Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

Abstract

Background Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. Methods Community-dwelling subjects aged � 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameterswere identified by means of a cross-validation method.

Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

“…It can also be observed that including MSE will improve the classification accuracy of the model across clinical tests, independent of the percentage of features selected. In addition, the multifactor test outperforms the single BBS assessment in all scenarios, which is consistent with previous studies which determined that a multifactor test is better at capturing the complex nature of falls [ 38 , 39 ]. Despite TUG having a higher AUC score than the multifactor test, it is important to point out that the latter is simultaneously assessing both mobility and balance, which are two of the main factors that affect falls.…”

“…They used a set of statistical, PE, and weighted permutation entropy (WPE) features to successfully estimate the SFBBS score, which can provide doctors with information on the fall risk of patients. Despite promising results, this study failed to implement a multifactorial assessment test which has been proven to be more effective than a single clinical tool at capturing the complex nature of falls [ 38 , 39 ]. Furthermore, the study did not compare the PE and MSE, as both tools were designed to measure the complexity of a signal.…”

Comparing Machine Learning Methods to Improve Fall Risk Detection in Elderly with Osteoporosis from Balance Data

“…Assessment models have been developed to support the identification of useful information for fall prevention. For example, a linear model to predict the risk of falling in older adults based on postural sway parameters presented a better performance (area under the receiver operating characteristic curve (AUC): 0.73; 95% CI: 0.63-0.83) than a model using exclusively clinical parameters (AUC: 0.67; 95% CI: 0.55-0.79) [13]. Other examples are the logistic regression models that were developed to predict the risk of falling in elder people [8][9][10][11][14][15][16], but the principal limitation of these models is the assumption of linearity between the dependent variable and the independent variables.…”

The Added Value of Combined Timed Up and Go Test, Walking Speed, and Grip Strength on Predicting Recurrent Falls in Chinese Community-dwelling Elderly

“…And "TUGT-, GS-, WS+" showed increased prognostic power toward discriminating recurrent-fallers than a single test, with the AUC increasing from 0.726 to 0.815. Previous studies suggested that gender and age should be controlled to provide better information about predictive value, 7 mainly because female gender was associated with a higher prevalence of falls 25 by the faster decline of bone mass, 26 and skeletal muscle mass 27 than men as well as agerelated reduction in muscle mass and muscle strength and deterioration of overall physical motor skills and abilities. 28 Meanwhile, other adjusted factors in our model, including IPAQ, cohabiting with others, living alone, walking aid, fall history, depression, osteoarthritis, and diabetes, have also been noted in previous studies.…”