用大模型数字孪生预测吸烟戒断消息的个人感知效果，精准度远超传统方法。
Personalized Prediction of Perceived Message Effectiveness Using Large Language Model Based Digital Twins
- 构建基于大模型的个人数字孪生，融合用户特征与历史反馈进行个性化预测。
- 在301名烟民数据上，准确率达49%~45%，比零样本大模型高12个百分点。
- 适合需个性化推送健康干预内容的mHealth平台，尤其关注个体差异时。
潜在干预对象对干预消息的感知效果（PME）对移动健康（mHealth）平台个性化推送戒烟信息至关重要。本研究评估大语言模型（LLMs）预测戒烟消息PME的能力。采用301名年轻吸烟者提供的3010条消息评分（5点李克特量表），对比三类方法：（1）基于标注数据的监督学习模型；（2）无需任务微调的零样本与少样本大模型；（3）融合个体特征与历史评分的LLM数字孪生。每名参与者测试3条保留消息，以准确率、Cohen's kappa和F1值评估性能。结果显示，数字孪生平均比零/少样本模型高12个百分点，比监督基线高13个百分点，准确率分别为0.49（内容质量）、0.45（应对支持）、0.49（戒烟支持），在简化3点量表上方向准确率达0.75、0.66、0.70。数字孪生预测分布更广，体现更强个体敏感性。结合个人画像与大模型可有效捕捉个体差异，优于监督与零/少样本方法。提升PME预测能力有助于实现mHealth中更精准的干预内容定制。基于大模型的数字孪生在戒烟及其他行为改变干预中具有应用潜力。
原文摘要 · Abstract (English)
Perceived message effectiveness (PME) by potential intervention end-users is important for selecting and optimizing personalized smoking cessation intervention messages for mobile health (mHealth) platform delivery. This study evaluates whether large language models (LLMs) can accurately predict PME for smoking cessation messages. We evaluated multiple models for predicting PME across three domains: content quality, coping support, and quitting support. The dataset comprised 3010 message ratings (5-point Likert scale) from 301 young adult smokers. We compared (1) supervised learning models trained on labeled data, (2) zero and few-shot LLMs prompted without task-specific fine-tuning, and (3) LLM-based digital twins that incorporate individual characteristics and prior PME histories to generate personalized predictions. Model performance was assessed on three held-out messages per participant using accuracy, Cohen's kappa, and F1. LLM-based digital twins outperformed zero and few-shot LLMs (12 percentage points on average) and supervised baselines (13 percentage points), achieving accuracies of 0.49 (content), 0.45 (coping), and 0.49 (quitting), with directional accuracies of 0.75, 0.66, and 0.70 on a simplified 3-point scale. Digital twin predictions showed greater dispersion across rating categories, indicating improved sensitivity to individual differences. Integrating personal profiles with LLMs captures person-specific differences in PME and outperforms supervised and zero and few-shot approaches. Improved PME prediction may enable more tailored intervention content in mHealth. LLM-based digital twins show potential for supporting personalization of mobile smoking cessation and other health behavior change interventions.
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