Objective: To investigate thestatus and control of blood lipid level among patients with type 2 diabetes mellitus（T2DM）in rural communities of Zhejiang Province，and to provide evidence for blood lipid control for T2DM. Methods: A sample of 10 343 patients with T2DM managed by communities from Jiashan，Suichang and Yongkang in 2016 were recruited. Through the diabetes registry system，physical examination and laboratory tests，data of demographic features，blood pressure，body mass index（BMI），waist circumstance（WC），glycated hemoglobin（HbA1c），total cholesterol（TC），triglyceride（TG），low-density lipoprotein cholesterol（LDL-C）and high-density lipoprotein cholesterol（HDL-C）were collected to learn the status of blood lipid control. Logistic regression analysis was conducted to explore the influencing factors for blood lipid control. Results: The control rate of TC，TG，LDL-C and HDL-C in patients with T2DM was 29.84%，58.72%，48.25% and 61.27%，respectively. About 11.76% of patients had all the four indicators in control，while 9.22% of patients failed in all. The higher control rates of all of the four indicators were seen in males than females，in older age，in lower BMI and in normal people than in central obese people（all P<0.05）. The results of multivariate logistic regression analysis showed that sex（OR=3.556，95%CI：3.070-4.119），age（OR=1.130，95%CI：1.060-1.204），WC（OR=0.989，95%CI：0.980-0.998）， BMI（OR=0.768，95%CI：0.688-0.857），systolic blood pressure（OR=0.991，95%CI：0.984-0.999），HbA1c level（OR=0.914，95%CI：0.876- 0.953），smoking（OR=0.768，95%CI：0.639-0.924）and drinking（OR=0.688，95%CI：0.536-0.884）were associated with the control of TC，TG，LDL-C and HDL-C in patients with T2DM. Conclusion The control rate of blood lipid is low in patients with T2DM in rural communities of Zhejiang Province，surveillance and interventions should be focused on sex，overweight/obesity，smoking，alcohol intake，blood glucose and blood pressure.

At present, fatigue state monitoring of upper limb movement generally relies solely on surface electromyographic signal (sEMG) to identify and classify fatigue, resulting in unstable results and certain limitations. This paper introduces the sEMG signal recognition and motion capture technology into the fatigue state monitoring process and proposes a fatigue analysis method combining an improved EMG fatigue threshold algorithm and biomechanical analysis. In this study, the right upper limb load elbow flexion test was used to simultaneously collect the biceps brachii sEMG signal and upper limb motion capture data, and at the same time the Borg Fatigue Subjective and Self-awareness Scale were used to record the fatigue feelings of the subjects. Then, the fatigue analysis method combining the EMG fatigue threshold algorithm and the biomechanical analysis was combined with four single types: mean power frequency (MPF), spectral moments ratio (SMR), fuzzy approximate entropy (fApEn) and Lempel-Ziv complexity (LZC). The test results of the evaluation index fatigue evaluation method were compared. The test results show that the method in this paper has a recognition rate of 98.6% for the overall fatigue state and 97%, 100%, and 99% for the three states of ease, transition and fatigue, which are more advantageous than other methods. The research results of this paper prove that the method in this paper can effectively prevent secondary injury caused by overtraining during upper limb exercises, and is of great significance for fatigue monitoring.
Electromyography/methods* ; Fatigue ; Humans ; Muscle Fatigue ; Muscle, Skeletal ; Upper Extremity