Physical inactivity remains a major public health concern, having associations with adverse health outcomes such as cardiovascular disease and type-2 diabetes. Mobile health applications present a promising avenue for low-cost, scalable physical activity promotion, yet often suffer from small effect sizes and low adherence rates, particularly in comparison to human coaching. Goal-setting is a critical component of health coaching that has been underutilized in adaptive algorithms for mobile health interventions. This paper introduces a modification to the Thompson sampling algorithm that places emphasis on individualized goal-setting by optimizing personalized reward functions. As a step towards supporting goal-setting, this paper offers a balanced approach that can leverage shared structure while optimizing individual preferences and goals. We prove that our modification incurs only a constant penalty on the cumulative regret while preserving the sample complexity benefits of data sharing. In a physical activity simulator, we demonstrate that our algorithm achieves substantial improvements in cumulative regret compared to baselines that do not share data or do not optimize for individualized rewards.

翻译：身体活动不足仍是重大公共卫生问题，与心血管疾病和2型糖尿病等不良健康结局相关。移动健康应用为低成本、可扩展的身体活动促进提供了有前景的途径，但常面临效应量小和依从性低的问题，尤其是与人工教练相比。目标设定是健康指导的关键组成部分，但在移动健康干预的自适应算法中尚未得到充分利用。本文提出对汤普森采样算法进行改进，通过优化个性化奖励函数，强调个体化目标设定。作为支持目标设定的一步，本文提供了一种平衡方法，既能利用共享结构，又能优化个体偏好与目标。我们证明该改进仅对累积遗憾产生常数惩罚，同时保留了数据共享的样本复杂度优势。在身体活动模拟器中，我们展示该算法相比不共享数据或未优化个体化奖励的基线方法，在累积遗憾上实现了显著改进。