Associations between Continuing Symptoms and Quality of Life in Post-COVID-19 Patients (2023)

COVID-19 is a multisystem disease that can cause various symptoms which last even after the acute stage and negatively impact the quality of life of patients. It is of utmost importance to comprehensively evaluate how COVID-19 affects not only patients’ physical and mental health, but also their family and social life. This knowledge plays a significant role in the creation of effective ways to assist those suffering from long COVID to address health-related quality of life issues in a timely manner.

Patterns in the relationship between acute COVID-19/long COVID-19 and quality of life:A cross-sectional study of patients attending a tertiary care hospital in Turkey

Objective:To determine the change in the quality of life(QoL)of patients who applied to a tertiary outpatient clinic according to their COVID-19 status.Methods:This cross-sectional study comprised 1370 participants.Short form-12(SF-12),which includes Physical Component Summary(PCS)and Mental Component Summary(MCS)domains,was used to evaluate the QoL.Different linear regression models created using PCS-12 and MCS-12 were dependent variables.Results:A total of 19.2% of participants had acute COVID-19,and 8.4%had long COVID-19.The most common sypmtoms were fatigue(72.6%),headache(42.5%),and joint pain(39.8%)in patients with long COVID-19.The model including all participants showed that long COVID-19 reduced the QoL in multivariate analysis for both MCS and PCS,while acute COVID-19 had no significant effect on the QoL comparing with those without COVID-19.Model that included participants with COVID-19 showed that long COVID-19 negatively affected the QoL in the multivariate model for PCS-12 and MCS-12.Variables that were significant in the multivariate model for those who had long COVID-19 were having a chronic disease and presence of ongoing symptoms.Females were disadvantaged for PCS-12 and MCS-12 in the multivariate models including all participants,and models including participants who have had COVID-19.Low educational group were disadvantaged for PCS-12 in the multivariate model including all participants.This group were also disadvantaged for PCS-12 and MCS-12 in the multivariate models including participants who had COVID-19.Conclusions:In studies,acute COVID-19 and long COVID-19 should be treated as separate categories.The effects of long COVID-19 should be considered when providing and planning health services.The effect of gender,and education,on QoL shows that health inequalities continue to be effective during the pandemic period.

Health-Related Quality of Life in HIV-positive Women on Long-Term Antiretroviral Therapy—A Study from Bangalore, South India

Introduction: Lifelong Anti-Retroviral Therapy (ART) promotes good quality of life and health among HIV-positive men and women. However, simplified newer and effective ART has not increased retention in care, or long-term ART adherence, especially among women. There are many factors that impede long-term adherence in women. This includes among other things female gender, depression, greater than once-daily dosing, longer time since HIV diagnosis, and patient beliefs. This study measures the quality of life in women whose ART durations range from one to fifteen years, using the standardized WHO Quality of Life questionnaire. Material and Methods: One hundred and fourteen women were divided into three groups based on ART duration. Group 1 had 37 women on ART for less than five years, Group 2 had 48 women on ART from 5 to 10 years and Group 3 had 29 women on ART for more than ten years. They were administered the WHO Quality of Life (QOL) questionnaire, which assesses QOL in six domains. QOL was considered poor in scores between 4 - 9.9, medium in scores of 10 - 14.9 and good in scores of 15 - 20. Results: Scores in all 3 groups were more than 85% in five domains and around 74.5% in the psychological domain. Domain mean scores were Physical 18 (CI 17.63 - 18.37), Psychological 14.9 (CI 14.55 - 15.25), Independence 18.6 (CI 18.33 - 18.87), Social relationships 17.5 (CI 17.07 - 17.93), Environmental 17.6 (CI 17.25 - 17.95), Spiritual, Religious, Personal beliefs, 17.4 (CI 16.93 - 17.87). Scores for women on long-term ART (Group 3) are not different from the other 2 groups and the p-values were not statistically significant. Conclusion: Women on long-term ART fare extremely well compared to other groups with more than 93% showing good QOL and none showing poor Quality of Health in spite of being on ART for a longer period of time than the other two groups. Despite a multitude of impeding factors, women who continue ART faithfully and consistently enjoy a good quality of health and life. Adequate preparation and a supportive health system are essential for ensuring long-term adherence, but the attitude and commitment of women are also critical.

