Pochai, Nopparat

Preferred name

Pochai, Nopparat

Alternative Name

Pochai, N.

Main Affiliation

School of Science

nopparat.po@kmitl.ac.th

Now showing 1 - 4 of 4

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Health Behavior and Emotional Responses of Thai National Team Athletes during the COVID-19 Pandemic: A Comparative Study of Individual and Team Sports
(2025-08-01)
Pluemsamran, Theeratheeta
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Pariyavuth, Pariya
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Pochai, Nopparat
;
Panurushthanon, Phichayavee
;
Punthipayanon, Sirichet
Objectives: COVID-19 significantly impacted athletes’ health behavior and emotional well-being. Disruptions to training routines, competition schedules, and social structures raised concerns about psychological resilience, especially for elite athletes. In this study, we (1) adapted and validated the Emotional State Questionnaire (EST-Q-2) for Thai athletes, (2) examined the emotional responses and associated health behavior patterns of Thai national team athletes during the COVID-19 pandemic, and (3) compared emotional states between individual and team sport athletes. Methods: We surveyed 280 Thai national team athletes (146 male, 134 female) preparing for the 19th Asian Games. Participants completed the culturally adapted EST-Q-2, measuring 5 dimensions: depression, general anxiety, panic disorder, fatigue, and insomnia. We compared emotional responses by sport type. Results: The most prominent symptoms reported were fatigue and insomnia (M = 3.26), general anxiety (M = 2.84), depression (M = 2.35), and panic disorder (M = 2.17). We found no statistically significant differences between individual and team sport athletes across emotional dimensions. The adapted EST-Q-2 demonstrated strong reliability (Cronbach’s α = 0.80). Conclusion: The COVID-19 pandemic adversely affected the emotional states and health behavior of Thai national athletes, with high levels of fatigue and sleep disturbances. The lack of significant differences between sport types indicates a universal psychological impact, underscoring the need for targeted mental health interventions regardless of sport category.
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THE EFFECTIVENESS OF COLD BEVERAGES VERSUS ICE-SLURRY DRINKS ON THE ATHLETIC PERFORMANCE OF THAI FUTSAL PLAYERS USING K-MEANS CLUSTERING
(2025-01-01)
Pariyavuth, Pariya
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Panurushthanon, Phichayavee
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Punthipayanon, Sirichet
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Klabchom, Kreethanat
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Thongtha, Kaboon
Cooling interventions during futsal halftime breaks show substantial individual variability in physiological responses, yet standardized protocols fail to account for athlete-specific thermal stress susceptibility. This study employed This study used K-means clustering to compare the effectiveness of cold beverages versus ice slurry and to identify distinct physiological response phenotypes for personalized cooling strategy optimization. Ten competitive male futsal players (22.4 ± 2.1 years; 68.5 ± 8.2 kg) completed a randomized crossover design. Following the Futsal Intermittent Shuttle-Run Protocol (FIRP), participants consumed either ice slurry (-1°C) or cold sports beverages (4°C) at 7.5 g/kg body mass during 10-minute recovery. Futsal-specific reactive agility tests (RAG-D, RAG-T), blood lactate, heart rate, urine specific gravity, and perceived exertion were measured. K-means clustering analysis with silhouette validation identified response patterns. Three distinct physiological phenotypes emerged (silhouette coefficient = 0.67). Cluster 1 (High-Response, n=4): elevated blood lactate (>8.0 mmol/L), highest cardiovascular stress, superior ice-slurry response. Cluster 2 (Moderate-Response, n=3): balanced responses to both modalities. Cluster 3 (Low-Response, n=3): conservative responses with maintained performance, preferential ice-slurry benefits. Strong correlations existed between body mass and response magnitude (r = 0.78, p < 0.01). Unsupervised machine learning effectively discerned unique cooling response phenotypes, facilitating evidence-based customization of cooling therapies. This signifies a substantial progression in the accuracy of sports performance enhancement.
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PRECISION HEALTH THROUGH WEARABLE TECHNOLOGY: K-MEANS CLUSTERING FOR CULTURALLY ADAPTED NCD PREVENTION
(2025-01-01)
Pochai, Nopparat
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Klabchom, Kreethanat
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Siriussawakul, Arunotai
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Pariyavuth, Pariya
;
Panurushthanon, Phichayavee
