Healthy NonObese Adults (20102012)
The objectives of the study were to
 Generate regression equations for predicting the resting metabolic rate (RMR) of 30 60year–old Australian males from age, height, mass and fatfree mass (FFM)
 Crossvalidate RMR prediction equations, which are currently used in Australia, against the measured and predicted values from the 30 –60yold males.
 Tested the hypothesis that the RMR prediction equations of Harris & Benedict and Schofield are inappropriate for 30 to60yold Australian males
 Explore FFM derived from the four compartment body composition model, and estimated from the sum of skinfold thicknesses, as predictors of RMR.
 males aged 3060
 able to give consent
 smokers
 not mass stable (+2 kg) over the last year
 suffering from disease known to affect energy metabolism
 taking medication known to affect energy metabolism
 history of clinical eating disorder
Commonly Used Equations:
Harris & Benedict (1919)
 RMR_{1} = using height and mass only
 RMR_{2} = using height, mass and age
Anthropometric:
Height and weight using standardized protocol
Skinfold measurements—2 or 3 at each site—arm, waist, gluteal, calf, femur, and breadths—biepicondylar humerus using standardized protocol
Clinical:
Resting heart rates monitored during measurements of RMR: Yes
FFM calculated? Yes, using a fourcompartment criterion method
Resting energy expenditure:
IC type: Douglas bag then using an O2 and CO2 analyzer
Rest before measure: 50 minutes Measurement length: 210 minute periods
Fasting length: 1112 hours
Exercise conditioning 24 prior to test?: No vigorous exercise during the preceding 36 hours
Room temp: 24+0.5° C
No. of measures &were they repeated? 2
Coefficient of variation? 0.989 with technical error of measurement (TEM) of 119 kJ/day
Equipment of Calibration: yes
Training of measurer? Not reported
Subject training of measuring process?: Yes
Dietary measures not taken.
Statistics:
Forward stepwise regression was used to predict RMR (kJ/d) from a) age, height, mass, and FFM predicted via the anthropometric variables; and b) age, height, mass and FFM estimated from the fourcompartment body composition model.
Dependent ttests were used to determine whether measured RMR was significantly different from those predicted by the equations of HarrisBenedict and Schofield; p <0.05.
Withdrawals not discussed.
 Skinfold thicknesses
 RMR
 FFM via a fourcompartment (fat mass, total body water, bone mineral mass and residual) body composition model.
Blinding was not used.
A power analysis demonstrated that 41 subjects would enable the detection of ( a= 0.05, power = 0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the equations currently used.
Sample: 41 males (mean age 44.8 ±8.6 y) (range 3058 y)
Mean weight: 83.50 ± 11.32 kg
Mean height: 179.1 ± 5.0 cm
BMI: 26.0 ± 3.2 kg/m^{2}
A multiple regression equation using mass, height and age as predictors correlated 0.745 with RMR measured via IC and the SEE=509 kJ/d (122 kcal/d)
Inclusion of FFM as a predictor increased both the correlation and the precision of prediction but there was no difference between FFM via the fourcompartment model (r = 0.816, SEE = 429 kJ/d (103 kcal/d)=best equation), and that predicted from skinfold thicknesses (r = 0.805, SEE= 441 kJ/d (105 kcal/d).
The regression equation of Harris & Benedict which uses age, height, and mass as predictors, overestimated the mean RMR of the subjects by 328 kJ/d (78 kcal/d) (p < 0.001). The absolute mean difference and total error were 524 and 632 kJ/d (125 kcal/d and 151 kcal/d) respectively. This is larger than the SEE=432 kJ/d (103 kcal/d) for the original equation (using mass, height, and age when establishing regression equations predicting the RMR . These are consistent across the range of measurement.
The total error [(Square root of sum (measured RMR minus predicted RMR) squared and then divided by n] for Harris & Benedict using height and mass was 974 kj/day (233 kcal/d) or 13.5% (Total error divided by measured RMR mean).
The total error [(Square root of sum (measured RMR minus predicted RMR) squared and then divided by n] for Harris & Benedict using age, height, and mass was 974 kJ/day (151 kcal/d) or 8.7% (Total error divided by measured RMR mean)
The original which uses just mass and height fared worse than the one that incorporated the additional variable of age.
The Harris & Benedict equations overpredict the RMR of 30 to60yold Australian males, by 4.25.0% (at two standard deviations above and below the mean). The error in kJ/d would be compounded when the RMR is multiplied by an activity factor to yield the total daily energy expenditure.
The RMR prediction equations ( one using mass, and one using mass and height) of Schofield also overpredicted the RMR mean of subjects by 421 and 418 kJ/d (p <0.001), with associated absolute mean differences of 551 and 549 kJ/d, respectively. The Schofield equations overpredict by 874 and 872 kJ/d, respectively at two standard deviations below the mean.
Overpredictions for the Schofield equations ranged from 194 kJ/d (or 46 kcal/d) (2.4%) at one standard deviation above the mean to 874 kJ/d (209 kcal/d) at two standard deviations below the mean (15.1%).
Our two best regression equations demonstrate that 66.7 and 64.8% of the variance in the criterion variable of RMR is explained by the best weighted combination of the predictor variables.
The databases of Harris & Benedict and Schofield had lower mean BMIs (21.2 and 22.7 kg/m^{2}, respectively), compared to the mean of 26.0 kg/m^{2} in this study.
