Methods Inf Med - Sensor-based fall risk assessment--an expert 'to go'.

Tópicos

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Resumo

CKGROUND: Falls are a predominant problem in our aging society, often leading to severe somatic and psychological consequences, and having an incidence of about 30% in the group of persons aged 65 years or above. In order to identify persons at risk, many assessment tools and tests have been developed, but most of these have to be conducted in a supervised setting and are dependent on an expert rater.OBJECTIVES: The overall aim of our research work is to develop an objective and unobtrusive method to determine individual fall risk based on the use of motion sensor data. The aims of our work for this paper are to derive a fall risk model based on sensor data that may potentially be measured during typical activities of daily life (aim #1), and to evaluate the resulting model with data from a one-year follow-up study (aim #2).METHODS: A sample of n = 119 geriatric inpatients wore an accelerometer on the waist during a Timed 'Up & Go' test and a 20 m walk. Fifty patients were included in a one-year follow-up study, assessing fall events and scoring average physical activity at home in telephone interviews. The sensor data were processed to extract gait and dynamic balance parameters, from which four fall risk models--two classification trees and two logistic regression models--were computed: models CT#1 and SL#1 using accelerometer data only, models CT#2 and SL#2 including the physical activity score. The risk models were evaluated in a ten-times tenfold cross-validation procedure, calculating sensitivity (SENS), specificity (SPEC), positive and negative predictive values (PPV, NPV), classification accuracy, area under the curve (AUC) and the Brier score.RESULTS: Both classification trees show a fair to good performance (models CT#1/CT#2): SENS 74%/58%, SPEC 96%/82%, PPV 92%/ 74%, NPV 77%/82%, accuracy 80%/78%, AUC 0.83/0.87 and Brier scores 0.14/0.14. The logistic regression models (SL#1/SL#2) perform worse: SENS 42%/58%, SPEC 82%/ 78%, PPV 62%/65%, NPV 67%/72%, accuracy 65%/70%, AUC 0.65/0.72 and Brier scores 0.23/0.21.CONCLUSIONS: Our results suggest that accelerometer data may be used to predict falls in an unsupervised setting. Furthermore, the parameters used for prediction are measurable with an unobtrusive sensor device during normal activities of daily living. These promising results have to be validated in a larger, long-term prospective trial.

Resumo Limpo

ckground fall predomin problem age societi often lead sever somat psycholog consequ incid group person age year order identifi person risk mani assess tool test develop conduct supervis set depend expert raterobject overal aim research work develop object unobtrus method determin individu fall risk base use motion sensor data aim work paper deriv fall risk model base sensor data may potenti measur typic activ daili life aim evalu result model data oneyear followup studi aim method sampl n geriatr inpati wore acceleromet waist time go test m walk fifti patient includ oneyear followup studi assess fall event score averag physic activ home telephon interview sensor data process extract gait dynam balanc paramet four fall risk modelstwo classif tree two logist regress modelswer comput model ct sl use acceleromet data model ct sl includ physic activ score risk model evalu tentim tenfold crossvalid procedur calcul sensit sen specif spec posit negat predict valu ppv npv classif accuraci area curv auc brier scoreresult classif tree show fair good perform model ctct sen spec ppv npv accuraci auc brier score logist regress model slsl perform wors sen spec ppv npv accuraci auc brier score conclus result suggest acceleromet data may use predict fall unsupervis set furthermor paramet use predict measur unobtrus sensor devic normal activ daili live promis result valid larger longterm prospect trial

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