BMC Med Inform Decis Mak - Sensors vs. experts - a performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients.

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground fall event contribut signific mortal morbid cost age popul order identifi person risk target prevent measur mani score assess tool develop often requir expertis cost implement recent research investig use wearabl inerti sensor provid object data motion featur can use assess individu fall risk automat far unknown well new method perform comparison convent fall risk assess tool aim research compar predict perform new sensorbas method convent establish method base prospect datamethod first studi phase inpati geriatr clinic took part motion measur use wireless triaxial acceleromet time upgo tug test m walk furthermor st thoma risk assess tool fall elder inpati stratifi perform multidisciplinari geriatr care team estim patient fall risk second followup phase studi particip interview one year includ fall activ assess predict perform tug stratifi team score compar furthermor two automat induc logist regress model base convent clinic assess data conv well sensor data sensor matchedresult among risk assess score geriatr team score sensit specif outperform stratifi tug induc logist regress model conv sensor achiev similar perform valu sensit specif auc lr model abl identifi person risk simpl scoresconclus sensorbas object measur motion paramet geriatr patient can use assess individu fall risk predict model perform match model base convent clinic assess data sensorbas measur use small wearabl devic may contribut signific inform convent method feasibl unsupervis set prospect research need assess costbenefit relat approach

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