Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective To explore the characteristics of fine motor deficits in the elderly individuals with MCI due to AD through a new wearable inertial motion capture device,and then construct a prediction model for MCI.Methods A total of 260 elderly subjects were recruited in community from November,2022 to April,2023,and based on diagnosis,they were divided into a MCI group(134 cases)and a control group(126 cases).A new wearable inertial motion capture device,which was self-designed and developed based on MEMS inertial sensor,was used to capture the fine mo-tor movements of the hands,and the obtained data were analyzed with a computerized assessment system to make the quantitative evaluation of fine motor.LASSO learning algorithm and logistic regression analysis were employed to identify the predictive factors for MCI,and then a nomo-gram was constructed based on these factors.ROC curve was plotted to evaluate the predictive ability of the model by calculating its AUC value.DC A,CIC,and Bootstrap method were applied to evaluate and validate the clinical utility and stability of the model.Results The total score of MoCA(22.18±2.84 vs 27.60±1.10)and scores of the dimensions were significantly lower in the MCI group than the control group(all P＜0.01).In the five digital assessment tasks,the MCI group showed obviously poorer fine motor performance of both hands than the control group(P＜0.05,P＜0.01).ROC curve analysis showed that the AUC value of our nomogram model in predicting MCI was 0.762(95％CI:0.705-0.819).DCA,CIC,and Bootstrap methods demonstra-ted good and relatively stable discrimination,calibration,and clinical applicability of the model.Conclusion MEMS inertial sensor motion capture technology can make digital evaluation of fine motor.For the elderly,fine motor deficits are significantly associated with risk for MCI.Our no-mogram model based on fine motion parameters shows good predictive efficacy in assessing the risk of MCI.

Objective:To characterize fine motor function in middle-aged and elderly individuals utilizing a novel wearable inertial motion capture device.Additionally, it seeks to investigate the relationship between fine motor deficits and overall cognitive function, as well as various cognitive dimensions.Methods:Participants aged 50 years and older were recruited between November 2022 and April 2023.The Montreal Cognitive Assessment Scale(MoCA)was employed to evaluate the cognitive function of the subjects, and a radar chart was utilized to illustrate the extent of impairment across different cognitive dimensions.An independent computerized fine motor evaluation system was developed using the motion capture technology of a novel wearable microelectromechanical system(MEMS)inertial sensor, enabling a quantitative assessment of fine motor skills.The differences in fine motor function characteristics between the two groups were compared.Spearman's correlation analysis and multivariate logistic regression were conducted to examine the relationship between fine motor deficits and cognitive dysfunction.Results:A total of 289 participants were recruited, among whom 140(48.4%)were classified into the cognitive impairment group.The mean MoCA scores for the cognitive impairment group and the non-cognitive impairment group were 22.2 ± 2.79 and 27.7 ± 1.19, respectively( P<0.001).The electronic assessment of fine motor function revealed that the motion parameters of hand function in the cognitive impairment group were significantly poorer across all three numerical evaluation tasks.Spearman's correlation analysis demonstrated a robust correlation between deficits in fine motor function and cognitive dysfunction.Furthermore, in the multiple logistic regression model, after adjusting for potential confounding factors including age, gender, and education level, a significant association between cognitive dysfunction and fine motor dysfunction persisted. Conclusions:A novel wearable motion capture technology was employed to facilitate the digital assessment of fine motor function.The findings revealed a significant correlation between deficits in fine motor function and cognitive dysfunction among middle-aged and elderly populations.

