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Postural sway occurs spontaneously during static stance as a result of the unstable properties of the body. In balance dysfunction, often a consequence of the normal aging process, the amplitude and frequency of this sway may exceed the limits of stability and contribute to a fall. Therefore, it is important to develop reliable and valid tools to assess postural sway and fall risk. When compared with force plate center of pressure (COP) measurements (one of the most commonly used postural sway assessment tools), accelerometers have been as good as, or better at discriminating postural sway differences between populations with and without balance deficits and between tasks requiring different levels of balance control. A wireless tri-axial accelerometer has recently come on the market that is smaller, less expensive, and more sensitive than those previously utilized in postural control studies. However, no reliability or validity studies on this device exist. Therefore, we aimed to assess the new accelerometer’s 1) test-retest reliability, 2) ability to discriminate between tasks with differing sensory conditions (i.e., visual and somatosensory), 3) ability to discriminate between individuals who are fallers versus non-fallers, and 4) and its concurrent validity with other fall risk screening tools in older adult populations. Twenty healthy and independent living older adults (mean age 81±4 years; 8 male and 12 female) participated in the reliability and balance task discrimination study. An accelerometer was taped to the lower back of the participant with a gait belt positioned just below. Participants completed three 30-second trials of the four classic Romberg conditions (standing on a firm or compliant surface with eyes open or closed) in random order. Following a 20 minute rest, participants underwent a second block of testing for a total of 24 trials. Raw data was collected at approximately 250 Hz and low-pass filtered at 55Hz. Data were transformed to correct for pelvic tilt and adjust for low frequency. Following processing of raw data, root mean squared (RMS), with the unit gravity (g), for the anterior-posterior (A-P) and mediolateral (M-L) acceleration data was calculated. Intraclass Correlation Coefficients (ICC) were used to test for reliability, while repeated measures analysis of variance (ANOVA) models were used to test for a main effect of balance condition on the A-P and M-L acceleration RMS.To validate the accelerometer-based balance assessment (ABA) as a fall risk screening tool, we recruited 95 healthy older adults (mean age 86±6 years) residing in five different independent living facilities. Participants reported the number of falls they had in the previous six months prior to testing. Participants then completed, in random order, the Berg Balance Scale (BBS), Timed up-and-go (TUG), Activities-specific Balance Confidence (ABC) scale, and the ABA. The ABA consisted of the same four Romberg conditions used in the reliability assessment with and without a cognitive task (counting backward by 3’s) for a total of eight balance conditions. The best attempt of two successful trials (the one with the lowest RMS values) for each condition was included in the data analysis. The fall risk discriminative power of various combinations of the TUG, BBS, ABC, and/or the ABA was evaluated using logistic regression and chi-square analyses. Spearman’s rank correlation coefficients were used to investigate the relationship between RMS, BBS, and the ABC scale measures. Pearson’s product-moment correlation was used to determine the relationship between RMS and TUG scores.ICCs were all good to excellent with values ranging from 0.736 to 0.972 for trial-to-trial and from 0.760 to 0.954 for block-to-block. There was a significant stepwise increase in A-P and M-L acceleration RMS from conditions 1 to 4. With respect to A-P sway, the mean acceleration RMS increases from conditions 1 to 2, 2 to 3, and 3 to 4 were 0.0031±0.0008g, 0.0035±0.0011g, and 0.0075±0.0022g, respectively. Similarly, the M-L mean acceleration RMS increased significantly from conditions 1 to 2, 2 to 3, and 3 to 4 (0.0025±0.0007g, 0.0068±0.0010g, and 0.0084±0.0018g, respectively). A-P and M-L acceleration RMS were statistically similar when standing on a firm surface. However, M-L acceleration RMS significantly exceeded A-P for condition 3 (F(1, 19) = 4.61, p = 0.045) and condition 4 (F(1, 19) = 6.68, p < 0.018).For the prediction of fall risk, all prediction models with the ABA, alone or in combination with the TUG, BBS, and ABC scale, had AUC’s = 0.80. Models without the ABA had AUC’s in the range of 0.45 to 0.75. In general, most of the prediction models were better at correctly identifying non-fallers than fallers, indicated by substantially larger specificity than sensitivity values. Only for the comparison of multiple fallers to non-fallers did a model, the combination of the TUG, BBS, and ABC scale with the ABA, exhibit both good sensitivity (85.0) and specificity (82.5). No clinical test on its own or in combination exhibited good sensitivity (all = 61.1). Surprisingly, the associations between the ABA and the TUG, BBS, and ABC scale were not only insignificant but extremely weak with correlation coefficients of 0.11, -0.04, and -0.02 for the TUG, BBS, and ABC scale, respectively. As expected, the TUG, BBS, and ABC scale were all significantly correlated with each other and in the proper direction.In summary, the accelerometer exhibited good to excellent trial-to-trial and block-to-block reliability. The accelerometer was also able to discriminate between visual and supporting surface conditions and acceleration axes. Though not significantly correlated with the clinical tests, the ABA alone was better than the TUG, BBS, and/or ABC at discriminating fallers from non-fallers. |