“Our findings can be summarized as:
The coefficient of determination and SEE. for the best RMR prediction equation were much higher and lower, respectively, than the equations of Schofield. Our SEE was also comparable with those for the equations of Harris & Benedict, which are based on 136 males, only 33 of whom were > 30 y of age. However, the 95% confidence interval at the mean of 841 kJ/d (i.e., 201 kcal/d) poses the question as to whether it was more acceptable to tolerate the error in the prediction or the difficulty of direct measurement.
Crossvalidation analyses suggest that equations need to be generated from a large database for predicting the RMR of Australian males.
FFM predicted from the sum of skinfold thicknesses, which has been validated against the criterion of FFM using the fourcompartment body composition model, should be further explored as an RMR predictor.”
University/Hospital:  Flinders University 
Limitations
 Higher % BF and heterogeneous sample; limited generalizability
 Small sample size; equations may need to be generated from large database
 Physical activity was not measured; may have affected the results
Quality Criteria Checklist: Primary Research


Relevance Questions  
1.  Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies)  Yes  
2.  Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about?  Yes  
3.  Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice?  Yes  
4.  Is the intervention or procedure feasible? (NA for some epidemiological studies)  Yes  
Validity Questions  
1.  Was the research question clearly stated?  N/A  
1.1.  Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified?  N/A  
1.2.  Was (were) the outcome(s) [dependent variable(s)] clearly indicated?  N/A  
1.3.  Were the target population and setting specified?  N/A  
2.  Was the selection of study subjects/patients free from bias?  Yes  
2.1.  Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study?  N/A  
2.2.  Were criteria applied equally to all study groups?  N/A  
2.3.  Were health, demographics, and other characteristics of subjects described?  N/A  
2.4.  Were the subjects/patients a representative sample of the relevant population?  N/A  
3.  Were study groups comparable?  N/A  
3.1.  Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT)  N/A  
3.2.  Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline?  N/A  
3.3.  Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.)  N/A  
3.4.  If cohort study or crosssectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis?  N/A  
3.5.  If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.)  N/A  
3.6.  If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")?  N/A  
4.  Was method of handling withdrawals described?  No  
4.1.  Were followup methods described and the same for all groups?  N/A  
4.2.  Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (crosssectional studies) described for each group? (Follow up goal for a strong study is 80%.)  N/A  
4.3.  Were all enrolled subjects/patients (in the original sample) accounted for?  N/A  
4.4.  Were reasons for withdrawals similar across groups?  N/A  
4.5.  If diagnostic test, was decision to perform reference test not dependent on results of test under study?  N/A  
5.  Was blinding used to prevent introduction of bias?  No  
5.1.  In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate?  N/A  
5.2.  Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.)  N/A  
5.3.  In cohort study or crosssectional study, were measurements of outcomes and risk factors blinded?  N/A  
5.4.  In case control study, was case definition explicit and case ascertainment not influenced by exposure status?  N/A  
5.5.  In diagnostic study, were test results blinded to patient history and other test results?  N/A  
6.  Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described?  Yes  
6.1.  In RCT or other intervention trial, were protocols described for all regimens studied?  N/A  
6.2.  In observational study, were interventions, study settings, and clinicians/provider described?  N/A  
6.3.  Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect?  N/A  
6.4.  Was the amount of exposure and, if relevant, subject/patient compliance measured?  N/A  
6.5.  Were cointerventions (e.g., ancillary treatments, other therapies) described?  N/A  
6.6.  Were extra or unplanned treatments described?  N/A  
6.7.  Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups?  N/A  
6.8.  In diagnostic study, were details of test administration and replication sufficient?  N/A  
7.  Were outcomes clearly defined and the measurements valid and reliable?  Yes  
7.1.  Were primary and secondary endpoints described and relevant to the question?  N/A  
7.2.  Were nutrition measures appropriate to question and outcomes of concern?  N/A  
7.3.  Was the period of followup long enough for important outcome(s) to occur?  N/A  
7.4.  Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures?  N/A  
7.5.  Was the measurement of effect at an appropriate level of precision?  N/A  
7.6.  Were other factors accounted for (measured) that could affect outcomes?  N/A  
7.7.  Were the measurements conducted consistently across groups?  N/A  
8.  Was the statistical analysis appropriate for the study design and type of outcome indicators?  Yes  
8.1.  Were statistical analyses adequately described and the results reported appropriately?  N/A  
8.2.  Were correct statistical tests used and assumptions of test not violated?  N/A  
8.3.  Were statistics reported with levels of significance and/or confidence intervals?  N/A  
8.4.  Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a doseresponse analysis)?  N/A  
8.5.  Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)?  N/A  
8.6.  Was clinical significance as well as statistical significance reported?  N/A  
8.7.  If negative findings, was a power calculation reported to address type 2 error?  N/A  
9.  Are conclusions supported by results with biases and limitations taken into consideration?  Yes  
9.1.  Is there a discussion of findings?  N/A  
9.2.  Are biases and study limitations identified and discussed?  N/A  
10.  Is bias due to study's funding or sponsorship unlikely?  No  
10.1.  Were sources of funding and investigators' affiliations described?  N/A  
10.2.  Was the study free from apparent conflict of interest?  N/A  