Displaying publications 61 - 62 of 62 in total

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  1. Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators
    Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR
    PLoS One, 2019;14(1):e0209638.
    PMID: 30605456 DOI: 10.1371/journal.pone.0209638
    k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.
    Matched MeSH terms: Physical Fitness
  2. Fulltext Prevalence of Frailty and its Association with Cognitive Status and Functional Fitness among Ambulating Older Adults Residing in Institutions within West Coast of Peninsular Malaysia
    Murukesu RR, Singh DKA, Subramaniam P, Tan XV, Mohamd Izhar IA, Ponvel P, et al.
    Int J Environ Res Public Health, 2019 11 26;16(23).
    PMID: 31779256 DOI: 10.3390/ijerph16234716
    AIM: There is limited information about the association between frailty, cognitive status and functional fitness in older adults living in institutions. We aimed to determine the prevalence of frailty and its association with cognitive status and functional fitness among pre-frail and frail Malaysian older adults residing in institutions on the west coast of Peninsular Malaysia.

    METHODS: This study included 302 ambulating Malaysian institutionalised older adults. Frailty was identified using Fried's frailty criteria. Cognitive status was assessed using the Mini Mental State Examination and Addenbrooke's Cognitive Examination. Functional fitness was assessed using the Senior Fitness test. The association between frailty groups, cognitive status and functional fitness was analysed using binary logistic regression.

    RESULTS: Prevalence of frailty, prefrailty and robustness in the older adults was 56.6%, 40.7% and 2.9%, respectively. Frailty was found to be associated with hypertension (OR 2.15, 95% CI: 1.11-4.16, p = 0.024), lower cognitive status (Addenbrooke's Cognitive Examination) (OR 0.98, 95% C.I: 0.96-0.99, p = 0.038), and lower dynamic balance and mobility (Timed Up and Go test) (OR 1.09, 95% CI: 1.01-1.16, p = 0.024).

    CONCLUSION: Frailty is highly prevalent among Malaysian institutionalised older adults. Hypertension, cognitive impairment and lower dynamic balance and mobility were found to be risk factors of frailty. Screening of frailty and its associated factors should be prioritized among institutionalised older adults in view of early prevention and rehabilitation.

    Matched MeSH terms: Physical Fitness
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