目的:利用潜类别增长模型(latent class growth model,LCGM)分析老年帕金森病患者用药依从性轨迹,并验证其影响因素。方法:对124例原发性老年帕金森病患者进行12个月随访调查,调查工具包括一般资料调查表和Morisky用药依从性量表。通过...目的:利用潜类别增长模型(latent class growth model,LCGM)分析老年帕金森病患者用药依从性轨迹,并验证其影响因素。方法:对124例原发性老年帕金森病患者进行12个月随访调查,调查工具包括一般资料调查表和Morisky用药依从性量表。通过潜类别增长模型识别患者用药依从性轨迹,采用有序多分类Logistic回归分析用药依从性轨迹的影响因素。结果:老年帕金森病患者用药依从性分为“高-持续型”“中-下降型”和“低-下降型”3种类型,且该3种类型文化程度、工作状态、用药种类、智力状态比较,差异有统计学意义(P<0.05)。有序多分类Logistic回归显示,工作状态、用药种类、智力状态是患者用药依从性轨迹的影响因素(P<0.05)。结论:老年帕金森患者用药依从性分为3种轨迹,工作状态、用药种类和智力状态是用药依从性轨迹类别的影响因素。展开更多
目的探讨帕金森病患者服药依从性轨迹及其影响因素。方法选择2020年10月—2021年6月滁州市5个社区招募的140例原发性帕金森病患者,进行12个月随访调查。调查工具包括社会资料调查表、临床资料调查表和Morisky服药依从性量表。通过潜类...目的探讨帕金森病患者服药依从性轨迹及其影响因素。方法选择2020年10月—2021年6月滁州市5个社区招募的140例原发性帕金森病患者,进行12个月随访调查。调查工具包括社会资料调查表、临床资料调查表和Morisky服药依从性量表。通过潜类别增长模型(Latent Class Growth Model,LCGM)识别患者服药依从性轨迹,利用有序多分类Logistic回归分析影响轨迹的因素。结果本研究共纳入127例患者,根据服药依从性轨迹可分为“高-稳定型”(11.8%)、“中-降低型”(28.4%)和“低-降低型”(59.8%)。单组重复测量方差分析显示,高-稳定型轨迹不同时间点服药依从得分差异无统计学意义(P>0.05),但中-降低型和低-降低型轨迹不同时间点服药依从得分差异有统计学意义(均P<0.05)。有序多分类Logistic回归显示,文化程度(P=0.022,OR=19.665,95%CI=1.536-251.817)、工作状态(P=0.004,OR=10.285,95%CI=2.082-50.814)、用药种类(P=0.022,OR=8.356,95%CI=1.356-51.498)和智力状态(P=0.017,OR=15.551,95%CI=1.628-148.497)是服药依从性轨迹的预测因素。结论帕金森病患者服药依从性可分为3种变化轨迹,文化程度、工作状态、用药种类和智力状态是影响服药依从性轨迹的主要因素。展开更多
Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of ...Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of athletes representing distinct burnout trajectories emerged from the analyses and(2)whether athlete burnout symptoms(reduced accomplishment,sport devaluation,and exhaustion)developed in tandem or whether some burnout dimensions predicted downstream changes in other dimensions(causal ordering model).Methods:One hundred and fifty-nine table tennis players in intensive training centers completed a self-reported athlete burnout measure across 3 time points within a 2-month period characterized by high demands.Data were analyzed through latent class growth analysis.Results:Results of latent class growth analysis showed 3 distinct trajectories for each athlete burnout dimension,indicating not only linear or quadratic change but also stability in longitudinal athlete burnout perceptions.Results also suggested that the 3 dimensions of athlete burnout did not develop in tandem.Rather,the likelihood of belonging to particular emerging trajectories of sport devaluation and physical/emotional exhaustion was significantly influenced by the athletes’perception of reduced accomplishment assessed at Time 1.Thus,reduced accomplishment predicted downstream changes in the 2 other athlete burnout dimensions.Conclusion:As a whole,these results highlighted that the multinomial heterogeneity in longitudinal athlete burnout symptoms needs to be accounted for in future research.展开更多
目的探讨潜变量增长混合模型(latent growth mixture modeling,GMM)和潜类增长模型(latent class growth model,LCGM)在识别儿童体重增长变化潜在类别上的应用。方法以大连市932名6~12岁学龄儿童的体检纵向数据为例。运用Mplus8.3软件...目的探讨潜变量增长混合模型(latent growth mixture modeling,GMM)和潜类增长模型(latent class growth model,LCGM)在识别儿童体重增长变化潜在类别上的应用。方法以大连市932名6~12岁学龄儿童的体检纵向数据为例。运用Mplus8.3软件构建不同性别儿童体质指数(body mass index,BMI)变化的GMM和LCGM模型。结果LCGM模型对男女学龄儿童的生长轨迹均识别出3个增长趋势不同的亚组:“稳定组”、“肥胖组”、“偏瘦组”;GMM模型对男性学龄儿童的生长轨迹识别出2个增长趋势不同的亚组:“稳定增长组”和“肥胖增长组”。结论GMM和LCGM模型可以识别学龄儿童BMI发展轨迹的异质性,拓展了描述儿童体重动态变化的方法研究。展开更多
