Colorectal cancer (CRC) screening is effective in reducing the burden of the disease. Participation rate is an important determinant of the magnitude of the effectiveness in population-based CRC screening. We summarize the current status of participation rate across the different organized CRC screening programs in China. The results indicate that the overall participation rates are low, and the acceptance of the screening protocols is poor in Chinese population. We conclude that the associated factors may include the poor health consciousness, low willingness-to-pay for screening, lack of personalized screening strategy and inadequate organization. Studies on the inventions to improve the willingness of screening and participation rate are of high demand and will provide important references in implementing large-scale CRC screening programs in China in the future.
At present, upper limb motor rehabilitation relies on specific rehabilitation aids, ignoring the initiative of upper limb motor of patients in the middle and late stages of rehabilitation. This paper proposes a fuzzy evaluation method for active participation based on trajectory error and surface electromyography (sEMG) for patients who gradually have the ability to generate active force. First, the level of motor participation was evaluated using trajectory error signals represented by computer vision. Then, the level of physiological participation was quantified based on muscle activation (MA) characterized by sEMG. Finally, the motor performance and physiological response parameters were input into the fuzzy inference system (FIS). This system was then used to construct the fuzzy decision tree (FDT), which ultimately outputs the active participation level. A controlled experiment of upper limb flexion and extension exercise in 16 healthy subjects demonstrated that the method presented in this paper was effective in quantifying difference in the active participation level of the upper limb in different force-generating states. The calculation results of this method and the active participation assessment method based on sEMG during the task cycle showed that the active participation evaluation values of both methods peaked in the initial cycle: (82.34 ± 9.3) % for this paper’s method and (78.44 ± 7.31) % for the sEMG method. In the subsequent cycles, the values of both showed a dynamic change trend of rising first and then falling. Trend consistency verifies the effectiveness of the active participation assessment strategy in this paper, providing a new idea for quantifying the participation level of patients in middle and late stages of upper limb rehabilitation without special equipment mediation.