Tag: nevin manimala

Evolution. 2026 May 5:qpag080. doi: 10.1093/evolut/qpag080. Online ahead of print.

ABSTRACT

Adams and Collyer (2019) evaluated the statistical performance of several approaches for quantifying morphological modularity and found that EMMLi had inflated type I error rates and a bias towards more complex models compared to the Covariance Ratio (CR) approach. They suggested that this may have been at least partly driven by the fact that AICc values from EMMLi do not incorporate trait numbers, but this was not verified. Here I present a performance analysis of a trait-number corrected EMMLi approach (“EMMLip”), showing that this ameliorates rates of false discovery and produces conservative results that favor less complex models. The corrected EMMLi approach was effective at differentiating models of modularity with varying between- and within-module covariation especially when effect size or dataset size were sufficiently large. While CR tests remained more effective at specifically detecting overall modularity, I found that CR tests are sensitive to varying within/between module covariation, and in some cases had inflated model misspecification between 2- and 3-module hypotheses. With this minor correction (albeit incomplete), the combination of EMMLip and CR tests becomes the best available toolkit for detecting and contrasting modularity hypotheses. This toolkit is however still imperfect, and I discuss future avenues for improvements.

PMID:42085682 | DOI:10.1093/evolut/qpag080

JMIR Biomed Eng. 2026 May 5;11:e86624. doi: 10.2196/86624.

ABSTRACT

BACKGROUND: Breast cancer-related lymphedema (BCRL) is a chronic complication that impairs quality of life through persistent fluid accumulation. While clinical guidelines recommend longitudinal surveillance, implementation is often limited by the logistical challenges of frequent in-clinic visits. Bioelectrical impedance analysis (BIA), specifically the segmental extracellular water-to-total body water (ECW/TBW) ratio, offers a noninvasive method for tracking fluid status. However, the technical agreement between in-clinic and patient-led home-based BIA systems, as well as the feasibility of long-term self-monitoring in real-world settings, remains to be fully established.

OBJECTIVE: Our primary objective was to evaluate the agreement between in-clinic and home-based BIA systems for key body composition and fluid-related parameters. Our secondary objective was to characterize longitudinal fluid patterns and diurnal variations in ECW/TBW ratios to assess the feasibility of home-based monitoring.

METHODS: This prospective, patient-driven, 12-month observational study enrolled breast cancer survivors at risk for lymphedema. Agreement between the in-clinic BIA system (InBody 770) and the home-based device (BWA ON) was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICCs), and the Lin concordance correlation coefficient (CCC). Longitudinal home-based ECW/TBW measurements were analyzed using linear mixed-effects models to evaluate diurnal differences (before-noon vs after-noon) across groups defined by limb dominance and BCRL status (International Society of Lymphology [ISL] stage 0 vs stage 1).

RESULTS: Over 12 months, ECW/TBW ratios measured by the home-based device demonstrated strong agreement with in-clinic measurements, showing minimal bias and high ICC/CCC values. Longitudinal analysis revealed that ECW/TBW changes did not follow uniform patterns within ISL stage categories, showing substantial physiological heterogeneity even among clinically stable groups. Diurnal analysis identified a small but statistically significant decrease in ECW/TBW ratios in the afternoon (P<.001). The magnitude of this decrease differed by limb dominance and BCRL status, with the most pronounced reduction observed in participants whose dominant arm was affected and who had a history of stage 1 lymphedema. ECW/TBW variability was driven more by within-individual factors (eg, measurement timing) than by between-individual differences.

CONCLUSIONS: Home-based segmental bioimpedance provides reliable longitudinal data and reveals granular fluid patterns not captured by conventional ISL staging alone. The significant impact of diurnal variation, particularly in relation to limb dominance, underscores the need for standardizing measurement protocols. Standardizing home-based measurements to a fixed monitoring time can minimize physiological noise and enhance the interpretability of long-term self-monitoring strategies for breast cancer survivors.

PMID:42085680 | DOI:10.2196/86624

JMIR Public Health Surveill. 2026 May 5;12:e89671. doi: 10.2196/89671.

ABSTRACT

BACKGROUND: With global aging, the burden of tuberculosis (TB) among older adults escalates, yet spatial studies on this group are scarce. In Chongqing, where 18.87% of the population are aged 65 years and older and TB burden is high, controlling older adult TB remains a major challenge.

