Stress is a pervasive determinant of mental health and a key target for mobile health interventions.
On-device language models (ODLMs) offer privacy-preserving inference without cloud dependency, yet their feasibility for health prediction under mobile resource constraints remains underexplored.
We evaluate ODLMs for multi-modal stress prediction using zero-shot prompting, measuring predictive accuracy alongside latency and throughput.
Our results show that objective sensor features marginally outperform subjective self-reports on average, and that lightweight sub-2B models achieve low latency with predictable resource usage.
Our findings highlight both the promise and the practical constraints of ODLMs for mobile mental health.