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PubMed2026

Improving insurance deduction identification: a hybrid artificial intelligence model using machine learning and expert systems.

PURPOSE: Financial challenges in healthcare systems worldwide, especially in low- and middle-income countries like Iran, have increased hospitals' reliance on insurance reimbursements. Unrecognized insurance deductions often cause severe financial shortages, making efficient deduction management crucial. This study aimed to design a hybrid intelligent system for identifying and predicting insurance deductions by combining machine learning and expert system frameworks. DESIGN/METHODOLOGY/APPROACH: A mixed-methods design was applied in four stages. First, a scoping review identified the causes and patterns of insurance deductions. Second, interviews with 15 insurance experts produced a validated checklist and a dataset from inpatient billing records. Third, using the CRISP-DM methodology, machine learning algorithms were developed and tested in SPSS Modeler alongside a fuzzy expert system developed in MATLAB. Finally, the model was validated using the holdout method. FINDINGS: Four categories of deduction drivers were identified: service provision, registration errors, document submission issues, and revenue conversion processes. The CHAID decision tree outperformed other algorithms with a 99% precision rate and the lowest Mean Absolute Error (9.43). A brief assessment of potential overfitting was conducted to ensure that the CHAID model's high accuracy was interpreted cautiously and supported by the validation results. The fuzzy expert system with validated rules was adaptable for deduction classification, especially for cases unsuitable for quantitative modeling. ORIGINALITY/VALUE: The hybrid model improves detection and prevention of deductions, offering actionable insights for hospital administrators, insurers, and policymakers. Its implementation can enhance hospital information systems, streamline claims processing, and optimize revenue management amid financial constraints.

Machine Learning↗

PubMed2026

Association between youth athletes' sports specialization and injuries: a systematic review and meta-analysis.

INTRODUCTION: The purpose of this study was to conduct a systematic review and meta-analysis to examine the specialization-injury relationship, and explore whether the specialization-injury relationship is moderated by study design, sport type, age, sex, and injury measurement type, injury mechanism, and anatomical location. METHODS: We searched eight databases by related keywords and assessed the quality of the included studies using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies and the Newcastle-Ottawa Scale. The Comprehensive Meta-Analysis (CMA) statistical software 3.7 examined heterogeneity, sensitivity, publication bias, overall effect size of specialization-injury relationship, and moderation effects. This review was prospectively registered in PROSPERO (CRD420251233318). Searches were conducted in eight electronic databases from inception to March, 2026. RESULTS: The 15 included studies showed moderate heterogeneity, stable sensitivity analyses, and no publication bias. The overall odds ratio of the sports specialization-injury relationship was 2.00 (95% confidence interval [1.58-2.55], p < .001). Moderation analyses indicated that sport type significantly influenced the specialization-injury relationship, whereas no significant moderation effects were observed for study design, sex, age, injury measurement type, injury mechanism, and injury anatomical location. CONCLUSIONS: The findings indicate a positive association between sport specialization and injury risk among youth athletes, with variation across sport participation contexts. Specifically, athletes in both contact and non-contact sports demonstrated higher pooled odds of injury than those involved in multiple sports. Although the evidence is heterogeneous and should be interpreted with caution, these findings highlight the potential role of diversified sport participation in relation to injury.

Humans↗

PubMed2026

Antimicrobial photodynamic therapy mediated by phenothiazine photosensitizers against Candida albicans and Candida auris: a systematic review.