基于MRSDAE-KPCA结合Bi-LST的滚动轴承剩余使用寿命预测

针对现有滚动轴承剩余使用寿命预测方法在提取数据特征时没有充分考虑数据的内部分布,且在构建健康因子时还需要专家经验进行人工提取等问题,提出一种基于流形正则化堆栈去噪自编码器、核主成分分析并结合双向长短时记忆网络的滚动轴承剩余使用寿命预测方法。首先采用无监督的堆栈去噪自编码器网络对原始振动数据进行深层特征提取,并使用核主成分分析法进一步降维,以提高健康因子的指标稳定性;然后在堆栈去噪自编码器中加入流形正则化,最大程度保留编码器隐藏层内部的数据分布结构,提高模型提取数据特征的有效性。最后使用双向长短时记忆网络预测轴承的剩余使用寿命,并采用AdaMax优化算法对网络模型的超参数进行自适应寻优。分析结果表明,提出的滚动轴承剩余使用寿命预测方法具有更高的精度。

基于SSA-SVR和LSTM相结合的混合模型预测锂电池的剩余寿命

锂电池的SOH和RUL可以判断电池管理系统故障的几率。文章提出一种预测SOH和RUL的混合模型。首先利用改进的带有自适应噪声的互补集合经验模态分解算法(ICEEMDAN)分解容量信号,然后分别利用SVR算法、LSTM对高频、低频信号进行预测,同时引入SSA优化SVR参数以提高精度,最后将各分量预测信号重组作为最终的预测结果。仿真结果表明,在不同数据集上各项预测评估指标均小于1%,该混合预测模型具有稳定性好、精度高和鲁棒性强等优点,适用于预测电池SOH和RUL。

Evaluation of Health-Related Quality of Life Associated with Provision of Healthcare to Stroke Patients Living at Home in Japan

Objective: As stroke mortality rates decline in Japan, a large proportion of disabled stroke survivors living in their homes are supported by informal caregivers or formal healthcare services. To evaluate the impact of healthcare provision on outcome of stroke patients living at home, this study investigated the associations of long-term care and health-related quality of life (HRQOL) in patients 1 year after stroke onset. Methods: Data on patient and caregiver characteristics, HRQOL of patients, and healthcare services for those living at home were prospectively collected from 426 patients with stroke at baseline and 12 months. Using general measures of HRQOL, namely, Short Form-36 (SF-36) and EuroQOL 5 dimension (EQ-5D), multivariate regression models were used to determine the contribution of variables to changes in HRQOL scores from discharge to the first year after stroke. Results: Five domains of SF-36—role-physical, vitality, social functioning, role- emotional, and mental health—were significantly improved 1 year after stroke. Factors affecting changes in the five domains of HRQOL were age, independence in activities of daily living, and cognitive function. Home care service was positively associated with role-physical, social functioning, and role-emotional. In addition, home rehabilitation and home bathing services were positively associated with social functioning. Conclusion: This study clarified that improvements of HRQOL 1 year after stroke were associated with use of home-based services involving home care service, home rehabilitation, and home bathing services. The use of home-based services contributed to the improved welfare of patients living at home.

基于Transformer组合模型的锂电池SOH和RUL预测

本论文采用了Transformer模型与多种深度学习模型的组合模型来预测电池的健康状态(SOH)和剩余使用寿命(RUL)。在NASA公开数据集合上进行了测试,使用电流、电压和温度来预测SOH,使用电流、电阻和阻抗来预测RUL。该模型首先利用卷积神经网络(convolution neural network,CNN)提取输入数据的空间特征,然后使用双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)提取输入数据的时间序列变化规律,再利用Transformer模型的多头注意力机制和前馈网络学习输入数据的特征表示,最后通过注意力机制进一步选取输入数据的时空特征中的重要部分,以共同预测SOH和RUL。实验结果表明,该模型在测试数据上的SOH预测均方误差(root mean square error,RMSE)达到0.08485,RUL预测的RMSE达到1.46,其效果均优于传统方法。因此,该深度学习模型能够有效地提高电池SOH和RUL的预测精度和稳定性。

Study on the quality of life and the factors influencing it in elderly patients

A society of advanced age is arriving with the increasing number of elderly patients. Little attention has been paid to the quality of life of elderly patients, which is decreasing gradually. This article aims to study the quality of life among elderly patients and explore the factors influencing it, in addition to exploring effective ways to improve the quality of life of elderly patients.

