Category: Statistics

Psychol Health Med. 2025 Apr 1:1-12. doi: 10.1080/13548506.2025.2486503. Online ahead of print.

ABSTRACT

Psychoeducation after a traumatic event can help reduce negative effects by providing a cognitive framework for one’s experience. It can also enable trauma survivors to increase their ability to help coping. The aim of this study was to examine the effect of psychoeducation on hope, loneliness and depression in nursing students who experienced 6 February 2023 Turkey earthquake. The study, which was conducted in experimental design, included 40 students who experienced the February 6 earthquake. 20 students were assigned to the psychoeducation group and 20 students to the control group. Students in the psychoeducation group received six sessions of psychoeducation, while students in the control group were not intervened. ‘Personal Information Form’, ‘Beck Hopelessness Scale’, ‘UCLA Loneliness Scale’ and ‘Beck Depression Scale’ were used to collect the data. Mean, standard deviation and percentage calculations, chi-square test and t test were used to analyze the data. Sociodemographic characteristics of the students in the psychoeducation and control groups were similar except for the place of residence (p > 0.05). It was determined that the mean score of the hope sub-dimension of the ‘Beck Hopelessness Scale’ was statistically significantly higher in the control group (p < 0.05). It was determined that there was no statistically significant difference between the students in the psychoeducation and control groups in terms of hopelessness levels and mean scores of ‘UCLA Loneliness Scale’ and ‘Beck Depression Scale’ (p > 0.05). There was a statistically significant difference between the students in the psychoeducation and control groups in terms of depression levels (p < 0.05). In conclusion, psychoeducation was partially effective on hope and depression and not on loneliness in students who experienced the earthquake. Students in the psychoeducation group had lower levels of depression. It is recommended that students experiencing natural disasters such as earthquakes should be monitored by a specialized psychologist and the duration of psychoeducation should be regulated.

PMID:40168674 | DOI:10.1080/13548506.2025.2486503

J Nurs Care Qual. 2025 Apr 1. doi: 10.1097/NCQ.0000000000000859. Online ahead of print.

ABSTRACT

BACKGROUND: Depression and anxiety are prevalent among patients with cancer, impacting their quality of life and treatment outcomes. Nurse-led interventions are promising but show variable effectiveness.

PURPOSE: This review assessed the effectiveness of nurse-led interventions in reducing depression and anxiety in patients with cancer.

METHODS: A systematic search identified studies evaluating nurse-led approaches for depression and anxiety. Meta-analysis used Cohen’s d with a random-effects model, and heterogeneity was assessed using the I2 statistic.

RESULTS: Eighteen studies (n = 2054) showed significant reductions in depression (-1.29, 95% confidence interval [CI]: -1.52 to -1.06) and anxiety (-1.31, 95% CI: -1.55 to -1.07). Effective strategies included self-care education, cognitive-behavioral therapy, and peer support. Moderate to high heterogeneity (I2 = 70%-75%) was partly resolved through sensitivity analyses.

CONCLUSIONS: Nurse-led interventions effectively reduce depression and anxiety in patients with cancer. Integrating these strategies into oncology care and standardizing protocols can further improve outcomes.

PMID:40168670 | DOI:10.1097/NCQ.0000000000000859

JMIR Form Res. 2025 Apr 1;9:e65000. doi: 10.2196/65000.

ABSTRACT

BACKGROUND: In the modern economy, shift work is prevalent in numerous occupations. However, it often disrupts workers’ circadian rhythms and can result in shift work sleep disorder. Proper management of shift work sleep disorder involves comprehensive and patient-specific strategies, some of which are similar to cognitive behavioral therapy for insomnia.

OBJECTIVE: Our goal was to develop and evaluate machine learning algorithms that predict physicians’ sleep advice using wearable and survey data. We developed a web- and app-based system to provide individualized sleep and behavior advice based on cognitive behavioral therapy for insomnia for shift workers.

METHODS: Data were collected for 5 weeks from shift workers (N=61) in the intensive care unit at 2 hospitals in Japan. The data comprised 3 modalities: Fitbit data, survey data, and sleep advice. After the first week of enrollment, physicians reviewed Fitbit and survey data to provide sleep advice and selected 1 to 5 messages from a list of 23 options. We handcrafted physiological and behavioral features from the raw data and identified clusters of participants with similar characteristics using hierarchical clustering. We explored 3 models (random forest, light gradient-boosting machine, and CatBoost) and 3 data-balancing approaches (no balancing, random oversampling, and synthetic minority oversampling technique) to predict selections for the 7 most frequent advice messages related to bedroom brightness, smartphone use, and nap and sleep duration. We tested our predictions under participant-dependent and participant-independent settings and analyzed the most important features for prediction using permutation importance and Shapley additive explanations.