OBJECTIVE: This study analyzed the spatiotemporal patterns of TB among adults aged 65 years and older in Chongqing, China, to inform local prevention and control strategies.

METHODS: The study data were obtained from the Tuberculosis Information Management System of China. Global and local spatial autocorrelation analyses were conducted using ArcGIS (version 10.7) to identify high-risk spatial clusters and visualize their distribution. Spatiotemporal scan statistics were performed using SaTScan (version 10.3.2) to detect clusters of TB cases among the older adult population. Statistical significance was set at P<.05.

RESULTS: The average annual incidence of TB among older adults in Chongqing was 69.59 per 100,000 population, with peaks occurring in spring and summer. The global Moran I ranged from 0.618 to 0.756 (P<.001 in all cases), indicating significant clustering. Persistent high-risk areas were identified in the northeastern and southeastern parts of Chongqing. Spatiotemporal scan statistics detected 1 most likely cluster (relative risk=3.52, 95% CI 3.37-3.68; log-likelihood ratio=1017.43; P<.001) and 3 secondary clusters.

CONCLUSIONS: Significant seasonal patterns of TB among older adults were observed in Chongqing. High-risk areas were predominantly concentrated in the northeastern and southeastern parts of the municipality. More targeted public health interventions are imperative.

PMID:42085675 | DOI:10.2196/89671

JMIR Hum Factors. 2026 May 5;13:e81638. doi: 10.2196/81638.

ABSTRACT

BACKGROUND: More than 20% of perinatal women experience depression, with suicide being a leading cause of maternal death in the United States. Professional societies emphasize the need to identify those at risk of developing perinatal depression to better target preventive care delivery during pregnancy.

OBJECTIVE: We evaluated receptivity to a machine learning-based predictive screener designed to identify women in the first trimester of pregnancy who were asymptomatic but were at risk for developing moderate to severe depression symptoms later in pregnancy.

METHODS: Our participants were adult pregnant women with negative first-trimester depression (Patient Health Questionnaire-9) screens at 1 of 4 obstetric practices. Of the 810 women who were clinically eligible, 787 were successfully contacted via their patient portal. Of these, 289 (36.7%) viewed the screener and 255 (88.2%) completed the 6-question predictive screener. In total, 51 (20%) were identified by the screener as being at risk for developing perinatal depression. Participants were asked a series of follow-up questions regarding the acceptability of the predictive screener and desired preventive resources. Chi-square tests were used to compare demographic characteristics, perceived benefits and concerns, and desired resources between those identified as at risk for depression and those who were not. Differences in acceptability ratings between the two risk groups were determined using nonparametric Mann-Whitney U tests.

RESULTS: On a 5-point Likert scale of agreement, participants found the screener questions easy to complete (median score 5, IQR 5-5) and felt comfortable sharing their answers with their obstetric care providers (median 5, IQR 4-5). Key perceived benefits of completing the screener included opportunities to seek preventive care (75/255, 29.4%) and to receive education on depression risk (66/255, 25.9%). Primary concerns about knowing one’s risk of future depression included worrying about developing depression (90/255, 35.3%) and a lack of prevention opportunities (39/255, 15.3%). Desired preventive resources included counseling (197/255, 77.3%), mind-body interventions (166/255, 65.1%) such as exercise, and prenatal classes or support groups (81/255, 31.8%).

CONCLUSIONS: Participants found the screener acceptable and felt comfortable receiving it through their patient portal. Specific preventive care options were commonly endorsed, several of which are scalable and evidence based. A minority of participants voiced addressable concerns about knowing their risk of developing depression in the future.

PMID:42085674 | DOI:10.2196/81638

JMIR Form Res. 2026 May 5;10:e82667. doi: 10.2196/82667.

ABSTRACT

BACKGROUND: The growing integration of artificial intelligence (AI) in higher education has transformed learning processes but also raised concerns about potential mental health risks. Medical students represent a particularly vulnerable group due to high academic stress and increasing reliance on generative AI tools for study and decision-making tasks. Despite this, the relationship between AI dependence and psychological distress remains underexplored in Latin American contexts.

OBJECTIVE: This study aimed to evaluate the association between generative AI dependence and levels of stress, anxiety, and depression among medical students.

