Department of Statistical Sciences, Wake Forest University, 1834 Wake Forest Road, Winston Salem, NC, 27109, United States, 1 336 758 5300.
Department of Surgery, Wake Forest School of Medicine, Winston Salem, NC, United States.
JMIR Mhealth Uhealth. 2024 Nov 6;12:e54735. doi: 10.2196/54735.
ViSi Mobile has the capability of monitoring a patient's posture continuously during hospitalization. Analysis of ViSi telemetry data enables researchers and health care providers to quantify an individual patient's movement and investigate collective patterns of many patients. However, erroneous values can exist in routinely collected ViSi telemetry data. Data must be scrutinized to remove erroneous records before statistical analysis.
The objectives of this study were to (1) develop a data cleaning procedure for a 1-year inpatient ViSi posture dataset, (2) consolidate posture codes into categories, (3) derive concise summary statistics from the continuous monitoring data, and (4) study types of patient posture habits using summary statistics of posture duration and transition frequency.
This study examined the 2019 inpatient ViSi posture records from Atrium Health Wake Forest Baptist Medical Center. First, 2 types of errors, record overlap and time inconsistency, were identified. An automated procedure was designed to search all records for these errors. A data cleaning procedure removed erroneous records. Second, data preprocessing was conducted. Each patient's categorical time series was simplified by consolidating the 185 ViSi codes into 5 categories (Lying, Reclined, Upright, Unknown, User-defined). A majority vote process was applied to remove bursts of short duration. Third, statistical analysis was conducted. For each patient, summary statistics were generated to measure average time duration of each posture and rate of posture transitions during the whole day and separately during daytime and nighttime. A k-means clustering analysis was performed to divide the patients into subgroups objectively.
The analysis used a sample of 690 patients, with a median of 3 days of extensive ViSi monitoring per patient. The median of posture durations was 10.2 hours/day for Lying, 8.0 hours/day for Reclined, and 2.5 hours/day for Upright. Lying had similar percentages of patients in low and high durations. Reclined showed a decrease in patients for higher durations. Upright had its peak at 0-2 hours, with a decrease for higher durations. Scatter plots showed that patients could be divided into several subgroups with different posture habits. This was reinforced by the k-means analysis, which identified an active subgroup and two sedentary ones with different resting styles.
Using a 1-year ViSi dataset from routine inpatient monitoring, we derived summary statistics of posture duration and posture transitions for each patient and analyzed the summary statistics to identify patterns in the patient population. This analysis revealed several types of patient posture habits. Before analysis, we also developed methodology to clean and preprocess routinely collected inpatient ViSi monitoring data, which is a major contribution of this study. The procedure developed for data cleaning and preprocessing can have broad application to other monitoring systems used in hospitals.
ViSi Mobile 具备在住院期间持续监测患者姿势的能力。分析 ViSi 遥测数据使研究人员和医疗保健提供者能够量化个体患者的运动，并研究许多患者的集体模式。然而，常规收集的 ViSi 遥测数据可能存在错误值。在进行统计分析之前，必须仔细检查数据以删除错误记录。
本研究的目的是（1）为 1 年住院患者 ViSi 姿势数据集开发数据清理程序，（2）将姿势代码合并为类别，（3）从连续监测数据中得出简洁的汇总统计信息，（4）使用姿势持续时间和转换频率的汇总统计信息研究患者姿势习惯的类型。
本研究检查了 Atrium Health Wake Forest Baptist Medical Center 的 2019 年住院 ViSi 姿势记录。首先，确定了 2 种错误类型，记录重叠和时间不一致。设计了一个自动程序来搜索所有记录中的这些错误。数据清理程序删除了错误记录。其次，进行了数据预处理。通过将 185 个 ViSi 代码合并为 5 个类别（躺着、斜倚、直立、未知、用户定义），简化了每个患者的分类时间序列。应用多数票过程去除短时间内的突发。第三，进行了统计分析。对于每个患者，生成汇总统计信息，以测量全天和白天及夜间的每个姿势的平均持续时间以及姿势转换率。进行了 k-均值聚类分析，以便客观地将患者分为亚组。
该分析使用了 690 名患者的样本，每位患者平均有 3 天的广泛 ViSi 监测。躺着的姿势持续时间中位数为 10.2 小时/天，斜倚的姿势持续时间中位数为 8.0 小时/天，直立的姿势持续时间中位数为 2.5 小时/天。躺着的患者在低时长和高时长的比例相似。斜倚的患者随着时长的增加而减少。直立的患者在 0-2 小时时达到峰值，随着时长的增加而减少。散点图显示，患者可以分为具有不同姿势习惯的几个亚组。k-均值分析也证实了这一点，该分析确定了一个活跃亚组和两个具有不同休息方式的静止亚组。
使用来自常规住院监测的 1 年 ViSi 数据集，我们为每个患者得出了姿势持续时间和姿势转换的汇总统计信息，并分析了汇总统计信息以识别患者人群中的模式。该分析揭示了几种患者姿势习惯。在分析之前，我们还开发了一种方法来清洁和预处理常规收集的住院 ViSi 监测数据，这是本研究的主要贡献。为数据清理和预处理开发的程序可以广泛应用于医院使用的其他监测系统。
