Sustaining the effectiveness of behavior change technologies remains a key challenge. AI self-modeling, which generates personalized portrayals of one's ideal self, has shown promise for motivating behavior change, yet prior work largely examines short-term effects. We present one of the first longitudinal evaluations of AI self-modeling in fitness engagement through a two-stage empirical study. A 1-week, three-arm experiment (visual self-modeling (VSM), auditory self-modeling (ASM), Control; N=28) revealed that VSM drove initial performance gains, while ASM showed no significant effects. A subsequent 4-week study (VSM vs. Control; N=31) demonstrated that VSM sustained higher performance levels but exhibited diminishing improvement rates after two weeks. Interviews uncovered a catalyst effect that fostered early motivation through clear, attainable goals, followed by habituation and internalization which stabilized performance. These findings highlight the temporal dynamics of personalized nudging and inform the design of behavior change technologies for long-term engagement.

翻译：行为改变技术如何维持长期有效性仍是关键挑战。AI自我建模通过生成个体理想自我的个性化表征，在促进行为改变方面展现出潜力，但既有研究主要关注短期效果。我们通过两阶段实证研究，首次对AI自我建模在健身参与中的长期效果进行纵向评估。为期1周的三组实验（视觉自我建模（VSM）、听觉自我建模（ASM）、对照组；N=28）表明，VSM能驱动初期表现提升，而ASM未产生显著效果。后续为期4周的研究（VSM vs. 对照组；N=31）显示，VSM能维持更高表现水平，但在两周后改善速率呈现递减趋势。访谈分析揭示了"催化剂效应"：清晰可达的目标在早期激发动机，随后习惯化与内化过程使表现趋于稳定。这些发现阐明了个性化助推的时间动态特征，为设计长期行为参与的改变技术提供了理论依据。