Multiple regression model for ascertaining the skeletal muscle mass index using grip strength and lifestyle factors in older outpatients.
Hisanori Otsubo, Yuri Ota, Tsuyoshi Suda and 6 others
PMID 40511314WHAT IT FOUND
A formula using body weight, grip strength, sex, comorbidities, meals, and activity predicts muscle mass index in older outpatients.
It helps when standard equipment is unavailable, but validation in other groups is needed before routine use.
Key findings
01The prediction model included sex, body weight, grip strength, comorbidity index, balanced meals per day, and working activity.
02Body weight was the strongest predictor, followed by grip strength, in the multiple regression analysis.
03The developed equation explained a high proportion of variance in skeletal muscle mass index, with an adjusted R-squared of 0.840.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
The study had a small sample size (142 participants) and potential selection bias because BIA was prescribed by only a limited number of gastroenterologists at a single hospital. The findings may not generalize to other populations or clinical settings due to the specific demographic and healthcare context. Nutrition counseling methods used to gather lifestyle data were less well-validated than those in prospective studies, which may affect the accuracy of the predictors. Cognitive function was not assessed, although most participants were able to comprehend the questionnaires. The study did not include patients with dialysis, limiting the applicability to those with specific renal conditions.
Declared interests
The authors declared no conflict of interest.
The easy way to misread this
Do not use this equation as a diagnostic tool without further validation in your specific population. The study was retrospective, had a small sample size, and used unvalidated lifestyle assessments, so the formula's accuracy in other clinical settings is uncertain.