Therapists or Helpers? Notes on a Youth-Type Free Clinic

This paper builds upon a helpful typology of free clinics that divides then into four major kinds - the street, neighborhood, youth, and sponsored. While the typology tends to weave among characteristics of clientele, locale, and source of support in setting up its units, it nonetheless has the advantage of being based on an empirical assessment of the major forms of clinic operations through the country. Youth clinics - the type that particularly concerns us here - are defined as generally organized by adults, service clubs, or official boards... because of their concern about drug use among high school students. Such clinics are distinctive from the other types in that they generally offer drug care which is limited to education and counseling. Our examination of the youth clinic model attempts to determine its distinctive characteristics vis-a-vis the remaining types of programs. In this regard, we hope to move information and insights about free clinics beyond the head-counting, diagnosis-tabulating stage and the sometimes (and quite understandable) self-congratulatory observations that have surrounded the early, innovative period of the free clinic movement

Influence of Acute Water Ingestion on Bioelectrical Impedance Analysis Estimates of Body Composition

Body composition estimation is a significant component of health and fitness assessments. Multi-frequency bioelectrical impedance analysis (MFBIA) uses multiple electrical frequencies that travel through body tissues in order to estimate fluid content and body composition. Prior to body composition assessments, it is common to implement a wet fast (i.e., a fasting period that allows water intake); however, the influence of a wet fast as compared to a dry fast (i.e., disallowing water intake) is relatively unknown. PURPOSE: To determine the effects of acute water consumption on MFBIA body composition estimates. METHODS: A randomized crossover study was conducted in 16 adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body mass index: 24.6 ± 4.7; body fat % [BF%]: 16.7 ± 8.1%). On two occasions, participants reported to the laboratory after an overnight food and fluid fast. After a baseline MFBIA assessment, participants either consumed 11 mL/kg of bottled water (W condition) or consumed no fluid as the control (CON condition). The 11 ml/kg dose of water corresponded to absolute intakes of 531 to 1360 mL. After the water consumption time point, MFBIA tests were performed every 10 minutes for one hour. Participants stood upright for the entire research visit. MFBIA estimates of body mass (BM), fat mass (FM), fat-free mass (FFM), and BF% were analyzed using 2 x 7 (condition x time) analysis of variance with repeated measures, follow-up pairwise comparisons, and evaluation of the partial eta-squared (ηp2) effect sizes. RESULTS: No variables differed between conditions at baseline. Condition x time interactions were present for all variables (BM: pp2=0.89; FM: p=0.0008, ηp2=0.30; BF%: p=0.005, ηp2=0.23) except FFM (p=0.69, ηp2=0.03). Follow-up testing indicated that BM was ~0.6 kg higher in W as compared to CON at all post-baseline time points (pp2=0.32), regardless of condition. CONCLUSION: Up to one hour after ingestion, acute water intake was exclusively detected as increased FM by MFBIA. This contrasts with the common belief that ingesting water prior to bioimpedance tests would result in inflated FFM and decreased BF%. Since body composition estimates never returned to baseline within the hour after water ingestion, it is not clear how long this effect would persist. These results suggest acute water ingestion can produce an inflation of MFBIA body fat estimates for at least one hour. These results indicate that water intake during fasting periods should be considered as part of pre-assessment standardization

Impact of Fluid Consumption on Estimates of Intracellular, Extracellular, and Total Body Water from Multi-Frequency Bioelectrical Impedance Analysis

