Risk Factors for Musculoskeletal Symptoms of Construction Workers: A Systematic Literature Review

Construction jobs are known among occupations with high risk of musculoskeletal disorders. An effective intervention should be then developed to minimize the risk of the disorders. Previous studies have reported a number of risk factors including extreme temperature, awkward posture, repetition, vibration, static loading, contact stress, and force. However, the results seem inconclusive, due to lack of evidence reported. The purpose of this research is to review the literature systematically that reported evidence of risk factors for musculoskeletal symptoms in construction jobs. The following search engine were utilized: Pubmed, Web of Science, Science Direct, and Taylor and Francis Online. A total of 1204 abstracts were screened. The resultsindicated that 15 studies met the selection criteria and were therefore considered for review. Significant risk factors with strong evidence (p < 0.001 or OR > 3) in developing musculoskeletal symptoms for construction jobs included age, race, years of experience, occupational stress, working hours/day, high physical workload,awkward posture, psychological demands and mental workload, physical strength, fast work pace, manual handling, working against force or vibration, effort, working in hot/cold/humid conditions, organizational, performance, social, performance, and perceived risk. This study will enhance the awareness for the construction industry in developing risk factors that occur in the field. In terms of research method, wefound most of the researches were performed cross-sectionally, using subjective measurement [self-reported] in collecting the data. Therefore, longitudinal study and mixed method between subjective and objective may use to measure the risk factors and musculoskeletal symptoms in future research. Keywords: risk factors, musculoskeletal symptoms, construction job

Comparison Between Key Success Factors in Safety Behavior in Small- and Medium-Sized Enterprises (SMEs) and Large Industries, and Development of a Hypothetic Model for Safety Behavior in Indonesian SMEs

The distinct characteristic between SMEs and large industries indicates a difference in the Key Success Factor (KSF) in safety behavior that must be considered. The purpose of this article is to identify possible differences in KSF between SMEs and large industries. The identification of KSFs is used to avoid the focal point of attention tocritical elements that is taken into account in efforts to implement safety improvement programs and it is conducted through a literature study. The result of the research shows that there are differences between KSFs in SMEs and large industries, although some KSFs represent the characteristics of both industries. In addition, a hypothetic model of the influence of KSFs to safety behavior in SMEs is proposed. Keywords: key success factors, safety behavior, SME

The Impact of Sleep Deprivation on the Level of Sleepiness, Fatigue, and Stress on Experiment Using Driving Simulator

Sleep prior to driving has been discussed widely in fatigue driving research focusing on how it affected driver on duty. This study is intended to compare the impact of prior normal sleep hour and sleep reduction during long-duration driving to subjective sleepiness, fatigue and stress level. To aim this objective, within-subject 2 x 2experiments was conducted (4 experiments condition). Sleep hour variable consists of ± 4 hours (var11) and ± 8 hours sleep (var12) before driving, and long duration driving consist of non-stop 5 hours driving (var21) ended with 60 minutes rest, and 2.5 hour driving x 2 sessions (var22) with 30 minutes break between session and ended with 30 minutes rest. Driving task conducted in laboratory started at ± 7 am to ± 1 pm using a simulator that set to highway and city route randomly. Thirteen participants were involved in these four experiments, each of them conducted in a different day in random fashion. Karolinska Sleepiness Scale (KSS/scale 1–9) and Visual Analogue Scale (0–10) were applied to rated subjective sleepiness and fatigue level, and saliva amylase was used to measure the participants’ stress level that was collected using Cocoro meter nipro. The result showed that sleepiness and fatigue level under sleep reduction condition was relatively higher compared to the normal sleep condition, while saliva amylase test result slightly increases after experiments, but cannot becategorized into stress condition yet. The conclusion is a duration and sleep hours before driving factors were induced fatigue, sleepiness and stress to driver, but lack of sleep has a higher impact compare to driving duration. Further research with another profession may give different results. Keywords: driving simulator, fatigue from driving, Karolinska Sleepiness Scale, sleep deprivation, stress leve

Age, Gender, and Muscle Strength: a Study Based on Indonesian Samples

Age and gender have been commonly used as a main criterion in accepting a job aplicant, but it is usually not clear how these affect job performance. While a number of recent studies have been done that describe the relationships between age, gender, and participants capacity (e.g., muscle strength), the results have been inconclusive. In Indonesia, in particular, such issues have been rarely investigated, and it is still important to study the issue since the relationships between these factors are population-specific. This study aimed at describing the relationships between age and muscle strength among workers for both genders. Ninety-six male and female workers (aged 18–65) were recruited in this study, and data on handgrip and lower back strength were collected. Findings of this study show that peak hand-grip strength occured at the age of around 35-40 years of age, regardless of gender. Maximum lower back strengh was identified at the age of 31-35 years old (for males) and 26-30 years old (for females). Comparisons between two extreme age groups (18-20 vs. 61-65 years of age) showed a mean strength decline of 50% for hand-grip and 30% for the lower back. For both protocols, female participants tended to have lower muscle strength (70-80% of their male counterparts). Findings of this study can be used as a basis in evaluating physical requirements of a job, and the corresponding factors (age and gender) relevant for the job

Teknik Jaringan Syaraf Tiruan Feedforward Untuk Prediksi Harga Saham Pada Pasar Modal Indonesia

