Feasibility of mobile phone application "Epilepsy care" for self-management of children and adolescents with epilepsy in Phramongkutklao hospital: A randomized controlled trial.

Epilepsy is a common neurological disorder in children. Mobile applications have shown potential in improving self-management for patients with chronic illnesses. To address language barriers, we developed the first Thai version of the "Epilepsy care" mobile application for children and adolescents with epilepsy in Thailand. A prospective, randomized controlled trial with 220 children and adolescents living with epilepsy who had a smartphone and were treated at the pediatric neurology clinic was conducted, with one group using the mobile application and the other receiving standard epilepsy guidance. The primary outcome assessed epilepsy self-management using the Pediatric Epilepsy Self-Management Questionnaire (PEMSQ) in the Thai version, which comprised 27 questions. These questions aimed to determine knowledge, adherence to medications, beliefs about medication efficacy, and barriers to medication adherence. The secondary outcome evaluated seizure frequency at baseline, 3, and 6 months after initiation of an application. Eighty-five participants who were randomized to a mobile application achieved significantly higher PEMSQ scores in the domain of barriers to medication adherence (p < 0.05) at 6 months follow-up. Other domains of PEMSQ showed no statistically significant difference. Baseline median seizure frequencies per month were 7 in the control group and 5.5 in the intervention group. At 3 and 6 months, these decreased significantly to 1.5 and 1 for the control group and 2.5 and 1 for the intervention group (p < 0.001). In addition, the study revealed that 94.9 % of the participants in a mobile application group were highly satisfied with using application. These findings suggest that the mobile application "Epilepsy care" may serve as an effective adjunctive therapy to enhance self-management and seizure control in children and adolescents with epilepsy.

Breakthrough infections, hospital admissions, and mortality after major COVID-19 vaccination profiles: A prospective cohort study.

Background: Several COVID-19 vaccination rollout strategies are implemented. Real-world data from the large-scale, government-mandated Central Vaccination Center (CVC), Thailand, could be used for comparing the breakthrough infection, across all available COVID-19 vaccination profiles. Methods: This prospective cohort study combined the vaccine profiles from the CVC registry with three nationally validated outcome datasets to assess the breakthrough COVID-19 infection, hospitalization, and death among Thais individuals who received at least one dose of the COVID-19 vaccine. The outcomes were analyzed by comparing vaccine profiles to investigate the shot effect and homologous effect. Findings: Of 2,407,315 Thais who had at least one dose of COVID-19 vaccine, 63,469 (2.75%) had breakthrough infection, 42,001 (1.79%) had been hospitalized, and 431 (0.02%) died. Per one vaccination shot added, there was an 18% risk reduction of breakthrough infection (adjusted hazard ratio [HR] 0.82, 95% confidence interval [CI] 0.80-0.82), a 25% risk reduction of hospitalization (HR 0.75, 95% CI 0.73-0.76), and a 96% risk reduction of mortality (HR 0.04, 95% CI 0.03-0.06). The heterologous two-shot vaccine profiles had a higher protective effect against infection, hospitalization, and mortality compared to the homologous counterparts. Interpretation: COVID-19 breakthrough infection, hospitalization, and death differ across vaccination profiles that had a different number of shots and types of vaccines. Funding: This study did not involve any funding.

Selecting the Most Important Features for Predicting Mild Cognitive Impairment from Thai Verbal Fluency Assessments.

Mild cognitive impairment (MCI) is an early stage of cognitive decline or memory loss, commonly found among the elderly. A phonemic verbal fluency (PVF) task is a standard cognitive test that participants are asked to produce words starting with given letters, such as "F" in English and "ก" /k/ in Thai. With state-of-the-art machine learning techniques, features extracted from the PVF data have been widely used to detect MCI. The PVF features, including acoustic features, semantic features, and word grouping, have been studied in many languages but not Thai. However, applying the same PVF feature extraction methods used in English to Thai yields unpleasant results due to different language characteristics. This study performs analytical feature extraction on Thai PVF data to classify MCI patients. In particular, we propose novel approaches to extract features based on phonemic clustering (ability to cluster words by phonemes) and switching (ability to shift between clusters) for the Thai PVF data. The comparison results of the three classifiers revealed that the support vector machine performed the best with an area under the receiver operating characteristic curve (AUC) of 0.733 (N = 100). Furthermore, our implemented guidelines extracted efficient features, which support the machine learning models regarding MCI detection on Thai PVF data.

Using Automatic Speech Recognition to Assess Thai Speech Language Fluency in the Montreal Cognitive Assessment (MoCA).

The Montreal cognitive assessment (MoCA), a widely accepted screening tool for identifying patients with mild cognitive impairment (MCI), includes a language fluency test of verbal functioning; its scores are based on the number of unique correct words produced by the test taker. However, it is possible that unique words may be counted differently for various languages. This study focuses on Thai as a language that differs from English in terms of word combinations. We applied various automatic speech recognition (ASR) techniques to develop an assisted scoring system for the MoCA language fluency test with Thai language support. This was a challenge because Thai is a low-resource language for which domain-specific data are not publicly available, especially speech data from patients with MCIs. Furthermore, the great variety of pronunciation, intonation, tone, and accent of the patients, all of which might differ from healthy controls, bring more complexity to the model. We propose a hybrid time delay neural network hidden Markov model (TDNN-HMM) architecture for acoustic model training to create our ASR system that is robust to environmental noise and to the variation of voice quality impacted by MCI. The LOTUS Thai speech corpus was incorporated into the training set to improve the model's generalization. A preprocessing algorithm was implemented to reduce the background noise and improve the overall data quality before feeding data into the TDNN-HMM system for automatic word detection and language fluency score calculation. The results show that the TDNN-HMM model in combination with data augmentation using lattice-free maximum mutual information (LF-MMI) objective function provides a word error rate (WER) of 30.77%. To our knowledge, this is the first study to develop an ASR with Thai language support to automate the scoring system of MoCA's language fluency assessment.