Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series

Kojima, Motonaga; Obuchi, Shuichi; Henmi, Osamu; Ikeda, Noriaki

doi:10.1589/jpts.20.243

Cited by 25 publications

(31 citation statements)

References 14 publications

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“…As the partial regression coefficient of WS with GS as a dependent variable was larger than that of GS with WS as a dependent variable for both sexes, the possibility of a path from WS to GS was indicated. A significant positive correlation between GS and WS has been reported; however, this relationship has not been verified using covariance structure analysis or logistic regression analysis. Although both GS and WS are included in the diagnostic criteria for both physical frailty, as proposed by Fried, and sarcopenia, as defined by the Group on Sarcopenia in Older People, the diagnostic flows of the two are different.…”

Section: Discussionmentioning

confidence: 87%

Assessment of potential clinical cascade between oral hypofunction and physical frailty: Covariance structure analysis in a cross‐sectional study

Hihara

Goto

Ichikawa

2019

J of Oral Rehabilitation

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No report has yet examined the impact of oral hypofunction on physical frailty in relation to intra‐relationships of physical frailty assessment items. The purpose of this study was to verify the potential of a clinical cascade between oral hypofunction and physical frailty, and especially to support the hypothesis that the influence of oral hypofunction on physical frailty is greater than the intra‐relationships among elements of physical frailty and that sex differences significantly affect these relationships. The participants were 272 older adults (101 men and 171 women; mean age 75.1 ± 7.5 years). Maximum occlusal force (MOF) and oral dryness (OD), as indicators of oral hypofunction, and grip strength (GS) and walking speed (WS), as indicators of physical frailty, were measured. Mutual relationship of four variables was verified using covariance structure analysis. In men, three paths from MOF to WS and GS and from WS to GS were confirmed, and those from MOF to WS and from WS to GS were found to be significant (P < .01). In women, three paths from MOF to WS and GS and from WS to GS were also confirmed, as with the men, and those from MOF to WS and from MOF to GS were found to be significant (P < .01). Model adaptability was shown to be good for both men and women. The results suggest our hypothesis was verified, and it is expected that the early detection of oral hypofunction, that is MOF, may be important for assessing physical frailty, especially in women.

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Section: Discussionmentioning

confidence: 87%

Assessment of potential clinical cascade between oral hypofunction and physical frailty: Covariance structure analysis in a cross‐sectional study

Hihara

Goto

Ichikawa

2019

J of Oral Rehabilitation

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“…The five best performing models, in terms of overall accuracy, specificity, and sensitivity, used neural networks [47,48], naive Bayesian classifier [41], Mahalanobis cluster analysis [46], and a decision tree [49]. The five worst performing models used regression [44,53,62,72] and a support vector machine [51]. Therefore, intelligent computing methods (neural networks, Bayesian classifiers, etc.)…”

Section: Discussionmentioning

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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“…Regression analysis suggested that AC-VT may have an advantage over other physical performance test parameters for assessing the risk of falling. Previous studies have suggested that acceleration variability was correlated with physical performance and can identify patients at a higher risk for falls (Kojima et al, 2008;Arai et al, 2011;Yamada et al, 2012). This is consistent with our study; previous studies investigating risk factors for falling have shown that gait analysis with accelerometery may be superior to other measures of body function and activity such as TUG, five-times-sit-to-stand, gait speed, and functional reach (Kojima et al, 2008;Arai et al, 2011;Doi et al, 2013) because accelerometery data can be used to comprehensively evaluate motor function, including lower extremity muscle strength, standing balance, gait speed, and gait disorder in the older adults, including frail individuals (Hausdorff et al, 2001;Hausdorff, 2005;Palombaro et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, trunk acceleration during gait showed higher discriminatory ability for the risk of falling among healthy older adults than other physical performance tests (Kojima et al, 2008;Doi et al, 2013). Variability in trunk acceleration as assessed by root mean square (RMS) and autocorrelation (AC) can successfully differentiate between fit and frail older adults (Moe-Nilssen and Helbostad, 2005;Senden et al, 2012).…”

Section: Introductionmentioning

confidence: 97%

“…Several recent studies have used accelerometers to assess the risk of falling with relatively high validity and reliability (Kojima et al, 2008;Caby et al, 2011;Senden et al, 2012). Specifically, trunk acceleration during gait showed higher discriminatory ability for the risk of falling among healthy older adults than other physical performance tests (Kojima et al, 2008;Doi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Accelerometry-based gait analysis predicts falls among patients with a recent fracture who are ambulatory

Matsumoto

Makabe

Morita

et al. 2015

International Journal of Rehabilitation Research

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The aim of this prospective study was to assess whether accelerometry-based gait analysis is associated with the incidence of falls among individuals who are ambulatory and recently had a fracture, and to compare the ability to discriminate body function and structure and activity measures. A total of 100 patients who fulfilled our inclusion criteria were enrolled. At hospital discharge, the following outcomes were assessed: timed-up-and-go test, five-times-sit-to-stand test, one-leg standing test, 5-m gait test, and gait analysis using a wireless three-axis accelerometer. Root mean square (RMS) and autocorrelation (AC) values were analyzed on the basis of the acceleration waveform. Follow-up assessment of falls and subsequent fractures was performed 12 months after the fracture. Eighty-five percent of the participants completed the follow-up at 12 months. During the 12 months of follow-up, 61 falls were reported by 34 patients (31 women, three men), for a fall rate of 40.0% (34/85). The fall group had significantly poorer body function and structure and activity measures compared with the no-fall group. In accelerometry-based gait analysis, RMS and AC in the vertical axis and RMS in the anteroposterior axis were significantly lower in the fall group compared with the no-fall group. Multivariate logistic regression analysis showed that only AC in the vertical axis was a significant predictor of falls. This finding suggests that trunk acceleration AC in the vertical axis showed good discriminative ability for predicting the incidence of falls among patients who are ambulatory and recently had a fracture.

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Comparison of Smoothness during Gait between Community Dwelling Elderly Fallers and Non-Fallers Using Power Spectrum Entropy of Acceleration Time-Series

Cited by 25 publications

References 14 publications

Assessment of potential clinical cascade between oral hypofunction and physical frailty: Covariance structure analysis in a cross‐sectional study

Assessment of potential clinical cascade between oral hypofunction and physical frailty: Covariance structure analysis in a cross‐sectional study

Review of fall risk assessment in geriatric populations using inertial sensors

Accelerometry-based gait analysis predicts falls among patients with a recent fracture who are ambulatory

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