Digital behaviour change systems increasingly rely on repeated, system-initiated messages to support users in everyday contexts. LLMs enable these messages to be personalised consistently across interactions, yet it remains unclear whether such personalisation improves individual messages or instead shapes users' perceptions through patterns of exposure. We explore this question in the context of LLM-generated JITAIs, which are short, context-aware messages delivered at moments deemed appropriate to support behaviour change, using physical activity as an application domain. In a controlled retrospective study, 90 participants evaluated messages generated using four LLM strategies: baseline prompting, few-shot prompting, fine-tuned models, and retrieval augmented generation, each implemented with and without Big Five Personality Traits to produce personality-aligned communication across multiple scenarios. Using ordinal multilevel models with within-between decomposition, we distinguish trial-level effects, whether personality information improves evaluations of individual messages, from person-level exposure effects, whether participants receiving higher proportions of personality-informed messages exhibit systematically different overall perceptions. Results showed no trial-level associations, but participants who received higher proportions of BFPT-informed messages rated the messages as more personalised, appropriate, and reported less negative affect. We use Communication Accommodation Theory for post-hoc analysis. These results suggest that personality-based personalisation in behaviour change systems may operate primarily through aggregate exposure rather than per-message optimisation, with implications for how adaptive systems are designed and evaluated in sustained human-AI interaction. In-situ longitudinal studies are needed to validate these findings in real-world contexts.

翻译：数字行为改变系统日益依赖系统发起的重复消息，在日常情境中为用户提供支持。大型语言模型使得这些消息能够在多次交互中保持个性化的一致性，但尚不清楚这种个性化是提升了个别消息的质量，还是通过暴露模式塑造了用户的整体感知。我们在LLM生成的适时适应性干预消息背景下探讨此问题——这类消息简短、情境感知，在认为适合支持行为改变的时刻发送，并以身体活动作为应用领域。在一项受控回顾性研究中，90名参与者评估了四种LLM策略生成的消息：基线提示、少样本提示、微调模型和检索增强生成，每种策略均分别实施包含与不包含大五人格特质两种版本，以在多个场景中产生人格特质对齐的沟通。通过采用包含组内-组间分解的序数多水平模型，我们区分了试验水平效应（人格信息是否提升个别消息的评价）与人际暴露效应（接收到较高比例人格特质信息消息的参与者是否表现出系统性的整体感知差异）。结果显示不存在试验水平关联，但接收到较高比例大五人格特质信息消息的参与者认为消息更具个性化、更恰当，并报告了更少的负面情绪。我们运用沟通适应理论进行事后分析。这些结果表明，行为改变系统中基于人格的个性化可能主要通过累积暴露而非单条消息优化发挥作用，这对持续人机交互中自适应系统的设计与评估具有启示意义。需要在真实情境中进行原位纵向研究以验证这些发现。