Background: A certain percentage of the vaccinated population initially did not want to get vaccinated but changed their minds (from 30% to 70%). By October 2022, World Bank reported that the Philippines had 77.8% COVID-19 vaccination rate. Knowing the factors that changed their decision can help improve the vaccination rate. Objective: This survey aimed to identify the factors that influence positive change in vaccination decisions. Methods: This survey was conducted in the Philippines among Filipinos aged 18-80 years old between March to April 2022. The dependent variable in the study was decision change, a binary variable coded as 1 for a vaccinated person who changed their decision from no to yes and 0 for an unvaccinated person who did not change their decision from yes to no. Results: Age (adjusted odds ratio [aOR] = 0.92, 95% CI = 0.89-0.96) and having a college degree (aOR=11.707, 95% CI=3.23-42.41) are related to changing decisions. Young and college degree holders are likely to change their decisions positively about getting vaccinated. Employer requirement also influences decision change because it affects a person's livelihood. High scores on vaccine confidence (aOR = 1.181, 95% CI = 1.12-1.25) and awareness (aOR = 1.318, 95% CI = 1.08-1.61) are associated with decision change. Conclusion Being young, educated, employed with a requirement to vaccinate, and having high vaccine awareness and confidence are strongly associated with a positive change in the decision to get vaccinated.
Vaccines ; Vaccination ; Philippines ; Awareness ; Covid-19

BACKGROUND AND OBJECTIVE

Self-assessment and metacognition can be practiced with an exam wrapper (EW). EW is a structured, metacognitive, and self-regulated learning strategy that involves guided self-reflection on an exam already taken to improve study habits. This research describes how internal medicine (IM) residents at two tertiary private hospitals performed in written examinations using an EW. The relationship between the residents' metacognition, the exam wrapper, and exam performance was also determined.

METHODS

This study employed a pre-experimental pre- and post-test design. The EW was constructed and tested for validity and reliability. It included (1) a description of study habits, (2) accuracy in self-efficacy perception and exam score prediction, (3) perceived reasons for exam mistakes, and (4) future study plans of residents. A complete enumeration of 24 IM residents was conducted. Respondents completed the Metacognitive Awareness Inventory (MAI) at the beginning of the study. The intervention consisted of (1) residents taking Exam 1: Gastroenterology, followed by EW; (2) Exam 2: Endocrinology and EW; then (3) Exam 3: Oncology, EW, and MAI. Scores were compared using a paired t-test or analysis of variance (ANOVA). The relationships between metacognition scores, the EW, and exam performance were determined using the Pearson correlation coefficient. The level of significance was set at p < 0.05.

RESULTS

The final EW comprises 16 items, with overall indices of content validity ratio of 0.72 and item-rated content validity of 0.8. The internal consistency coefficient is 0.65 (Kuder-Richardson 20). Nineteen out of 24 residents (79.17%) completed the study. Mean exam percentage scores were 57.97%, 42%, and 51.16% for Exams 1, 2, and 3, respectively. Exam 2 differed significantly from the other two exams (p = 0 and p = 0.04). EWs for the first two exams were not significantly different and revealed: (1) top study habits included studying right before an exam and skimming the textbook; (2) 68.42% vs. 63.16% accuracy of selfefficacy perception; (3) 26.32% vs. 31.58% accuracy of grade estimation; (4) 31.58% vs. 26.32% accuracy of error analysis; (5) most errors were due to not reading about the topic, and (6) most planned to “read more." Mean MAI scores were 36.79 ± 9.10 (pretest) and 36.05 ± 9.44 (post-test) (p = 0.81). All correlations were not statistically significant.

CONCLUSION

Residents performed poorly during exams, crammed their studies, preferred low-impact learning strategies, and lacked self-reflection skills and metacognition monitoring. Time issues related to reading or studying were common. There is no significant relationship between metacognition score and exam wrapper use or exam performance in IM residency trainees.