RESULTS: We found that the clusters were distinguished by work shifts and behavioral patterns. For example, one cluster had days with low sleep duration and the lowest sleep quality when there was a day shift on the day before and a midnight shift on the current day. Our advice prediction models achieved a higher area under the precision-recall curve than the baseline in all settings. The performance differences were statistically significant (P<.001 for 13 tests and P=.003 for 1 test). Sensitivity ranged from 0.50 to 1.00, and specificity varied between 0.44 and 0.93 across all advice messages and dataset split settings. Feature importance analysis of our models found several important features that matched the corresponding advice messages sent. For instance, for message 7 (darken the bedroom when you go to bed), the models primarily examined the average brightness of the sleep environment to make predictions.

CONCLUSIONS: Although our current system requires physician input, an accurate machine learning algorithm shows promise for automatic advice without compromising the trustworthiness of the selected recommendations. Despite its decent performance, the algorithm is currently limited to the 7 most popular messages. Further studies are needed to enable predictions for less frequent advice labels.

PMID:40168666 | DOI:10.2196/65000

J Med Internet Res. 2025 Apr 1;27:e52694. doi: 10.2196/52694.

ABSTRACT

BACKGROUND: As the number of people diagnosed with cancer continues to increase, self-management has become crucial for patients recovering from cancer surgery or undergoing chemotherapy. Technology has emerged as a key tool in supporting self-management, particularly through interventions that promote physical activity, which is important for improving health outcomes and quality of life for patients with cancer. Despite the growing availability of digital tools that facilitate physical activity tracking, high-level evidence of their long-term effectiveness remains limited.

OBJECTIVE: This study aimed to investigate the effect of long-term physical activity on patients with cancer by categorizing them into active and inactive groups based on step count time-series data using the mobile health intervention, the Walkon app (Swallaby Co, Ltd.).

METHODS: Patients with cancer who had previously used the Walkon app in a previous randomized controlled trial were chosen for this study. Walking step count data were acquired from the app users. Biometric measurements, including BMI, waist circumference, blood sugar levels, and body composition, along with quality of life (QOL) questionnaire responses (European Quality of Life 5 Dimensions 5 Level version and Health-related Quality of Life Instrument with 8 Items), were collected during both the baseline and 6-month follow-up at an outpatient clinic. To analyze step count patterns over time, the concept of sample entropy was used for patient clustering, distinguishing between the active walking group (AWG) and the inactive walking group (IWG). Statistical analysis was performed using the Shapiro-Wilk test for normality, with paired t tests for parametric data, Wilcoxon signed-rank tests for nonparametric data, and chi-square tests for categorical variables.

RESULTS: The proposed method effectively categorized the AWG (n=137) and IWG (n=75) based on step count trends, revealing significant differences in daily (4223 vs 5355), weekly (13,887 vs 40,247), and monthly (60,178 vs 174,405) step counts. Higher physical activity levels were observed in patients with breast cancer and younger individuals. In terms of biometric measurements, only waist circumference (P=.01) and visceral fat (P=.002) demonstrated a significant improvement exclusively within the AWG. Regarding QOL measurements, aspects such as energy (P=.01), work (P<.003), depression (P=.02), memory (P=.01), and happiness (P=.05) displayed significant improvements solely in the AWG.

CONCLUSIONS: This study introduces a novel methodology for categorizing patients with cancer based on physical activity using step count data. Although significant improvements were noted in the AWG, particularly in QOL and specific physical metrics, differences in 6-month change between the AWG and IWG were statistically insignificant. These findings highlight the potential of digital interventions in improving outcomes for patients with cancer, contributing valuable insights into cancer care and self-management.

TRIAL REGISTRATION: Clinical Research Information Service by Korea Centers for Diseases Control and Prevention, Republic of Korea KCT0005447; https://tinyurl.com/3zc7zvzz.

PMID:40168661 | DOI:10.2196/52694

JMIR Res Protoc. 2025 Apr 1;14:e67737. doi: 10.2196/67737.

ABSTRACT

BACKGROUND: Glaucoma is the leading cause of irreversible blindness worldwide, causing continuous and progressively worsening damage to visual function, which leads to vision loss. Optic nerve protection is an important treatment for glaucoma with controlled intraocular pressure (GPCI), but to date, there is no universally accepted effective optic nerve protection agent. Acupuncture can protect the optic nerve by increasing blood flow to the eye. However, fear of pain or the limitations of treatment place and time lead to poor patient compliance. Press-needle therapy is a characteristic of traditional Chinese medicine (TCM) external treatment methods; its safety is high, the effect is fast and lasting, it is easy to conduct, and it has high patient compliance.

