A prospective field study for sensor-based identification of fall risk in older people with dementia

Aim of this study was to make a fall prognosis in a cohort of older people with dementia in short-term (2 month), mid-term (4 month) and long-term (8 month) intervals using accelerometry during the subjects' everyday life.

The study was designed as a longitudinal cohort study. The subjects were recruited from a nursing home and geriatric assessment tests were conducted at baseline. Each subject underwent four visits and was measured at each visit for one week. Gait episodes were detected and gait parameters were extracted from these episodes. These gait parameters were combined with the falls occurred during the study. A decision tree induction method was used to analyze the data.

Forty subjects participated in the study, whereby 12 drop-outs were registered. The geriatric assessment tests were unable to distinguish between the groups (AUC < 0.6). The evaluation of the models induced with the decision tree classification showed a rate of correctly classified gait episodes of 88.4% for short-term, 74.8% for mid-term, and 88.5 % for long-term monitoring.

We concluded that it is possible to classify gait episodes of fallers and non-fallers in people with dementia during everyday life using accelerometry.