文摘目的:利用潜类别增长模型(latent class growth model,LCGM)分析老年帕金森病患者用药依从性轨迹,并验证其影响因素。方法:对124例原发性老年帕金森病患者进行12个月随访调查,调查工具包括一般资料调查表和Morisky用药依从性量表。通过潜类别增长模型识别患者用药依从性轨迹,采用有序多分类Logistic回归分析用药依从性轨迹的影响因素。结果:老年帕金森病患者用药依从性分为“高-持续型”“中-下降型”和“低-下降型”3种类型,且该3种类型文化程度、工作状态、用药种类、智力状态比较,差异有统计学意义(P<0.05)。有序多分类Logistic回归显示,工作状态、用药种类、智力状态是患者用药依从性轨迹的影响因素(P<0.05)。结论:老年帕金森患者用药依从性分为3种轨迹,工作状态、用药种类和智力状态是用药依从性轨迹类别的影响因素。
文摘目的探讨帕金森病患者服药依从性轨迹及其影响因素。方法选择2020年10月—2021年6月滁州市5个社区招募的140例原发性帕金森病患者,进行12个月随访调查。调查工具包括社会资料调查表、临床资料调查表和Morisky服药依从性量表。通过潜类别增长模型(Latent Class Growth Model,LCGM)识别患者服药依从性轨迹,利用有序多分类Logistic回归分析影响轨迹的因素。结果本研究共纳入127例患者,根据服药依从性轨迹可分为“高-稳定型”(11.8%)、“中-降低型”(28.4%)和“低-降低型”(59.8%)。单组重复测量方差分析显示,高-稳定型轨迹不同时间点服药依从得分差异无统计学意义(P>0.05),但中-降低型和低-降低型轨迹不同时间点服药依从得分差异有统计学意义(均P<0.05)。有序多分类Logistic回归显示,文化程度(P=0.022,OR=19.665,95%CI=1.536-251.817)、工作状态(P=0.004,OR=10.285,95%CI=2.082-50.814)、用药种类(P=0.022,OR=8.356,95%CI=1.356-51.498)和智力状态(P=0.017,OR=15.551,95%CI=1.628-148.497)是服药依从性轨迹的预测因素。结论帕金森病患者服药依从性可分为3种变化轨迹,文化程度、工作状态、用药种类和智力状态是影响服药依从性轨迹的主要因素。
基金supported by the French Federation of Table Tennis.
文摘Purpose:The purposes of this study were to examine the trajectories of athlete burnout across a 2-month period characterized by high physical,psychological,and social demands to explore(1)whether several subgroups of athletes representing distinct burnout trajectories emerged from the analyses and(2)whether athlete burnout symptoms(reduced accomplishment,sport devaluation,and exhaustion)developed in tandem or whether some burnout dimensions predicted downstream changes in other dimensions(causal ordering model).Methods:One hundred and fifty-nine table tennis players in intensive training centers completed a self-reported athlete burnout measure across 3 time points within a 2-month period characterized by high demands.Data were analyzed through latent class growth analysis.Results:Results of latent class growth analysis showed 3 distinct trajectories for each athlete burnout dimension,indicating not only linear or quadratic change but also stability in longitudinal athlete burnout perceptions.Results also suggested that the 3 dimensions of athlete burnout did not develop in tandem.Rather,the likelihood of belonging to particular emerging trajectories of sport devaluation and physical/emotional exhaustion was significantly influenced by the athletes’perception of reduced accomplishment assessed at Time 1.Thus,reduced accomplishment predicted downstream changes in the 2 other athlete burnout dimensions.Conclusion:As a whole,these results highlighted that the multinomial heterogeneity in longitudinal athlete burnout symptoms needs to be accounted for in future research.
文摘目的探讨潜变量增长混合模型(latent growth mixture modeling,GMM)和潜类增长模型(latent class growth model,LCGM)在识别儿童体重增长变化潜在类别上的应用。方法以大连市932名6~12岁学龄儿童的体检纵向数据为例。运用Mplus8.3软件构建不同性别儿童体质指数(body mass index,BMI)变化的GMM和LCGM模型。结果LCGM模型对男女学龄儿童的生长轨迹均识别出3个增长趋势不同的亚组:“稳定组”、“肥胖组”、“偏瘦组”;GMM模型对男性学龄儿童的生长轨迹识别出2个增长趋势不同的亚组:“稳定增长组”和“肥胖增长组”。结论GMM和LCGM模型可以识别学龄儿童BMI发展轨迹的异质性,拓展了描述儿童体重动态变化的方法研究。
