Usefulness of chair stand time as a surrogate of gait speed in diagnosing sarcopenia

Nishimura, Takayuki; Arima, Kei; Okabe, Takuhiro; Mizukami, Satoshi; Tomita, Yoshihito; Kanagae, Mitsuo; Goto, Hisashi; Horiguchi, Itsuko; Abe, Yasuyo; Aoyagi, Kiyoshi

doi:10.1111/ggi.12766

Cited by 35 publications

(24 citation statements)

References 6 publications

(8 reference statements)

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“…When it is not feasible to measure the usual gait speed, we recommend the 5CST as a surrogate measure of gait speed in community-dwelling older adults [ 93 ]. Based on the study by Nishimura et al, a gait speed of 1.0 m/s correlates with a cut-off of ≥12 s for 5CST [ 93 ]; this is also recommended by AWGS 2019 [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

Singapore multidisciplinary consensus recommendations on muscle health in older adults: assessment and multimodal targeted intervention across the continuum of care

Chew

Kayambu

Lew³

et al. 2021

BMC Geriatr

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Background The rapidly aging societies worldwide and in Singapore present a unique challenge, requiring an integrated multidisciplinary approach to address high-value targets such as muscle health. We propose pragmatic evidence-based multidisciplinary consensus recommendations for the assessment and multi-modal management of muscle health in older adults (≥65 years) across the continuum of care. Methods The recommendations are derived from an in-depth review of published literature by a multidisciplinary working group with clinical experience in the care of the older population in both acute and community settings. Results The panel recommends screening for muscle impairment using the SARC-F questionnaire, followed by assessment for low muscle strength (handgrip strength or 5-times chair stand test ≥10 s as a surrogate for lower limb strength) to diagnose possible/probable sarcopenia. For uncomplicated cases, lifestyle modifications in exercise and diet can be initiated in the community setting without further assessment. Where indicated, individuals diagnosed with possible/probable sarcopenia should undergo further assessment. Diagnosis of sarcopenia should be based on low muscle strength and low muscle mass (bioimpedance analysis, dual-energy X-ray absorptiometry or calf circumference as a surrogate). The severity of sarcopenia should be determined by assessment of physical performance (gait speed or 5-times chair stand test ≥12 s as a surrogate for gait speed). To treat sarcopenia, we recommend a combination of progressive resistance-based exercise training and optimization of nutritional intake (energy, protein and functional ingredients). High quality protein in sufficient quantity, to overcome anabolic resistance in older adults, and distributed throughout the day to enable maximum muscle protein synthesis, is essential. The addition of resistance-based exercise training is synergistic in improving the sensitivity of muscle protein synthesis response to the provision of amino acids and reducing anabolic resistance. An expected dose-response relationship between the intensity of resistance-based training, lean mass and muscle strength is described. Conclusions Reviewed and endorsed by the Society of Rehabilitation Medicine Singapore and the Singapore Nutrition and Dietetics Association, these multidisciplinary consensus recommendations can provide guidance in the formulation of comprehensive and pragmatic management plans to improve muscle health in older adults in Singapore and Asia.

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Section: Resultsmentioning

confidence: 99%

Singapore multidisciplinary consensus recommendations on muscle health in older adults: assessment and multimodal targeted intervention across the continuum of care

Chew

Kayambu

Lew³

et al. 2021

BMC Geriatr

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“…Previous studies have shown that tandem balance test, gait speed, CST and HGS are valid and reliable measures to assess physical performance ( 22 , 23 ). Moreover, gait speed, CST and HGS have proven to be associated with sarcopenia according to the EWGSOP definition in community dwelling older adults ( 20 , 24 , 25 ). Unfortunately, the results of this study in geriatric outpatients did not show a single suitable physical performance measure to identify older individuals with sarcopenia.…”

Section: Discussionmentioning

confidence: 99%

“…AUC was defined as low <0.70, acceptable 0.70–0.80, excellent 0.80–0.90 and outstanding >0.90. To test diagnostic accuracy, CST and HGS were dichotomized: CST ≥13 seconds ( 14 , 20 ), and HGS <20 kilograms for females and <30 kilograms for males were considered low ( 21 ). Statistical analyses were performed using Statistical Package for Social Sciences (SPSS Inc, Chicago, USA), version 22.…”

