Tag: nevin manimala

BMC Public Health. 2025 Apr 11;25(1):1369. doi: 10.1186/s12889-025-22362-7.

ABSTRACT

BACKGROUND: Disasters represent significant public health challenges, particularly for vulnerable populations. In the Republic of Korea, both natural and man-made disasters, exacerbated by urbanization and socioeconomic disparities, have exposed weaknesses in disaster preparedness and public health resilience. This scoping review examines health outcomes and associated risk factors from past disasters in Korea.

METHODS: A comprehensive search was conducted in the PubMed, DBpia, KISS, and RISS databases for studies published between April 2004 and April 2022, following the PRISMA Extension for Scoping Reviews guidelines. Eighty-three studies met the inclusion criteria. Data were analyzed using a narrative synthesis approach to distinguish direct and indirect health effects. Key outcomes were categorized into socioeconomic, physical, mental, social, and environmental risk factors.

RESULTS: Among the 83 reviewed studies, natural disasters accounted for 50.6% of the total, man-made disasters for 22.9%, and mass trauma events for 26.5%. Most studies (78.3%) focused on disaster survivors, with cross-sectional designs predominating (90.4%). Approximately half (51.8%) of the studies used primary data, with the remainder being based on secondary sources. Regression was the most common method for statistical analysis (75.9%). Frequently reported direct health outcomes included physical injuries such as fractures, burns, and respiratory issues, along with mental health conditions such as post-traumatic stress disorder and depression. Natural disasters were particularly associated with physical injuries, while both natural and man-made disasters had a significant impact on mental health. Vulnerable groups-older adults, women, unmarried individuals, and those with lower socioeconomic status-faced disproportionate higher risk for both physical and mental health. Indirect health impacts such as heightened anxiety, emotional distress, and weakened social cohesion were common in economically disadvantaged and disaster-prone communities, in which recovery was further hindered due to limited access to healthcare and support services.

CONCLUSIONS: These findings highlight the need for strategies aimed at disaster risk reduction that prioritize health equity, integrate mental health services, and address environmental vulnerabilities. Future research should focus on longitudinal studies to track evolving health outcomes.

PMID:40217450 | DOI:10.1186/s12889-025-22362-7

J Act Sedentary Sleep Behav. 2023 Aug 1;2(1):16. doi: 10.1186/s44167-023-00025-5.

ABSTRACT

BACKGROUND: Studies to date that investigate combined impacts of health behaviors, have rarely examined device-based movement behaviors alongside other health behaviors, such as smoking, alcohol, and sleep, on cardiometabolic health markers. The aim of this study was to identify distinct classes based on device-assessed movement behaviors (prolonged sitting, standing, stepping, and sleeping) and self-reported health behaviors (diet quality, alcohol consumption, and smoking status), and assess associations with cardiometabolic health markers in older adults.

METHODS: The present study is a cross-sectional secondary analysis of data from the Mitchelstown Cohort Rescreen (MCR) Study (2015-2017). In total, 1,378 older adults (aged 55-74 years) participated in the study, of whom 355 with valid activPAL3 Micro data were included in the analytical sample. Seven health behaviors (prolonged sitting, standing, stepping, sleep, diet quality, alcohol consumption, and smoking status) were included in a latent class analysis to identify groups of participants based on their distinct health behaviors. One-class through to six-class solutions were obtained and the best fit solution (i.e., optimal number of classes) was identified using a combination of best fit statistics (e.g., log likelihood, Akaike’s information criteria) and interpretability of classes. Linear regression models were used to test associations of the derived classes with cardiometabolic health markers, including body mass index, body fat, fat mass, fat-free mass, glycated hemoglobin, fasting glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very-low-density lipoprotein cholesterol, systolic and diastolic blood pressure.

