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We examined the measurement properties of fall-related psychological instruments with a sample of 133 older adults (M age = 74.4 years, SD = 9.4). Measures included the Comprehensive Falls Risk Screening Instrument, Falls-efficacy Scale-International (FES-I), Activities-specific Balance Confidence (ABC), modified Survey of Activities and Fear of Falling in the Elderly (mSAFFE), Consequences of Falling (CoF), Physical Activity Scale for the Elderly (PASE), and 36-Item Short-Form Health Survey (SF-36). The FES-I, ABC, mSAFFE, and CoF were significantly correlated with each other, with SF-36, and with mobility. The ABC and mSAFFE were significantly correlated with PASE. The ABC differentiated between fallers and nonfallers and predicted total falls risk. Findings can assist with the selection of psychological instruments in a falls risk screening context. (Contains 3 tables.)

Background. Functional dependence and the risks of disability increase with age. The loss of independence is thought to be partially due to a decrease in physical activity. However, in populations, accurate measurement of physical activity is challenging and may not provide information on functional impairment. Methods. This study therefore assessed physical functionality and physical activity level in a group of nonagenarians (11 men/11 women: 93 [+ or -] 1 years, 66.6 [+ or -] 2.4 kg. body mass index [BMI] = 24 [+ or -] 1 kg/[m.sup.2]) and a group of participants aged 60-74 years (17 men/15 women: 70 [+ or -] 1 years, 83.3 [+ or -] 3.0 kg, BMI = 29 [+ or -] 1 kg/[m.sup.2]) from the Louisiana Healthy Aging Study. Physical activity level was calculated from total energy expenditure (TEE) and resting metabolic rate (RMR). Physical functionality was assessed using the Reduced Continuous Scale Physical Functional Performance Test (CS-PFP10). Results. Nonagenarians had lower absolute (p < .001) and adjusted (p < .007) TEE compared to participants aged 60-74 years which was attributed to a reduction in both RMR and physical activity level. Nonagenarians also had reduced functional performance (p < .001) which was correlated with activity level (r = 0.68, p < .001). Conclusions. When compared to individuals aged 60-74 years, 73% of the reduction in TEE in nonagenarians can be attributed to a reduction in physical activity level, the remaining being accounted for by a reduction in RMR. The reduced physical activity in nonagenarians is associated with less physical functionality. This study provides the first objective comparison of physical functionality and actual levels of physical activity in older individuals.

Since falls are the leading cause of injury and injurious death among older adults, the development of techniques for prediction and prevention of falls are of great value to the aging society. The purpose of this study is to examine a single descriptor of walking dynamics, derived from an inverted-pendulum model, that is capable of distinguishing older adults with a history of falls from those without. In the study, we investigated difference between the motion trajectory of a person's center of mass (COM) calculated by the inverted pendulum model and that measured by a 3D motion capture system. As a pilot study, we recruited and tested two groups of participants. One group has 15 participants who fell in the last 12 months and thus is called the fallers group and the other has 16 participants of similar ages who did not fall in the same time period. The test results reveal that the errors between the model-calculated COM and the measured COM for the two groups are significantly different. Three machine learning (ML) methods were implemented to help classify the test data. The results of the study demonstrate that the proposed distinguisher can separate the individuals with a fall history from those without a fall history and, therefore, it may be potentially used to predict the risk of falls of an older adult. [ABSTRACT FROM AUTHOR]

The purpose of this investigation was to determine associations among race, education, and income and falls risk scores from the Comprehensive Falls Risk Screening Instrument (CFRSI). Participants (N = 626) were Black (n = 318) and White (n = 308) older adults (M age = 72.62, SD = 9.31). The CFRSI assessed history, physical functioning, medication, vision, and home environment, and produced an average total falls risk score. A 3 (Income) × 3 (Education) × 2 (Race) MANCOVA showed main effects for income and race. Univariate tests revealed that low-income participants had greater scores for history, physical functioning, and total falls risk than high income participants. Similarly, Black participants had greater scores on history, physical functioning, medication, and total falls risk than White participants. Low income and Black race were associated with greater falls risk, and professionals should consider these disparities when designing community-based falls prevention programs. [ABSTRACT FROM PUBLISHER]

Falling is a serious problem in an aged society such that assessment of the risk of falls for individuals is imperative for the research and practice of falls prevention. This paper introduces an application of several machine learning methods for training a classifier which is capable of classifying individual older adults into a high risk group and a low risk group (distinguished by whether or not the members of the group have a recent history of falls). Using a 3D motion capture system, significant gait features related to falls risk are extracted. By training these features, classification hypotheses are obtained based on machine learning techniques (K Nearest-neighbour, Naive Bayes, Logistic Regression, Neural Network, and Support Vector Machine). Training and test accuracies with sensitivity and specificity of each of these techniques are assessed. The feature adjustment and tuning of the machine learning algorithms are discussed. The outcome of the study will benefit the prediction and prevention of falls.