Fungal infections caused by Candida albicans and Candida auris represent an increasing clinical challenge, particularly due to biofilm formation and rising antifungal resistance. Antimicrobial photodynamic therapy (aPDT) has emerged as a potential alternative strategy, with phenothiazine-based photosensitizers being among the most extensively investigated compounds. This systematic review aimed to evaluate the application of phenothiazine-mediated aPDT in in vitro studies against C. albicans and C. auris. A comprehensive search was conducted in PubMed, Embase, and Scopus, including studies published within the last 10 years. Forty in vitro studies met the eligibility criteria and were synthesized descriptively due to substantial methodological heterogeneity. Overall, aPDT was associated with reductions in fungal viability, with generally greater effects reported in planktonic models compared with biofilms. Methylene blue was the most frequently investigated photosensitizer, applied across a broad range of concentrations and dosimetric parameters, resulting in variable antifungal responses. Other phenothiazine derivatives, including toluidine blue O, dimethyl methylene blue, new methylene blue, and S137, were also associated with antifungal activity under specific experimental conditions but remain comparatively underexplored. Studies involving C. auris were less frequent and suggested lower susceptibility compared with C. albicans, particularly in biofilm models. Given the substantial variability in experimental protocols, especially regarding photosensitizer concentration, light parameters, and biofilm maturation, the findings should be interpreted with caution and limit direct comparison across studies. These findings support the antifungal potential of phenothiazine-mediated aPDT while emphasizing the need for methodological standardization and expanded investigation of C. auris.

Photochemotherapy↗

PubMed2026

Symptoms of Anxiety in Adults with ADHD: A Systematic Review and Meta-Analysis of Case-Control Studies.

OBJECTIVE: Research suggests there is a higher prevalence of anxiety in individuals with ADHD compared to those without ADHD. However, estimated prevalence rates vary greatly due to differences in methodologies used across studies. This study is the first meta-analysis investigating differences in anxiety measure scores between adults with and without ADHD, and possible moderators of this effect. METHOD: Our analysis included 58 studies that compared anxiety scores in adults with/without ADHD (N = 18,821; k = 112). RESULTS: The average effect size (Hedges's g) of anxiety scores in those with ADHD relative to controls was 0.77 (SE = 0.066); a medium effect. The only significant moderator was the type of comparison group used, with comparisons to non-clinical control groups (g = 0.954, SE = 0.08) yielding significantly larger effects than comparisons to clinical control groups (g = 0.437, SE = 0.09). None of the other moderator variables examined (Method of characterising ADHD, Anxiety Measure Type, Anxiety Measure Focus, Age or Gender) moderated the effect. There was some evidence of publication bias, therefore results should be interpreted with this in mind. CONCLUSION: Overall, this study indicates that ADHD is associated with higher levels of anxiety symptomology. This has important implications in the diagnosis and treatment of individuals presenting with ADHD or anxiety, including the consideration and screening of each of these conditions as potentially co-occurring with the other. Adaptions made to clinical practice in line with this would better support this population, improve symptom management and overall quality of life. Suggestions for future research are discussed.

Humans↗

PubMed2026

Association between fruit and vegetable intake and risk of depression: A systematic review and meta-analysis of prospective cohort studies.

Depression is a leading cause of global disability, and identifying modifiable lifestyle factors is a public health priority. We conducted a systematic review and dose-response meta-analysis of prospective cohort studies examining the association between fruit and vegetable intake and incident depression. PubMed, Web of Science, Scopus, Embase, and Google Scholar were searched through November 2025. Data on study characteristics, dietary assessment, outcomes, effect estimates, and covariates were independently extracted by two reviewers. Random-effects models were used to calculate pooled relative risks (RRs), and linear and non-linear dose-response relationships were assessed. Heterogeneity was evaluated using I2, and evidence certainty was rated with GRADE. Thirteen cohorts with 385,449 participants and 26,592 depression cases were included. Highest versus lowest combined fruit and vegetable intake was associated with a 37% lower risk of depression (RR: 0.63; 95% CI: 0.50, 0.80). Each 200 g/day increase corresponded to a 16% risk reduction (RR: 0.84; 95% CI: 0.74-0.94). Separate analyses showed that fruit and vegetable intake reduced depression risk by 16% and 12%, respectively, with a non-linear dose-response for vegetables. These findings indicate that higher consumption of fruits and vegetables is associated with lower depression risk, supporting dietary strategies as a potential approach for mental health prevention. However, given the observational nature of the included studies, these results should be interpreted with caution, as residual confounding may partially account for the observed associations. Registration: This study was registered at PROSPERO (CRD420261279998).

Humans↗

PubMed2026

Innovation-related perception as a key driver of alternative protein acceptance: evidence from an early-stage model for cultivated meat and algae-/microalgae-based alternative protein products in Italy.

