1.Wearable stoop-assist device in reducing incidence of low back pain.
Ziguo LUO ; Yong YU ; Yunjian GE
Chinese Journal of Medical Instrumentation 2013;37(4):264-268
According to human biomechanics the ideal static equilibrium model of stooped human body was built, based on which a wearable stoop-assist device (WSAD) as an intervention to reduce the load on the erector spinae was developed. Electromyography (EMG) experiments were conducted to evaluate the effectiveness of the WSAD. Results showed that the integrated EMG of the thoracic erector spinae (TES), the lumbar erector spinae (LES), the latissimus dorsi (LD) and the rectus abdominis (RA) were reduced by 43%, 48%, 32% and 14% respectively, when Sagittal trunk bent forward to 90 degrees from the vertical. Therefore, by reducing back erector spinae activity, the WSAD could reduce the incidence of developing LBP for those who adopt the prolonged stooped posture in work.
Humans
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Incidence
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Low Back Pain
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epidemiology
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prevention & control
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Posture
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Protective Devices
2.Wearable Stoop-assist Device in Reducing Incidence of Low Back Pain
Ziguo LUO ; Yong YU ; Yunjian GE
Chinese Journal of Medical Instrumentation 2013;(4):264-268
According to human biomechanics the ideal static equilibrium model of stooped human body was built, based on which a wearable stoop-assist device(WSAD) as an intervention to reduce the load on the erector spinae was developed. Electromyography(EMG) experiments were conducted to evaluate the effectiveness of the WSAD. Results showed that the integrated EMG of the thoracic erector spinae(TES), the lumbar erector spinae(LES), the latissimus dorsi(LD) and the rectus abdominis(RA) were reduced by 43%, 48%, 32% and 14% respectively, when Sagittal trunk bent forward to 90o from the vertical. Therefore, by reducing back erector spinae activity, the WSAD could reduce the incidence of developing LBP for those who adopt the prolonged stooped posture in work.
3.The relationship between comorbidity factors and in-hospital mortality in patients with carbapenem-resistant Klebsiella pneumoniae pneumonia
Yan WANG ; Jia CUI ; Dandan WANG ; Chunyue GE ; Yunjian HU ; Xiaoman AI
Chinese Journal of Preventive Medicine 2024;58(11):1705-1710
This study aimed to explore the relationship between comorbidity factors and in-hospital mortality related to factors in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) pneumonia. This study collected clinical data from 218 patients with CRKP pneumonia in Beijing hospital from November 2011 to December 2023, analyzed the number of comorbidities carried by CRKP pneumonia patients, comorbidity patterns, Charlson Comorbidity Index (CCI) scores, and comorbidity of underlying diseases, and explored the relationship between various indicators and comorbidity factors and in-hospital mortality in CRKP pneumonia patients. The Ward.D cluster analysis was performed on the comorbidities of patients and used to draw heatmaps. Using a multiple logistic regression model, a nomogram model was constructed to predict in-hospital mortality in patients with CRKP pneumonia. This study included 218 patients with CRKP pneumonia. The results showed that there were significant differences in the age ( P=0.003), comorbidities such as heart failure ( P<0.001), arrhythmia ( P=0.002), chronic liver disease ( P=0.003), chronic kidney disease ( P=0.002), CCI score ( P=0.007), total number of comorbidities ( P<0.001), and comorbidity patterns (respiratory/immune/psychiatric disease patterns and cardiovascular/tumor/metabolic disease patterns, P=0.003) between the survival and death groups of CRKP pneumonia patients. The multiple logistic regression showed that cardiovascular/tumor/metabolic disease patterns ( P=0.030), CCI score ( P=0.040), concomitant heart failure ( P=0.011), and concomitant arrhythmia ( P=0.025) were independent risk factors for in-hospital mortality in patients with CRKP pneumonia. The nomogram model for predicting the risk of in-hospital mortality in patients with CRKP pneumonia, constructed based on the identified risk factors, had an area under the ROC curve of 0.758. Both the ROC curve and validation curve indicated that the nomogram model had stable performance in predicting in-hospital mortality in patients with CRKP pneumonia. In summary, comorbidity factors are risk factors for predicting in-hospital mortality in patients with CRKP pneumonia, and the role of comorbidity factors in in-hospital mortality in patients with CRKP pneumonia should be taken seriously.
4.The relationship between comorbidity factors and in-hospital mortality in patients with carbapenem-resistant Klebsiella pneumoniae pneumonia
Yan WANG ; Jia CUI ; Dandan WANG ; Chunyue GE ; Yunjian HU ; Xiaoman AI
Chinese Journal of Preventive Medicine 2024;58(11):1705-1710
This study aimed to explore the relationship between comorbidity factors and in-hospital mortality related to factors in patients with carbapenem-resistant Klebsiella pneumoniae (CRKP) pneumonia. This study collected clinical data from 218 patients with CRKP pneumonia in Beijing hospital from November 2011 to December 2023, analyzed the number of comorbidities carried by CRKP pneumonia patients, comorbidity patterns, Charlson Comorbidity Index (CCI) scores, and comorbidity of underlying diseases, and explored the relationship between various indicators and comorbidity factors and in-hospital mortality in CRKP pneumonia patients. The Ward.D cluster analysis was performed on the comorbidities of patients and used to draw heatmaps. Using a multiple logistic regression model, a nomogram model was constructed to predict in-hospital mortality in patients with CRKP pneumonia. This study included 218 patients with CRKP pneumonia. The results showed that there were significant differences in the age ( P=0.003), comorbidities such as heart failure ( P<0.001), arrhythmia ( P=0.002), chronic liver disease ( P=0.003), chronic kidney disease ( P=0.002), CCI score ( P=0.007), total number of comorbidities ( P<0.001), and comorbidity patterns (respiratory/immune/psychiatric disease patterns and cardiovascular/tumor/metabolic disease patterns, P=0.003) between the survival and death groups of CRKP pneumonia patients. The multiple logistic regression showed that cardiovascular/tumor/metabolic disease patterns ( P=0.030), CCI score ( P=0.040), concomitant heart failure ( P=0.011), and concomitant arrhythmia ( P=0.025) were independent risk factors for in-hospital mortality in patients with CRKP pneumonia. The nomogram model for predicting the risk of in-hospital mortality in patients with CRKP pneumonia, constructed based on the identified risk factors, had an area under the ROC curve of 0.758. Both the ROC curve and validation curve indicated that the nomogram model had stable performance in predicting in-hospital mortality in patients with CRKP pneumonia. In summary, comorbidity factors are risk factors for predicting in-hospital mortality in patients with CRKP pneumonia, and the role of comorbidity factors in in-hospital mortality in patients with CRKP pneumonia should be taken seriously.