Faculty Bibliography

CONTEXT/BACKGROUND:Traditional medical education is shifting to incorporate learning technologies and online educational activities with traditional face-to-face clinical instruction to engage students, especially at remote clinical training sites. OBJECTIVE:To describe and evaluate the effectiveness of the blended learning format (combining online and face-to-face instruction) for third-year osteopathic medical students during their pediatric rotation. METHODS:Third-year medical students who completed the 4-week clerkship in pediatrics during the 2014-2015 academic year were divided into a standard learning group and a blended learning group with online activities (discussion boards, blogs, virtual patient encounters, narrated video presentations, and online training modules). Comprehensive Osteopathic Medical Achievement Test scores and final course grades were compared between the standard learning and blended learning groups. Students in the blended learning group completed a postsurvey regarding their experiences. RESULTS:Of 264 third-year students who completed the 4-week clerkship in pediatrics during the 2014-2015 academic year, 78 (29.5%) participated in the blended learning supplement with online activities. Of 53 students who completed the postsurvey in the blended learning group, 44 (83.0%) agreed or strongly agreed that "The integration of e-learning and face-to-face learning helped me learn pediatrics." Open-ended comments supported this overall satisfaction with the course format; however, 26 of 100 comments reflected a desire to increase the amount of clinical exposure and face-to-face time with patients. No statistical differences were seen between the standard learning (n=186) and blended learning (n=78) groups with regard to Comprehensive Osteopathic Medical Achievement Test scores (P=.321). Compared with the standard learning group, more students in the blended learning group received a final course grade of honors (P=.015). CONCLUSION/CONCLUSIONS:Results of this study support the use of blended learning in a clinical training environment. As more medical educators use blended learning, it is important to investigate the best balance between learning with technology and learning in a face-to-face setting. Online activities may enhance but should never fully replace face-to-face learning with real patients.

Muscularity, or the proportion of adipose tissue-free body mass (ATFM) as skeletal muscle (SM), provides valuable body composition information, especially for age-related SM loss (i.e., sarcopenia). Limited data from elderly cadavers suggest a relatively constant SM/ATFM ratio, 0.540 +/- 0.046 for men (mean +/- SD, n = 6) and 0.489 +/- 0.049 for women (n = 7). The aim of the present study was to examine the magnitude and constancy of the SM/ATFM ratio in healthy adults. Whole-body SM and ATFM were measured using multi-scan magnetic resonance imaging. The SM/ATFM ratio was 0.528 +/- 0.036 for men (n = 139) and 0.473 +/- 0.037 for women (n = 165). Multiple regression analysis indicated that the SM/ATFM ratio was significantly influenced by sex, age, body weight, and race. The four factors explained 50% of the observed between individual variation in the SM/ATFM ratio. After adjusting for age, body weight, and race, men had a larger SM/ATFM ratio than women. Both older men and women had a lower SM/ATFM ratio than younger subjects, although the relative reduction was greater in men. After adjustment for sex, age, and body weight, there were no significant differences in the SM/ATFM ratios between Asian, Caucasian, and Hispanic subjects. In contrast, African-American subjects had a significantly greater SM/ATFM ratio than subjects in the other three groups. In addition, the SM/ATFM ratio was significantly lower in AIDS patients than corresponding values in healthy subjects.

Skeletal muscle is a large compartment that can now be quantified using research and clinically applicable regional and whole-body methods. The most important advances are the two imaging methods, computed tomography (CT) and magnetic resonance imaging (MRI). Both CT and MRI can serve as regional and whole-body reference methods when evaluating other approaches for estimating skeletal muscle mass. Imaging methods also afford the opportunity to quantify both anatomic skeletal muscle and the smaller adipose-tissue free skeletal muscle component. Other available methods for estimating skeletal muscle, either regional or at the whole body level, include dual-energy x-ray absorptiometry, in vivo neutron activation analysis-whole body counting, anthropometry, ultrasound, bioimpedance analysis, and urinary metabolite markers. Each method is reviewed in the context of the aging process, cost, availability, practicality, and desired accuracy. New insights should be possible when skeletal muscle mass, measured using these methods, is combined with other descriptors of muscle biochemical and mechanical function.

BACKGROUND:Skeletal muscle (SM) is a large body compartment of biological importance, but it remains difficult to quantify SM with affordable and practical methods that can be applied in clinical and field settings. OBJECTIVE:The objective of this study was to develop and cross-validate anthropometric SM mass prediction models in healthy adults. DESIGN/METHODS:SM mass, measured by using whole-body multislice magnetic resonance imaging, was set as the dependent variable in prediction models. Independent variables were organized into 2 separate formulas. One formula included mainly limb circumferences and skinfold thicknesses [model 1: height (in m) and skinfold-corrected upperarm, thigh, and calf girths (CAG, CTG, and CCG, respectively; in cm)]. The other formula included mainly body weight (in kg) and height (model 2). The models were developed and cross-validated in nonobese adults [body mass index (in kg/m(2)) < 30]. RESULTS:Two SM (in kg) models for nonobese subjects (n = 244) were developed as follows: SM = Ht x (0.00744 x CAG(2) + 0.00088 x CTG(2) + 0.00441 x CCG(2)) + 2.4 x sex - 0.048 x age + race + 7.8, where R:(2) = 0.91, P: < 0.0001, and SEE = 2.2 kg; sex = 0 for female and 1 for male, race = -2.0 for Asian, 1.1 for African American, and 0 for white and Hispanic, and SM = 0.244 x BW + 7.80 x Ht + 6.6 x sex - 0.098 x age + race - 3.3, where R:(2) = 0.86, P: < 0.0001, and SEE = 2.8 kg; sex = 0 for female and 1 for male, race = -1.2 for Asian, 1.4 for African American, and 0 for white and Hispanic. CONCLUSION/CONCLUSIONS:These 2 anthropometric prediction models, the first developed in vivo by using state-of-the-art body-composition methods, are likely to prove useful in clinical evaluations and field studies of SM mass in nonobese adults.

BACKGROUND:Increasing body mass index (BMI) is associated with progressively lower serum HDL-cholesterol concentrations, although the underlying body-composition compartment accounting for this unfavorable lipid change remains uncertain. OBJECTIVE:Because growing evidence favors a role of lean tissue in HDL homeostasis, the hypothesis was tested that non-adipose tissue components of body mass explain the inverse association of HDL cholesterol and BMI. DESIGN/METHODS:Fasting serum lipid concentrations and body composition [total, subcutaneous, and visceral adipose tissue; adipose tissue-free mass (ATFM); and skeletal muscle by whole-body magnetic resonance imaging and body cell mass by 40K counting) were evaluated in healthy adults. Body-composition compartments were expressed as height2-normalized indexes. RESULTS:An inverse correlation was observed between serum HDL cholesterol and BMI in women (n = 68; R2 = 0.08, P = 0.023) and men (n = 61; R2 = 0.07, P = 0.046). Significant inverse correlations (P = 0.005-0.02) were also observed between HDL cholesterol and nonadipose components (ie, ATFM, skeletal muscle, and body cell mass) but not between HDL cholesterol and any adipose tissue component. The association between HDL cholesterol and ATFM remained significant after serum triacylglycerol was controlled for. When BMI was entered into the HDL cholesterol-ATFM regression model, BMI was not a significant independent variable. The strongest correlate of serum triacylglycerol was visceral adipose tissue (P = 0.002 for both women and men). CONCLUSIONS:Lean tissues and body cell mass appear to account in part for the long-observed inverse association of HDL cholesterol and BMI. These observations suggest a link between nonadipose tissue compartments and the greater cardiovascular risk associated with high BMI.