Alternative proteins are increasingly considered part of the transition toward more sustainable food systems, yet their diffusion depends critically on consumer acceptance. This study investigates the early-stage acceptance of two alternative protein categories in Italy-cultivated meat and algae-/microalgae-based alternative protein products. Focusing on the first three phases of acceptance, the analysis examines how innovation-related perception (IRP) shapes consumer perceived value (CPV), consumer perceived risk (CPR), and subsequent affective (AFF), cognitive (COG), and conative (CON) responses. Data were collected through an online survey administered to 238 Italian respondents and analysed using partial least squares structural equation modelling (PLS-SEM). The results show that IRP is the main upstream driver of early-stage acceptance in both product domains: more favourable perceptions strongly increase perceived value and reduce perceived risk. In turn, CPV exerts a much stronger influence than CPR on both affective and cognitive attitudes. A tentative cross-model comparison suggests only a descriptive variation in the final transition toward conative acceptance: affective and cognitive responses were both significant in the two models, with a relatively larger affective coefficient for cultivated meat and more balanced coefficients for algae-/microalgae-based products. Overall, the findings support a process-based interpretation of alternative protein acceptance and highlight the central role of innovation-related perception in shaping early consumer responses. These results provide relevant implications for communication strategies, product positioning, and policy actions aimed at improving the acceptability of alternative proteins in food cultures characterised by strong culinary traditions.

Italy↗

PubMed2026

Comparative profiling of microbial community structure, enzyme potential, metabolic features, and volatile composition in craft and Jiafan Huangjiu processes.

Craft Huangjiu and Jiafan Huangjiu represent two distinct industrial Huangjiu product outcomes with contrasting volatile profiles. This study compared craft Huangjiu (L70) and Jiafan Huangjiu (L79) to characterize their physicochemical, microbial, gene-level functional, metabolic, and volatile features. Because L70 involved mid-fermentation addition of finished Huangjiu, this comparison was not intended to isolate the sole effect of fermentation interruption versus continued fermentation. L79 showed more extensive carbon and nitrogen utilization, with lower residual substrates and higher ethanol and acetic acid contents than L70, whereas L70 retained a less complete fermentation state. At the volatile level, GC-MS and volatile metabolomics consistently showed an ester-enriched profile in L79 and a more alcohol-dominant profile in L70. FlavorDB-based putative annotation and threshold-based OAV analysis further indicated distinct database-assigned descriptor distributions and potential odor-active compounds, with more OAV > 1 ester-related compounds in L79. Metagenomic analysis showed that L70 was dominated by Lactobacillus acetotolerans, whereas L79 contained higher relative abundances of Saccharomyces cerevisiae, Aspergillus oryzae, Aspergillus flavus, and Fructilactobacillus fructivorans. Metagenomic functional annotation showed higher representation of hydrolysis-related CAZy genes and ester-related enzyme annotations in L79. KEGG-based pathway mapping further indicated greater gene-level potential for ethanol-, acetate-, and acetyl-CoA-related metabolism in L79. Accordingly, the L70 profile should be interpreted as the integrated final-product outcome of process intervention, exogenous input, and subsequent fermentation. The findings provide a comparative basis for future flavor regulation and process optimization in Huangjiu and other fermented alcoholic beverages.

Volatile Organic Compounds↗

PubMed2026

Pharmacological and non-pharmacological modulation of striatal dopamine release: a meta-analysis of [11C]raclopride PET studies.

