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Although the goal of rectal cancer treatment is to restore gastrointestinal continuity, some patients with rectal cancer develop a permanent stoma (PS) after sphincter-saving operations. Although many studies have identified the risk factors and causes of PS, few have precisely predicted the probability of PS formation before surgery. To validate whether an artificial intelligence model can accurately predict PS formation in patients with rectal cancer after sphincter-saving operations. Patients with rectal cancer who underwent a sphincter-saving operation at Taipei Medical University Hospital between January 1, 2012, and December 31, 2021, were retrospectively included in this study. A machine learning technique was used to predict whether a PS would form after a sphincter-saving operation. We included 19 routinely available preoperative variables in the artificial intelligence analysis. To evaluate the efficiency of the model, 6 performance metrics were utilized: accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiving operating characteristic curve. In our classification pipeline, the data were randomly divided into a training set (80% of the data) and a validation set (20% of the data). The artificial intelligence models were trained using the training dataset, and their performance was evaluated using the validation dataset. Synthetic minority oversampling was used to solve the data imbalance. A total of 428 patients were included, and the PS rate was 13.6% (58/428) in the training set. The logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest, decision tree and light gradient boosting machine (LightGBM) algorithms were employed. The accuracies of the logistic regression (LR), Gaussian Naïve Bayes (GNB), Extreme Gradient Boosting (XGB), Gradient Boosting (GB), random forest (RF), decision tree (DT) and light gradient boosting machine (LightGBM) models were 70%, 76%, 89%, 93%, 95%, 79% and 93%, respectively. The area under the receiving operating characteristic curve values were 0.79 for the LR model, 0.84 for the GNB, 0.95 for the XGB, 0.95 for the GB, 0.99 for the RF model, 0.79 for the DT model and 0.98 for the LightGBM model. The key predictors that were identified were the distance of the lesion from the anal verge, clinical N stage, age, sex, American Society of Anesthesiologists score, and preoperative albumin and carcinoembryonic antigen levels . Integration of artificial intelligence with available preoperative data can potentially predict stoma outcomes after sphincter-saving operations. Our model exhibited excellent predictive ability and can improve the process of obtaining informed consent.

The Brief COPE Inventory has been proven as acceptable psychometric properties to examine coping strategies among cancer patients. However, most psychometric testing studies have been carried out in Western countries, raising concerns about the properties’ relevance and applicability in other cultural contexts. This study aimed to present psychometric properties of the Brief COPE in a sample of patients with advanced cancer in Indonesia. Specifically, we intended to examine the factorial structure and the measure’s validity and reliability. This study included 440 patients from the original study who completed the Indonesian version of Brief COPE. We used exploratory factor analysis and confirmatory factor analysis to assess factor structure and evaluate the structural model fit, respectively. Reliability was demonstrated by internal consistency represented by Cronbach’s alpha coefficient. The factor analysis identified a 21-items scale with 5-factors (avoidance, religion and acceptance, social support coping, problem solving and distraction). Confirmatory factor analysis demonstrated a good model fit. For the whole scale and its subscales Cronbach’s alpha coefficients were acceptable signifying good reliability. Convergent, divergent validity and contrast group comparison were evidenced by significant correlations among subscales and the other instruments used. This study shows that the Indonesian version of Brief COPE is a reliable and valid instrument to measure coping in advanced cancer patients and is ready for use amongst this population in the Indonesian cultural context.

This study investigates the impact of technology-assisted sports training on the physiological and psychological performance of recreational exercisers (non-athletes), with particular attention to the moderating role of sport involvement (SI). A quasi-experimental design was employed, with 48 participants randomly assigned to either an experimental group (technology-assisted training) or a control group (traditional coaching) for an eight-week training program. Performance measures included exercise self-efficacy (ESE) and squat speed (SS). Data were analyzed using ANCOVA and linear mixed models. The results showed that technology-assisted training significantly improved SS (p = 0.026), but had no significant effect on ESE (p = 0.905). Furthermore, SI moderated the relationship between training method and ESE: participants with low SI demonstrated significant improvements in ESE under traditional coaching (p = 0.006), whereas those with high SI showed no significant differences between training methods. These findings suggest that while sports technology can enhance physical performance, it does not necessarily improve exercise self-efficacy. For individuals with low sport involvement, traditional coaching remains essential, highlighting the importance of combining technology with interpersonal interaction. Future training strategies should be customized according to participants’ levels of sport involvement to optimize both performance and psychological motivation, thereby promoting broader health engagement and exercise participation.

Objectives: To investigate whether chronotype is a moderator variable that also interacts with shift type and whether they jointly influence the attention performance of nurses working in acute and critical care units.Methods: We adopted a longitudinal research design focusing on nurses working rotating shifts in the emergency room and intensive care units at a medical center. A total of 40 complete samples were obtained. Data analysis was conducted using the generalized estimating equations in SAS 9.4.Results: The mean (SD) age of the participants was 26.35 (2.12) years. After controlling for age, gender, and sleep duration, an interaction effect was discovered between a specific chronotype and shift type; that is, the interaction effect between chronotype and shift type was only significant when comparing late-types working the night shift with early- and intermediate-types working the night shift (B = −18.81, P = .011). The least squares means of the mean reaction time of the interaction effects between the 2 chronotype groups and the 3 shift types found that the mean reaction time of late-types working the night shift was 11.31 ms (P = .044) slower compared with working the day shift.Conclusions: The chronotype is a moderator variable between shift type and mean reaction time, such that matching the chronotype of nurses in acute and critical care units with the appropriate shift type improved their mean reaction time. It is hoped that the results of this study could serve as a reference for acute and critical care nurses when scheduling their shifts.

