Small Language Model-Based Intermediate Interpretations for Predicting Nurse Stress and Its Factors from Step Count Data

In this paper, we propose a hybrid approach that combines Small Language Model (SLM)-based interpretation with machine learning (ML)-based prediction to analyze stress levels and related factors from step-count data. While several datasets exist for predicting mental health conditions from sensor data, most do not explicitly address the underlying factors associated with stress. To explore this issue, we collect step-count data from 30 nurses, together with stress assessments (QIDS: Quick Inventory of Depressive Symptomatology) and stress factor ratings based on six questionnaire items measured on a 4-point Likert scale, collected over 8 days within 4 weeks. We evaluate the proposed approach through two tasks. The first task examines how intermediate textual interpretations relate to stress presence estimation. Under our baseline experimental settings, BERT (Bidirectional Encoder Representations from Transformers) with intermediate stress interpretations achieved the highest accuracy (0.74), compared with BERT using raw step-count representations (step count: 0.63, distance: 0.59) and a prompt-based approach. The second task evaluates the association between intermediate interpretations and stress factor ranking. In this setting, BERT with intermediate stress interpretations achieved a ranking accuracy of 0.60, compared to 0.56 when using step-count sequences without interpretation. Higher correlations were observed for work-related stress factors such as “workplace relationships,” “busy work,” “heavy work responsibilities,” and “lack of time off.” Overall, these results suggest that intermediate textual representations derived from step-count data can be useful for stress analysis under baseline conditions, while avoiding causal claims about stress determinants.