The dopaminergic system has long been a central focus of functional neuroimaging. Positron emission tomography (PET) with the D2/D3 receptor radioligand [11C]raclopride remains the most widely used method for indirectly quantifying striatal dopamine release in vivo. However, no previous meta-analysis has studied the relative magnitude and regional distribution of dopamine release across different interventions or cognitive interventions overall. To address this gap, in this meta-analysis of 92 [11C]raclopride PET studies (n = 1640), we compared the magnitude and regional distribution of dopamine release induced by amphetamine, methylphenidate, ketamine, alcohol, and cognitive challenges with and without reward. Amphetamine induced approximately four-fold greater dopamine release than cognitive challenges (10.9 vs. 2.7%, p < 0.001), and approximately twice that of alcohol (4.8%, p < 0.001), with effects comparable to methylphenidate (11.5%) and slightly greater than ketamine (9.8%). Psychostimulant-induced increase in synaptic dopamine was greater in putamen and ventral striatum than in caudate, whereas alcohol preferentially engaged ventral striatum. Dopamine release did not differ between rewarded and non-rewarded cognitive tasks in the ventral striatum (p > 0.14) or overall striatum (p > 0.10). Methylphenidate-induced increases in synaptic dopamine appeared to attenuate with advancing age, whereas cognitive challenges were associated with greater dopamine release in older individuals. These findings demonstrate that individual pharmacological and cognitive interventions differ markedly in both magnitude and regional pattern of dopamine release. They also suggest that [¹¹C]raclopride PET may have limited sensitivity for distinguishing reward-related from non-reward-related dopamine release. These findings have implications for the design and interpretation of future neuroimaging studies of dopaminergic function in health and disease.

Journal Article↗

PubMed2026

Redefining the real problem in psychedelic trials: Why fighting the Lessebo matters more than blinding integrity.

Imperfect blinding is not specific to psychedelic trials. In randomized trials, treatment allocation is frequently correctly guessed, yet blinding integrity is rarely assessed outside of psychedelic research and is generally not considered a barrier in regulatory evaluation. The intense debate in psychedelics may reflect a broader double standard affecting mental health research, when uncertainties arising from imperfect blinding are confounded by those linked to patient-reported outcome measures. Indeed, people living with mental disorders are often viewed as unreliable reporters, despite well-documented limitations of clinician-rated scales and the absence of robust biological markers of symptomatic change. Importantly, it is the maintenance of reasonable doubt of treatment allocation that sustains internal validity and ethical feasibility of placebo-controlled designs, rather than perfect blinding. Concerns about expectancy bias in psychedelic trials are closely tied to blinding debates. When allocation is inferred, expectations may cluster in the arm perceived as active or in stereotyped experiences and influence outcomes differently in active and control arms, leading to a risk of lessebo, a negative placebo effect due to the negative expectation related to receiving a placebo. However, we argue that an underrecognized mechanism of lessebo is disappointment. This risk may reflect insufficient clinical management of disappointment rather than pre-treatment expectation alone. We therefore propose shifting the emphasis from preserving inevitably imperfect blinding towards mitigating disappointment in both arms. Establishing non-stereotyped expectations prior to treatment through structured psychoeducation, strengthened therapeutic alliance, and realistic preparation would help avoid lessebo effects. Such strategies would enhance ethical rigor, interpretability, and the clinical usefulness of psychedelic trials.

Humans↗

PubMed2026

Care navigation for older adults after stroke - A systematic review and meta-analyses to guide social prescribing.

INTRODUCTION: Stroke affects many older people worldwide, and patient navigation and social prescribing (e.g., care navigation) may help recovery. We aimed to synthesize evidence on the effect of care navigation for people living with the effects of a stroke (PLWS) on anxiety, depression, quality of life, and well-being. Our secondary focus was to explore these models in rural settings. METHODS: We conducted a systematic review following guidelines, and searched for peer-reviewed randomized controlled trials for older adults (60 years+ or group mean age in this range) who had a stroke and received patient navigation or social prescribing. Two authors independently screened citations at Level 1 (title and abstract) and Level 2 (full text). The date of the last updated search was December 5, 2025. We synthesized data quantitatively using meta-analyses (random effects model and standard mean difference). RESULTS: We identified 11 studies using patient navigation, but no social prescribing interventions. The total number of PLWS participants at baseline was 7829 with an average mean age of 68 years (43% women). There were no differences between groups for anxiety or quality of life for PLWS, but there was a difference favouring the intervention for depression, although the findings were no longer significant with sensitivity analyses. Thus, results should be interpreted with caution. Only two studies provided data for caregivers, with mixed findings. No studies focused on well-being or rural settings. CONCLUSIONS: Care navigation for PLWS needs more research, including testing social prescribing within stroke rehabilitation in rural and urban locations. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2025 CRD420251077958.

