PDF(Pubmed)
Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：潜在类别分析（LCA）识别异质群体中的不同群体，但是它在加速测量评估的身体活动和久坐行为中的应用尚未得到系统的探索。我们进行了系统的范围审查，以描述LCA在加速度测量中的应用。
方法：PubMed中的综合搜索，WebofScience,CINHAL,SPORTDiscus,Embase确定了到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纳入标准,差异通过共识解决.选择了将LCA应用于加速度测量或组合的加速度测量/自我报告测量的研究。提取的数据包括研究特征以及加速度计和LCA方法。
结果：在2555篇论文中发现，共筛选了66篇全文论文,和12篇论文(11篇横截面，包括来自8项独特研究的1个队列）。研究样本量为217-7931（平均值2249，标准偏差2780）。在8个独特的研究中，潜在类别变量包括体力活动(100%)和久坐行为(75%)。大约三分之二（63％）的研究仅使用加速度测量法，而38％的研究将加速度测量法和自我报告相结合来得出潜在类别。LCA模型中基于加速度计的变量包括按星期几的测量值(38%)，工作日与周末（13%），周平均值（13%），分分钟/天（13%），性别特异性z得分（13%），和每小时（13％）。指导选择最终类别数量和模型拟合的标准因研究而异，包括贝叶斯信息标准(63%)，实质性知识(63%)，熵（50%），Akaike信息标准(50%)，样本量（50%），引导似然比测试（38%），和目视检查（38%）。这些研究探索了多达5项(25%)，6（38%），或7+（38%）类，以3（50%）结尾，4（13%），或5（38％）最终课程。
结论：这篇综述探讨了LCA在身体活动和久坐行为中的应用，并确定了利用LCA进行未来研究的改进领域。LCA用于识别独特的分组作为数据缩减工具，结合自我报告和加速度测量，并将不同的身体活动强度和久坐行为组合在一个LCA模型或单独的模型中。
方法：PubMed中的综合搜索，WebofScience,CINHAL,SPORTDiscus,Embase确定了到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纳入标准,差异通过共识解决.选择了将LCA应用于加速度测量或组合的加速度测量/自我报告测量的研究。提取的数据包括研究特征以及加速度计和LCA方法。
结果：在2555篇论文中发现，共筛选了66篇全文论文,和12篇论文(11篇横截面，包括来自8项独特研究的1个队列）。研究样本量为217-7931（平均值2249，标准偏差2780）。在8个独特的研究中，潜在类别变量包括体力活动(100%)和久坐行为(75%)。大约三分之二（63％）的研究仅使用加速度测量法，而38％的研究将加速度测量法和自我报告相结合来得出潜在类别。LCA模型中基于加速度计的变量包括按星期几的测量值(38%)，工作日与周末（13%），周平均值（13%），分分钟/天（13%），性别特异性z得分（13%），和每小时（13％）。指导选择最终类别数量和模型拟合的标准因研究而异，包括贝叶斯信息标准(63%)，实质性知识(63%)，熵（50%），Akaike信息标准(50%)，样本量（50%），引导似然比测试（38%），和目视检查（38%）。这些研究探索了多达5项(25%)，6（38%），或7+（38%）类，以3（50%）结尾，4（13%），或5（38％）最终课程。
结论：这篇综述探讨了LCA在身体活动和久坐行为中的应用，并确定了利用LCA进行未来研究的改进领域。LCA用于识别独特的分组作为数据缩减工具，结合自我报告和加速度测量，并将不同的身体活动强度和久坐行为组合在一个LCA模型或单独的模型中。
