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Fulltext Physical predictors for retention and dismissal of professional soccer head coaches: an analysis of locomotor variables using logistic regression pipeline

Sousa H, Musa RM, Clemente FM, Sarmento H, Gouveia ÉR

Front Sports Act Living, 2023;5:1301845.
PMID: 38053523 DOI: 10.3389/fspor.2023.1301845

INTRODUCTION: Soccer has enormous global popularity, increasing pressure on clubs to optimize performance. In failure, the tendency is to replace the Head coach (HC). This study aimed to check the physical effects of mid-season replacements of HCs, investigating which external load variables can predict retention or dismissal.
METHODS: The data was collected in training and matches of a professional adult male soccer team during three complete seasons (2020/21-2022/2023). The sample included 6 different HCs (48.8 ± 7.4 years of age; 11.2 ± 3.9 years as a HC). The 4 weeks and 4 games before and after the replacement of HCs were analysed. External load variables were collected with Global Positioning System (GPS) devices. A logistic regression (LR) model was developed to classify the HCs' retention or dismissal. A sensitivity analysis was also conducted to determine the specific locomotive variables that could predict the likelihood of HC retention or dismissal.
RESULTS: In competition, locomotor performance was better under the dismissed HCs, whereas the new HC had better values during training. The LR model demonstrated a good prediction accuracy of 80% with a recall and precision of 85% and 78%, respectively, amongst other model performance indicators. Meters per minute in games was the only significant variable that could serve as a potential physical marker to signal performance decline and predict the potential dismissal of an HC with an odd ratio of 32.4%.
DISCUSSION: An in-depth analysis and further studies are needed to understand other factors' effects on HC replacement or retention.
Fulltext Surveillance of Injury Types, Locations, and Intensities in Male and Female Tennis Players: A Content Analysis of Online Newspaper Reports

Musa RM, Hassan I, Abdullah MR, Azmi MNL, P P Abdul Majeed A, Abu Osman NA

Int J Environ Res Public Health, 2021 12 01;18(23).
PMID: 34886410 DOI: 10.3390/ijerph182312686

The popularity of modern tennis has contributed to the increasing number of participants at both recreational and competitive levels. The influx of numerous tennis participants has resulted in a wave of injury occurrences of different types and magnitudes across both male and female players. Since tennis injury harms both players' economic and career development, a better understanding of its epidemiology could potentially curtail its prevalence and occurrences. We used online-based tennis-related injury reports to study the prevalence, location types, and injury intensities in both male and female tennis players for the past five years. It is demonstrated from the chi-square analysis that injury occurrences are significantly associated with a specific gender (χ2(18) = 50.773; p = 0.001), with male players having a higher risk of injury manifestation (68.10%) as compared with female players (31.90%). Nonetheless, knee, hip, ankle, and shoulder injuries are highly prevalent in both male and female players. Moreover, the injury intensities are distributed across gender (χ2(2) = 0.398; p = 0.820), with major injuries being dominant, followed by minor injuries, whilst a few cases of career-threatening injuries were also reported. It was similarly observed that male players recorded a higher degree of both major, minor, and career-threatening injuries than female players. In addition, male players sustained more elbow, hip, knee, shoulder, and thigh injuries than female players. Whereas, female players mostly suffered from Achilles and back injuries, ankle and hamstring injuries affected both genders. The usage of online newspaper reports is pivotal in characterizing the epidemiology of tennis-related injuries based on locations and gender to better understand the pattern and localization of injuries, which could be used to address the problem of modern tennis-related injuries.
The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach

Taha Z, Musa RM, P P Abdul Majeed A, Alim MM, Abdullah MR

Hum Mov Sci, 2018 Feb;57:184-193.
PMID: 29248809 DOI: 10.1016/j.humov.2017.12.008

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.
Fulltext A longitudinal analysis of injury characteristics among elite and amateur tennis players at different tournaments from electronic newspaper reports

Musa RM, Hassan I, Abdullah MR, Latiff Azmi MN, Abdul Majeed APP, Abu Osman NA

Front Public Health, 2022;10:835119.
PMID: 36033746 DOI: 10.3389/fpubh.2022.835119