METHODS: A cross-sectional study was conducted with 187 human medicine students from a Peruvian university during the first academic semester of 2025. The Dependence on Artificial Intelligence Scale and the Depression, Anxiety, and Stress Scale-21 were applied. Negative binomial regression models, both crude and adjusted for sex, age, income, and year of study, were used to assess associations, reporting rate ratios (RRs) and 95% CIs.

RESULTS: Participants had a median age of 22 (IQR 19-24) years, and 58.8% (110/187) were female. The median Dependence on Artificial Intelligence Scale score was 10 (IQR 7-14). Generative AI dependence showed significant correlations with anxiety (ρ=0.336, 95% CI 0.22-0.44) and depression (ρ=0.316, 95% CI 0.20-0.43) and a smaller correlation with stress (ρ=0.277, 95% CI 0.16-0.39). In the adjusted regression models, each 1-point increase in generative AI dependence was associated with a 5% higher expected anxiety score (RR 1.05, 95% CI 1.01-1.09; P=.01) and a 4% higher depression score (RR 1.04, 95% CI 1.01-1.08; P=.03), whereas the association with stress was positive but nonsignificant (RR 1.03, 95% CI 1.00-1.07; P=.08). Fifth-year students had significantly greater anxiety levels than their sixth-year peers (RR 1.82, 95% CI 1.09-3.01; P=.02). No significant effects were observed for sex, age, or income.

CONCLUSIONS: This study empirically examined generative AI dependence as a distinct behavioral construct and its association with mental health symptoms in medical students. Unlike prior research, this study evaluated psychological dependence on generative AI and modeled its relationship with anxiety and depression using appropriate count-based regression techniques. By providing early evidence from a Latin American context, it contributes to the emerging field of digital mental health and medical education research. These findings underscore the need for universities to promote balanced and responsible AI use, integrate digital literacy with mental health support strategies, and develop preventive policies that mitigate potential maladaptive reliance on generative AI tools.

PMID:42085672 | DOI:10.2196/82667

JMIR Form Res. 2026 May 5;10:e75133. doi: 10.2196/75133.

ABSTRACT

BACKGROUND: Wearable activity trackers are useful tools to track and monitor physical activity (PA), especially considering their use in free-living environments. Users often see moderate improvements in step count, but consistent increases at various intensities of PA are inconclusive. While wearable research is growing, no known studies specifically examine the relationship between how the use of self-selected social features on wearables affects PA and exercise self-efficacy.

OBJECTIVE: This study aims to compare weekly PA, approximating moderate-to-vigorous intensity, of adults from the New York City metropolitan area assigned to either use or not use social engagement PA features on their device. Exercise self-efficacy was also measured. Additionally, a preliminary examination into the use of 3 different social features was conducted to inform where controlled parameters on feature use may be needed in future work.

METHODS: The researchers conducted a real-world pilot study by recruiting wearable users aged 18 years and older in the New York City area to wear their devices in free-living environments. After consent, participants were randomized into 1 of 2 conditions: the condition that involved use of the social engagement PA features or the condition that did not for 8 weeks. Participants submitted objective data from their device and completed a self-efficacy measure at baseline, week 4, and week 8. Those in the intervention group also answered questions about which social feature they used the most throughout the study.

RESULTS: Data from 123 participants were analyzed using mixed methods analysis. Principal findings included no difference between wearable social feature users and nonusers in weekly PA (P=.55) or exercise self-efficacy (P=.47). There was an overall effect of time across the repeated measures on PA (P=.006) with an average increase of 72 (SD 3) minutes. Secondary findings highlight the need to control for the use of only a single social feature to identify more concrete effects. An effect of time was found across the repeated measures (P=.01) in the intervention group, showing an increase of 49 to 126 minutes of PA, depending on the feature used most. The mixed methods analysis also found that exercise self-efficacy did not significantly change based on which social feature was used most (P=.24).

CONCLUSIONS: Consistent with other literature, this pilot study demonstrates that using wearables can lead to increases in PA and that sharing one’s PA data with others may amplify the effect. However, the novelty of this study is that although carefully implied, specific social features on a wearable may have a greater effect than others. This study identified the need for further investigation into which features may be more effective. With the increased prevalence of device ownership, knowing if certain social features lead to greater increases in PA may help those encouraging PA behavior change.

