纵向混合型建模方法,以捕捉运动对情感反应的异质性,测量在研究波和研究波中测量
Sarah J Schmiege1, Laura K Kaizer2, Courtney J Stevens3
1Department for Biostatistics and Informatics, Center for Innovative Design and Analysis, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA. Sarah.Schmiege@cuanschutz.edu.
Journal of behavioral medicine
|January 13, 2026
概括
纵向混合模型揭示了个人在运动过程中随着时间的推移体验情感反应的不同模式. 这项研究确定了具有稳定,增加或减少情绪反应的子组,为运动行为维护提供了洞察力.
科学领域:
- 行为科学 行为科学
- 生物统计学 生物统计学
- 心理学 心理学 心理学
背景情况:
- 了解运动期间情感反应的个体差异对于促进长期的运动行为维持至关重要.
- 此前对这一队列的分析显示了稳定的平均情感反应,但这项研究调查了这些反应随着时间的推移的潜在异质性.
- 纵向混合模型提供先进的统计方法来识别人口内的不同变化模式.
研究的目的:
- 应用纵向混合物建模技术,特别是隐性类增长分析/增长混合物建模 (LCGA/GMM) 和重复测量隐性配置分析 (RMLPA),以根据运动期间情感反应模式识别不同的子组.
- 为了检查在16周的时间内,个体对运动的情感反应是否有显著的差异.
- 探索已识别的潜在类对练习结果的理论影响.
主要方法:
- 利用了一项为期16周的随机试验的数据,涉及201名接受运动干预的女性.
- 应用LCGA/GMM来分析波级情绪反应 (4个时间点) 和RMLPA来分析运动期间分钟间隔的情绪反应.
- 采用加权分析,将潜伏类成员身份与理论结果联系起来,例如VO2max变化和计划行为理论构造.
主要成果:
- LCGA/GMM确定了三种截然不同的平均情感反应模式:"稳定"",高,增加"和"减少".
- 在分析运动期间每分钟间隔的情感反应时,RMLPA揭示了四个不同的子组.
- 以人为中心的分析证实了随着时间的推移和运动会内情感反应的显著异质性.
结论:
- 纵向混合模型有效地捕捉了对运动的情感反应的个人层面异质性,挑战了统一稳定的平均反应的概念.
- 已识别的潜在类提供了对运动期间个体体验的更细致的理解,可能会为量身定制的运动干预提供信息.
- 方法比较突出了建模选择 (例如,波平面与内部分析) 对解释纵向数据和理论发展的影响.
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