Aged↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2026

Heterogeneity in Teriflunomide Treatment Arms: A Systematic Review and Meta‑Regression of Randomised Multiple Sclerosis Trials.

BACKGROUND: Teriflunomide is widely used as an active comparator in Phase 3 randomised trials for relapsing multiple sclerosis (RMS). Temporal changes in disease activity within teriflunomide-treated cohorts have not been systematically examined. OBJECTIVES: To assess temporal trends in relapse and disability outcomes across teriflunomide arms of Phase 3 multiple sclerosis (MS) trials and identify predictors of between-trial heterogeneity. METHODS: We performed a systematic review and meta-analysis of Phase 3 randomised controlled trials including a teriflunomide arm. PubMed, Scopus, and ClinicalTrials.gov were searched up to October 2025. Annualised relapse rate (ARR) and 12- and 24-week confirmed disability worsening (CDW) were extracted together with baseline characteristics. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Random-effects meta-analyses, meta-regression, and sensitivity analyses were performed. RESULTS: Twelve teriflunomide cohorts from eight trials involving 4,900 adults with RMS were included. ARR ranged from 0.11 to 0.37 with substantial heterogeneity (I2 = 94%). Trial start year was inversely associated with ARR and explained a large proportion of between-study variability in exploratory meta-regression analyses. Confirmed disability worsening outcomes also showed substantial heterogeneity with a weaker trend toward lower event rates in more recent trials. CONCLUSION: Teriflunomide-treated trial populations have shifted toward lower relapse activity over time, and trial start year was the principal predictor of between-trial heterogeneity in ARR in exploratory analyses. These findings most plausibly reflect evolving recruitment and diagnostic practices rather than changes in drug efficacy. Accounting for these temporal dynamics is essential when interpreting outcomes from RMS trial using teriflunomide as comparator.

Humans↗

PubMed2026

The Statistical Fragility of Saline Nasal Irrigation for Rhinosinusitis: A Systematic Review.

OBJECTIVE: To assess the statistical fragility of randomized controlled trials (RCTs) evaluating high-volume saline nasal irrigation (SNI) for rhinosinusitis using fragility analysis. DATA SOURCES: PubMed, MEDLINE, and Embase were searched for RCTs published between May 1976 and January 2026. REVIEW METHODS: This study was reported as per PRISMA guidelines. RCTs that compared high-volume SNI to non-irrigation standard care for acute, recurrent, or chronic rhinosinusitis, and reported ≥ 1 dichotomous outcome, were included. Fragility index (FI), the minimum number of event reversals needed to alter statistical significance, and fragility quotient (FQ), FI normalized to sample size, were calculated for statistically significant dichotomous outcomes. Reverse FI (rFI) and reverse FQ (rFQ) were calculated for non-significant outcomes. RESULTS: Eight RCTs were included, yielding 38 dichotomous outcomes. Eight outcomes (21.1%) were statistically significant. The overall combined median FI was 5 (FQ 0.062), with similar FI values between significant and non-significant outcomes. In over one-fifth of outcomes, loss to follow-up exceeded FI. Analysis of principal dichotomous outcomes from studies demonstrated a median FI of 6 (FQ 0.092), with five of eight (62.5%) outcomes non-significant. CONCLUSION: RCTs evaluating SNI for rhinosinusitis exhibit moderate-to-high statistical fragility, with small outcome changes capable of reversing study conclusions. Because fragility analysis was limited to dichotomous outcomes while many primary endpoints were continuous, our findings should be interpreted as complementary rather than comprehensive appraisals of RCTs. Future RCTs with larger sample sizes, reduced bias, and pre-specified fragility considerations are needed to better define the clinical role of SNI.

Rhinosinusitis↗

PubMed2026

Macroprolactinemia as a diagnostic pitfall in hyperprolactinemia: a systematic review and quantitative synthesis.