A Research on the Relationship between Intestinal Flora and Human Longevity

The exploration of human life and health is advancing with the changes of the times.With the growth of age,the occurrence of chronic diseases of human immunity and organ system is frequent,which has a serious impact on human health.Genes,environment and other random factors determine the outcome of longevity,and intestinal flora is considered to be a decisive factor affecting human health and longevity,mainly because of its huge impact on human immunity,growth and development.The study of the relationship between intestinal flora and longevity is beneficial to improve the health status of the elderly and improve the overall life level of human beings,which has great scientific research value.This review will review the role of intestinal flora in longevity.

State of health and remaining useful life prediction for lithiumion batteries based on differential thermal voltammetry and a long and short memory neural network

As the lithium-ion battery is widely applied,the reliability of the battery has become a high-profile content in recent years.Accurate estimation and prediction of state of health(SOH)and remaining useful life(RUL)prediction are crucial for battery management systems.In this paper,the core contribution is the construction of a datadriven model with the long short-term memory(LSTM)network applicable to the time-series regression prediction problem with the integration of two methods,data-driven methods and feature signal analysis.The input features of model are extracted from differential thermal voltammetry(DTV)curves,which could characterize the battery degradation characteristics,so that the accurate prediction of battery capacity fade could be accomplished.Firstly,the DTV curve is smoothed by the Savitzky-Golay filter,and six alternate features are selected based on the connection between DTV curves and battery degradation characteristics.Then,a correlation analysis method is used to further filter the input features and three features that are highly associated with capacity fade are selected as input into the data driven model.The LSTM neural network is trained by using the root mean square propagation(RMSprop)technique and the dropout technique.Finally,the data of four batteries with different health levels are deployed for model construction,verification and comparison.The results show that the proposed method has high accuracy in SOH and RUL prediction and the capacity rebound phenomenon can be accurately estimated.This method can greatly reduce the cost and complexity,and increase the practicability,which provides the basis and guidance for battery data collection and the application of cloud technology and digital twin.

基于间接健康特征优化与多模型融合的锂电池SOH-RUL联合预测

准确预测锂电池健康状态(SOH)与电池剩余使用寿命(RUL)对提高电池安全性能具有重要意义。而当前针对SOH和RUL的预测,存在着间接健康特征选取困难,以及使用数据驱动方法缺乏不确定性表达的问题。为此,该文提出一种基于间接健康特征优化与多模型融合的锂电池SOH-RUL联合预测方法。首先从充电电压曲线中采集多个健康特征,并通过特征并行融合方法和注意力机制进行优化处理得到间接健康特征(IHF)。然后引入贝叶斯模型平均(BMA)方法来解决预测过程中的不确定性问题,将其与支持向量回归(SVR)和长短期记忆神经网络(LSTM)相结合,构建SVR-BMA融合模型和LSTM-BMA融合模型,并分别进行SOH和RUL预测;通过自适应噪声完备集合经验模态分解(CEEMDAN)方法从SOH预测阶段的容量预测结果中提取出RUL预测的输入特征,以实现SOH和RUL的联合预测。最后利用CALCE数据集进行性能测试,实验结果表明,所提方法能有效提高SOH和RUL预测的准确性和可靠性。

基于长期护理保险探讨社区健康随访管理与失能居民生活质量的关联性

目的探究社区健康随访管理与长期护理保险(简称长护险)居家照护居民生活质量的关系,为构建以失能居民为中心的整合式社区居家医疗护理服务模式提供依据。方法根据纳入排除标准,按照方便原则选取上海市闵行区梅陇社区2021年1月1日至12月31日参保长护险居家照护的居民。参保满1年以后,由培训合格的社区医师使用36条简明健康状况调查(the 36-item short form health survey,SF-36)量表面对面调查研究对象的生活质量,基于居民电子健康档案、社区慢性病管理系统、长护险管理系统,结合现场问卷调查,采集研究对象的基本人口学信息、生活方式、罹患疾病种类、慢性病共病状况及参加社区提供的健康随访管理情况等。采用横断面分析方法,评估社区健康随访管理与长护险居家照护参保居民生活质量之间的关系。结果230人(57.64%)实际接受社区卫生服务中心提供的健康随访管理,调查结果显示其SF-36量表的总体健康、生理职能、躯体疼痛、精神健康、活力、情感职能维度得分均高于未接受随访的人群,且组间差异具有统计学意义(P值均<0.05)。此外,接受社区健康随访管理者SF-36量表健康变化分数高于未接受随访者(P=0.003),提示健康状况较好。控制人口学及混杂因素后,进一步分析表明,社区卫生服务中心提供的健康随访管理与长护险居民SF-36量表的总体健康、生理职能、躯体疼痛、精神健康、活力维度有显著正相关(P值均<0.05)。结论社区卫生服务中心提供的健康随访管理对失能居民的生活质量有显著的正向影响。社区健康随访管理是提高长护险居家照护服务质量的有效途径。