PMID:42085670 | DOI:10.2196/75133

JMIR Nurs. 2026 May 5;9:e88075. doi: 10.2196/88075.

ABSTRACT

BACKGROUND: The integration of digital health technologies into nursing education in Saudi Arabia requires reliable tools to assess nursing informatics competency and digital technology self-efficacy among students.

OBJECTIVE: This study aimed to evaluate the reliability and validity of the Canadian Nursing Informatics Competency Assessment Scale (C-NICAS) and Digital Technology Self-Efficacy (DT-SE) scale among undergraduate nursing students at a Saudi university.

METHODS: A descriptive cross-sectional survey of 243 undergraduate nursing students at the University of Ha’il was conducted using the C-NICAS and DT-SE. Internal consistency was examined using Cronbach α, and construct validity was assessed using exploratory and confirmatory factor analyses.

RESULTS: A total of 243 students participated (mean C-NICAS score 54.0, SD 16.9; mean DT-SE score 2.7, SD 0.56). Both scales showed good internal consistency (C-NICAS total α=0.90; DT-SE α=0.80). C-NICAS demonstrated a multidimensional factor structure with an acceptable model fit (comparative fit index=1.00; root mean square error of approximation=0.081), whereas DT-SE showed a 3-factor structure with a suboptimal confirmatory model fit (comparative fit index=0.76, root mean square error of approximation=0.146).

CONCLUSIONS: The C-NICAS and the DT-SE are suitable for assessing informatics competency and digital self-efficacy among undergraduate nursing students at this institution, although further refinement of the DT-SE may improve model fit. These validated tools can inform curriculum reform at this and similar institutions in Saudi Arabia and support the digital health goals of Saudi Vision 2030.

PMID:42085668 | DOI:10.2196/88075

JMIR Med Educ. 2026 May 5;12:e85967. doi: 10.2196/85967.

ABSTRACT

BACKGROUND: Three-dimensional modalities are increasingly being used as adjuncts for medical trainees learning about complex anatomical concepts, such as congenital heart disease.

OBJECTIVE: This study aimed to evaluate the use of 2 such modalities, 3D-printed models, and augmented reality (AR), in improving medical students’ understanding and knowledge retention of congenital heart disease when compared to traditional teaching methods.

METHODS: A prospective cohort pilot study was performed with 26 first-year medical students. Students were randomly assigned to receive a 30-minute teaching session using traditional slide-based lecture, 3D-printed model, or AR. Participants completed a 16-question pretest consisting of 4 basic general cardiology questions and 6 questions each regarding the anatomy and physiology of tetralogy of Fallot and hypoplastic left heart syndrome. Participants completed a posttest immediately following the teaching session, as well as a delayed posttest 3 weeks later.

RESULTS: When comparing overall and subsection posttest scores, the AR group obtained perfect immediate posttest scores at a significantly increased rate compared to the lecture and 3D model groups (6/9, 67% vs 1/8, 13% and 1/9, 11%, respectively; large effect size Cramér V=0.57; P=.02). Participants in the lecture group reported difficulty understanding cardiac anatomy and physiology using only 2D diagrams, whereas those in the 3D-printed model and AR groups almost unanimously reported improved visualization of complex cardiac defects, which enhanced their understanding.

CONCLUSIONS: Due to the visuospatial benefits of 3D-printed models and AR, there is potential for use in medical education to improve students’ knowledge of complex anatomical and physiological concepts. Students who received teaching using 3D-printed models or AR overwhelmingly reported improved 3D visualization of congenital cardiac defects compared to those who were taught via lecture. Additionally, AR and 3D-printed models offer practical opportunities for implementation into medical education curricula as both adjunct and stand-alone teaching modalities.

PMID:42085666 | DOI:10.2196/85967

Health Promot Int. 2026 May 5;41(3):daag058. doi: 10.1093/heapro/daag058.