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s.

Background Cardiovascular disease (CVD) is the leading cause of excessive and premature mortality in patients with bipolar disorder (BD). Despite immune cells participating considerably in the pathogenesis of CVD, limited data are available regarding leukocyte phenotypes in patients with BD and CVD. This study aimed to evaluate associations between circulating leukocyte subset and CVD among patients with BD. Methods A total of 109 patients with BD-I and cardiologist-confirmed CVD diagnosis (i.e., case) were matched with 109 BD-I patients without CVD (i.e., control) according to the age (± 2 years), sex, and date of most recent psychiatric admission because of acute mood episode (± 2 years). Leukocyte subset data were retrieved from complete blood count tests performed on the next morning after the most recent acute psychiatric admission. Results During the most recent acute psychiatric hospitalization, circulating monocyte counts in the case group were significantly higher than those in the age- and sex-matched controls ( p  = 0.020). In addition, monocyte–lymphocyte ratios (MLRs) in the case group were significantly higher than those in the control group ( p  = 0.032). Multiple logistic regression showed that together with serum levels of uric acid and manic symptoms, circulating monocyte counts (95% CI, OR: 1.01–1.05) and MLRs (95% CI, OR: 1.01–1.09) were significantly associated with CVD in patients with BD, respectively. Conclusions Monocyte activation in an acute manic episode may play a critical role in the pathogenesis of CVD among patients with BD. Future research is required to investigate markers of monocyte activation and indices of cardiovascular structure and function across the different mood states of BD.

Background Studies on examining the relationship between physical activity patterns and frailty are lacking. This study examined physical activity patterns in older people and investigated the relationship between physical activity and frailty as well as identifying the predictors of frailty. Methods We used a nationally representative longitudinal database, the Taiwan Longitudinal Study of Aging (TLSA) database, and data for a 20-year period were extracted and analyzed. A total of 5131 participants aged ≥ 60 years in 1996 were included in the current analysis. Information regarding demographic characteristics, frailty, physical activity, comorbidities, oral health, and depressive symptoms was extracted from the TLSA database. Physical activity patterns were examined using group-based trajectory modeling from 1996 to 2015. Potential predictors were examined by performing multivariate logistic regression. Results Four trajectories of the physical activity pattern were found: consistently physically inactive (33.7%), consistently physically active (21.5%), incline (21.6%), and decline (23.2%). Throughout the period, the trajectories of the four groups significantly differed from each other at year 2015, with the incline and decline groups exhibiting the lowest and highest frailty scores, respectively ( p  < 0.001). Older age, male, poor oral health, diabetes, chronic kidney disease, and depressive symptoms were identified as risk factors for frailty. Conclusion Physical activity reduces the risk of chronic conditions, which contributes to healthy longevity. This study can guide the development of future research and interventions to manage frailty in older people, particularly in considering previous physical activity trajectories within the life course.

Background Poor oral health affects quality of life; oral health literacy studies are increasing as it plays an essential role in promoting oral health. However, little is known regarding the gender differences in oral health literacy and oral health-related quality of life (OHRQoL) among older adults. This study aimed to explore the gender differences in oral health literacy and OHRQoL among community-dwelling older adults in Taiwan. Methods A cross-sectional study design with convenience sampling was undertaken to recruit participants at two community service centres. Data were collected using a structured survey consisted of the demographic characteristics, instrumental activities of daily living, nutrition assessment, oral health literacy and OHRQoL. The logistic regression was used to examine the gender differences in the relationship between oral health literacy and OHRQoL. Results A total of 202 participants completed the survey. Of which 56.4% (n = 114) were female. Logistic regression analyses showed that after controlling for age, instrumental activities of daily living, nutrition, education level, and average monthly income, better oral health literacy was associated with better oral health quality of life ( p  = 0.006) in men. Conclusions The relationship between oral health literacy and OHRQoL was only significant for men. No significant relationship between women’s oral health literacy and their OHRQoL. However, good OHRQoL is an integral part of overall health, but it is affected by differences in oral health and the accessibility of healthcare services. We suggest that gender-specific oral health literacy education should be offered through community health-education programs.

Few studies have examined the prevalence of food intake difficulties and their associated factors among residents with dementia in long-term care facilities in Taiwan. The purpose of the study was to identify the best cutoff point for the Chinese Feeding Difficulty Index (Ch-FDI), which evaluates the prevalence of food intake difficulties and recognizes factors associated with eating behaviors in residents with dementia. A cross-sectional design was adopted. In total, 213 residents with dementia in long-term care facilities in Taiwan were recruited and participated in this study. The prevalence rate of food intake difficulties as measured by the Chinese Feeding Difficulty Index (Ch-FDI) was 44.6%. Factors associated with food intake difficulties during lunch were the duration of institutionalization (beta = 0.176), the level of activities of daily living-feeding (ADL-Q1) (beta = -0.235), and the length of the eating time (beta = 0.416). Associated factors during dinner were the illuminance level (beta = -0.204), sound volume level (beta = 0.187), ADL-Q1 (beta = -0.177), and eating time (beta = 0.395). Food intake difficulties may potentially be associated with multiple factors including physical function and the dining environment according to the 45% prevalence rate among dementia residents in long-term care facilities.

Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the \"Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub\", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.