长期护理保险对中老年人生活满意度的影响--基于CHARLS数据的实证分析

基于积极应对人口老龄化的背景,选用中国健康与养老追踪调查(CHARLS)数据库,结合我国当前的长期护理保险制度背景,运用多时点双重差分模型研究长期护理保险对我国中老年人生活满意度的影响。研究结果发现,长期护理保险对中老年人生活满意度有显著的改善效应,通过异质性分析发现该效应在性别、婚姻状态和孩子数量层面存在异质性,即长期护理保险对男性、其他婚姻状态和少子的中老年人生活满意度有显著的改善效应;并通过机制分析发现,长期护理保险会通过增加代际经济资助对中老年人的生活满意度产生改善效应。研究建议:加快推进我国长期护理保险的试点进程,并扩大长期护理保险的覆盖范围,将城乡居民纳入到保障范围内。

基于数据预处理和VMD-LSTM-GPR的锂离子电池剩余寿命预测

锂离子电池的剩余使用寿命(RUL)是健康管理中重要参数,其准确评估对于保证电池设备的安全稳定运行非常重要。该文提出一种数据预处理联合变分模态分解(VMD)、长短期记忆网络(LSTM)和高斯回归过程(GPR)的预测框架。首先选取充放电循环过程中的信息作为间接健康因子(HI),并通过核主元分析方法(KPCA)实现间接HI的特征提取,完成数据预处理;其次通过VMD-LSTM方法实现健康因子的分解、预测和重构,并将重构得到的数据应用于RUL预测的GPR模型,完成预测模型搭建;最后以NASA锂电池数据集作为算法测试数据,结果表明,所提取的健康因子能够准确跟踪锂电池的退化过程;所提预测方法能够准确地估计电池的剩余寿命,同时具有较高的可靠性和稳定性。

基于CEEMDAN-LSTM组合的锂离子电池寿命预测方法

针对目前锂离子电池寿命预测结果不准确的问题,提出了一种多模态分解的锂离子电池组合预测模型,从而学习锂离子电池退化过程的微小变化.该方法在单一长短期记忆(LSTM)预测模型的基础上,采用了自适应噪声完全集成的经验模态分解(CEEMDAN)算法将锂电池容量分为主退化趋势和若干局部退化趋势,然后使用长短期记忆神经网络(LSTMNN)算法分别对所分解的若干退化数据进行寿命预测,最后将若干预测结果进行有效集成.结果表明,所提出的CEEMDANLSTM锂离子电池组合预测模型最大平均绝对百分比误差不超过1.5%,平均相对误差在3%以内,且优于其他预测模型.

基于CAE与LSTM的航空发动机剩余寿命预测

通过深度学习方法构建航空发动机的健康状况评估模型,并在此模型基础上进行剩余寿命预测。基于卷积自编码器构建航空发动机的健康因子(health indicator,HI),以其HI值反映健康状况;通过长短期记忆网络(long short-term memory,LSTM)建立HI与剩余寿命的特征关系,实现剩余寿命预测。经比较验证,此间接方法的预测精度优于多层感知机、支持向量回归等浅层神经网络,以及卷积神经网络、多层LSTM等直接预测的深度学习方法。

