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

BMC Medical Informatics and Decision Making

Research Article

Gerhard Nemitz¹  Hubertus Meyer zu Schwabedissen¹  Mareike Schulze²  Anja Rehwald²  Michael Marschollek²  Klaus-Hendrik Wolf³  Matthias Gietzelt³ 

[1] Braunschweig Medical Center, Department for Geriatric Medicine, Gliesmaroder Straße 29, 38106, Braunschweig, Germany;Hanover Medical School, Peter L. Reichertz Institute for Medical Informatics, Carl-Neuberg-Str. 1, 30625, Hanover, Germany;University of Braunschweig - Institute of Technology, Peter L. Reichertz Institute for Medical Informatics, Mühlenpfordtstr. 23, 38106, Braunschweig, Germany;

关键词: Negative Predictive Value;    Fall Risk;    Risk Assessment Tool;    Brier Score;    Fall Event;   

DOI  :  10.1186/1472-6947-11-48

 received in 2011-03-07, accepted in 2011-06-28,  发布年份 2011

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Marschollek et al; licensee BioMed Central Ltd. 2011