Pattern-based clustering of daily weigh-in trajectories using dynamic time warping.
Pattern-based clustering of daily weigh-in trajectories using dynamic time warping.
复制标题
DOI：
10.1111/biom.13773
复制
发表时间：
2023-09
期刊：
影响因子：
1.9
通讯作者：
Wrobel, Julia
中科院分区：
文献类型：
--
作者：
Bothwell, Samantha;Kaizer, Alex;Peterson, Ryan;Ostendorf, Danielle;Catenacci, Victoria;Wrobel, Julia
“智能”秤是一种用于频繁监测体重变化和称重行为的新工具。这些量表使研究人员有机会发现个人体重随时间变化的频率模式，以及这些模式与整体体重减轻的关系。我们的激励数据来自一项为期 18 个月的行为减肥研究，研究对象为 55 名超重或肥胖的成年人，他们被要求每天称重。遵守每日称重程序会为每个受试者生成一个二进制时间序列，表明参与者是否在某一天称重。为了通过时不变模式而不是整体依从性来表征称重，我们建议使用具有动态时间规整（DTW）的分层聚类。我们进行了广泛的模拟研究，以评估 DTW 与欧几里德距离和杰卡德距离相比的性能，以恢复依从时间序列中的潜在模式。此外，我们使用集群验证指数 (CVI) 在单一、平均、完整和 Ward 链接下比较集群性能，并评估内部和外部 CVI 如何比较聚类二进制时间序列。我们应用模拟的结论来聚类我们的真实数据并总结观察到的称重模式。我们的分析发现，坚持轨迹模式与减肥显着相关。
“Smart”-scales are a new tool for frequent monitoring of weight change as well as weigh-in behavior. These scales give researchers the opportunity to discover patterns in the frequency that individuals weigh themselves over time, and how these patterns are associated with overall weight loss. Our motivating data come from an 18-month behavioral weight loss study of 55 adults classified as over-weight or obese who were instructed to weigh themselves daily. Adherence to daily weigh-in routines produces a binary times series for each subject, indicating whether a participant weighed in on a given day. To characterize weigh-in by time-invariant patterns rather than overall adherence, we propose using hierarchical clustering with dynamic time warping (DTW). We perform an extensive simulation study to evaluate the performance of DTW compared to Euclidean and Jaccard distances to recover underlying patterns in adherence time series. In addition, we compare cluster performance using cluster validation indices (CVIs) under the single, average, complete, and Ward linkages and evaluate how internal and external CVIs compare for clustering binary time series. We apply conclusions from the simulation to cluster our real data and summarize observed weigh-in patterns. Our analysis finds that the adherence trajectory pattern is significantly associated with weight loss.
登录
查看更多内容
影响因子：
8
作者：
Arbelaitz, Olatz;Gurrutxaga, Ibai;Perona, Inigo
通讯作者：
Perona, Inigo
影响因子：
11.8
作者：
Guijo-Rubio, David;Manuel Duran-Rosal, Antonio;Hervas-Martinez, Cesar
通讯作者：
Hervas-Martinez, Cesar
影响因子：
8
作者：
Brun, Marcel;Sima, Chao;Dougherty, Edward R.
通讯作者：
Dougherty, Edward R.
影响因子：
5
作者：
Liang, Maohan;Liu, Ryan Wen;Lu, Feng
通讯作者：
Lu, Feng
影响因子：
3.8
作者：
Linde, JA;Jeffery, RW;Boyle, RG
通讯作者：
Boyle, RG