The non-complexity of tennis, coupled with its health benefits, renders it appealing and encourages varying competitions at different levels of age, gender, and expertise. However, the rapid increase in the participation rates witnesses a surge in injury occurrences, prompting the need for in-depth analysis to facilitate immediate intervention. We employed a media content analysis technique in which tennis-associated articles published in the last 5 years were examined. A total of 207 news reports were gathered and screened for analysis. Subsequently, 71 articles were excluded from the study due to content duplications or summary updates of existing news articles, while 23 news articles were also excluded from the study due to inappropriateness. Finally, 113 news reports directly related to injury in tennis were coded and analyzed. We examined various types of injuries reported from the screened articles with respect to their status (fresh, recurrent, and recovery) across expertise levels i.e., elite, or amateur. Similarly, the incidence of injury occurrences based on the types of tournaments the players engage in was also investigated. A chi-square analysis was employed to achieve the objectives of the study. Occurrences of tennis-associated injuries are disseminated across expertise levels [ χ ( 18 ) 2 = 16.542; p = 0.555], with knee, hip, elbow, and shoulder injuries being highly prevalent in both elite and amateur players. Nevertheless, it was noted that elite players suffered a staggering 72.60% of injury-related problems, while amateur players sustained 27.40% of injuries. Moreover, the status of injury spreads based on types of tournaments [ χ ( 4 ) 2 = 3.374; p = 0.497], with higher occurrences of fresh and recurrent injuries, while low recovery rates were observed. The findings further demonstrated that injuries are sustained regardless of tournament types [ χ ( 36 ) 2 = 39.393; p = 0.321]. However, most of the injuries occurred at international tournaments (85%). Whereas, only 5.30% of the injuries occurred at national/regional tournaments while 9.70% were unidentified. It could be deduced from the findings of this investigation that elite players are more prone to injuries compared with amateur players. Furthermore, the most common tennis-related injuries affect the lower, trunk, and upper regions of the body, respectively. A large number of the reported tennis injuries are fresh and recurrent, with a few recoveries. The international tennis tournaments are highly attributed to injury occurrences as opposed to the national/regional tournaments. The application of the media-based data mining technique is non-trivial in projecting injury-related problems that could be used to facilitate the development of an injury index peculiar to the tennis sport for prompt intervention.
Fulltext Psychometric properties and gender invariance of the 8-item emotion regulation questionnaire (ERQ-8) among Chinese university students

Zhao M, Kuan G, Zhou K, Musa RM, Majeed APPA, Kueh YC

PLoS One, 2024;19(1):e0296035.
PMID: 38166088 DOI: 10.1371/journal.pone.0296035

BACKGROUND: To assess emotion regulation strategies in a clear and direct manner, Emotion Regulation Questionnaire (ERQ) was developed based on the process model of emotion regulation. ERQ primarily assesses an individual's propensity for reappraisal (a cognitive change in the individual's psychological state in specific situations) and expressive suppression (a regulatory response where an individual alters their emotional response after the onset of an emotional reaction). Recent studies have suggested that the abbreviated 8-item version of the ERQ exhibits comparable model fit to the original version. The present study aimed to explore the psychometric properties and assess cross-gender invariance of the ERQ-8 in Chinese university students.
METHODS: University students from Jiangsu Province participated in this study. Participants completed self-report surveys assessing emotion regulation strategies. It was conducted from May 2022 to July 2022. The study employed confirmatory factor analysis (CFA) to assess the two-factor model of ERQ-8 and measurement invariance across male and female samples.
RESULTS: The mean age of 1534 participants was 19.83 years (SD = 1.54), and the majority were female (70.4%). The initial ERQ-10 model with ten items demonstrated good fit for all indicators, CFI (Comparative Fit index) = 0.967, TLI (Tucker-Lewis Index) = 0.957, RMSEA (Root Mean Square Error of Approximation) = 0.043, SRMR (Standardised Root Mean Square Residual) = 0.029. However, to assess the fit of the previously proposed ERQ-8 model, two items (Q1 and Q3) were excluded. The fit of the ERQ-8 model was further improved (CFI = 0.989, TLI = 0.984, RMSEA = 0.029, SRMR = 0.021). All item loadings exceeded or were equal to 0.573. Internal consistency analysis based on the ERQ-8 model revealed Cronbach's alpha values of 0.840 for reappraisal and 0.745 for suppression, and corresponding composite reliability (CR) values of 0.846 and 0.747, respectively. Test-retest reliability, assessed using the intraclass correlation coefficient (ICC) (95% CI) within a one-week interval, ranged from 0.537 to 0.679. The correlation coefficient between the two factors was 0.084, significantly below 0.85, which suggested a low correlation between the two factors. The results of the invariance analysis across gender demonstrated that the values of ΔCFI and ΔTLI were both below 0.01. It was supported the gender invariance of the ERQ-8 among university students.
CONCLUSION: The eight-item ERQ demonstrated validity and reliability in evaluating emotion regulation strategies, and measurement invariance was observed across gender among university students. The ERQ-8 may prove to be a practical and cost-effective tool, particularly in time-constrained situations.
Fulltext The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, et al.