CONTEXT: Macroprolactinemia is a well-recognized cause of hyperprolactinemia and an important diagnostic pitfall in endocrine practice. However, interpretation of published quantitative prolactin data remains sparse as studies vary in confirmation method, assay platform, polyethylene glycol (PEG) recovery cutoff, and reporting of prolactin measurement. EVIDENCE ACQUISITION: PubMed, Embase, Scopus, Web of Science, the Cochrane Library, and Google Scholar were systematically searched. Eligible studies reported macroprolactinemia-specific quantitative prolactin data in patients with confirmed macroprolactinemia defined by PEG precipitation, gel filtration chromatography (GFC), or both. Two reviewers independently performed study selection, data extraction, and quality assessment. Findings were summarized using study-level descriptive synthesis. The review was prospectively registered in PROSPERO and conducted in accordance with PRISMA 2020 guidelines. EVIDENCE SYNTHESIS: Forty-five studies encompassing 2853 macroprolactinemia cases from 21 413 screened patients with hyperprolactinemia across 22 countries were included. Among 33 studies eligible for primary quantitative analysis, the median study-level central total prolactin attributed to macroprolactinemia was 61.4 ng/mL ([IQR] 42.0-80.0; range 28.1-137.6), and the median study-level post-PEG monomeric prolactin was 11.7 ng/mL (IQR 8.3-13.2; range 4.0-17.0)). The median study-level maximum total prolactin was 264.5 ng/mL (IQR 97.0-425.5; range 81.8-663.0); extreme elevations were attributable to coexisting prolactinomas. CONCLUSION: In confirmed macroprolactinemia, total prolactin elevation is typically moderate, and post-PEG monomeric prolactin is usually within or near the normal range. The post-PEG monomeric prolactin value, rather than percent recovery alone, is the most informative parameter for distinguishing isolated macroprolactinemia from coexisting true hyperprolactinemia. These quantitative benchmarks may help clinicians to avoid unnecessary investigation or treatment.

Humans↗

PubMed2026

Comparative effectiveness and safety of pharmacological interventions for sleep outcomes in chronic non-cancer pain: a systematic review and network meta-analysis.

Sleep disturbances are highly prevalent among individuals with chronic non-cancer pain and are associated with worse pain severity and poorer prognosis. The comparative trade-offs between the effectiveness and safety of available pharmacotherapies for sleep outcomes in this population remain poorly defined. Ninety-eight RCTs involving 28,920 participants (mean age 53.2 years, 71.2% female) were included. Moderate-certainty evidence demonstrated that melatonin significantly improved sleep quality compared with placebo (standardized mean difference [SMD] = -0.60, 95%CI: -0.98, -0.22). Ten agents (e.g., amitriptyline, oxycodone, gabapentin, pregabalin, duloxetine) also showed statistically significant improvements in subjective sleep quality (SMD = -0.24 to -1.07), but most effects were supported by low-certainty evidence and were accompanied by an increased risk of adverse events (odds ratio [OR] = 1.90 to 37.00). Conversely, melatonin was not associated with an increased risk (OR = 0.88, 95%CI: 0.19, 3.94). Our findings indicate that melatonin shows promise as a safe, adjunctive option for improving sleep quality in this population, but larger, condition-specific trials are warranted to confirm these effects. Other pharmacological agents are limited by lower-certainty and unfavorable safety profiles. These results should be interpreted cautiously given limited direct comparisons, heterogeneous chronic pain populations, the high proportion of trials at high risk of bias, and the predominance of subjective sleep outcomes.

Humans↗

PubMed2026

Self-selected goals outperform assigned goals in reducing mobile phone usage: Evidence from a randomized controlled trial.

Excessive smartphone use is increasingly recognized as a public-health concern, yet scalable approaches to help individuals regulate daily use remain limited. We examine whether allowing individuals to self-select reduction goals improves behavioral and psychological outcomes when incentives and average goal levels are held constant across conditions. In a twelve-week randomized controlled trial, (N = 149; over 9000 person-day observations), participants were assigned to (i) a self-selected condition (choosing a 10%, 20%, or 30% reduction in daily phone use), (ii) an assigned condition (assigned a 14% reduction goal), or (iii) a no-goal control condition. Participants who selected their own goals reduced phone use by 26 min more per day (73% larger reduction) and achieved their goals 11 percentage points more often than those assigned goals, despite identical incentives and average goal levels. Reductions in phone use and higher goal achievement were associated with improvements in perceived addiction, depressive, and anxiety symptoms. These psychological outcomes were secondary endpoints. Although the between-group estimates generally followed the same directional pattern as the behavioral outcomes, the sample size for these analyses was limited and the between-group differences were not statistically significant. These findings should therefore be interpreted with caution. Overall, the results provide causal field evidence that self-selection under this goal-setting design can improve behavioral outcomes. Allowing individuals to choose their own goals may strengthen engagement and support healthier digital behavior. Incorporating opportunities for goal-selection may represent a simple addition to digital-health and public-health interventions aimed at helping individuals moderate smartphone use and improve well-being.