RESULTS: In total, 355 participants (89% of participants who were given the activPAL3 Micro) were included in the latent class analysis. Mean participant ages was 64.7 years and 45% were female. Two distinct classes were identified: “Healthy time-users” and “Unhealthy time-users”. These groups differed in their movement behaviors, including physical activity, prolonged sitting, and sleep. However, smoking, nutrition, and alcohol intake habits among both groups were similar. Overall, no clear associations were observed between the derived classes and cardiometabolic risk markers.

DISCUSSION: Despite having similar cardiometabolic health, two distinct clusters were identified, with differences in key behaviors such as prolonged sitting, stepping, and sleeping. This is suggestive of a complex interplay between many lifestyle behaviors, whereby one specific behavior alone cannot determine an individual’s health status. Improving the identification of the relation of multiple risk factors with health is imperative, so that effective and targeted interventions for improving health in older adults can be designed and implemented.

PMID:40217447 | DOI:10.1186/s44167-023-00025-5

J Act Sedentary Sleep Behav. 2024 Jun 13;3(1):14. doi: 10.1186/s44167-024-00053-9.

ABSTRACT

OBJECTIVE: Traditional intensity-based physical activity measures and variable-centered statistics may not fully capture the complex associations between sitting time, physical activity, and obesity indices. This study investigates the associations between device-measured sitting, standing and different modes of physical activity (i.e., slow walking, brisk-walking, cycling and high-intensity activity) and measured body mass index (BMI) in children using person-based latent profile analyses and Partial Least Squared-structural equation modeling (PLS-SEM).

METHODS: A total of 344 children (11.5 ± 0.81 years, boys n = 139) wore a triaxial accelerometer (Fibion®) on their thigh for eight days, and their weight and height were measured at school. Latent profile analysis formed profiles including BMI, total sitting time, and physical activities, and their associations were further studied with PLS-SEM.

RESULTS: The latent profile analysis indicates that high levels of physical activity always coincide with low sitting time. Both normal weight and overweight/obesity can coexist with low physical activity and prolonged sitting. The PLS-SEM results highlight a cascade-like sequence in the relationship between various types of physical activity, sitting time, and BMI. This sequence begins with light-intensity activities, such as standing, progresses to higher-intensity activities, and ultimately through reduced sitting time (sample mean= -0.01; effect size = 0.0001; p = 0.02), mediates a decline in BMI (sample mean= -0.06; effect size = 0.0036; p = 0.01). The most positive effects on sitting time and BMI occur when this pattern is adhered to consistently, suggesting that omitting steps could negatively impact the associations.

CONCLUSION: These findings suggest that persuading children to increase physical activity incrementally, starting from low-intensity activities such as standing and slow walking to activity types with higher intensities, possibly influence BMI by mediating reduced sitting time. This approach is particularly inclusive for overweight and obese children, taking into account the potential challenges they may encounter when performing activity types with high intensity. These cross-sectional associations need to be verified with longitudinal and experimental designs.

PMID:40217444 | DOI:10.1186/s44167-024-00053-9

J Act Sedentary Sleep Behav. 2025 Jan 9;4(1):1. doi: 10.1186/s44167-024-00071-7.

ABSTRACT

BACKGROUND: While recent studies, primarily among Asian cohorts, have linked adherence to 24-hour movement behavior (24-HMB) guidelines with improved mental health-some of which show sex differences-few studies have explored these relationships among older adults from the United States.

METHODS: National Health and Nutrition Examination Survey data from 2011-2018 were examined in 2,812 older adults (≥ 65years). Those considered adherent to 24-HMB guidelines had a sleep duration of 7-8 h./night, moderate-vigorous physical activity (MVPA) ≥ 150 min/wk., and sedentary behavior (SB) < 8 h./day. Sleep duration, SB, and MVPA were self-reported, with SB and MVPA obtained from the validated Global Physical Activity Questionnaire. Depression was measured using the Patient Health Questionnaire (PHQ-9), with a score of ≥ 10 indicating depression. Logistic regression was used to evaluate overall and sex-stratified associations between non-adherence to all three behaviors, combinations of two behaviors, or individual behavior guidelines, with odds of depression, adjusted for putative confounders.