Human ; Metacognition ; Self-control ; Self-regulation

BACKGROUND

Stunting remains a major public health issue in Indonesia, particularly in regions like Pandeglang Regency, where prevalence rates are high. Understanding the contributing factors is essential for targeted interventions.

OBJECTIVE

This study aimed to analyze factors associated with stunting among toddlers using a mixed-methods approach.

METHODS

A cross-sectional mixed-methods study was conducted in two health centers in Banten Province from December 2021 to January 2022. Quantitative data were collected using a structured questionnaire and analyzed using chi-square tests (pRESULTS

There is a relationship between maternal knowledge, history of exclusive breastfeeding, history of supplementary feeding, support from health workers, and socio-economic status with the prevalence of stunting. However, the pattern of exclusive breastfeeding and seeking health services in the working areas of Bangkonol Health Center and Kaduhejo Health Center, Pandeglang Regency, is not good enough and needs to be improved. Health workers need to educate mothers with toddlers about patterns of providing complementary feeding, food preparation, and storage, and basic health practices to prevent stunting.

RESULTS

There is a relationship between maternal knowledge, history of exclusive breastfeeding, history of supplementary feeding, support from health workers, and socio-economic status with the prevalence of stunting. However, the pattern of exclusive breastfeeding and seeking health services in the working areas of Bangkonol Health Center and Kaduhejo Health Center, Pandeglang Regency, is not good enough and needs to be improved. Health workers need to educate mothers with toddlers about patterns of providing complementary feeding, food preparation, and storage, and basic health practices to prevent stunting.

Human ; Infant: 1-23 Months ; Child Preschool: 2-5 Yrs Old ; Public Health ; Prevalence ; Methods ; Indonesia ; Health ; Growth Disorders ; Comprehension

The neurophysiological mechanisms by which exercise improves cognitive function have not been fully elucidated. A comprehensive and systematic review of current domestic and international neurophysiological evidence on exercise improving cognitive function was conducted from multiple perspectives. At the molecular level, exercise promotes nerve cell regeneration and synaptogenesis and maintains cellular development and homeostasis through the modulation of a variety of neurotrophic factors, receptor activity, neuropeptides, and monoamine neurotransmitters, and by decreasing the levels of inflammatory factors and other modulators of neuroplasticity. At the cellular level, exercise enhances neural activation and control and improves brain structure through nerve regeneration, synaptogenesis, improved glial cell function and angiogenesis. At the structural level of the brain, exercise promotes cognitive function by affecting white and gray matter volumes, neural activation and brain region connectivity, as well as increasing cerebral blood flow. This review elucidates how exercise improves the internal environment at the molecular level, promotes cell regeneration and functional differentiation, and enhances the brain structure and neural efficiency. It provides a comprehensive, multi-dimensional explanation of the neurophysiological mechanisms through which exercise promotes cognitive function.
Animals ; Humans ; Brain/physiology* ; Cognition/physiology* ; Exercise/physiology* ; Nerve Regeneration/physiology* ; Neuronal Plasticity/physiology*

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Repeated transcranial magnetic stimulation (rTMS) is one of the commonly used brain stimulation techniques. In order to investigate the effects of rTMS on the excitability of different types of neurons, this study is conducted to investigate the effects of rTMS on the cognitive function of mice and the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons from the perspective of electrophysiology. In this study, mice were randomly divided into glutaminergic control group, glutaminergic magnetic stimulation group, gamma-aminobutyric acid energy control group, and gamma-aminobutyric acid magnetic stimulation group. The four groups of mice were injected with adeno-associated virus to label two types of neurons and were implanted optical fiber. The stimulation groups received 14 days of stimulation and the control groups received 14 days of pseudo-stimulation. The fluorescence intensity of calcium ions in mice was recorded by optical fiber system. Behavioral experiments were conducted to explore the changes of cognitive function in mice. The patch-clamp system was used to detect the changes of neuronal action potential characteristics. The results showed that rTMS significantly improved the cognitive function of mice, increased the amplitude of calcium fluorescence of glutamergic neurons and gamma-aminobutyric neurons in the hippocampus, and enhanced the action potential related indexes of glutamergic neurons and gamma-aminobutyric neurons. The results suggest that rTMS can improve the cognitive ability of mice by enhancing the excitability of hippocampal glutaminergic neurons and gamma-aminobutyric neurons.
Animals ; Mice ; Hippocampus/cytology* ; Transcranial Magnetic Stimulation ; Neurons/physiology* ; Male ; Cognition/physiology* ; gamma-Aminobutyric Acid/metabolism* ; Action Potentials/physiology*