Humans↗

PubMed2026

An introductory practical guide to secondary data analysis in pediatric urology.

INTRODUCTION: Secondary data analysis (SDA) has become an increasingly important approach in pediatric urology, enabling the study of long-term outcomes, care variation, and disparities in populations with chronic or congenital urologic conditions. With the growing availability of large datasets, a structured approach to designing and conducting SDA studies is increasingly relevant. OBJECTIVES: To provide an introductory, practical guide to SDA in pediatric urology by (1) summarizing commonly used data sources with representative studies, (2) outlining a stepwise approach to designing and executing SDA studies, and (3) highlighting key methodological considerations, limitations, and opportunities for future work. STUDY DESIGN: Narrative review of existing literature and commonly used datasets relevant to pediatric urology, including administrative claims, hospital encounter databases, clinical registries, electronic health record networks, and population-based surveys. RESULTS: Data sources differ in scope, clinical granularity, longitudinal follow-up, and representativeness, and each is suited to specific research questions. We present a practical workflow for SDA, including dataset selection, cohort definition, and analytic planning. Linkage across datasets can provide a more comprehensive view of care patterns and outcomes, although feasibility is influenced by legal, technical, and data-quality constraints. DISCUSSION: SDA enables population-level analyses and the study of rare conditions that are challenging to evaluate through single-center or prospective designs. However, careful cohort definition, feasibility assessment, and awareness of data limitations are essential to ensure validity and interpretability. CONCLUSION: SDA provides a scalable, cost-efficient framework for generating meaningful evidence in pediatric urology. Continued efforts to harmonize data elements, improve linkage infrastructure, and support cross-institution collaboration will enhance the quality and impact of future research. This article provides a practical framework and examples to support the design and execution of SDA studies.

Humans↗

PubMed2026

The effects of visuomotor training and tDCS stimulation on visuomotor integration and visual processing: an electrophysiological approach.

BACKGROUND: Visuomotor integration coordinates visual and motor cortical activity to produce goal-directed responses and can be indexed by Rolandic Mu-rhythm suppression and visual evoked potential (VEP) P100 parameters. Perceptual-motor training improves visuomotor performance, and transcranial direct current stimulation (tDCS) over primary motor cortex (M1) has been reported to enhance motor learning when paired with training. This study examined whether anodal M1 tDCS augments the effects of Senaptec visuomotor training in healthy adults. METHODS: Sixty participants were randomized to active anodal tDCS (five 10-minute sessions, 1 mA; n = 31) or sham (n = 29) over M1 immediately before each Senaptec training session; 53 completed all sessions and post-testing. Outcomes were Mu-suppression ratios, VEP P100 latency and amplitude, and Senaptec measures of visual sensitivity and visuomotor control. RESULTS: Active tDCS produced no augmentation of any outcome, with no significant group × time interaction for any measure, consistent across composite and task-level analyses. Training alone produced no change in Mu suppression or visuomotor control. By contrast, both groups showed significant training-related gains in visual sensitivity, including near-far quickness and stereopsis, accompanied by shorter P100 latencies and larger amplitudes, indicating more efficient early visual processing. CONCLUSIONS: A clear dissociation emerged: training produced robust improvements in early visual processing, whereas neither tDCS nor training altered sensorimotor (Mu) or visuomotor-control measures. The tDCS results should be interpreted cautiously given the modest dose and limited power to detect small effects, rather than as evidence of inefficacy. Tablet-based perceptual training enhanced visual processing independent of neuromodulation.

Humans↗