RESULTS: Among the full sample, non-adherence to all three 24-HMB guidelines was associated with 1.7 [95% confidence interval (CI):1.1, 3.1; p = 0.02] higher odds of depression versus those that adhered to all three behaviors. After sex stratification, the association only persisted among males [OR = 2.5 (95% CI:1.1, 5.4); p = 0.02]. Within the overall sample, higher odds of depression were observed for those who did not adhere to the SB + sleep duration guidelines and the sleep duration + MVPA guidelines. Sex-stratified findings revealed that associations only remained significant in males. While in the overall sample of older adults, non-adherence to the sleep duration guideline was associated with 2.1 (95% CI:1.4, 3.3; p = 0.001) higher odds of depression compared to those that adhered to the guideline.

CONCLUSIONS: Results provide evidence of associations between non-adherence to 24-HMB and higher odds of depression, specifically in older males, suggesting a potential sex-specific effect that warrants further investigation. Future studies using longitudinal designs are needed to confirm these findings and explore the mechanisms underlying these associations.

PMID:40217442 | DOI:10.1186/s44167-024-00071-7

J Act Sedentary Sleep Behav. 2024 Jul 5;3(1):17. doi: 10.1186/s44167-024-00056-6.

ABSTRACT

BACKGROUND: Adherence to the Canadian 24-Hour Movement Guidelines (24 H-MG) has been associated with a reduced risk of developing various chronic conditions. However, its association with frailty and all-cause mortality has not been investigated. Therefore, our primary and secondary objective was to investigate the association between adherence to the 24 H-MG and frailty and mortality, respectively.

METHODS: This study included 2739 individuals (age = 50.6 ± 18.1 years; male = 1370 (50.0%)) from the 2005-2006 cycle of the National Health and Nutrition Examination Survey (NHANES). Frailty was quantified with a 46-item frailty index and analyzed cross-sectionally using linear regression. All-cause mortality data were obtained from the National Death Index and was analyzed prospectively over 10 years using Cox regression. The primary exposure variable was six individual and combined 24 H-MG components including the moderated-to-vigorous physical activity, light physical activity, sedentary time, recreational screen time, sleep, and strength training guidelines. All analyses were stratified into two age groups (younger: 20-64 and older adults 65 + years).

RESULTS: Our cross-sectional analyses demonstrated an inverse dose-response relationship between the number of individual 24 H-MG components met and frailty level in adults aged 20-64 (β = -0.439 (95% C.I. = -0.551:-0.328)) and 65+ (β = -0.322 (95% C.I. = -0.490:-0.154)). Of the individual guideline components, following the moderate-to-vigorous physical activity (MVPA) guideline in individuals aged 20-64 and the recreational screen time guideline in adults aged 65 + was associated with lower frailty (p < 0.001). There was no clear prospective relationship between adherence to the combined 24 H-MG and mortality. Of the individual guideline components, only meeting the MVPA guideline component in the 65 + group was prospectively associated with reduced mortality risk (HR = 0.48 (95% C.I. = 0.25-0.93)).

CONCLUSION: Adherence to the Canadian 24 H-MG may be protective against frailty. Increasing MVPA and decreasing recreational screen time may be important behaviors to consider for frailty prevention and should be investigated further.

PMID:40217427 | DOI:10.1186/s44167-024-00056-6

Phys Rev Lett. 2025 Mar 28;134(12):127101. doi: 10.1103/PhysRevLett.134.127101.

ABSTRACT

The study of first passage time for diffusing particles reaching target states is foundational in various practical applications, including diffusion-controlled reactions. In large systems, first passage times statistics exhibit a biscaling behavior, challenging the use of a single timescale. In this work, we present a biscaling theory for the probability density function of first passage times in confined compact processes, applicable to both Euclidean and fractal domains and for diverse geometries. Our theory employs two distinct scaling functions: one for short times, capturing initial dynamics in unbounded systems, and the other for long times, which is sensitive to finite size effects. The combined framework is argued to provide a complete expression for first passage time statistics across all timescales. As our detailed calculations show, the theory describes various scenarios with and without external force fields, for active and thermal settings, and in the presence of resetting when a nonequilibrium steady state emerges.