ABSTRACT

Healthcare costs not subsidized by the government and are covered by patients, are known as out-of-pocket healthcare expenditure (OOPHE). In Australia, OOPHE disproportionately impacts Aboriginal households, particularly in rural and remote regions. Currently no patient reported measures (PRM) to assess OOPHE exist, despite being an identified priority in Aboriginal communities. This study developed and psychometrically evaluated (validity and test-retest reliability) of an OOPHE PRM for Aboriginal households in outer regional to remote areas. This Aboriginal led study was governed by an Aboriginal Governance Group, which involved a 4-stage process: (i) identification of community-derived OOPHE themes; (ii) item development and expert judgment quantification; (iii) exploratory factor analysis (EFA) to determine factor structure through pilot testing with Aboriginal participants; and (iv) assessment of reliability and stability through test-retest methods. Stage 1 identified OOPHE themes (i.e. barriers, financial strain), informing development of a 15 item PRM in Stage 2. In Stage 3, 39 Aboriginal participants completed Test 1, with EFA revealing a two-factor model; Factor 1 (8 items, internal consistency = 0.91) and Factor 2 (6 items, internal consistency = 0.85). In Stage 4, 32 participants completed Test 2, with over 60% of items showing substantial to perfect agreement (κ = 0.61-0.87) and scale-level reliability as good to excellent (ICC = 0.75-0.92). Two items performed poorly and were removed, resulting in a final 13-item PRM. The OOPHE PRM demonstrates promising psychometric properties as a culturally grounded measure of OOPHE burden among Aboriginal families, supporting advocacy for equitable policy, funding, and health system reform.

PMID:42085664 | DOI:10.1093/heapro/daag058

JMIR Form Res. 2026 May 5;10:e90261. doi: 10.2196/90261.

ABSTRACT

BACKGROUND: Outdoor secondhand smoke (SHS) remains a public health concern, particularly around designated outdoor smoking areas where nonsmokers may pass through or remain nearby. Although prior studies have quantified outdoor SHS concentrations, fewer have examined how many people may be present within a plausible exposure setting. Estimating the exposure-opportunity level requires methods that are feasible, scalable, and minimally intrusive.

OBJECTIVE: This study aimed to evaluate the feasibility of using passive Wi-Fi packet sensing, calibrated with brief on-site observation, to estimate the number of smokers and passersby within a plausible SHS exposure range at a public outdoor smoking area in Japan.

METHODS: We conducted a formative field study at a designated outdoor smoking area at the Asia Pacific Trade Center in Osaka, Japan. A passive Wi-Fi packet sensor collected timestamps, anonymized device identifiers, organizationally unique identifiers, and received signal strength indicator (RSSI) values from October 13 to 29, 2023. The main analysis focused on October 28, 2023, a high-footfall event day selected for direct calibration. Episodes were classified using empirically derived RSSI thresholds, and class-specific calibration ratios were applied to estimate day-level counts.

RESULTS: Of 128,313 anonymized detections recorded on October 28, 90.3% (115,950/128,313) occurred during business hours. Among these, 8.6% (n=11,068) identifiers were detected more than once. Dwell time could be calculated for 1.4% (n=1817) of the identifiers, and 0.5% (n=659) eligible presence episodes remained after preprocessing. During a 30-minute validation window, smokers and passersby were counted manually within a 25-m radius. During the validation window, 6230 signal records formed 104 stays, with a mean stay duration of 9.89 (SD 7.89) minutes. During the validation window, direct observation recorded 14 smokers and 207 passersby within the 25-m radius. Applying the rule-based classification and calibration ratios to business hours data yielded estimated day totals of 262 smokers and 3907 passersby within the plausible SHS exposure range. Estimated smoker counts showed 2 peaks, around noon and 4 PM, whereas passerby volume peaked around midday. In an exploratory analysis, a random forest model using stay duration, mean RSSI, and RSSI variability achieved an accuracy of 0.95, sensitivity of 0.75, specificity of 0.97, and area under the receiver operating characteristic curve of 0.99.

CONCLUSIONS: This formative field study suggests that passive Wi-Fi packet sensing, combined with brief on-site observation, can be used to estimate population-level exposure opportunity around an outdoor smoking area. The method identified substantial numbers of potentially exposed passersby in a high-footfall public setting. Although the findings are site specific and preliminary, they indicate that exposure-count metrics may complement concentration-based and survey-based SHS research. Further studies incorporating repeated validation, direct pollutant monitoring, and multiple sites are needed to refine the method and strengthen its usefulness for tobacco control and public health decision-making.

PMID:42085663 | DOI:10.2196/90261