Multi-frequency bioelectrical impedance analysis (MFBIA) is able to distinguish between total body water (TBW), extracellular water (ECW) and intracellular water (ICW). Low-frequency currents are thought to primarily pass through ECW, while high-frequency currents pass through all body fluids (i.e., TBW). ICW can then be estimated by subtracting ECW from TBW. As such, MFBIA may have utility for monitoring health conditions resulting in water retention within specific fluid compartments. However, the sensitivity of fluid estimates from MFBIA is not fully established. PURPOSE: To evaluate the effects of acute fluid ingestion on body water estimates produced by a MFBIA analyzer. METHODS: Sixteen adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body fat %: 16.7 ± 8.1%) participated in a randomized crossover study consisting of two conditions: 1) no fluid ingestion (control; C); and 2) acute ingestion of 11 mL/kg of bottled water (W). In both conditions, participants reported to the laboratory after an overnight food and fluid fast for serial assessments using 8-point standing MFBIA. An initial MFBIA assessment was performed at baseline, followed by a 5-minute period during which water was ingested (W condition) or the participant continued to rest in the lab (C condition). Beginning 10 minutes after this time period, participants were assessed by MFBIA every 10 minutes for one hour. Participants stood upright for the entirety of each research visit. Analysis of variance with repeated measures was used to examine differences in MFBIA estimates of body mass (BM), TBW, ECW, and ICW between conditions and across time. Follow-up pairwise comparisons were performed and partial eta-squared (ηp2) effect sizes were calculated. RESULTS: A group-by-time interaction was present for BM (pp2: 0.89) but not TBW (p=0.74; ηp2: 0.03), ECW (p=0.85; ηp2: 0.02), or ICW (p=0.87; ηp2: 0.05). Follow-up indicated that BM did not differ between conditions at baseline but was ~0.6 ± 0.2 kg higher in the W condition as compared to C at all post-baseline time points (pp2: 0.29 to 0.38). No significant effects were observed for ECW. CONCLUSION: The lack of change in body fluids with acute water ingestion likely indicates that: 1) within one hour, ingested water has not been assimilated into body fluids to the extent that it is detectable by MFBIA; or 2) the quantity of fluid ingestion is below the detection limits of the MFBIA analyzer. In support of the first point, it is likely that bioelectrical currents do not penetrate the gastrointestinal tract, meaning fluids contained therein are unlikely to be detected by MFBIA as fluids

Body Fat Gain Automatically Increases Lean Mass by Changing the Fat-Free Component of Adipose Tissue

Estimating alterations in lean mass in response to various training interventions is a primary concern for many investigations. However, previous reports have suggested that lean mass estimates from weight loss interventions may be significantly altered by attempting to correct for changes in the fat-free component of adipose tissue (FFAT). This component, consisting primarily of water and protein, has been estimated as ~15% of adipose tissue (AT) mass. While a preliminary examination of this correction method has been conducted in the instance of weight loss, it has yet to be investigated after a period of purposeful weight gain and resistance training. PURPOSE: To examine the impact of corrections for FFAT on estimates of lean mass accretion during a period of weight gain and resistance training. METHODS: Twenty-one resistance trained males underwent 6 weeks of supervised training and followed a hypercaloric diet in order to elicit weight gain. Body composition was assessed pre- and post-intervention via dual energy x-ray absorptiometry (DXA). AT was estimated using DXA-derived fat mass (FM) in the equation: AT = FM/0.85. FFAT was then estimated via the equation: FFAT = 0.15 × AT. Lastly, FFAT was subtracted from DXA-derived lean mass (LMDXA) to yield the new corrected lean mass value (cLM). Changes in LMDXA and cLM in response to the training intervention were calculated, and dependent samples T-tests were employed to determine if significant differences were present between changes in LMDXA and cLM. RESULTS: Significant differences (p ≤ 0.001) were noted for estimates of LM gain, with a larger increase observed for LMDXA as compared to cLM (LMDXA :2.42 ± 1.58kg; cLM: 2.14 ± 1.65kg). CONCLUSION: Correcting DXA-derived LM for the fat-free component of adipose tissue reduces the magnitude of LM accretion after a period of weight gain. However, while LM estimates did significantly differ, the small degree to which they differed indicates questionable practical relevance of such corrections in future investigations

Agreement Between 4-Compartment Model and 7-Site Ultrasonography for Tracking Weight Training-Induced Changes in Body Composition

Tracking body composition changes provides valuable information in a variety of contexts, including aging, disease, and lifestyle interventions. The 4-Compartment (4C) model is widely accepted as a criterion molecular-level method for evaluating body composition by integrating data from dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and bioimpedance spectroscopy (BIS). Ultrasonography (US) is another method of body composition estimation that evaluates subcutaneous adipose tissue at various body sites. PURPOSE: To evaluate the agreement between body composition changes detected by a molecular-level 4C model and a 7-site skinfold thickness-based US method in response to weight training and a hypercaloric diet. METHODS: Seventeen adult males (age:­­ 22.5 ± 2.4 y, body mass: 72.8 ± 11.6 kg, body fat % [BF%]: 14.0 ± 4.8%) who were moderately resistance-trained completed a 6-week period of supervised resistance training in conjunction with overfeeding via provision of a high-calorie, carbohydrate/protein dietary supplement. At the beginning and end of this period, body composition was evaluated via 4C model, necessitating assessments via DXA, ADP, and BIS. Additionally, body composition was estimated via US by utilizing subcutaneous adipose tissue thicknesses at seven sites on the body as described by Jackson and Pollock. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by the 4C model and US were compared using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: ΔFM and ΔFFM were significantly correlated between methods (ΔFM: r=0.48 [95% confidence interval {CI}: 0.002 to 0.78]; ΔFFM: r=0.87 [95% CI: 0.66 to 0.95]. However, both ΔFM (4C: 0.6 ± 1.2 kg; US: 2.8 ± 2.5 kg) and ΔFFM (4C: 3.3 ± 1.6 kg; US: 1.0 ± 3.4 kg) significantly differed between methods (p \u3c 0.001). The total error for ΔFM and ΔFFM estimates was 3.1 kg (95% CI: 3.0 to 3.2 kg). 4C and US predicted the same direction of change in ΔFFM but not ΔFM, based on equivalence testing with an equivalence interval equal to 4C change. Proportional bias was observed for both ΔFM and ΔFFM. CONCLUSION: Although changes in body composition were correlated between methods, ΔFM and ΔFFM significantly differed between 4C and US. As compared to the 4C, US detected a greater proportion of increased body mass as FM rather than FFM. Overall, the magnitude of differences in body composition changes do not support the interchangeability of 4C and US. Although tracking body composition changes provides valuable information, it is important to take into account that different assessment methods may produce varying results in response to a given intervention