基于LSTM‑ES‑RVM的滚动轴承剩余寿命预测方法

为解决滚动轴承在寿命预测时精度不高,且性能退化趋势及波动范围难以预测等问题,提出了基于LSTM‑ES‑RVM的滚动轴承剩余寿命预测方法。在无先验知识或人工经验的干扰下,利用长短期记忆(Long Short‑Term Memory,LSTM)网络直接对频率数据进行特征提取,构建退化过程的初步健康指标(Health Indicator,HI);为了消除HI曲线的局部剧烈振荡,提出了带斜率的极端拐点(Extreme Inflection Point with a Slope,ES)模型改善其整体单调性;使用相关向量机(Relevance Vector Machine,RVM)模型对HI曲线进行趋势预测,实现了滚动轴承的剩余寿命(Remaining Useful Life,RUL)预测。实验结果表明,所提方法相较于对比方法具有较好的预测精度。

中医护理结合综合护理在腰椎间盘突出症患者中的应用

目的探讨中医护理结合综合护理在腰椎间盘突出症患者中的应用效果。方法选择2021年3月—2022年3月医院收治的86例腰椎间盘突出症患者,随机分为对照组和试验组。对照组采取常规护理,试验组采取中医护理结合综合护理,包括日常护理、情志调节、平衡膳食、中医特色护理等。观察两组VAS评分、直腿抬高角度,SAS、SDS评分,压疮、下肢静脉血栓等并发症发生率,躯体、角色、情感、社会功能等生活质量评分。结果试验组VAS评分(1.19±0.54)相比对照组VAS评分(3.40±0.79)明显更低,直腿抬高角度(66.14±4.87)相比对照组(61.72±4.89)明显更高,SAS(39.33±5.19)、SDS(40.40±5.67)评分均明显低于对照组SAS(45.88±5.51)、SDS(48.88±5.32)评分,并发症总发生率2.33%明显低于对照组18.60%,躯体功能(66.40±6.20)、角色功能(79.53±6.95)、情感功能(82.07±8.30)、社会功能(79.37±7.98)等各项生活质量评分均显著高于对照组躯体功能(54.07±6.45)、角色功能(65.65±5.99)、情感功能(76.77±7.25)、社会功能(74.67±7.25)评分,差异有统计学意义(P<0.05)。结论对腰椎间盘突出症患者实施中医护理结合综合护理,有利于降低疼痛感,恢复身体健康,缓解焦虑、抑郁等负面情绪,改善心理状态,减少并发症发生,提高生活质量,护理效果较好。

基于双重注意力机制的电池SOH估计和RUL预测编解码模型

锂电池的健康状态(SOH)和剩余使用寿命(RUL)精确评估对电池安全稳定运行极为重要,而现有预测模型内部运行机制透明性低,导致评估可靠性较差。文中提出一种基于双重注意力机制的双向长短期记忆网络编解码模型进行SOH估计和RUL预测。编码侧的特征注意力机制和解码侧的时序注意力机制不仅通过动态分配特征和时序信息的权重提升了模型预测精度,还通过可视化权重的方法实现了模型可解释性。最后,利用NASA和CALCE公开的电池数据集进行实验测试,验证了所提方法具有较高的精度和可靠性。

以患者需求为导向的个体化护理对老年长期卧床并发下肢深静脉血栓患者健康行为、自护能力的影响

目的 探究以患者需求为导向的个体化护理对老年长期卧床并发下肢深静脉血栓患者健康行为、自护能力的影响。方法 根据河南省人民医院以患者需求为导向的个体化护理措施推行时间为截点,将推行前,2022年1月至2022年6月予以常规护理措施的60例老年长期卧床并发下肢深静脉血栓患者纳入对照组;将推行后,即2022年7月至2022年12月给予以患者需求为导向的个体化护理措施的60例老年长期卧床并发下肢深静脉血栓患者纳入观察组。比较两组患者干预前及干预3个月后患者健康行为[中文版健康增强生活方式量表(筛选版)(HELP-CS)]、健康赋能[简化版老年人健康赋能量表(HES)]、自护能力[老年人自我护理能力量表(SASE)]、生活质量[简版老年人生活质量问卷(OPQOL-brief)]变化。结果 干预3个月后与干预前相比,两组患者HELP-CS、HES、SASE、OPQOL-brief评分均有升高,且观察组患者评分高于对照组,差异有统计学意义(P<0.05)。结论 以患者需求为导向的个体化护理可以增强老年长期卧床并发DVT患者健康行为、健康赋能和自护能力,从而提升其生活质量。