PMID:40215509 | DOI:10.1103/PhysRevLett.134.127101

Phys Rev Lett. 2025 Mar 28;134(12):123404. doi: 10.1103/PhysRevLett.134.123404.

ABSTRACT

Experimental control over the strength and angular dependence of interactions between atoms is a key capability for advancing quantum technologies. Here, we use microwave dressing to manipulate and enhance Rydberg-Rydberg interactions in an atomic ensemble. By varying the cloud length relative to the blockade radius and measuring the statistics of the light retrieved from the ensemble, we demonstrate a clear enhancement of the interaction strength due to microwave dressing. These results are successfully captured by a theoretical model that accounts for the excitation dynamics, atomic density distribution, and phase-matched retrieval efficiency. Our approach offers a versatile platform for further engineering interactions by exploiting additional features of the microwave fields, such as polarization and detuning, opening pathways for new quantum control strategies.

PMID:40215508 | DOI:10.1103/PhysRevLett.134.123404

Phys Rev Lett. 2025 Mar 28;134(12):123402. doi: 10.1103/PhysRevLett.134.123402.

ABSTRACT

We demonstrate the existence and stability of one-dimensional (1D) topological kink configurations immersed in higher-dimensional bosonic gases and nonlinear optical setups. Our analysis pertains, in particular, to the two- and three-dimensional extended Gross-Pitaevskii models with quantum fluctuations describing droplet-bearing environments but also to the two-dimensional cubic-quintic nonlinear Schrödinger equation containing higher-order corrections to the nonlinear refractive index. Contrary to the generic dark soliton transverse instability, the kink structures are generically robust under the interplay of low-amplitude attractive and high-amplitude repulsive interactions. A quasi-1D effective potential picture dictates the existence of these defects, while their stability is obtained numerically and analytically through linearization analysis and direct dynamics in the presence of external fluctuations showcasing their unprecedented resilience. These “generic” (across different models) findings should be detectable in current cold atom and optics experiments, offering insights toward controlling topological excitations.

PMID:40215489 | DOI:10.1103/PhysRevLett.134.123402

JMIR Ment Health. 2025 Apr 11;12:e67381. doi: 10.2196/67381.

ABSTRACT

BACKGROUND: Numerous mental health apps purport to alleviate depressive symptoms. Strong evidence suggests that brief cognitive behavioral therapy (bCBT)-based mental health apps can decrease depressive symptoms, yet there is limited research elucidating the specific features that may augment its therapeutic benefits. One potential design feature that may influence effectiveness and user experience is the inclusion of virtual agents that can mimic realistic, human face-to-face interactions.

OBJECTIVE: The goal of the current experiment was to determine the effect of conversational and animation features of a virtual agent within a bCBT-based mental health app on depressive symptoms and user experience in college students with and without depressive symptoms.

METHODS: College students (N=209) completed a 2-week intervention in which they engaged with a bCBT-based mental health app with a customizable therapeutic virtual agent that varied in conversational and animation features. A 2 (time: baseline vs 2-week follow-up) × 2 (conversational vs non-conversational agent) × 2 (animated vs non-animated agent) randomized controlled trial was used to assess mental health symptoms (Patient Health Questionnaire-8, Perceived Stress Scale-10, and Response Rumination Scale questionnaires) and user experience (mHealth App Usability Questionnaire, MAUQ) in college students with and without current depressive symptoms. The mental health app usability and qualitative questions regarding users’ perceptions of their therapeutic virtual agent interactions and customization process were assessed at follow-up.

