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  1. Lim E, Lim R, Suhaimi A, Chan BT, Wahab AKA
    J Back Musculoskelet Rehabil, 2018;31(6):1041-1047.
    PMID: 30149436 DOI: 10.3233/BMR-171042
    BACKGROUND: Low frequency sound wave stimulation therapy has become increasingly popular in the rehabilitation fields, due to its ease, less fatiguing and time efficient application.

    OBJECTIVE: This 12-week pilot study examines the efficacy of applying low frequency sound wave stimulation (between 16-160 Hz) through both hands and feet on relieving pain and improving functional ability in patients with chronic back pain.

    METHODS: Twenty-three participants with chronic shoulder (eleven participants) or low back pain (twelve participants) underwent a 12-week vibration therapy program of three sessions per week. A low frequency sound wave device comprising four piezoelectric vibration-type tactile tranducers enclosed in separate 5-cm diameter circular plates, which generate sinusoidal vibratory stimuli at a frequency of 16-160 Hz, was used in this study. Primary outcome measure was pain sensation measured using the Visual Analogue Scale (P-VAS). The secondary outcome measures were pain-related disability measured using the pain disability index (PDI) and quality of life measured using the SF-12.

    RESULTS: At week 12, significant reductions in pain sensation and pain-related disability were observed, with mean reductions of 3.5 points in P-VAS and 13.5 points in the PDI scores. Sixty-five percent of the participants had a reduction of at least 3 points on the P-VAS score, while 52% participants showed a decrease of at least 10 points in the PDI score. Significant improvement was observed in the SF-12 physical composite score but not the mental composite score.

    CONCLUSIONS: The preliminary findings showed that passive application of low frequency sound wave stimulation therapy through both hands and feet was effective in alleviating pain and improving functional ability in patients with chronic back pain.

  2. Islam MA, Hamzaid NA, Ibitoye MO, Hasnan N, Wahab AKA, Davis GM
    Clin Biomech (Bristol, Avon), 2018 10;58:21-27.
    PMID: 30005423 DOI: 10.1016/j.clinbiomech.2018.06.020
    BACKGROUND: Investigation of muscle fatigue during functional electrical stimulation (FES)-evoked exercise in individuals with spinal cord injury using dynamometry has limited capability to characterize the fatigue state of individual muscles. Mechanomyography has the potential to represent the state of muscle function at the muscle level. This study sought to investigate surface mechanomyographic responses evoked from quadriceps muscles during FES-cycling, and to quantify its changes between pre- and post-fatiguing conditions in individuals with spinal cord injury.

    METHODS: Six individuals with chronic motor-complete spinal cord injury performed 30-min of sustained FES-leg cycling exercise on two days to induce muscle fatigue. Each participant performed maximum FES-evoked isometric knee extensions before and after the 30-min cycling to determine pre- and post- extension peak torque concomitant with mechanomyography changes.

    FINDINGS: Similar to extension peak torque, normalized root mean squared (RMS) and mean power frequency (MPF) of the mechanomyography signal significantly differed in muscle activities between pre- and post-FES-cycling for each quadriceps muscle (extension peak torque up to 69%; RMS up to 80%, and MPF up to 19%). Mechanomyographic-RMS showed significant reduction during cycling with acceptable between-days consistency (intra-class correlation coefficients, ICC = 0.51-0.91). The normalized MPF showed a weak association with FES-cycling duration (ICC = 0.08-0.23). During FES-cycling, the mechanomyographic-RMS revealed greater fatigue rate for rectus femoris and greater fatigue resistance for vastus medialis in spinal cord injured individuals.

    INTERPRETATION: Mechanomyographic-RMS may be a useful tool for examining real time muscle function of specific muscles during FES-evoked cycling in individuals with spinal cord injury.

  3. Zamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, et al.
    Front Public Health, 2021;9:782203.
    PMID: 34869194 DOI: 10.3389/fpubh.2021.782203
    The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.
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