Mental health care increasingly uses data from smartphones and wearable technology to monitor symptoms, daily behavior, and early warning signs. But these datasets are large, multimodal, and difficult for clinicians to interpret, which is where large language models (LLMs) come in. The LLM scaling interpretation would enable larger longitudinal studies, broader remote monitoring, early detection of relapse, and more personalized treatment adjustments. Additionally, LLM assistance can provide faster feedback, create personalized explanations, maintain engagement between visits, which supports more responsive and human-centered care. This would also allow clinicians to allocate resources more effectively and support early intervention programs to move mental health care toward a preventive, data-informed model. This study provides insight into where LLMs perform well, such as long-term data, high quality data, and worsening anxiety/depression patterns, and where they perform poorly, including improvement trends and passive technology interpretation. Clinically, this research matters because it supports scalable, personalized, real-time psychiatric care and could significantly reduce clinician burden.