RESULTS: Mixed ANOVA (analysis of variance) results demonstrated a significant decrease in symptoms of depression (P=.002; mean [SD]=5.5 [4.86] at follow-up vs mean [SD]=6.35 [4.71] at baseline), stress (P=.005; mean [SD]=15.91 [7.67] at follow-up vs mean [SD]=17.02 [6.81] at baseline), and rumination (P=.03; mean [SD]=40.42 [12.96] at follow-up vs mean [SD]=41.92 [13.61] at baseline); however, no significant effect of conversation or animation was observed. Findings also indicate a significant increase in user experience in animated conditions. This significant increase in animated conditions is also reflected in the user’s ease of use and satisfaction (F(1, 201)=102.60, P<.001), system information arrangement (F(1, 201)=123.12, P<.001), and usefulness of the application (F(1, 201)=3667.62, P<.001).

CONCLUSIONS: The current experiment provides support for bCBT-based mental health apps featuring customizable, humanlike therapeutic virtual agents and their ability to significantly reduce negative symptomology over a brief timeframe. The app intervention reduced mental health symptoms, regardless of whether the agent included conversational or animation features, but animation features enhanced the user experience. These effects were observed in both users with and without depressive symptoms.

PMID:40215483 | DOI:10.2196/67381

JMIR Aging. 2025 Apr 11;8:e69504. doi: 10.2196/69504.

ABSTRACT

BACKGROUND: Polypharmacy, the concurrent use of multiple medications, is prevalent among older adults and associated with increased risks for adverse drug events including falls. Deprescribing, the systematic process of discontinuing potentially inappropriate medications, aims to mitigate these risks. However, the practical application of deprescribing criteria in emergency settings remains limited due to time constraints and criteria complexity.

OBJECTIVE: This study aims to evaluate the performance of a large language model (LLM)-based pipeline in identifying deprescribing opportunities for older emergency department (ED) patients with polypharmacy, using 3 different sets of criteria: Beers, Screening Tool of Older People’s Prescriptions, and Geriatric Emergency Medication Safety Recommendations. The study further evaluates LLM confidence calibration and its ability to improve recommendation performance.

METHODS: We conducted a retrospective cohort study of older adults presenting to an ED in a large academic medical center in the Northeast United States from January 2022 to March 2022. A random sample of 100 patients (712 total oral medications) was selected for detailed analysis. The LLM pipeline consisted of two steps: (1) filtering high-yield deprescribing criteria based on patients’ medication lists, and (2) applying these criteria using both structured and unstructured patient data to recommend deprescribing. Model performance was assessed by comparing model recommendations to those of trained medical students, with discrepancies adjudicated by board-certified ED physicians. Selective prediction, a method that allows a model to abstain from low-confidence predictions to improve overall reliability, was applied to assess the model’s confidence and decision-making thresholds.

RESULTS: The LLM was significantly more effective in identifying deprescribing criteria (positive predictive value: 0.83; negative predictive value: 0.93; McNemar test for paired proportions: χ²₁=5.985; P=.02) relative to medical students, but showed limitations in making specific deprescribing recommendations (positive predictive value=0.47; negative predictive value=0.93). Adjudication revealed that while the model excelled at identifying when there was a deprescribing criterion related to one of the patient’s medications, it often struggled with determining whether that criterion applied to the specific case due to complex inclusion and exclusion criteria (54.5% of errors) and ambiguous clinical contexts (eg, missing information; 39.3% of errors). Selective prediction only marginally improved LLM performance due to poorly calibrated confidence estimates.

CONCLUSIONS: This study highlights the potential of LLMs to support deprescribing decisions in the ED by effectively filtering relevant criteria. However, challenges remain in applying these criteria to complex clinical scenarios, as the LLM demonstrated poor performance on more intricate decision-making tasks, with its reported confidence often failing to align with its actual success in these cases. The findings underscore the need for clearer deprescribing guidelines, improved LLM calibration for real-world use, and better integration of human-artificial intelligence workflows to balance artificial intelligence recommendations with clinician judgment.

PMID:40215480 | DOI:10.2196/69504