The demand for mental health services has risen substantially in recent years, leading to challenges in meeting patient needs promptly. Virtual agents capable of emulating motivational interviews (MI) have emerged as a potential solution to address this issue, offering immediate support that is especially beneficial for therapy modalities requiring multiple sessions. However, developing effective patient simulation methods for training MI dialog systems poses challenges, particularly in generating syntactically and contextually correct, and diversified dialog acts while respecting existing patterns and trends in therapy data. This paper investigates data-driven approaches to simulate patients for training MI dialog systems. We propose a novel method that leverages time series models to generate diverse and contextually appropriate patient dialog acts, which are then transformed into utterances by a conditioned large language model. Additionally, we introduce evaluation measures tailored to assess the quality and coherence of simulated patient dialog. Our findings highlight the effectiveness of dialog act-conditioned approaches in improving patient simulation for MI, offering insights for developing virtual agents to support mental health therapy.