University of California, San Diego, School of Medicine, La Jolla, CA, United States.
JMIR Mhealth Uhealth. 2015 Dec 31;3(4):e108. doi: 10.2196/mhealth.4292.
Chronic diseases such as diabetes require high levels of medication adherence and patient self-management for optimal health outcomes. A novel sensing platform, Digital Health Feedback System (Proteus Digital Health, Redwood City, CA), can for the first time detect medication ingestion events and physiological measures simultaneously, using an edible sensor, personal monitor patch, and paired mobile device. The Digital Health Feedback System (DHFS) generates a large amount of data. Visual analytics of this rich dataset may provide insights into longitudinal patterns of medication adherence in the natural setting and potential relationships between medication adherence and physiological measures that were previously unknown.
Our aim was to use modern methods of visual analytics to represent continuous and discrete data from the DHFS, plotting multiple different data types simultaneously to evaluate the potential of the DHFS to capture longitudinal patterns of medication-taking behavior and self-management in individual patients with type II diabetes.
Visualizations were generated using time domain methods of oral metformin medication adherence and physiological data obtained by the DHFS use in 5 patients with type II diabetes over 37-42 days. The DHFS captured at-home metformin adherence, heart rate, activity, and sleep/rest. A mobile glucose monitor captured glucose testing and level (mg/dl). Algorithms were developed to analyze data over varying time periods: across the entire study, daily, and weekly. Following visualization analysis, correlations between sleep/rest and medication ingestion were calculated across all subjects.
A total of 197 subject days, encompassing 141,840 data events were analyzed. Individual continuous patch use varied between 87-98%. On average, the cohort took 78% (SD 12) of prescribed medication and took 77% (SD 26) within the prescribed ±2-hour time window. Average activity levels per subjects ranged from 4000-12,000 steps per day. The combination of activity level and heart rate indicated different levels of cardiovascular fitness between subjects. Visualizations over the entire study captured the longitudinal pattern of missed doses (the majority of which took place in the evening), the timing of ingestions in individual subjects, and the range of medication ingestion timing, which varied from 1.5-2.4 hours (Subject 3) to 11 hours (Subject 2). Individual morning self-management patterns over the study period were obtained by combining the times of waking, metformin ingestion, and glucose measurement. Visualizations combining multiple data streams over a 24-hour period captured patterns of broad daily events: when subjects rose in the morning, tested their blood glucose, took their medications, went to bed, hours of sleep/rest, and level of activity during the day. Visualizations identified highly consistent daily patterns in Subject 3, the most adherent participant. Erratic daily patterns including sleep/rest were demonstrated in Subject 2, the least adherent subject. Correlation between sleep /rest and medication ingestion in each individual subject was evaluated. Subjects 2 and 4 showed correlation between amount of sleep/rest over a 24-hour period and medication-taking the following day (Subject 2: r=.47, P<.02; Subject 4: r=.35, P<.05). With Subject 2, sleep/rest disruptions during the night were highly correlated (r=.47, P<.009) with missing doses the following day.
Visualizations integrating medication ingestion and physiological data from the DHFS over varying time intervals captured detailed individual longitudinal patterns of medication adherence and self-management in the natural setting. Visualizing multiple data streams simultaneously, providing a data-rich representation, revealed information that would not have been shown by plotting data streams individually. Such analyses provided data far beyond traditional adherence summary statistics and may form the foundation of future personalized predictive interventions to drive longitudinal adherence and support optimal self-management in chronic diseases such as diabetes.
糖尿病等慢性病需要高水平的药物依从性和患者自我管理，以实现最佳健康结果。一种新型传感平台，数字健康反馈系统（Proteus Digital Health，加利福尼亚州雷德伍德城），首次可以同时检测药物摄入事件和生理测量值，使用可食用传感器、个人监测贴片和配对的移动设备。数字健康反馈系统（DHFS）生成大量数据。对这个丰富数据集的可视化分析可能会深入了解慢性病患者在自然环境中的药物依从性纵向模式，以及之前未知的药物依从性与生理测量值之间的潜在关系。
我们旨在使用现代可视化分析方法来表示来自 DHFS 的连续和离散数据，同时绘制多种不同的数据类型，以评估 DHFS 捕捉个体 2 型糖尿病患者药物使用行为和自我管理的纵向模式的潜力。
使用时域方法对 5 例 2 型糖尿病患者的口服二甲双胍药物依从性和 DHFS 使用获得的生理数据进行可视化。DHFS 捕获了在家中的二甲双胍依从性、心率、活动和睡眠/休息。开发了算法来分析不同时间段的数据：整个研究期间、每天和每周。在可视化分析后，计算了所有受试者之间睡眠/休息与药物摄入之间的相关性。
共分析了 197 个受试者日，包含 141840 个数据事件。个别连续贴片的使用情况在 87%至 98%之间变化。平均而言，该队列服用了 78%（SD 12）的规定药物，在规定的±2 小时时间窗口内服用了 77%（SD 26）。每个受试者的平均活动水平范围为每天 4000-12000 步。活动水平和心率的组合表明受试者之间的心血管健康水平不同。整个研究期间的可视化捕获了漏服剂量的纵向模式（其中大部分发生在晚上）、个别受试者的摄入时间以及药物摄入时间范围，从 1.5-2.4 小时（受试者 3）到 11 小时（受试者 2）不等。通过将醒来时间、二甲双胍摄入时间和血糖测量时间组合起来，获得了研究期间早晨的个人自我管理模式。在 24 小时期间内将多个数据流组合在一起的可视化捕获了广泛的日常事件模式：当受试者早晨起床、测试血糖、服用药物、上床睡觉、睡眠/休息时间和白天活动水平时。可视化识别出受试者 3 中高度一致的日常模式，受试者 3 是最遵守的参与者。在最不遵守的受试者 2 中，显示了不规则的日常模式，包括睡眠/休息。评估了每个受试者之间睡眠/休息与药物摄入之间的相关性。受试者 2 和 4 显示了 24 小时内的睡眠时间/休息量与第二天的药物服用之间的相关性（受试者 2：r=.47，P<.02；受试者 4：r=.35，P<.05）。与受试者 2 一样，夜间睡眠/休息中断与第二天的漏服剂量高度相关（r=.47，P<.009）。
通过在不同时间间隔内整合来自 DHFS 的药物摄入和生理数据的可视化，捕获了在自然环境中药物依从性和自我管理的详细个人纵向模式。同时可视化多个数据流，提供了丰富的数据表示，揭示了单独绘制数据流不会显示的信息。这种分析提供了远远超出传统依从性汇总统计数据的数据，可能为未来的个性化预测干预奠定基础，以促进慢性病（如糖尿病）的纵向依从性并支持最佳自我管理。
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