“…In the present study, we selected a prediction equation from a previous study to estimate the aLM from MT‐ulna combined with body height: aLM = 4.89 x MT‐ulna (cm) x body height (m) – 9.15. Our recent study confirmed the precision for Japanese older adults (total error = 1.38 kg; no systematic bias).…”
Section: Methodssupporting
confidence: 83%
“…A previous study reported that there was no systematic bias and an acceptable amount of total error (1.38 kg) for the prediction equation that was used in this study. In the present study, a different sample of Japanese older adults was used and then similar results were confirmed that the prediction equation predicted aLM with no systematic bias and 1.69 kg of total error.…”
Section: Discussionmentioning
confidence: 74%
“…Recently, we developed prediction equations for estimating aLM (which includes FFAT) from ultrasound‐derived muscle thickness, and the forearm ulna (MT‐ulna) was the single best predictor . For sarcopenia screening, a single ultrasound measurement may be advantageous for community‐based physical examinations . Fortunately, subcutaneous adipose tissue layer thickness can be measured using the same ultrasound images that are used to measure MT.…”
Our results indicated that a single ultrasound forearm measurement can be used to accurately estimate DXA-derived aLM-minus-FFAT in Japanese older adults, which may be advantageous for community-based physical examinations.
“…In the present study, we selected a prediction equation from a previous study to estimate the aLM from MT‐ulna combined with body height: aLM = 4.89 x MT‐ulna (cm) x body height (m) – 9.15. Our recent study confirmed the precision for Japanese older adults (total error = 1.38 kg; no systematic bias).…”
Section: Methodssupporting
confidence: 83%
“…A previous study reported that there was no systematic bias and an acceptable amount of total error (1.38 kg) for the prediction equation that was used in this study. In the present study, a different sample of Japanese older adults was used and then similar results were confirmed that the prediction equation predicted aLM with no systematic bias and 1.69 kg of total error.…”
Section: Discussionmentioning
confidence: 74%
“…Recently, we developed prediction equations for estimating aLM (which includes FFAT) from ultrasound‐derived muscle thickness, and the forearm ulna (MT‐ulna) was the single best predictor . For sarcopenia screening, a single ultrasound measurement may be advantageous for community‐based physical examinations . Fortunately, subcutaneous adipose tissue layer thickness can be measured using the same ultrasound images that are used to measure MT.…”
Our results indicated that a single ultrasound forearm measurement can be used to accurately estimate DXA-derived aLM-minus-FFAT in Japanese older adults, which may be advantageous for community-based physical examinations.
“…Although not reported within this paper, we previously noted that two of those prediction equations selected by Nijholt et al included systematic error . Over the last couple of years, we have published several prediction equations for estimating DXA‐derived appendicular lean mass in older adults . Unfortunately, those equations were not included in the article by Nijholt et al Interestingly, a single site measurement of forearm muscle thickness was found to be good predictor of DXA‐derived lean soft tissue mass in older Caucasian adults, and the equation was also found to be accurate in older Japanese adults .…”
mentioning
confidence: 78%
“…In summary, our previous studies suggest that forearm muscle thickness measurements are a tolerable and less demanding assessment to use for older adults, and ultrasound estimated appendicular lean mass from the forearm muscle thickness may be a useful indicator for evaluating muscularity in older adults. Although additional research is needed, our recent work along with others noted within the Nijholt et al review may be useful with the development of ultrasound evaluation for health screenings as well as for the primary diagnosis of sarcopenia.…”
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.