Introduction: Non-communicable diseases (NCDs) represent a critical global health challenge, accounting for 71% of deaths worldwide, with disproportionate burden in underserved populations including religious communities. Thai Buddhist monks face exceptionally high NCD prevalence attributed to sedentary lifestyles, dietary constraints, and limited physical activity opportunities inherent to monastic practices. This study introduces a novel machine learning framework for culturally-adapted health monitoring, addressing the urgent need for scalable, technology-driven solutions in traditional religious communities globally. Methods: This cross-sectional study employed an innovative K-means clustering approach to analyze wearable device data from 28 Thai Buddhist monks over one month (November-December 2024). Polar Pacer Pro devices captured daily step counts, average heart rate (beats per minute), and energy expenditure (kilocalories). Following Min-Max normalization, unsupervised K-means clustering identified distinct physical activity phenotypes. Optimal cluster determination utilized the Elbow Method through Within-Cluster Sum of Squares (WCSS) analysis. This represents the first application of unsupervised machine learning for health pattern recognition in monastic populations, demonstrating methodological innovation in small-sample clustering validation for culturally-specific healthcare contexts. Results: K-means clustering successfully identified distinct activity profiles within the monastic population, revealing significant heterogeneity in physical activity patterns. The analysis differentiated monks into meaningful clusters based on step count (range: 700-18,650 daily steps), heart rate (50-87 bpm), and energy consumption (1,623-3,758 kcal) profiles. Cluster centroids demonstrated clear stratification: low-activity groups (1,079-3,763 steps daily) representing 39% of participants with sedentary behavior patterns, moderate-activity clusters (4,495-5,164 steps), and high-activity groups (9,381-12,664 steps) approaching recommended cardiovascular health guidelines. These quantitative classifications provide empirical foundations for precision health interventions tailored to individual risk profiles. Discussion: The research evaluates scalable digital health system that holds great potential for implementation in many religious and cultural groups globally. The clustering approach was able to identify actionable health phenotypes that inform targeted NCD prevention strategies that do not violate cultural restrictions. Low-activity clusters could be seen as the straightforward target of intervention, and the higher-activity cluster indicates that the promotion of physical activity practices is successful in the traditional approach. The methodology is a potentially effective method that is feasible in resource-restricted environments, about consumer-level wearable technology and open-source machine learning algorithms. Strategies of cultural adaptation, such as the application of Buddhist walking meditation and mindful movements in health promotion, serve as sustainable avenues of health promotion at the community level. This framework offers a replicable template related to health disparities in religious community members around the world (estimated 500+ million) and can lead to Sustainable Development Goal 3 (Good Health and Well-being) via innovative and culturally relevant digital health technologies. These results can guide policymaking based on evidence related to community health initiatives and illustrate how the precision health vision can empower traditional in the development of precision health to address modern NCDs in contemporary society.
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A Biomathematical Clustering Framework for Classifying Neuromechanical Performance Profiles in Amateur Boxers
(2026-01-01)
Punthipayanon, Sirichet
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Chottidao, Monchai
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Manilam, Surasak
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Thongtha, Kaboon
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Pochai, Nopparat
Boxing effectiveness is strongly influenced by neuromuscular power generation and the efficient transmission of force through the lower extremities; however, traditional evaluation methods tend to assess these variables in isolation across athletes. A biomathematical clustering strategy provides a structured approach to extracting coherent performance patterns from complex, multidimensional biomechanical datasets. The present investigation proposes a biomathematical clustering model to categorise amateur boxers and quantify between-cluster variation, thereby facilitating tailored training interventions. A cohort of 30 amateur competitors underwent a series of standardised biomechanical tests, including Muscle Power (MP), reaction time (RT), rear-leg ground reaction force (GRF) relative to body mass, and maximal cross-punch (MCP) force output. Before analysis, all variables were rescaled through min–max normalisation. Unsupervised classification was executed via K-means clustering. The quality of clustering was assessed using indices of compactness and separation, specifically the Dunn Index and Davies–Bouldin Index. In contrast, the appropriate cluster count was determined using within-cluster sum of squares (WCSS) interpreted via the elbow method. Statistical procedures, including post hoc testing and effect size computation, were applied to evaluate intergroup differences. Additionally, principal component analysis (PCA) was utilised to project the data into a reduced-dimensional space for clearer visual interpretation of cluster distinctiveness. All computational procedures were implemented in Python. The analysis supported a three-cluster configuration. Cluster 3 (40%) exhibited superior performance characteristics, including elevated MP (7,700 ± 3,500 W), reduced RT (0.18 ± 0.03 s), and greater rear-leg GRF (1.55 ± 0.18 BW) relative to Cluster 2 (p ≤ 0.002; d = 1.35–2.45). In contrast, Cluster 2 (46.7%) was characterised by diminished MP (4,000 ± 1,400 W) and prolonged RT (0.26 ± 0.07 s), whereas Cluster 1 (13.3%) showed moderate values across all measured variables. The biomathematical clustering framework successfully distinguishes discrete neuromechanical profiles among amateur boxers, thereby enabling cluster-specific training strategies and enhancing individual performance optimisation.

Pochai, Nopparat

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