Increased fall risk is associated with elevated co-contraction about the ankle during static balance challenges in older adults

Nelson-Wong, Erika; Appell, Ryan; McKay, Mike; Nawaz, Hannah; Roth, Joanna; Sigler, Robert; Third, Jacqueline; Walker, Mark F.

doi:10.1007/s00421-011-2094-x

Cited by 61 publications

(46 citation statements)

References 31 publications

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“…Muscle co-contraction may be a contributing factor. Greater muscle co-contraction between the tibialis anterior and gastrocnemius has been reported in fall-prone elderly compared to healthy elderly 39 , and high levels of co-contraction have also been associated with aging 40, 41 . These co-contractions may create correlations between the AP and ML data which standard entropy measures cannot address, but polar coordinates, constructed using both AP and ML time series, may retain some of these correlations.…”

Section: 0 Discussionmentioning

confidence: 99%

Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers

et al. 2015

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The health and financial cost of falls has spurred research to differentiate the characteristics of fallers and non-fallers. Postural stability has received much of the attention with recent studies exploring various measures of entropy. This study compared the discriminatory ability of several entropy methods at differentiating two paradigms in the center-of-pressure (COP) of elderly individuals: 1.) eyes open (EO) versus eyes closed (EC) and 2.) fallers (F) versus non-fallers (NF). Methods were compared using the area under the curve (AUC) of the receiver-operating characteristic (ROC) curves developed from logistic regression models. Overall, multiscale entropy (MSE) and composite multiscale entropy (CompMSE) performed the best with AUCs of 0.71 for EO/EC and 0.77 for F/NF. When methods were combined together to maximize the AUC, the entropy classifier had an AUC of for 0.91 the F/NF comparison. These results suggest researchers and clinicians attempting to create clinical tests to identify fallers should consider a combination of every entropy method when creating a classifying test. Additionally, MSE and CompMSE classifiers using polar coordinate data outperformed rectangular coordinate data, encouraging more research into the most appropriate time series for postural stability entropy analysis.

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Section: 0 Discussionmentioning

confidence: 99%

Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers

et al. 2015

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“…Adaptive capacity of the postural control system was assessed with the sharpened Romberg test [34]. Subjects stood barefoot on their dominant leg for up to 20 seconds.…”

Section: Methodsmentioning

confidence: 99%

The Complexity of Standing Postural Control in Older Adults: A Modified Detrended Fluctuation Analysis Based upon the Empirical Mode Decomposition Algorithm

et al. 2013

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Human aging into senescence diminishes the capacity of the postural control system to adapt to the stressors of everyday life. Diminished adaptive capacity may be reflected by a loss of the fractal-like, multiscale complexity within the dynamics of standing postural sway (i.e., center-of-pressure, COP). We therefore studied the relationship between COP complexity and adaptive capacity in 22 older and 22 younger healthy adults. COP magnitude dynamics were assessed from raw data during quiet standing with eyes open and closed, and complexity was quantified with a new technique termed empirical mode decomposition embedded detrended fluctuation analysis (EMD-DFA). Adaptive capacity of the postural control system was assessed with the sharpened Romberg test. As compared to traditional DFA, EMD-DFA more accurately identified trends in COP data with intrinsic scales and produced short and long-term scaling exponents (i.e., αShort, αLong) with greater reliability. The fractal-like properties of COP fluctuations were time-scale dependent and highly complex (i.e., αShort values were close to one) over relatively short time scales. As compared to younger adults, older adults demonstrated lower short-term COP complexity (i.e., greater αShort values) in both visual conditions (p>0.001). Closing the eyes decreased short-term COP complexity, yet this decrease was greater in older compared to younger adults (p<0.001). In older adults, those with higher short-term COP complexity exhibited better adaptive capacity as quantified by Romberg test performance (r2 = 0.38, p<0.001). These results indicate that an age-related loss of COP complexity of magnitude series may reflect a clinically important reduction in postural control system functionality as a new biomarker.

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“…This oversimplifies geriatric fall risk, which is more accurately modeled by a continuum of fall risk with fuzzy boundaries between multiple risk categories, such as low, moderate, and high fall risk. Sensors that measure whole body motion [19], ground reaction forces [20], and electromyographic signals [21] provide objective, quantitative measures for fall risk assessment. However, the associated equipment is typically located in a gait laboratory and requires a time consuming setup that is difficult to practically integrate into typical clinic schedules.…”

Section: Introductionmentioning

confidence: 99%

Review of fall risk assessment in geriatric populations using inertial sensors

Howcroft

Kofman

Lemaire

2013

J NeuroEngineering Rehabil

308

209

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BackgroundFalls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.MethodsForty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.ResultsInertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).ConclusionsInertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

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Increased fall risk is associated with elevated co-contraction about the ankle during static balance challenges in older adults

Cited by 61 publications

References 31 publications

Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers

Comparing Postural Stability Entropy Analyses to Differentiate Fallers and Non-fallers

The Complexity of Standing Postural Control in Older Adults: A Modified Detrended Fluctuation Analysis Based upon the Empirical Mode Decomposition Algorithm

Review of fall risk assessment in geriatric populations using inertial sensors

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