Abstract
BackgroundFall 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.MethodsIn 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.ResultsAmong 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.ConclusionsSensor-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.
Highlights
Fall events contribute significantly to mortality, morbidity and costs in our ageing population
Despite promising first results of this sensorbased approach developed by the authors [18], it remains unclear how well the new methods perform in comparison with conventional fall risk assessment tools
The Timed Up&Go test results in Table 2 show an overall classification accuracy of 50%, where a high sensitivity of 90% is pitted against a very low specificity of 22%
Summary
Fall events contribute significantly to mortality, morbidity and costs in our ageing population. 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 It is well-known that fall events constitute an important factor with regard to mortality, morbidity and costs in our aging population. Oliver et al conclude that even the best tools are not able to identify the majority of fallers [9] Keeping this in mind along with the often time-consuming nature of fall risk assessment tests (e.g. the Performance-Oriented Mobility Assessment, POMA [13]) that frequently require expert knowledge, several research groups have developed the idea to perform a sensorbased automatic or semi-automatic assessment using wearable inertial sensors [14,15,16,17]. Despite promising first results of this sensorbased approach developed by the authors [18], it remains unclear how well the new methods perform in comparison with conventional fall risk assessment tools
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