Erickson, Jacob L.

Abstract Jagim, AR, Camic, CL, Askow, A, Luedke, J, Erickson, J, Kerksick, CM, Jones, MT, and Oliver, JM. Sex differences in resting metabolic rate among athletes. J Strength Cond Res 33(11): 3008–3014, 2019—The purpose of this study was to compare differences in resting metabolic rate (RMR) between sexes in Division III National Collegiate Athletic Association (NCAA) collegiate athletes and to identify predictors of RMR. Sixty-eight male (M) (age: 20.1 ± 1.5 years; height: 181.8 ± 5.9 cm; body mass (BM): 93.7 ± 16.3 kg; and body fat%: 16.3 ± 8.6%) and 48 female (F) athletes (age: 19.4 ± 1.3 years; height: 166.5 ± 6.0 cm; BM: 63.4 ± 12.7 kg; and body fat%: 21.5 ± 6.3%) participated in a single day of testing, which included determination of RMR using indirect calorimetry and air displacement plethysmography to measure fat mass and fat-free mass (FFM). An independent-samples t -test was used to compare differences in body composition and RMR between sexes, and regression analysis was used to identify predictors of RMR. Men had a significantly higher absolute RMR (M: 2,481 ± 209 vs. F: 1,553 ± 193 kcals·d −1 ; p < 0.001), but when adjusted for BM (M: 25.6 ± 8.3 vs. F: 25.9 ± 2.5 kcals·kg −1 BM per day; p = 0.82) and FFM (M: 31.1 ± 10.6 vs. F: 33.6 ± 3.8 kcals·kg −1 FFM per day; p = 0.12), these differences became nonsignificant. Regression analysis indicated that BM in both men (β = 0.73) and women (β = 0.88) was the strongest predictor of RMR. The results of the current study indicate minimal differences in RMR between sexes among athletic populations when adjusted for BM and FFM. In the current group of athletes, BM seems to account for the largest variability in RMR.

Abstract Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875–1881, 2018—The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (−165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.

AbstractRationale, aims and objectivesAchieving control over elevated lipid parameters, particularly low‐density lipoprotein (LDL)‐cholesterol, is an acknowledged quality indicator in primary care. The Centers for Disease Control and Prevention (CDC)'s model for investigation of outbreaks (person–place–time) can be applied to the analysis of quality indicators.MethodsA sample of 322 family medicine patients for whom lipid levels were ordered was extracted. LDL > 100 mg/dL was cross‐tabulated by personal characteristics [age group, gender, body mass index (BMI), diagnoses], month (time) and ordering department (place).ResultsAge (except one age category), gender, time and location were not related to LDL > 100 mg/dL after adjustment for covariates. All levels of BMI above normal elevated the risk of LDL > 100 mg/dL [BMI 25–29.9: odds ratio (OR) = 3.41, confidence interval (CI) = 1.61–7.23, P = 0.0014; BMI 30–34.9: OR = 2.93, CI = 1.28–6.70, P = 0.0109; BMI ≥ 35: OR = 2.75, CI = 1.19–6.37, P = 0.0181]. Patients with coronary artery disease (CAD) and diabetes mellitus (DM) were at reduced risk for LDL > 100 mg/dL (CAD: OR = 0.47, CI = 0.24–0.91, P = 0.0254; DM: OR = 0.28, CI = 0.14–0.55, P = 0.0002).ConclusionAn outbreak investigation model is useful for analysing variations in this quality indicator. Patients with higher BMI and those not diagnosed with CAD or DM type I/II may be considered for intensified lipid lowering using quality improvement efforts. These might include counselling for lifestyle changes or medication therapy depending upon their calculated cardiac risk.

AbstractRationale, aims and objectivesThis study was undertaken to assess the frequency of lipid screening in comparison with the United States Preventive Services Task Force guideline in a sample of family medicine patients. In addition, we sought to determine the association between testing frequency and achievement of lipid targets.MethodsA random sample was extracted from 271 patients from among all patients cared for in our Department of Family Medicine for whom lipid screening was ordered from March to September 2012 and who had ≥2 well‐defined cardiovascular risk factors. Lipid testing frequency was classified in three ways: semi‐annual or less often (0–12 tests over 6 years), annual or less often (0–6 tests), or biennial (0–3 tests).ResultsMultiple logistic regression analysis revealed that the predictors of lipid screening more often than semi‐annually were age ≥60 years [odds ratio (OR) = 3.7] and diabetes mellitus (DM) (OR = 30.6). Predictors of screening more often than annually were DM (OR = 4.3), hypertension (OR = 2.1), family history of premature coronary artery disease (OR = 5.6) and statin treatment (OR = 3.5). Lipid goal attainment was not associated with testing frequency except with regard to low‐density lipoprotein levels (P = 0.043, P < 0.001, P = 0.005, by semi‐annual, annual and biennial, respectively) and total cholesterol levels (P = 0.015, P = 0.025 by semi‐annual and annual, respectively).ConclusionsQuestionable high frequency of lipid testing was detected even when the more conservative approach of annual monitoring was assumed. Frequency of testing was not associated with goal attainment for most parameters. Physicians should request the lipid testing based on overall risk assessment and person variability in accordance with published guidelines.