Diabetes, a chronic disease that occurs when the pancreas does not produce enough insulin or when the body cannot effectively utilize its insulin, is increasingly recognized as a significant health burden and affects many older adults. Poor sleep quality in diabetic seniors worsens the diabetes condition, but most seniors are tend to regard poor sleep quality as a usual event and do not seek treatment. This study aims to detect poor sleep quality in diabetic seniors through passive in-home monitoring to inform intervention (e.g., seeking diagnosis and treatment) to improve the physical and mental health of diabetic seniors. We derive sensor-based classification models using data from motion sensors installed in each apartment zone (bedroom, living room, kitchen, and bathroom) and a contact sensor on the main door from 39 seniors. Diabetes and poor sleep quality labeling are done based on psychosocial survey data. Our evaluation of the model reveals that (i) diabetes classification using features related to kitchen activity achieved perfect precision, (ii) sleep quality classification in diabetic seniors achieved the best results using Naïve Bayes and features related to night activity. Correlation analysis also reveals that seniors with diabetes are more likely to have poor sleep quality due to frequently voiding at night. Our findings can help community caregivers to monitor the sleep quality of diabetic seniors.