标题： 考虑不确定性的智能移动健康干预决策点调度 显示英文标题

标题： Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions

摘要： 及时的决策对于移动健康（mHealth）干预的有效性至关重要。在称为“决策点”的预定义时间点，智能mHealth系统，如及时适应性干预（JITAIs），从传感器或调查数据中估计个体的生物行为环境，并决定是否以及如何进行干预。对于针对习惯性行为（例如口腔卫生）的干预措施，有效性通常取决于在目标行为可能发生前不久提供支持。当前的做法是在用户提供的行为时间之前以固定间隔（例如一小时）安排决策点，且固定间隔对所有个体都相同。然而，这种一刀切的方法对于有不规律作息的个体效果不佳，常常在目标行为已经发生后安排决策点，从而使干预无效。在本文中，我们提出了SigmaScheduling，一种基于预测行为时间不确定性的动态安排决策点的方法。当行为时间更可预测时，SigmaScheduling会将决策点安排得更接近预测的行为时间；当时间不确定性较高时，SigmaScheduling会提前安排决策点，从而增加及时干预的可能性。我们使用Oralytics一项10周试验中68名参与者的实际数据评估了SigmaScheduling。SigmaScheduling提高了至少70%的情况下决策点在刷牙事件之前的可能性，从而保留了干预的机会并影响行为。我们的结果表明，SigmaScheduling可以推动精准mHealth的发展，特别是对于针对时间敏感的习惯性行为（如口腔卫生或饮食习惯）的JITAIs。 显示英文摘要

摘要： Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior is likely to occur. Current practice schedules decision points at a fixed interval (e.g., one hour) before user-provided behavior times, and the fixed interval is kept the same for all individuals. However, this one-size-fits-all approach performs poorly for individuals with irregular routines, often scheduling decision points after the target behavior has already occurred, rendering interventions ineffective. In this paper, we propose SigmaScheduling, a method to dynamically schedule decision points based on uncertainty in predicted behavior times. When behavior timing is more predictable, SigmaScheduling schedules decision points closer to the predicted behavior time; when timing is less certain, SigmaScheduling schedules decision points earlier, increasing the likelihood of timely intervention. We evaluated SigmaScheduling using real-world data from 68 participants in a 10-week trial of Oralytics, a JITAI designed to improve daily toothbrushing. SigmaScheduling increased the likelihood that decision points preceded brushing events in at least 70% of cases, preserving opportunities to intervene and impact behavior. Our results indicate that SigmaScheduling can advance precision mHealth, particularly for JITAIs targeting time-sensitive, habitual behaviors such as oral hygiene or dietary habits.