Tracking Resistance Training-Induced Changes in Body Composition via 3-Dimensional Optical Scanning

Tracking changes in body composition is potentially useful for monitoring health status, disease risk, and results of lifestyle interventions. In active individuals, evaluating body composition changes over time may provide useful information regarding the effectiveness of nutrition and exercise programs. PURPOSE: The purpose of this study was to compare changes in body composition estimates obtained from a 4-compartment (4C) model and a 3-dimensional optical (3DO) scanner in resistance-trained males. METHODS: Twenty resistance-trained males underwent assessments via 4C and 3DO before and after 6 weeks of supervised resistance training plus overfeeding with a high-calorie protein/carbohydrate supplement. To generate the 4C model, tests were performed using dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by 3DO were compared with the reference 4C model using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: Both ΔFM (mean ± SD: 4C: 0.6 ± 1.1 kg; 3DO: 1.9 ± 1.9 kg) and ΔFFM (4C: 3.2 ± 1.7 kg; 3DO: 1.9 ± 1.4 kg) differed between methods (p \u3c 0.002). The correlation (r) for ΔFM was 0.49 (95% confidence interval [CI]: 0.06 to 0.77) and was 0.42 (95% CI: -0.03 to 0.73) for ΔFFM. The total error for ΔFM and ΔFFM estimates was 2.1 kg. ΔFFM demonstrated equivalence between methods based on a ± 2 kg (~62% of 4C change) equivalence interval, whereas ΔFM failed to exhibit equivalence even with a 100% equivalence interval. Proportional bias was observed for ΔFM but not ΔFFM. CONCLUSION: Our data indicate that changes in FM and FFM detected by a 3D scanner did not exhibit strong agreement with changes detected by a 4C model. However, within the context of our study, agreement in FFM changes was superior to agreement in FM changes based on the results of equivalence testing and lack of proportional bias in FFM changes. Therefore, depending on the level of accuracy needed, the error in FFM changes observed for the 3D scanner may be potentially acceptable for some applications. Future research should investigate the utility of 3D scanners for monitoring changes in body composition and anthropometric variables in healthy and clinical populations, as well as investigate novel body phenotypes that may be associated with disease risk or health status

Clinical Practice Guidelines for Antimicrobial Prophylaxis in Surgery

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140217/1/sur.2013.9999.pd

Influence of Subject Presentation on Body Composition Estimates from Dual-Energy X-Ray Absorptiometry, Air Displacement Plethysmography, and Bioelectrical Impedance Analysis