Objective:To characterize fine motor function in middle-aged and elderly individuals utilizing a novel wearable inertial motion capture device.Additionally, it seeks to investigate the relationship between fine motor deficits and overall cognitive function, as well as various cognitive dimensions.Methods:Participants aged 50 years and older were recruited between November 2022 and April 2023.The Montreal Cognitive Assessment Scale(MoCA)was employed to evaluate the cognitive function of the subjects, and a radar chart was utilized to illustrate the extent of impairment across different cognitive dimensions.An independent computerized fine motor evaluation system was developed using the motion capture technology of a novel wearable microelectromechanical system(MEMS)inertial sensor, enabling a quantitative assessment of fine motor skills.The differences in fine motor function characteristics between the two groups were compared.Spearman's correlation analysis and multivariate logistic regression were conducted to examine the relationship between fine motor deficits and cognitive dysfunction.Results:A total of 289 participants were recruited, among whom 140(48.4%)were classified into the cognitive impairment group.The mean MoCA scores for the cognitive impairment group and the non-cognitive impairment group were 22.2 ± 2.79 and 27.7 ± 1.19, respectively( P<0.001).The electronic assessment of fine motor function revealed that the motion parameters of hand function in the cognitive impairment group were significantly poorer across all three numerical evaluation tasks.Spearman's correlation analysis demonstrated a robust correlation between deficits in fine motor function and cognitive dysfunction.Furthermore, in the multiple logistic regression model, after adjusting for potential confounding factors including age, gender, and education level, a significant association between cognitive dysfunction and fine motor dysfunction persisted. Conclusions:A novel wearable motion capture technology was employed to facilitate the digital assessment of fine motor function.The findings revealed a significant correlation between deficits in fine motor function and cognitive dysfunction among middle-aged and elderly populations.

With the acceleration of aging process and the increase of average life in China, the number of disabled elderly in China is also growing rapidly, which undoubtedly brings a heavy care burden and pressure to families and society.Facing the great challenges brought by aging, the increasing evidences show that only by reducing the incidence of disease and disability from the source, improving the health level of the elderly, prolonging their healthy life and realizing healthy aging, we can fundamentally alleviate the great pressure brought by the aging society.The concept of motor function maintenance and health promotion of the elderly has become an important link and breakthrough to realize healthy aging and active health.Facing the requirements of active health medicine and the development trend of science and technology in the future, it is imperative to deeply study the motion characteristics and function maintenance of the elderly, and promote the development of sports medicine with innovative technologies such as internet, big data and artificial intelligence.In the future, we will continue to improve the assessment and maintenance system for motor function of the elderly, develop and optimize functional maintenance products that meet the exercise needs of the elderly, and actively build an elderly-friendly sports environment support system, thereby promoting the healthy China strategy and realize healthy aging to the greatest extent.

Objective To evaluate the clinical efficacy of dragon-tiger contending needling at Xi-cleft points plus electroacupuncture in treating postherpetic neuralgia (PHN).Method Seventy PHN patients were randomized into a treatment group of 36 cases and a control group of 34 cases. The treatment group was intervened by dragon-tiger contending needling at Xi-cleft points plus electroacupuncture, while the control group was treated with electroacupuncture alone. In the 4-week treatment, the Visual Analogue Scale (VAS) scores right before and after the 1st, 2nd, 6th, 9th and 12th treatment sessions were recorded, “the maximum pain intensity since the last treatment session” and “real-time analgesic effect” were observed, and the total therapeutic efficacy was also evaluated.Result The real-time analgesic effects right after the 1st, 2nd and 6th treatments in the treatment group were significantly better than those in the control group (P<0.05). The “maximum pain intensities since the last treatment session” prior to the 2nd, 6th, 9th, and 12th treatment sessions were significantly different from the pain intensity before the intervention in the two groups (P<0.05). The “maximum pain intensities since the last treatment session” prior to the 6th, 9th, and 12th treatment sessions were significantly different from those in the control group (P<0.05). The total effective rate and recovery plus markedly-effective rate were respectively 97.2% and 77.8% in the treatment group, versus 91.2% and 59.2% in the control group. There was a statistically significant difference in the recovery plus markedly-effective rate between the two groups (P<0.05).Conclusion Dragon-tiger contending needling at Xi-cleft points plus electro- acupuncture is an effective approach in treating PHN; it acts efficiently and can produce a satisfactory real-time analgesic effect.