Evaluation of Falls Risk in Community-Dwelling Older Adults Using Body-Worn Sensors

Abstract

Background: Falls are the most common cause of injury and hospitalization and one of the principal causes of death and disability in older adults worldwide. This study aimed to determine if a method based on body-worn sensor data can prospectively predict falls in community-dwelling older adults, and to compare its falls prediction performance to two standard methods on the same data set. Methods: Data were acquired using body-worn sensors, mounted on the left and right shanks, from 226 community-dwelling older adults (mean age 71.5 ± 6.7 years, 164 female) to quantify gait and lower limb movement while performing the ‘Timed Up and Go’ (TUG) test in a geriatric research clinic. Participants were contacted by telephone 2 years following their initial assessment to determine if they had fallen. These outcome data were used to create statistical models to predict falls. Results: Results obtained through cross-validation yielded a mean classification accuracy of 79.69% (mean 95% CI: 77.09–82.34) in prospectively identifying participants that fell during the follow-up period. Results were significantly (p < 0.0001) more accurate than those obtained for falls risk estimation using two standard measures of falls risk (manually timed TUG and the Berg balance score, which yielded mean classification accuracies of 59.43% (95% CI: 58.07–60.84) and 64.30% (95% CI: 62.56–66.09), respectively). Conclusion: Results suggest that the quantification of movement during the TUG test using body-worn sensors could lead to a robust method for assessing future falls risk.

Do functional mobility tests predict the risk of falls in community-dwelling elderly?

“…Greene et al (2012) (27) Table 4). Of these, only the TUG and BERG Tests can be performed meta-analysis because they have enough studies for the analytical tool.…”

Early identification of declining balance in higher functioning older adults, an inertial sensor based method

“…The BBS has been shown to be highly correlated with TUG time [12] (also referred to as manual TUG). As such it was selected as the clinical reference standard of balance for comparison with the quantitative TUG.…”

“…Quantitative TUG parameters were calculated from the raw sensor data, these are described in detail elsewhere [11,12]. In summary temporal, spatio-temporal, turn and tri-axial angular velocity parameters were extracted for further analyses.…”

“…The TUG is quick, easy to administer and has been shown to have some predictive power for falls and mobility impairment [9,10]. Additionally, the application of wireless inertial sensors to the lower limbs during the TUG allows measurements such as cadence, stance phase duration and angular velocity to be recorded [11,12]. These objective measures have been combined statistically to improve the accuracy of falls risk assessment as compared to TUG time [11,12].…”

“…Additionally, the application of wireless inertial sensors to the lower limbs during the TUG allows measurements such as cadence, stance phase duration and angular velocity to be recorded [11,12]. These objective measures have been combined statistically to improve the accuracy of falls risk assessment as compared to TUG time [11,12]. As altered balance is associated with falls risk these inertial sensor based measures may have utility in identifying early changes in balance for higher functioning groups [4].…”