OBJECTIVE: The objective of the trial is to evaluate the safety and clinical efficacy of press-needle therapy and investigate whether it can improve visual function by regulating eye blood circulation in patients with GPCI.

METHODS: In total, 192 participants aged 18-75 years with GPCI from the Mianyang Central Hospital, the Mianyang Hospital of Traditional Chinese Medicine, and the Mianyang Wanjiang Eye Hospital will participate in this study. Participants will be allocated to 2 treatment groups (experimental and control groups) in a ratio of 1:1 and will undergo press-needle therapy and sham press-needle therapy, respectively, for the same 4-week period. Primary outcomes will include the best-corrected visual acuity (BCVA), optical coherence tomography angiography (OCTA), color Doppler flow imaging (CDFI), and visual field assessment results. Secondary outcomes will include the intraocular pressure (IOP) and traditional Chinese medicine (TCM) clinical symptom scales. The primary outcomes and safety assessments will be measured at baseline and 4 weeks thereafter, while the secondary outcomes will be measured at baseline and 1, 2, 3, and 4 weeks thereafter.

RESULTS: Recruitment and data collection began in February 2023. The final outcomes are expected in September 2025. As of October 2024, the project had recruited 220 eligible participants, of whom 192 (87.3%) will complete the study, exceeding initial projections for the study time frame. The remainder of the participants will provide the ability to explore cross-level interactions that could not be statistically powered at the outset. The strengths of the project include rigorous data collection, good retention rates, and high compliance rates.

CONCLUSIONS: This study will provide data on the effects of press-needle therapy on visual function and ocular circulation in patients with GPCI, and these results will help demonstrate whether acupuncture can improve patients’ visual function by regulating ocular circulation, thus providing a clinical and theoretical basis for the wider application of acupuncture therapy in GPCI.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2300067862；https://tinyurl.com/mrxd58x9.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67737.

PMID:40168659 | DOI:10.2196/67737

Medwave. 2025 Apr 1;25(3):e3024. doi: 10.5867/medwave.2025.03.3024.

ABSTRACT

INTRODUCTION: Malignant neoplasms of the oral cavity represent a public health problem worldwide with an increasing incidence, especially in young populations. In Ecuador, the epidemiology of these conditions has been little studied, limiting timely diagnosis and management. The study aims to identify the distribution of hospitalizations and deaths from oral cavity malignant neoplasms in Ecuador during the pre-pandemic years, from 2015 to 2019, describing spatiotemporal patterns by province, gender, and age.

METHODS: An observational and descriptive study was conducted using the database of hospitalizations and deaths of the National Institute of Statistics and Census of Ecuador. Variables such as age, gender, province, and location of the neoplasms were analyzed, and frequencies, proportions, and crude rates were calculated.

RESULTS: Between 2015 and 2019, 4444 hospitalizations and 726 deaths were reported, with a notable increase in 2019. Males predominated in all the studied years. Malignant neoplasms of unspecified sites and other sites of the tongue had the highest frequency of cases and deaths. Geographically, the provinces of Loja, El Oro, Cañar, Carchi, and Bolivar had the highest rates.

CONCLUSIONS: The study shows an increase in hospitalizations and deaths due to malignant neoplasms of the oral cavity between 2015 and 2019, highlighting the urgency of implementing public health strategies aimed at prevention, early detection, and timely treatment of this disease.

PMID:40168654 | DOI:10.5867/medwave.2025.03.3024

JMIR Public Health Surveill. 2025 Apr 1;11:e69048. doi: 10.2196/69048.

ABSTRACT

BACKGROUND: Optimizing sentinel surveillance site allocation for early pathogen detection remains a challenge, particularly in ensuring coverage of vulnerable and underserved populations.

OBJECTIVE: This study evaluates the current respiratory pathogen surveillance network in Brazil and proposes an optimized sentinel site distribution that balances Indigenous population coverage and national human mobility patterns.

METHODS: We compiled Indigenous Special Health District (Portuguese: Distrito Sanitário Especial Indígena [DSEI]) locations from the Brazilian Ministry of Health and estimated national mobility routes by using the Ford-Fulkerson algorithm, incorporating air, road, and water transportation data. To optimize sentinel site selection, we implemented a linear optimization algorithm that maximizes (1) Indigenous region representation and (2) human mobility coverage. We validated our approach by comparing results with Brazil’s current influenza sentinel network and analyzing the health attraction index from the Brazilian Institute of Geography and Statistics to assess the feasibility and potential benefits of our optimized surveillance network.

