Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

BACKGROUND: Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. METHODS: In a first study phase, 119 inpatients of a geriatric clinic took part in motion measurements using a wireless triaxial accelerometer during a Timed Up&Go (TUG) test and a 20 m walk. Furthermore, the St. Thomas Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) was performed, and the multidisciplinary geriatric care team estimated the patients' fall risk. In a second follow-up phase of the study, 46 of the participants were interviewed after one year, including a fall and activity assessment. The predictive performances of the TUG, the STRATIFY and team scores are compared. Furthermore, two automatically induced logistic regression models based on conventional clinical and assessment data (CONV) as well as sensor data (SENSOR) are matched. RESULTS: Among the risk assessment scores, the geriatric team score (sensitivity 56%, specificity 80%) outperforms STRATIFY and TUG. The induced logistic regression models CONV and SENSOR achieve similar performance values (sensitivity 68%/58%, specificity 74%/78%, AUC 0.74/0.72, +LR 2.64/2.61). Both models are able to identify more persons at risk than the simple scores. CONCLUSIONS: Sensor-based objective measurements of motion parameters in geriatric patients can be used to assess individual fall risk, and our prediction model's performance matches that of a model based on conventional clinical and assessment data. Sensor-based measurements using a small wearable device may contribute significant information to conventional methods and are feasible in an unsupervised setting. More prospective research is needed to assess the cost-benefit relation of our approach.

Daily activities and fall risk--a follow-up study to identify relevant activities for sensor-based fall risk assessment.

The demographic change will lead to an increase in the incidence of falls in the elderly. Technological progress allows for unobtrusive physical activity measurement with miniature sensors, e.g. accelerometers. Yet it is unclear which activities or activity patterns are associated with an increased fall risk. The aim of the research for this paper is to identify daily physical activities associated with a high fall risk. A one-year follow-up study was conducted with n=50 geriatric patients who took part in a telephone interview to assess fall events, their consequences and a set of daily physical activities. Descriptive analysis of the data shows that there are marked differences between fallers (n=21) and non-fallers (n=29) in the overall activity level, the amount of shopping activity and associated locomotion, and in the intensity of light household work. The results confirm that there are differences in typical daily activities between fallers and non-fallers that may be used as parameters to enhance fall prediction models.