Objective To analyze the correlation between frailty syndrome and lower limb motor function in hospitalized elderly patients. Methods Convenience sampling method was used to select inpatients aged 65 and above from the Center of Gerontology and Geriatrics of West China Hospital, Sichuan University between December 2022 and May 2023. The FRAIL Scale, Short Physical Performance Battery (SPPB), and Timed Up and Go Test (TUGT) were used to evaluate the degree of frailty and lower limb motor function, and to explore the correlation between frailty and lower limb motor function. Results A total of 501 elderly patients were included, including 325 males (64.9%) and 176 females (35.1%); 256 cases of frailty (51.1%), 161 cases of pre-frailty (32.1%), and 84 cases of non-frailty (16.8%). The incidence of frailty in hospitalized elderly male patients was higher than that in female patients (P<0.05); The incidence of frailty in patients aged 80-99 was higher than that in patients aged 65-79 (P<0.05). The Spearman correlation analysis results showed that all dimensions of SPPB were negatively correlated with frailty (P<0.001), while TUGT was positively correlated with frailty (r=0.776, P<0.001). The results of multiple linear regression analysis showed that the worse the motor function of the lower limbs, the higher the debilitation score. Conclusions Frailty syndrome in hospitalized elderly patients is closely related to lower limb motor function. Lower limb motor function assessment can be used to predict the onset of frailty in clinical practice, and interventions to improve lower limb motor function can be used to improve the frailty of elderly patients.
ObjectiveTo systematically review studies investigating the rate of falls among Chinese elderly. MethodsThe CNKI, VIP, CBM, WanFang Data, EMbase, The Cochrane Library and PubMed databases were electronically searched to identify cross-sectional studies on the rate of falls in Chinese elderly published from January 1st, 2000 to December 31st, 2021. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies. Meta-analysis was then performed using Stata 16.0 software. Results A total of 54 cross-sectional studies, involving 111 098 cases were included. The results of meta-analysis showed that the rate of falls among Chinese elderly was 19.3% (95%CI 16.9% to 21.6%). Subgroup analyses showed that the fall rates were 16.1% and 21.9% for males and females, respectively. The rates for 60-69, 70-79, and >80 years age groups were 16.3%, 21.7%, and 27.3%, respectively. The rates for the North, South, East, Southwest, and Central parts of China were 16.6%, 17.9%, 18.7%, 22.0%, and 25.8%, respectively. For the urban and rural elderly, the rates were 16.4% and 23.1%, respectively. The rates for those with or without spouses were 24.2% and 26.8%, respectively, while for the solitary and non-solitary elderly were 21.1% and 17.8%, respectively. The rates for elderly with or without exercise habits were 22.1% and 27.1%, respectively. ConclusionThe rate of falls is high among Chinese elderly, especially among females, older individuals, those in Southwest China, rural individuals, those without spouse, solitary individuals and those without exercise habits.
ObjectiveTo evaluate the reading speed and related factors of normally-sighted middle-aged and elderly people, and compare with those assessed in age-related macular degeneration (AMD) patients.MethodsProspective case control study. Participants aged 45 to 85 years old with junior high school or above education and BCVA no less than 0.6 from the community around the First Hospital of Tsinghua University were recruited. People with ocular and nervous system diseases were excluded. AMD patients without other ocular and nervous system diseases, with the BCVA of their better eye above 0.05, were recruited from the Low Vision Clinic of the First Hospital of Tsinghua University. The best corrected vision, contrast sensitivity, and reading acuity were tested. Reading speed was evaluated with IReST Chinese version. Single factor correlation analysis was used to assess different factors which may be related with the reading speed, then multiple linear regression analysis was conducted further.ResultsFrom January to April, 2016, 53 volunteers aged 60.7±8.8 years old participated in the survey including 17 males and 36 females. Their median of best vision acuity both distance and near was 1.0, and their average reading speed was 244±55 characters/min. The average reading speed of younger participants in the middle-aged group (45-59 years old) was statistically faster (P＜0.05) than the elderly group (≥60 years old), which was 267±53 and 227±51 characters/min separately. The reading speed was correlated with age (r=－0.476, P=0.000), gender (t=－2.291, P=0.031), educational level (t=2.656, P=0.011), reading habits (t=7.346, P=0.000), best corrected distance vision (r=－0.293, P=0.033), best corrected near vision (r=－0.460, P=0.001), and reading acuity (r=－0.558, P=0.000) by single factor correlation analysis. Further analysis with multiple linear regression showed that reading acuity, gender, education level, and reading habits were significantly correlated with reading speed (β=－283.312, 28.303, －37.700, －45.505; P=0.000, 0.022, 0.019, 0.023). Totally 22 AMD patients aged 77.4±8.2 (59-90) years old joined the study from September 2016 to August 2018, including 16 males and 6 females. The median reading speed was 118 characters/min. Compared with the normally-sighted elderly, there were more males in AMD patients (χ2=3.981, P=0.046). They were older (t=－4.285, P=0.000), with significant poorer visual acuity (t=-6.176, P=0.000) and lower reading speed (t=-5.719, P=0.000). Significant correlation was found between reading speed and best binocular distance or near vision, reading acuity and contrast sensitivity (r=－0.771, －0.805, －0.776, 0.511; P=0.000, 0.000, 0.000, 0.015), no statistic relationship was found between reading speed and age(r=0.021, P=0.926) or gender(Z=－0.382, P=0.703) in AMD patients.ConclusionsThe reading speed of normally-sighted people declined with age. Reading acuity may be a better predictive factor than distance vision for reading function. Compared with normally-sighted group, the reading acuity and reading speed of AMD patients was significantly lower. The main factor affecting their reading speed was the severity of their visual impairment.
Objective To broaden the current understanding of the usage willingness about artificial intelligence (AI) robots and relevant influence factors for elderly patients. Methods The elderly patients in the inpatient ward, outpatient department and physical examination of the Department of Geriatrics, West China Hospital of Sichuan University were selected by convenient sampling for investigation between February and April 2020, to explore the willingness of elderly patients to use AI robots and related influencing factors. Results A total of 446 elderly patients were included. There were 244 males and 202 females. The willingness to use AI robots was (14.40±3.62) points. There were statistically significant differences among the elderly patients with different ages, marital status, living conditions, educational level, current health status, current vision status, current hearing status, self-care ability and family support in their willingness to use AI robots (P<0.05). Multiple linear regression analysis showed that age, education level and family support were the influencing factors of use intention (P<0.05). Among the elderly patients, 60.76% had heard of AI robots, but only 28.03% knew the medical application of AI robots, and only 13.90% had used AI robot services. Most elderly patients (>60%) thought that some adverse factors may reduce their usage willingness, like “the price is too expensive” and “the use is complex, or I don’t know how to use”. Conclusions Elderly patients’ cognition of AI robots is still at a low level, and their willingness to use AI robots is mainly affected by age, education level and family support. It is suggested to consider the personalized needs of the elderly in terms of different ages, education levels and family support, and promote the cheap and user-friendly AI robots, so as to improve the use of AI robots by elderly patients.
