Cláudia Tonelo

Falls are a major cause of morbidity and mortality among older adults. While the Timed Up and Go (TUG) test has recently been identified as the best predictor of falls, it should not be used in isolation to identify individuals at risk. This study aims to develop a predictive model by combining the TUG test with fall risk factors that involve intrinsic and extrinsic elements to predict future falls in Portuguese community-dwelling adults aged 50–60, 60–70, and 70 years or older. A total of 403 participants aged 50 or older completed a questionnaire on demographic information and fall risk factors, underwent the TUG test, and were monitored for 12 months to record falls. ROC curve analysis demonstrated that the TUG test alone effectively distinguished fallers from non-fallers exclusively among adults aged 50–60, with a cut-off time of 6.9 s. Multivariate logistic regression defined three predictive models based on age groups, with ROC curve results as follows: 50–60 (AUC = 0.825, cut-off = 18.1), 60–70 (AUC = 0.754, cut-off = 17.8), and 70 or older (AUC = 0.708, cut-off = 24.8). These findings are clinically significant, demonstrating that the TUG test combined with a few self-reported questions can efficiently identify individuals at risk of falling in just a few minutes, without requiring specialized equipment.

Falls are a major cause of morbidity and mortality among older adults. While the Timed Up and Go (TUG) test has recently been identified as the best predictor of falls, it should not be used in isolation to identify individuals at risk. This study aims to develop a predictive model by combining the TUG test with other risk factors associated with falls in Portuguese community-dwelling adults aged 50–60, 60–70, and 70 years or older. A total of 403 participants aged 50 or older completed a questionnaire on demographic information and fall risk factors, underwent the TUG test, and were monitored for 12 months to record falls. ROC curve analysis demonstrated that the TUG test alone effectively distinguished fallers from non-fallers exclusively among adults aged 50–60, with a cut-off time of 6.9 seconds. Multivariate logistic regression defined three predictive models based on age groups, with ROC curve results as follows: 50–60 (AUC=0.825, cut-off=18.1), 60–70 (AUC=0.754, cut-off=17.8), and 70 or older (AUC=0.708, cut-off=24.8). These findings are clinically significant, demonstrating that the TUG test combined with a few self-reported questions can efficiently identify individuals at risk of falling in just a few minutes, without requiring specialized equipment.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Falls are a major health problem among older adults. About a third of community-dwelling adults aged 65 years or over fall each year. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling and changes in function of musculoskeletal and sensory systems, associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless falls can be prevented by screening to detect risk factors. Objective: To understand if FallSensing screening tool is able to determine the fall risk of community-dwelling adults aged 50 years or over. Methods: FallSensing screening tool is a technological solution for fall risk screening, including a software, a pressure platform and two inertial sensors. The screening software includes questions about sociodemographic and health and lifestyle behaviors, a detailed explanation about procedures to accomplish six functional tests (Grip Strength, Timed Up and Go, 30 seconds Sit-to-Stand, Step test, 4 Stage Balance test “modified” and 10 meters Walking Speed) and three questionnaires concerning environmental risk, activities and participation profile related to mobility and self-efficacy for exercise. The questions registers information about demographic and anthropometric data (age, sex, height and weight), Fear of Falling, history of fall (previous 12 months), health conditions, medication, sedentary lifestyle, upper extremities assistance to stand from a chair, living settings, alcohol habits, self-perceived health and unintentional or involuntary weight lost. We will recruit community-dwelling adults aged 50 years or over, independent to be standing and walking with or without walking aids in different regions of continental Portugal. In order to be a representative sample of Portuguese population, a minimum of 385 participants is needed. Results: The development of the FallSensing screening tool started in October 2015, and the screening of participants started in June 2016 and continues by the time of submission. A set of seniors’ university, parish councils, physical therapy clinics and other community facilities have consented to participate in the study. Conclusions: In order to develop a multifactorial screening tool for fall risk in community-dwelling adults, it is essential to identify key risk factors for falls and select the best method to evaluate them. FallSensing screening tool, gathers a multifactorial assessment, designed and studied through the best evidence, which seek to identify factors that influence the fall risk. Understanding these factors, will allow that preventive strategies could be implemented and decrease falls rates.

BACKGROUND Falls are a major health problem among older adults. The risk of falling can be increased by polypharmacy, vision impairment, high blood pressure, environmental home hazards, fear of falling, and changes in the function of musculoskeletal and sensory systems that are associated with aging. Moreover, individuals who experienced previous falls are at higher risk. Nevertheless, falls can be prevented by screening for known risk factors. OBJECTIVE The objective of our study was to develop a multifactorial, instrumented, screening tool for fall risk, according to the key risk factors for falls, among Portuguese community-dwelling adults aged 50 years or over and to prospectively validate a risk prediction model for the risk of falling. METHODS This prospective study, following a convenience sample method, will recruit community-dwelling adults aged 50 years or over, who stand and walk independently with or without walking aids in parish councils, physical therapy clinics, senior’s universities, and other facilities in different regions of continental Portugal. The FallSensing screening tool is a technological solution for fall risk screening that includes software, a pressure platform, and 2 inertial sensors. The screening includes questions about demographic and anthropometric data, health and lifestyle behaviors, a detailed explanation about procedures to accomplish 6 functional tests (grip strength, Timed Up and Go, 30 seconds sit to stand, step test, 4-Stage Balance test “modified,” and 10-meter walking speed), 3 questionnaires concerning environmental home hazards, and an activity and participation profile related to mobility and self-efficacy for exercise. RESULTS The enrollment began in June 2016 and we anticipate study completion by the end of 2018. CONCLUSIONS The FallSensing screening tool is a multifactorial and evidence-based assessment which identifies factors that contribute to fall risk. Establishing a risk prediction model will allow preventive strategies to be implemented, potentially decreasing fall rate. REGISTERED REPORT IDENTIFIER RR1-10.2196/10304

Purpose: National Institute for Health and Care Excellence (NICE) has recently published quality standards for assessment of fall risk and preventing further falls. According to the standards, multifactorial fall risk assessments should include: identification of falls history; analysis of gait, balance, mobility and muscle strength, among other factors. Despite being based on subjective analysis or simple timing and not being multifactorial, physiotherapists and physicians quite often use these tests as reference scales to differentiate between lower and higher risk of falling. Instrumented TUG has been recently reported to provide important additional information to the overall score. Objective: To explore a case-based approach of fall risk assessment to identify the most relevant and informative risk factors that in combination could better define a person risk profile. Materials and Methods: A multifactorial assessment of fall risk through questionnaires, standard functional tests, tests instrumented with inertial sensors, and force platforms has been studied within a group aged 55-80 years old. Different fall risk factors and fall risk assessment methods were analyzed in a case-based descriptive study. Results & Discussion: Subjects at higher risk of falling were identified based on their detailed profiles. A set of features were obtained from the instrumented standard tests differing significantly between subjects presenting higher or lower fall risk. Therefore, instrumenting conventional tests with wearables containing inertial sensors and force platforms gives more detailed and quantitative insights. This information can be used to better define and tailor fall prevention exercises and to improve the follow-up of the evolution of the subject.