Digital mental health (DMH) interventions, such as text-message-based lessons and activities, offer immense potential for accessible mental health support. While these interventions can be effective, real-world experimental testing can further enhance their design and impact. Adaptive experimentation, utilizing algorithms like Thompson Sampling for (contextual) multi-armed bandit (MAB) problems, can lead to continuous improvement and personalization. However, it remains unclear when these algorithms can simultaneously increase user experience rewards and facilitate appropriate data collection for social-behavioral scientists to analyze with sufficient statistical confidence. Although a growing body of research addresses the practical and statistical aspects of MAB and other adaptive algorithms, further exploration is needed to assess their impact across diverse real-world contexts. This paper presents a software system developed over two years that allows text-messaging intervention components to be adapted using bandit and other algorithms while collecting data for side-by-side comparison with traditional uniform random non-adaptive experiments. We evaluate the system by deploying a text-message-based DMH intervention to 1100 users, recruited through a large mental health non-profit organization, and share the path forward for deploying this system at scale. This system not only enables applications in mental health but could also serve as a model testbed for adaptive experimentation algorithms in other domains.

数字心理健康（DMH）干预，如基于短消息的课程和活动，为心理健康支持提供巨大潜力。利用自适应实验和算法（如Thompson Sampling）进行连续改进和个性化设计，以提高用户体验奖励并为社会行为科学家收集足够的统计数据分析，但对于不同真实环境的影响仍需进一步探索。本文介绍了一个经过两年开发的软件系统，可在收集数据的同时，使用Bandit和其他算法来调整基于短消息的DMH干预组件，并与传统的均匀随机非自适应实验进行对比评估。通过在一个大型心理健康非盈利组织中招募1100名用户，并展示该系统在规模化部署方面的前景，证明该系统不仅适用于心理健康领域，也可作为其他领域自适应实验算法的模型试验平台。

利用自适应赌博实验来提高和研究心理健康的参与度