatening events and experiencing fewer positive emotions in the past 2 weeks was the only statistically significant predictor of pandemic-related threatening events in dreams. These findings suggest that the propensity to experience threatening dream content, including pandemic-related threatening events, is more of a stable trait rather than a daily fluctuating feature of dreams. In light of the threat simulation theory, it could be argued that individual variation in the proneness to simulate threatening events adaptively interacts with daily experiences to modulate threatening dream content.

PMID:41924860 | DOI:10.1111/jsr.70336

Int J Artif Organs. 2026 Apr 2:3913988261435494. doi: 10.1177/03913988261435494. Online ahead of print.

ABSTRACT

BACKGROUND: Type 1 diabetes (T1D) management remains particularly challenging in the presence of external disturbances such as stress or physical activity (PA). The glycemic impact of PA is still not fully understood and lacks standardized modeling approaches. This work seeks to describe the relationship between PA, time-in-range and glucose predictability in people with T1D (PwT1D).

METHODS: This study uses data from the Type 1 Diabetes and Exercise Initiative (T1DEXI) clinical trial, the largest trial to date in PwT1D undergoing both free-living and structured exercise. A characterization pipeline extracts summary statistics including glucose, carbohydrate intake and PA signals. These features are used to perform unsupervised clustering of subjects using various techniques. The relevance of the cluster-defining variables is then assessed, and their relationship to the performance of a long short-term memory (LSTM) neural network trained to forecast glucose 1 h into the future is analyzed.

RESULTS: The spectral clustering algorithm successfully separates individuals into three groups based on glycemic control metrics. Results indicate that subjects with higher levels of weekly PA exhibit lower prediction errors. This suggests that regular PA enhances the predictability of glucose trends, enabling more accurate forecasting models.

CONCLUSION: Since fear of hypoglycemia remains one of the main barriers to PA in PwT1D, these findings are particularly relevant: regular exercise not only promotes better glycemic regulation but also improves the performance of predictive models, which could strengthen automated insulin delivery systems, support more reliable decision-support tools and contribute to safer and more confident engagement in PA.

PMID:41924857 | DOI:10.1177/03913988261435494

Int J Artif Organs. 2026 Apr 2:3913988261429899. doi: 10.1177/03913988261429899. Online ahead of print.

ABSTRACT

BACKGROUND: Elderly patients requiring emergency intubation and invasive mechanical ventilation (IMV) in the emergency department (ED) face substantial mortality risk. However, prediction models utilizing only ED-obtainable variables for early prognostication remain limited.

OBJECTIVE: To develop and internally validate a prognostic model using variables available at ED intubation to predict in-hospital mortality in elderly patients.

METHODS: This retrospective cohort study included 273 patients aged ⩾ 65 years who underwent emergency intubation and IMV initiation in the ED of a tertiary hospital between June 2019 and June 2024. Candidate predictors included demographics, comorbidities, Glasgow Coma Scale (GCS), vasopressor use, admission lactate, PaO2/FiO2 ratio, shock index, and baseline laboratory values. Multiple imputation addressed missing data. Least absolute shrinkage and selection operator (LASSO) logistic regression identified optimal predictors, followed by multivariate logistic regression. Internal validation employed bootstrap resampling (1000 iterations). Model performance was assessed via discrimination (C-statistic), calibration (calibration plot, Brier score), and clinical utility (decision curve analysis).

RESULTS: The overall in-hospital mortality rate was 72.2% (197/273). After LASSO selection and clinical adjudication, the final model included age, GCS score, admission lactate, and vasopressor requirement as independent predictors. The model demonstrated excellent discrimination (C-statistic 0.834, 95% CI: 0.784-0.884), good calibration, and superior net benefit across threshold probabilities of 0.3-0.8 compared to default strategies. Bootstrap-corrected optimism was minimal (optimism-corrected C-statistic 0.827). Sensitivity analyses confirmed model robustness.

CONCLUSIONS: A parsimonious model incorporating age, GCS score, admission lactate, and vasopressor use accurately predicts in-hospital mortality in elderly patients undergoing emergency intubation and IMV, using only variables readily available at the time of ED presentation. This tool has the potential to facilitate early, evidence-informed shared decision-making.

PMID:41924851 | DOI:10.1177/03913988261429899

Ann Med. 2026 Dec;58(1):2647569. doi: 10.1080/07853890.2026.2647569. Epub 2026 Apr 2.

ABSTRACT

BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, effective models for predicting frailty risk are lacking. This study utilizes machine learning-based models to early identify community-dwelling older adults, enhancing risk assessment accuracy and guiding targeted interventions to slow frailty progression.

METHODS: A cross-sectional analysis of data from 1,156 older adults across 31 community health centers in Nanjing, conducted between January and October 2024, was performed. Independent predictors of frailty were identified using univariate analysis and the least absolute shrinkage and selection operator. The dataset was divided into 70% training and 30% testing subsets. Six machine learning (ML) models were developed and their performances compared. The SHapley Additive exPlanations (SHAP) method was applied to interpret the models, and a web-based risk calculator was created.

RESULTS: Our dataset showed that 22.3% of older adults were frail. Significant predictors of frailty were identified as age, education, medicine, vegetable, cognitive status, number of diseases, hemoglobin, total cholesterol, and neutrophil-to-lymphocyte ratio. Among the six ML models, Categorical Boosting (CatBoost) exhibited the highest performance, attaining an AUROC of 0.886 in the training set and 0.831 in the testing set.

CONCLUSIONS: The developed CatBoost model and web calculator can be employed by general practitioners to proactively identify high-risk community-dwelling older adults, thereby enabling timely interventions to mitigate the progression of frailty. The tool’s simplicity and replicability effectively facilitate the promotion and management of frailty prevention within the community.

PMID:41924849 | DOI:10.1080/07853890.2026.2647569

Nurs Outlook. 2026 Mar 31;74(3):102749. doi: 10.1016/j.outlook.2026.102749. Online ahead of print.

ABSTRACT

BACKGROUND: National nursing organizations and policy frameworks call for incorporating social determinants of health (SDOH) into nursing curricula to improve workforce readiness and promote health equity. Although providers acknowledge the importance of SDOH, gaps remain in preparing prelicensure nursing students to address systemic and community health factors.

PURPOSE: This project assessed the effectiveness of the Toward Health Equity and Literacy (2HEAL) program’s online modules in improving BSN students’ perceived competence in addressing SDOH.

METHODS: The 2HEAL team created a series of asynchronous, interactive modules covering various SDOH topics. BSN students (n= 346) completed the modules between August 2023 and March 2025. A retrospective self-assessment survey measured changes in students’ perceived competence. Data from 1,131 surveys compared pre- and post-module scores. Net Promoter Scores (NPS) evaluated module acceptability.

FINDINGS: Students reported a statistically significant increase in perceived competency across all SDOH topics appearing in more than one module, with an overall average increase of +0.50 points. Post-module scores showed students self-reported proficiency in 68% of assessed SDOH domains. The greatest gains were in civic engagement (+0.63), while environmental health showed the smallest increase (+0.26). Modules achieved an average NPS of +36, indicating high learner satisfaction.

DISCUSSION: This project demonstrates that structured, competency-based digital modules can improve BSN students’ perceived competence in addressing SDOH. Results highlight improvement in addressing civic engagement, immigrant health, and health literacy, and reveal ongoing gaps in climate and environmental health education. The 2HEAL model provides a scalable way to incorporate SDOH training into pre-licensure programs.

PMID:41924837 | DOI:10.1016/j.outlook.2026.102749