To predict the condition of stock price, several technical analysis models have been used and expanded such as MACD, Fourier Transform, Accumulator Swing Index , Stochastic Oscillator etc. For input they are using the various prices such as Open, high, low , close , volume, BID, ASK price, and the output is a graphic that shows the decision whether to sell, buy or hold. Another method to determine the stock price by using Fundamental Analysis method. Fundamental method is an analysis that is based on the ratio or financial report from the existing company. Neural Network System Technology has been implemented in various applications especially in introduce the pattern. This power has attracted several people to use Neural Network for medical, Finance, Investment and marketing. Assuming that the prediction of the output system (next output prediction) is deterministic, than the suitable N.N model to predict it is Feed Forward. The prediction of the stock price is the complex interaction between unstable market and unknown random processes factor. The data from stock price can be determined by time series. If we have daily data from a certain period, for example : Xt(t = 1,2,...) than the stock price for the next period (t+h) can be predicted (the timing used can be in hourly, daily, weekly, monthly or yearly). To get the good prediction, the inputs from several aspects of the share prices have to be input in Neural Network after that the weighing principal can be adapted to minimize the wrong prediction in the first future steps. By using the final weighing, an action is done to done to minimize the total error in the second future steps. Due to that, the risk of Investor's decision to sell or buy the stock can be minimized. This paper will discuss on how to use and implement Time Series Neural Network to predict the stock market in Semen Gresik (SMGR) and Gudang Garam (GGRM

Comparison Between Key Success Factors in Safety Behavior in Small- and Medium-Sized Enterprises (SMEs) and Large Industries, and Development of A Hypothetic Model for Safety Behavior in Indonesian SMEs

The distinct characteristic between SMEs and large industries indicates a difference in the Key Success Factor (KSF) in safety behavior that must be considered. The purpose of this article is to identify possible differences in KSF between SMEs and large industries. The identification of KSFs is used to avoid the focal point of attention tocritical elements that is taken into account in efforts to implement safety improvement programs and it is conducted through a literature study. The result of the research shows that there are differences between KSFs in SMEs and large industries, although some KSFs represent the characteristics of both industries. In addition, a hypothetic model of the influence of KSFs to safety behavior in SMEs is proposed. Keywords: key success factors, safety behavior, SME

Risk Factors for Musculoskeletal Symptoms of Construction Workers: A Systematic Literature Review

Construction jobs are known among occupations with high risk of musculoskeletal disorders. An effective intervention should be then developed to minimize the risk of the disorders. Previous studies have reported a number of risk factors including extreme temperature, awkward posture, repetition, vibration, static loading, contact stress, and force. However, the results seem inconclusive, due to lack of evidence reported. The purpose of this research is to review the literature systematically that reported evidence of risk factors for musculoskeletal symptoms in construction jobs. The following search engine were utilized: Pubmed, Web of Science, Science Direct, and Taylor and Francis Online. A total of 1204 abstracts were screened. The resultsindicated that 15 studies met the selection criteria and were therefore considered for review. Significant risk factors with strong evidence (p < 0.001 or OR > 3) in developing musculoskeletal symptoms for construction jobs included age, race, years of experience, occupational stress, working hours/day, high physical workload,awkward posture, psychological demands and mental workload, physical strength, Fast work pace, manual handling, working against force or vibration, effort, working in hot/cold/humid conditions, organizational, performance, social, performance, and perceived risk. This study will enhance the awareness for the construction industry in developing risk factors that occur in the field. In terms of research method, wefound most of the researches were performed cross-sectionally, using subjective measurement [self-reported] in collecting the data. Therefore, longitudinal study and mixed method between subjective and objective may use to measure the risk factors and musculoskeletal symptoms in future research. Keywords: risk factors, musculoskeletal symptoms, construction job

The Impact of Sleep Deprivation on the Level of Sleepiness, Fatigue, and Stress on Experiment Using Driving Simulator

Sleep prior to driving has been discussed widely in fatigue driving research focusing on how it affected driver on duty. This study is intended to compare the impact of prior normal sleep hour and sleep reduction during long-duration driving to subjective sleepiness, fatigue and stress level. To aim this objective, within-subject 2 x 2experiments was conducted (4 experiments condition). Sleep hour variable consists of ± 4 hours (var11) and ± 8 hours sleep (var12) before driving, and long duration driving consist of non-stop 5 hours driving (var21) ended with 60 minutes rest, and 2.5 hour driving x 2 sessions (var22) with 30 minutes break between session and ended with 30 minutes rest. Driving task conducted in laboratory started at ± 7 am to ± 1 pm using a simulator that set to highway and city route randomly. Thirteen participants were involved in these four experiments, each of them conducted in a different day in random Fashion. Karolinska Sleepiness Scale (KSS/scale 1–9) and Visual Analogue Scale (0–10) were applied to rated subjective sleepiness and fatigue level, and saliva amylase was used to measure the participants' stress level that was collected using Cocoro meter nipro. The result showed that sleepiness and fatigue level under sleep reduction condition was relatively higher compared to the normal sleep condition, while saliva amylase test result slightly increases after experiments, but cannot becategorized into stress condition yet. The conclusion is a duration and sleep hours before driving factors were induced fatigue, sleepiness and stress to driver, but lack of sleep has a higher impact compare to driving duration. Further research with another profession may give different results. Keywords: driving simulator, fatigue from driving, Karolinska Sleepiness Scale, sleep deprivation, stress leve