A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior.
A systematic scoping review of latent class analysis applied to accelerometry-assessed physical activity and sedentary behavior.
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DOI：
10.1371/journal.pone.0283884
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发表时间：
2024
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
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潜在类别分析（LCA）确定不同群体内的异质性人口，但其应用加速度计评估的体力活动和久坐行为尚未系统地探讨。我们进行了一个系统的范围审查，以描述应用LCA加速度。在PubMed、Web of Science、CINHAL、SPORTDiscus和Embase中进行的综合检索确定了截至2021年12月31日发表的研究。使用Covidence，两名研究人员独立评估了入选标准，并通过协商一致解决了差异。选择了将LCA应用于加速度测量或加速度测量/自我报告测量组合的研究。提取的数据包括研究特征以及加速度计和LCA方法。在发现的2555篇论文中，筛选了66篇全文论文，纳入了来自8项独特研究的12篇论文（11篇横断面，1篇队列）。研究样本量范围为217-7931（平均值2249，标准差2780）。在8项独特的研究中，潜在类别变量包括身体活动（100%）和久坐行为（75%）的测量。大约三分之二（63%）的研究仅使用加速度计，38%的研究结合加速度计和自我报告来推导潜在类别。LCA模型中基于加速度计的变量包括按一周中的几天（38%）、工作日与周末（13%）、每周平均值（13%）、二分分钟/天（13%）、性别特异性z评分（13%）和每小时（13%）进行的测量。指导最终类别数和模型拟合选择的标准在研究中各不相同，包括贝叶斯信息标准（63%）、实质性知识（63%）、熵（50%）、赤池信息标准（50%）、样本量（50%）、Bootstrap似然比检验（38%）和目视检查（38%）。这些研究探索了多达5（25%），6（38%）或7+（38%）类，最后以3（50%），4（13%）或5（38%）类结束。本综述探讨了LCA在体力活动和久坐行为中的应用，并确定了未来利用LCA进行研究的改进领域。LCA被用来识别独特的分组作为数据简化工具，结合联合收割机自我报告和加速度计，并结合联合收割机不同的体力活动强度和久坐行为在一个LCA模型或单独的模型。
Latent class analysis (LCA) identifies distinct groups within a heterogeneous population, but its application to accelerometry-assessed physical activity and sedentary behavior has not been systematically explored. We conducted a systematic scoping review to describe the application of LCA to accelerometry. Comprehensive searches in PubMed, Web of Science, CINHAL, SPORTDiscus, and Embase identified studies published through December 31, 2021. Using Covidence, two researchers independently evaluated inclusion criteria and discrepancies were resolved by consensus. Studies with LCA applied to accelerometry or combined accelerometry/self-reported measures were selected. Data extracted included study characteristics and both accelerometry and LCA methods. Of 2555 papers found, 66 full-text papers were screened, and 12 papers (11 cross-sectional, 1 cohort) from 8 unique studies were included. Study sample sizes ranged from 217–7931 (mean 2249, standard deviation 2780). Across 8 unique studies, latent class variables included measures of physical activity (100%) and sedentary behavior (75%). About two-thirds (63%) of the studies used accelerometry only and 38% combined accelerometry and self-report to derive latent classes. The accelerometer-based variables in the LCA model included measures by day of the week (38%), weekday vs. weekend (13%), weekly average (13%), dichotomized minutes/day (13%), sex specific z-scores (13%), and hour-by-hour (13%). The criteria to guide the selection of the final number of classes and model fit varied across studies, including Bayesian Information Criterion (63%), substantive knowledge (63%), entropy (50%), Akaike information criterion (50%), sample size (50%), Bootstrap likelihood ratio test (38%), and visual inspection (38%). The studies explored up to 5 (25%), 6 (38%), or 7+ (38%) classes, ending with 3 (50%), 4 (13%), or 5 (38%) final classes. This review explored the application of LCA to physical activity and sedentary behavior and identified areas of improvement for future studies leveraging LCA. LCA was used to identify unique groupings as a data reduction tool, to combine self-report and accelerometry, and to combine different physical activity intensities and sedentary behavior in one LCA model or separate models.
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影响因子：
3.1
作者：
Patnode CD;Lytle LA;Erickson DJ;Sirard JR;Barr-Anderson DJ;Story M
通讯作者：
Story M
影响因子：
3.7
作者：
Miranda VPN;Coimbra DR;Bastos RR;Miranda Júnior MV;Amorim PRDS
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Amorim PRDS
影响因子：
3.8
作者：
Wurpts IC;Geiser C
通讯作者：
Geiser C
DOI：
10.1016/j.sapharm.2016.11.011
发表时间：
2017-11-01
影响因子：
3.9
作者：
Schreiber, James B.
通讯作者：
Schreiber, James B.
影响因子：
4
作者：
Jones, Sydney A.;Wen, Fang;Evenson, Kelly R.
通讯作者：
Evenson, Kelly R.