Section: Methodsmentioning

confidence: 99%

Single Physical Performance Measures Cannot Identify Geriatric Outpatients With Sarcopenia

et al. 2018

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Background: Sarcopenia is highly prevalent in the older population and is associated with several adverse health outcomes. Equipment to measure muscle mass and muscle strength to diagnose sarcopenia is often unavailable in clinical practice due to the related expenses while an easy physical performance measure to identify individuals who could potentially have sarcopenia is lacking. Objectives: This study aimed to assess the association between physical performance measures and definitions of sarcopenia in a clinically relevant population of geriatric outpatients. Design, setting and participants: A cross-sectional study was conducted, consisting of 140 community-dwelling older adults that were referred to a geriatric outpatient clinic. No exclusion criteria were applied. Measurements: Physical performance measures included balance tests (side-by-side, semi-tandem and tandem test with eyes open and -closed), four-meter walk test, timed up and go test, chair stand test, handgrip strength and two subjective questions on mobility. Direct segmental multi-frequency bioelectrical impedance analysis was used to measure muscle mass. Five commonly used definitions of sarcopenia were applied. Diagnostic accuracy was determined by sensitivity, specificity and area under the curve.Results: Physical performance measures, i.e. side-by-side test, tandem test, chair stand test and handgrip strength, were associated with at least one definition of sarcopenia. Diagnostic accuracy of these physical performance measures was poor. Conclusions: Single physical performance measures could not identify older individuals with sarcopenia, according to five different definitions of sarcopenia.

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“…It is also clinically plausible that higher grip strength (as a marker of upper limb strength) and shorter chair stands time (more representative of lower limb strength) were common determinants of all three performance metrics. Indeed, sarcopenia (low muscle mass and/or strength), of which both grip strength and the five chair stands test are indicative measures (50), has been associated with reduced gait speed and poor functional outcomes in older people (51)(52)(53). In our models, slower chair stands time was associated with a decline in UGS, MGS, and GSR once time increased beyond 14.2 s, 13 s, and 10.6 s, respectively; whilst increases in UGS, MGS, and GSR began at values of 13.4 s, 13 s, and 10.6 s, respectively.…”

Section: Common Featuresmentioning

confidence: 99%

Comparison of gait speed reserve, usual gait speed, and maximum gait speed of adults aged 50+ in Ireland using explainable machine learning

Davis

Knight

Hernández

et al. 2021

Preprint

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Gait speed is a measure of general fitness. Changing from usual (UGS) to maximum (MGS) gait speed requires a general effort across many body systems. The difference, MGS − UGS, is defined as gait speed reserve (GSR). In the present study, using 3925 participants aged 50+ from Wave 3 of The Irish Longitudinal Study on Ageing (TILDA), we used a gradient boosted trees-based stepwise feature selection pipeline for the discovery of clinically relevant predictors of GSR, UGS, and MGS using a shortlist of 88 features across 5 categories (socio-demographics/anthropometrics/medical history; cardiovascular system; physical strength; sensory; and cognitive/psychological). The TreeSHAP explainable machine learning package was used to analyse the input-output relationships of the three models. The mean R2adj (SD) from 5-fold cross validation on training data and the R2adj score on test data for the models are: 0.38 (0.04) and 0.41 for UGS; 0.45 (0.04) and 0.46 for MGS; and 0.19 (0.02) and 0.21 for GSR. Features selected for the UGS model were: age, chair stands time, body mass index, grip strength, number of medications, resting state pulse interval, mean motor reaction time in the choice reaction time test, height, depression score, sit-to-stand difference in diastolic blood pressure, and left visual acuity. The features selected for the MGS model were: age, grip strength, repeated chair stands time, body mass index, education, mean motor reaction time in the choice reaction time test, number of medications, height, the standard deviation of the mean reaction time in the sustained attention to response task, mean heart rate at resting state, fear of falling, MOCA errors, orthostatic intolerance during active stand, smoking status, total heart beat power during paced breathing, the root mean square of successive differences between heartbeats during paced breathing, and visual acuity. Finally, the features chosen for the GSR model were: mean motor reaction time in the choice reaction time test, grip strength, education, chair stands time, MOCA errors, accuracy proportion in the sound induced flash illusion (two beeps and one flash with stimulus-onset asynchrony of +150 ms), fear of falling, height, age, sex, orthostatic intolerance, MMSE errors, and number of cardiovascular conditions. MGS and UGS were more explainable than GSR. All three models contain features from all five categories. There were common features to all three models (age, grip strength, chair stands time, mean motor reaction time in the choice reaction time test, and height), but also some features unique to each of them. Overall, findings on all three models were clinically plausible and support a network physiology approach to the understanding of predictors of performance-based tasks. By employing an explainable machine learning technique, our observations may help clinicians gain new insights into the multisystem predictors of gait speed and gait speed reserve in older adults.

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Usefulness of chair stand time as a surrogate of gait speed in diagnosing sarcopenia

Abstract: Chair stand time might be useful as a surrogate of gait speed when screening for sarcopenia. Geriatr Gerontol Int 2017; 17: 659-661.

Cited by 35 publications

References 6 publications

Singapore multidisciplinary consensus recommendations on muscle health in older adults: assessment and multimodal targeted intervention across the continuum of care

Singapore multidisciplinary consensus recommendations on muscle health in older adults: assessment and multimodal targeted intervention across the continuum of care

Single Physical Performance Measures Cannot Identify Geriatric Outpatients With Sarcopenia

Comparison of gait speed reserve, usual gait speed, and maximum gait speed of adults aged 50+ in Ireland using explainable machine learning

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