睡眠見守りセンサーデータの因果分析　その2

潜在成長モデルによるアプローチ

抄録

In this paper, we applied structural equation modeling (SEM) and latent growth model (LGM) to evaluate the effects of vital signs and environmental data, which were obtained during 90 minutes after falling asleep, on sleep quality. The data used for the analysis is obtained by a care support device used in an elderly care facility. Applying SEM analysis to the data 90 minutes after falling asleep revealed factors that deepen sleep. Then applying LGM analysis, we found that each sleep level factors have a different sleep-deepening effect. Statistical analysis environment R and the lavaan package were used for both SEM and LGM analysis in this paper. The results show that SEM and LGM can be used to build a rational model of the effects of vital signs and environmental conditions on deepening sleep.