RESULTS: The current Brazilian network includes 199 municipalities, representing 3.6% (199/5570) of the country’s cities. The optimized sentinel site design, while keeping the same number of municipalities, ensures 100% coverage of all 34 DSEI regions while rearranging 108 (54.3%) of the 199 cities from the existing flu sentinel system. This would result in a more representative sentinel network, addressing gaps in 9 of 34 previously uncovered DSEI regions, which span 750,515 km² and have a population of 1.11 million. Mobility coverage would improve by 16.8 percentage points, from 52.4% (4,598,416 paths out of 8,780,046 total paths) to 69.2% (6,078,747 paths out of 8,780,046 total paths). Additionally, all newly selected cities serve as hubs for medium- or high-complexity health care, ensuring feasibility for pathogen surveillance.

CONCLUSIONS: The proposed framework optimizes sentinel site allocation to enhance disease surveillance and early detection. By maximizing DSEI coverage and integrating human mobility patterns, this approach provides a more effective and equitable surveillance network, which would particularly benefit underserved Indigenous regions.

PMID:40168644 | DOI:10.2196/69048

J Phys Chem A. 2025 Apr 1. doi: 10.1021/acs.jpca.4c08371. Online ahead of print.

ABSTRACT

Accurate modeling of complex physical systems often requires accounting for many-body interactions. Traditional statistical physics methods, such as Monte Carlo, transfer matrix, cluster approximations, and others, face significant computational challenges. This study introduces a unified tensor algorithm that efficiently incorporates interactions up to the third nearest neighbor. We applied our algorithm to a system of 1,3,5-tris(4-pyridyl)benzene and copper on Au(111). Many-body interactions were considered in two ways: by expressing them through pairwise interactions and by explicitly considering DFT energies for each many-body configuration. This led to both quantitative and qualitative differences in the results. The most significant difference is the lower thermal stability of the “superflower” phase and its subsequent replacement by a disordered structure with higher density. The developed unified tensor algorithm opens up new possibilities for the accurate modeling of complex systems taking into account many-body interactions.

PMID:40168638 | DOI:10.1021/acs.jpca.4c08371

Chaos. 2025 Apr 1;35(4):043102. doi: 10.1063/5.0249690.

ABSTRACT

Multi-state quantum molecular dynamics is one of the most accurate methodologies for predicting rates and yields of different chemical reactions. However, the generation of potential energy surfaces (PES), transition dipoles, and non-adiabatic couplings from ab initio calculations become a challenge, especially because of the exponential growth of computational cost as the number of electrons and molecular modes increases. Thus, machine learning (ML) emerges as a novel technique to compute molecular properties using fewer resources. Yet, the validity of ML methodologies continues in constant development, particularly for high-energy regions where conventional ab initio sampling is reduced. We test the accuracy of the potential energy surfaces interpolated with machine learning (ML) techniques in the solution of the time-dependent Schrödinger equation for the conventional IR+UV bond-breaking process of semi-heavy water. We perform a statistical analysis of the differences in expectation values and dissociation probabilities, which depend on the number of ab initio points selected to generate the machine learning potential energy surface (ML-PES). The energy differences of the electronic excited state modify population transfer from the ground state by driving with a UV laser pulse. We consider as the exact solution the photodynamics implemented with analytical expressions of the electronic ground X~1A1 and excited A~1B1 states. The results of the mean bond distance and dissociation probabilities suggest that ML-PES is suitable for dynamics calculations around the Franck-Condon region, and that standard interpolation methods are more efficient for multistate dynamics that involve dissociative and repulsive energy regions of the electronic states. Our work contributes to the continued inclusion of ML tools in molecular dynamics to obtain accurate predictions of dissociation yields with fewer computational resources and non-written rules to follow in multi-state dynamics calculations.

PMID:40168612 | DOI:10.1063/5.0249690

Chaos. 2025 Apr 1;35(4):043106. doi: 10.1063/5.0233640.

ABSTRACT

In neuroscience, delayed synaptic activity plays a pivotal and pervasive role in influencing synchronization, oscillation, and information-processing properties of neural networks. In small rhythm-generating networks, such as central pattern generators (CPGs), time-delays may regulate and determine the stability and variability of rhythmic activity, enabling organisms to adapt to environmental changes, and coordinate diverse locomotion patterns in both function and dysfunction. Here, we examine the dynamics of a three-cell CPG model in which time-delays are introduced into reciprocally inhibitory synapses between constituent neurons. We employ computational analysis to investigate the multiplicity and robustness of various rhythms observed in such multi-modal neural networks. Our approach involves deriving exhaustive two-dimensional Poincaré return maps for phase-lags between constituent neurons, where stable fixed points and invariant curves correspond to various phase-locked and phase-slipping/jitter rhythms. These rhythms emerge and disappear through various local (saddle-node, torus) and non-local (homoclinic) bifurcations, highlighting the multi-functionality (modality) observed in such small neural networks with fast inhibitory synapses.

PMID:40168609 | DOI:10.1063/5.0233640