Latent class growth modelling for the evaluation of intervention outcomes: example from a physical activity intervention.
Latent class growth modelling for the evaluation of intervention outcomes: example from a physical activity intervention.
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DOI：
10.1007/s10865-021-00216-y
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发表时间：
2021-10
影响因子：
3.1
通讯作者：
Forsell Y
中科院分区：
文献类型：
--
作者：
Lampousi AM;Möller J;Liang Y;Berglind D;Forsell Y
干预研究通常假设结果的变化在整个人群中是同质的，但这一假设可能并不总是成立。本文介绍了如何潜在类增长模型（LCGM）可以在干预研究中进行，使用实证的例子，并讨论了这种方法的挑战和潜在的影响。该分析包括110名参与平行随机对照试验的行动不便的年轻人，他们接受了为期12周的移动的应用程序（n = 55）或监督健康计划（n = 55）。主要结局是在基线、干预后6周、12周和1年评估的加速度计测量的中度至剧烈体力活动（MVPA）水平（min/d）。使用配对t检验估计MVPA从基线至1年的平均变化。LCGM用于确定MVPA的轨迹。逻辑回归模型被用来确定潜在的预测轨迹。基线和1年MVPA水平之间无显著差异（4.8分钟/天，95% CI：-1.4，10.9）。通过LCGM确定了四种MVPA轨迹，即“正常/降低"、"正常/升高"、”正常/快速升高“和”高/升高“。年龄较小和基线MVPA较高的个体更可能具有MVPA增加的轨迹。LCGM发现了身体活动的隐藏轨迹，这些轨迹不是由平均模式表示的。这种方法可以在干预研究中提供重要的见解。为了获得更高的准确性，建议包括更大的样本量。 本文的在线版本（10.1007/s10865-021-00216-y）包含补充材料，可供授权用户使用。
Intervention studies often assume that changes in an outcome are homogenous across the population, however this assumption might not always hold. This article describes how latent class growth modelling (LCGM) can be performed in intervention studies, using an empirical example, and discusses the challenges and potential implications of this method. The analysis included 110 young adults with mobility disability that had participated in a parallel randomized controlled trial and received either a mobile app program (n = 55) or a supervised health program (n = 55) for 12 weeks. The primary outcome was accelerometer measured moderate to vigorous physical activity (MVPA) levels in min/day assessed at baseline, 6 weeks, 12 weeks, and 1-year post intervention. The mean change of MVPA from baseline to 1-year was estimated using paired t-test. LCGM was performed to determine the trajectories of MVPA. Logistic regression models were used to identify potential predictors of trajectories. There was no significant difference between baseline and 1-year MVPA levels (4.8 min/day, 95% CI: −1.4, 10.9). Four MVPA trajectories, ‘Normal/Decrease’, ‘Normal/Increase’, ‘Normal/Rapid increase’, and ‘High/Increase’, were identified through LCGM. Individuals with younger age and higher baseline MVPA were more likely to have increasing trajectories of MVPA. LCGM uncovered hidden trajectories of physical activity that were not represented by the average pattern. This approach could provide significant insights when included in intervention studies. For higher accuracy it is recommended to include larger sample sizes. The online version of this article (10.1007/s10865-021-00216-y) contains supplementary material, which is available to authorized users.
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影响因子：
3.1
作者：
Curran, PJ;Muthén, BO
通讯作者：
Muthén, BO
DOI：
10.20982/tqmp.05.1.p011
发表时间：
2009-01-01
期刊：
TUTORIALS IN QUANTITATIVE METHODS FOR PSYCHOLOGY
影响因子：
--
作者：
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通讯作者：
Louvet, Benoit
DOI：
10.1111/j.1530-0277.2000.tb02070.x
发表时间：
2000-06-01
影响因子：
3.2
作者：
Muthén, B;Muthén, LK
通讯作者：
Muthén, LK
DOI：
10.1080/10705511.2013.824781
发表时间：
2013-10-01
期刊：
Structural equation modeling : a multidisciplinary journal
影响因子：
--
作者：
Tein JY;Coxe S;Cham H
通讯作者：
Cham H
影响因子：
3
作者：
Bjorkman, Frida;Ekblom-Bak, Elin;Ekblom, Bjorn
通讯作者：
Ekblom, Bjorn