RESUMEN
Objective: The incidence of stroke worldwide is increasing year by year. With the enhancement of public health awareness, people's demand for the quality of stroke rehabilitation is getting higher and higher, so better quality care measures are needed in the treatment of stroke. Based on this, this paper explores the impact of a new type of nursing care measure, the complementary health care model combined with condition tracking, on stroke patients. Methods: 238 stroke patients were randomly divided into a conventional group (n=119) and a combined group (n=119). 238 stroke patients were randomly divided into conventional group (n=119) and combined group (n=119). The conventional group received routine care, in which doctors and nursing carried out their own work without cooperation after the patients were admitted to the hospital; the combined group received a complementary health care model and condition tracking, in which doctors and nurses jointly checked the rooms, discussed cases, jointly formulated treatments and nursing care plans, and jointly formulated the patients' discharge and rehabilitation plans after the patients were admitted to the hospital. Before the intervention, at the time of discharge, and 6 months after discharge, the neurological function of the patients in both groups was assessed using the National Institutes of Health Stroke Scale (NIHSS) and the Fugl-Meyer (FMA) scale, the cognitive function of the patients in both groups was assessed using the Montreal Cognitive Assessment (MoCA) scale and the Measured Mental State Examination (MMSE), and the cognitive function of the patients in both groups was assessed using the General Self-Efficacy Scale (GSS) and the Montreal Cognitive Assessment (MCA) scale. General Self-Efficacy Scale (GSES) to assess self-efficacy, Exercise Adherence Questionnaire (EAQ) to assess adherence to functional exercise and Specific Quality of Life Scale (SSQoL-12) to assess the quality of life of patients in both groups, and the self-developed satisfaction with nursing care to assess patients' satisfaction with the care model. Results: Before the intervention, there was no difference in the National Institutes of Health Stroke Scale (NIHSS), the Fugl-Meyer Assessment (FMA), the Montreal Cognitive Assessment (MoCA), the Mental State Examination (MMSE), the General Self-Efficacy Scale (GSES), the Exercise Adherence Questionnaire (EAQ) and the Stroke-Specific Quality of Life Scale-12 (SSQoL-12) scores between the two groups (P > .05). At discharge and six months later, NIHSS scores continued to decrease in both groups, with the joint group being lower than the conventional group (P < .05); scores for all other items continued to increase, with the joint being higher than the conventional group (P < .05). Satisfaction with care was higher in the combined group than in the conventional group (P < .05). Conclusion: The complementary healthcare model combined with condition tracking can effectively promote the prognosis of rehabilitation of stroke patients, and has a positive effect in promoting the recovery of neurological and cognitive functions, strengthening self-efficacy, and improving the quality of life, which can be promoted in the clinic.
RESUMEN
BACKGROUND AND OBJECTIVE: The application of robot technology in fracture reduction ensures the minimal invasiveness and accurate operation process. Most of the existing robot assisted fracture reduction systems don't have the function of bone collision detection, which is very important for system safety. In view of the deficiencies in the research of this field, a broken bone collision detection method based on the slope ratio of force curve was proposed in this paper, which could realize the real-time detection. METHODS: In order to analyze the factors influencing the slope of force curve, a collision mechanical model based on three-element viscoelastic model was established. The effects of four factors on the slope ratio of the force curve were studied based on the mechanical model. The proposed collision detection model was analyzed in detail. By drawing slope ratio curves under various experimental conditions, the universality of the collision detection model was proved; by comparative simulation, the differences between the slope ratio curves before and after optimization were analyzed. The factors that affect the performance of the detection model were also analyzed. RESULTS: The results of collision experiments show that the increase of moving speed of distal bone and soft tissue mass reduces the slope ratio, while the increase of collision angle increases the slope ratio. In the verification experiment, the minimum main peak of KRopt curve is 14.16 and the maximum is 220.7, the maximum interference value before the peak is 6.1. When the detection threshold is 10, the model can detect the collision state of the broken bone. It is also proved that after optimization, the model can effectively filter out invalid waveforms and reduce the occurrence of false detections. When a=5 and b=40, the detection model has sufficient stability and a low detection time delay. CONCLUSION: This research developed a broken bone collision detection method based on the slope ratio of the force curve. After optimization, the method has good adaptability under a variety of experimental conditions. The collision of broken bones can be judged by setting an appropriate detection threshold. The application of this method in the robot fracture reduction system will improve the safety of the system.
