Novel Equations to Estimate Resting Energy Expenditure during Sitting and Sleeping

Abstract

<b><i>Introduction:</i></b> Young and early middle-aged office workers spend most of the day sitting or sleeping. Few studies have used a metabolic chamber to report sitting resting energy expenditure (REE) or sleeping metabolic rate (SMR) estimation equations. This study aimed to develop novel equations for estimating sitting REE and SMR, and previously published equations for SMR were compared against measured values. <b><i>Methods:</i></b> The relationships among sitting REE, SMR, and body composition measured in clinical trials were analyzed. The body composition (fat-free mass [FFM] and fat mass) and energy metabolism of 85 healthy young and early middle-aged Japanese individuals were measured using dual-energy X-ray absorptiometry and a metabolic chamber, respectively. Novel estimate equations were developed using stepwise multiple regression analysis. Estimates of SMR using a new equation and 2 published equations were compared against measured SMR. <b><i>Results:</i></b> The sitting mREE and mSMR were highly correlated (<i>r</i> = 0.756, <i>p</i> < 0.01). The new FFM-based estimate accounted for 50.4% of the variance in measured sitting REE (mREE) and 82.3% of the variance in measured SMR (mSMR). The new body weight-based estimate accounted for 49.3% of the variance in sitting mREE and 82.2% of the variance in mSMR. Compared with mSMR, the SMR estimate using an FFM-based published equation was slightly underestimated. <b><i>Conclusion:</i></b> These novel body weight- and FFM-based equations may help estimate sitting REE and SMR in young and early middle-aged adults. Previous SMR estimated FFM-based equations were slightly underestimated against measured SMR; however, we confirmed the previous SMR estimate equations could be useful. This finding suggests that sitting REE and SMR can be easily estimated from individual characteristics and applied in clinical settings.

Revised Harris–Benedict Equation: New Human Resting Metabolic Rate Equation

“…In recent years, in the context of adapting prediction equations to specialized categories of individuals and situations, several prediction equations have been developed that are specific to particular diseases [ 13 , 14 , 15 , 16 , 17 ], ethnicity [ 18 , 19 ], special categories such as athletes [ 20 , 21 ], overweight and obese people [ 22 , 23 ], children [ 24 ], and people after middle age [ 25 ]. Several attempts to create new RMR prediction equations using individual characteristics, such as free fat mass (FFM), have emerged from time to time but they require the use of body composition analysis devices [ 26 ]. Some of the widely used prediction equations are listed below.…”