受依从性影响的个体内研究自适应数据收集
Adaptive data collection for intra-individual studies affected by adherence
AI总结：
本文针对受依从性影响的个体内研究，提出一种基于控制图和设计优化的自适应EMA触发机制，平衡数据质量与受试者负担，经模拟和真实数据验证，优于现有方法。
摘要：
近年来，移动技术在生态瞬时评估（EMA）和生态瞬时干预（EMI）中的应用使得在医学领域收集适合个体内变异性研究的数据变得更加容易。然而，特别是在数据收集过程中使用自我报告时，在数据质量与受试者负担之间取得平衡存在困难。在本文中，我们针对一种特定的EMA设置解决了这一问题，该设置旨在在自我报告变量的高/低值处向受试者提交一项高要求任务。我们采用了一种受控制图方法和设计优化技术启发的动态方法，以获得一种数据收集的EMA触发机制，该机制同时考虑了自我报告变量的个体变异性以及依从率。我们在模拟环境和来自耳鸣纵向研究的真实大规模数据上测试了该算法。Wilcoxon-Mann-Whitney秩和检验表明，与随机时间表和具有静态阈值的基于规则的算法（当前最先进的方法）相比，该算法往往具有更高的F1分数和效用。总之，该算法被证明在平衡数据质量和参与者负担方面是有效的，特别是正如所进行的分析所表明的，在数据收集受依从性影响的研究中。
英文摘要：
Recently the use of mobile technologies in Ecological Momentary Assessments (EMA) and Interventions (EMI) has made it easier to collect data suitable for intra-individual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subjects. In this paper, we address this problem for a specific EMA setting which aims to submit a demanding task to subjects at high/low values of a self-reported variable. We adopt a dynamic approach inspired by control chart methods and design optimization techniques to obtain an EMA triggering mechanism for data collection which takes into account both the individual variability of the self-reported variable and of the adherence rate. We test the algorithm in both a simulation setting and with real, large-scale data from a tinnitus longitudinal study. A Wilcoxon-Mann-Whitney Rank Sum Test shows that the algorithm tends to have both a higher F1 score and utility than a random schedule and a rule-based algorithm with static thresholds, which are the current state-of-the-art approaches. In conclusion, the algorithm is proven effective in balancing data quality and the burden placed on the participants, especially, as the analysis performed suggest, in studies where data collection is impacted by adherence.
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