PeerJ Comput Sci, 2021;7:e374.
PMID: 33817022 DOI: 10.7717/peerj-cs.374

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Rashid M, Sulaiman N, P P Abdul Majeed A, Musa RM, Ab Nasir AF, Bari BS, et al.

Front Neurorobot, 2020;14:25.
PMID: 32581758 DOI: 10.3389/fnbot.2020.00025

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.
A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, et al.

PeerJ Comput Sci, 2021;7:e432.
PMID: 33954231 DOI: 10.7717/peerj-cs.432

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
Fulltext Patterns in assignment submission times: Analysis of factors contributing to undergraduate students' commitment to core-curriculum related course

Husin Musawi Maliki AB, Mohd Isa AM, Nazarudin MN, Abdullah MR, Mat-Rasid SM, Musa RM

Heliyon, 2024 Feb 29;10(4):e26214.
PMID: 38420391 DOI: 10.1016/j.heliyon.2024.e26214

Co-curricular activities equip students with essential skills and knowledge for personal and professional growth. Despite their importance, many students exert minimal effort to complete the assigned tasks. Instructors perceive that the lack of emphasis on final exams in co-curricular subjects reduces student effort and commitment. Moreover, poor time management and lack of effort in completing tasks have increased across various subjects in recent years. Therefore, it is important to investigate the factors that contribute to student commitment towards co-curricular subjects. In this study, the submission status of 339 tasks was retrieved from the student learning system to measure student commitment based on whether tasks were submitted on time, delayed, or not submitted. A chi-square test f was used to investigate the relationship between students' demographic characteristics and their commitment. The findings revealed a significant association between student commitment and the type of task given (p 0.05), indicating that the year of the study could not determine the level of commitment to the course. Overall, these findings could be used to guide the preparation of tasks and assignments in co-curricular subjects to enhance student commitment and holistic development.
Fulltext Locomotor performance parameters as predictors of high-performing male soccer teams. A multiple-season study on professional soccer

Makar P, Musa RM, Silva RM, Muracki J, Trybulski R, Altundağ E, et al.

Sci Rep, 2024 Nov 18;14(1):28547.
PMID: 39558131 DOI: 10.1038/s41598-024-80181-z

This study aims to explore the interplay between locomotor demands and goal differentials to better understand their combined influence on overall success. Spanning three competitive seasons within the male Turkish Super League, this study analyzed all participating teams across 124 matches. Locomotor demands, including total distance (m) covered (TD), distances covered (m) at different speed thresholds (0.21-2.0 m/s; 2.01-4.0 m/s; 4.01-5.5 m/s; and 5.5-7.7 m/s), and the number of accelerations in range of 5.5-7.0 m/s (n), were quantified using an optical tracking system. Subsequently, regression models were employed to predict the total points earned by all teams over the three seasons. The logistic regression model, tailored to predict team categorization as high-points earners (HPE) or low-points earners (LPE) based on locomotor variables, exhibited a mean accuracy of 74%. Notably, total distance covered, running speed intervals between 4.4 and 5.5 m/s, and the number of accelerations in range of 5.5-7.0 m/s emerged as significant predictors of team success. Our findings highlight the pivotal role of running speed (4.01-5.5 m/s), number of accelerations, and total distance in predicting success for high-performing teams. Coaches can leverage these insights to refine training programs, thereby optimizing team performance, and fostering success in competitive environments.
Fulltext Kids motor performances datasets

Maliki ABHM, Abdullah MR, Nadzmi A, Zainoddin MAR, Puspitasari IM, Jibril NFA, et al.

Data Brief, 2021 Feb;34:106582.
PMID: 33354597 DOI: 10.1016/j.dib.2020.106582

These datasets described the data of the Motor Performance Index for 7 years old kids in Malaysia based on Malaysia's physical fitness test SEGAK. This database has been designed and created with data analysis to create the index from the factor and variable of the test and the test was conducted in the majority of the national primary school in Malaysia. Gender, state of origin, and residential location of the school were the factors used to categorize the participant of the test. The factor of age, weight, height, body mass index (BMI), power, flexibility, coordination, and speed were used for the measurement to relate with the participant's physical fitness. Kids Motor Performances Index data can be reused for talent identification in sport talent scout and to create a baseline for kid's biology growth specifically in gross motor skills and cognitive growth measurement.

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