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms

The bidirectional closed-loop motor imagery brain-computer interface (MI-BCI) is an emerging method for active rehabilitation training of motor dysfunction, extensively tested in both laboratory and clinical settings. However, no standardized method for evaluating its rehabilitation efficacy has been established, and relevant literature remains limited. To facilitate the clinical translation of bidirectional closed-loop MI-BCI, this article first introduced its fundamental principles, reviewed the rehabilitation training cycle and methods for evaluating rehabilitation efficacy, and summarized approaches for evaluating system usability, user satisfaction and usage. Finally, the challenges associated with evaluating the rehabilitation efficacy of bidirectional closed-loop MI-BCI were discussed, aiming to promote its broader adoption and standardization in clinical practice.
Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Imagery, Psychotherapy/methods*

With the continuous advancement of neuroimaging technologies, clinical research has discovered the phenomenon of cognitive-motor dissociation in patients with disorders of consciousness (DoC). This groundbreaking finding has provided new impetus for the development and application of brain-computer interface (BCI) in clinic. Currently, BCI has been widely applied in DoC patients as an important tool for assessing and assisting behaviorally unresponsive individuals. This paper reviews the current applications of BCI in DoC patients, focusing four main aspects including consciousness detection, auxiliary diagnosis, prognosis assessment, and rehabilitation treatment. It also provides an in-depth analysis of representative key techniques and experimental outcomes in each aspect, which include BCI paradigm designs, brain signal decoding method, and feedback mechanisms. Furthermore, the paper offers recommendations for BCI design tailored to DoC patients and discusses future directions for research and clinical practice in this field.
Humans ; Brain-Computer Interfaces ; Consciousness Disorders/physiopathology* ; Electroencephalography ; Brain/physiopathology* ; Consciousness

Clinical grading diagnosis of disorder of consciousness (DOC) patients relies on behavioral assessment, which has certain limitations. Combining multi-modal technologies and brain-computer interface (BCI) paradigms can assist in identifying patients with minimally conscious state (MCS) and vegetative state (VS). This study collected electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals under motor BCI paradigms from 14 DOC patients, who were divided into two groups based on clinical scores: 7 in the MCS group and 7 in the VS group. We calculated event-related desynchronization (ERD) and motor decoding accuracy to analyze the effectiveness of motor BCI paradigms in detecting consciousness states. The results showed that the classification accuracies for left-hand and right-hand movement tasks using EEG were 93.28% and 76.19% for the MCS and VS groups, respectively; the classification precisions using fNIRS were 53.72% and 49.11% for these groups. When combining EEG and fNIRS features, the classification accuracies for left-hand and right-hand movement tasks in the MCS and VS groups were 95.56% and 87.38%, respectively. Although there was no statistically significant difference in motor decoding accuracy between the two groups, significant differences in ERD were observed between different consciousness states during left-hand movement tasks ( P < 0.001). This study demonstrates that motor BCI paradigms can assist in assessing the level of consciousness, with EEG being more sensitive for evaluating residual motor intention intensity. Moreover, the ERD feature of motor intention intensity is more sensitive than BCI classification accuracy.
Humans ; Brain-Computer Interfaces ; Spectroscopy, Near-Infrared/methods* ; Electroencephalography/methods* ; Consciousness Disorders/diagnosis* ; Male ; Movement ; Adult ; Female ; Intention ; Persistent Vegetative State/diagnosis*