Body composition assessment devices are commonly employed to track changes associated with exercise or nutritional interventions. However, many individuals undergo body composition assessments with little to no pre-testing standardization of dietary intake or physical activity, potentially introducing error into their results. PURPOSE: To examine the validity of unstandardized body composition assessments relative to standardized assessments using three common body composition assessment devices. METHODS: Twenty-three resistance-trained males (Mean ± SD; 21.6 ± 2.6 years; 71.3 ± 6.8 kg; 177.4 ± 5.9 cm; 17.4 ± 4.1% DXA-derived percent body fat [%BF]) underwent paired body composition assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and single-frequency bioelectrical impedance analysis (BIA). Each participant’s initial standardized body composition assessments were performed in the morning following an overnight food and fluid fast and 12 hours of exercise and caffeine abstention, and all unstandardized assessments were performed later during the same day following ad libitum daily activities. Unstandardized estimates of %BF and fat-free mass (FFM) for each device were compared with device-specific standardized values using paired-samples t-tests, line of identity analysis, evaluation of validity metrics, Bland-Altman analysis, and equivalence testing. RESULTS: The total error between standardized and unstandardized %BF estimates was 0.66% for DXA [95% confidence interval {CI}: 0.56-0.76%], 1.60% for ADP [95% CI: 1.50-1.70%], and 1.85% for BIA [95% CI: 1.75-1.95%]. The total error for FFM estimates was 0.75kg for DXA [95% CI: 0.65-0.85kg], 1.15kg for ADP [95% CI: 1.06-1.25kg], and 1.68 kg for BIA [95%CI: 1.58-1.78]. %BF estimates did not differ between paired measurements for DXA (p = 0.17) or ADP (p = 0.10) but differed between BIA (p \u3c 0.001) assessments. Similarly, FFM estimates did not differ between paired measurements for DXA (p = 0.40) or ADP (p = 0.78) but differed between BIA assessments (p \u3c 0.001). All paired assessments for each outcome produced regression line slopes which differed from the line of identity (p \u3c 0.001). Only BIA %BF estimates exhibited an intercept that differed from the line of identity (p \u3c 0.001). No proportional bias was detected for any outcome. Equivalence was demonstrated between %BF estimates for DXA but not ADP or BIA, based on a ±1%BF equivalence interval. Equivalence was demonstrated for all FFM estimates except BIA, based on a ±1kg equivalence interval. CONCLUSION: Our findings suggest that DXA body composition estimates are more robust when conducted in an unstandardized state relative to ADP or BIA. These results can inform the choice of body composition assessment methodology when pre-testing standardization is not possible

Relationship Between Rate and Composition of Mass Gain During Overfeeding Plus Resistance Training

A common goal among athletes is to gain additional body mass (BM), particularly fat-free mass (FFM), in order to improve muscle strength, power, and overall performance. Athletes typically undergo BM accretion over a specific period of time in conjunction with high-volume resistance training (RT) in order to preferentially gain FFM and promote concomitant muscular performance improvements. PURPOSE: The purpose of this study was to examine the relationship between the rate of BM gain and the proportion of BM gained as FFM versus fat mass (FM) during a 6-week period of overfeeding and resistance training. METHODS: 21 resistance-trained males (mean ± SD: age = 22.6 ± 2.5 years; height = 177.8 ± 6.8 cm; BM = 73.3 ± 12.3 kg, body fat % = 14.8 ± 5.1%, bench press [BP] 1-repetition maximum [1RM] = 1.3 ± 0.3, leg press [LP] 1RM = 3.3 ± 0.9) were recruited and assigned to 6 weeks of RT for 3-days/week and instructed to consume a high-calorie protein/carbohydrate supplement daily. Prior to the intervention, participants performed 1RM tests for the BP and LP exercises to assess training status, with the minimum requirement for study participation being BP 1RM ≥ 1.0xBM and LP 1RM ≥ 2.0xBM. At baseline and post-intervention, body composition assessments were performed using dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and bioimpedance spectroscopy (BIS) in order to produce a criterion 4-compartment model. Simple linear regression was performed to determine if the relative rate of mass gain predicted the composition of mass gain (calculated as the change in fat-free mass divided by the change in body mass). Assumptions of normality, outliers, homogeneity, and independence were examined and addressed as needed. RESULTS: The change in BM, FM, and FFM were (mean ± SD) 5.6 ± 2.3%, 1.3 ± 14.8%, and 6.0 ± 2.1%, respectively. In the regression model, the relative rate of mass gain significantly predicted the composition of mass gain (β: -0.81 [-1.11, -0.50], mean [95% confidence interval]). Based on these data, for every 1% increase in the rate of relative mass gain, the percent of mass gained as FFM decreased by approximately 10% (with a 95% confidence interval of -6 to -13%). A rate of mass gain of 0.93%/week corresponded to 100% of mass being gained as FFM, with slower rates allowing for simultaneous FFM gain and FM loss. CONCLUSION: For individuals who are moderately well-trained with respect to resistance training, a rate of BM gain of ~1%/week may allow for nearly all mass to be gained as FFM while slower rates may allow for simultaneous increases in FFM and decreases in FM

Influence of acute water ingestion and prolonged standing on raw bioimpedance and subsequent body fluid and composition estimates

This study evaluated the influence of acute water ingestion and maintaining an upright posture on raw bioimpedance and subsequent estimates of body fluids and composition. Twenty healthy adults participated in a randomized crossover study. In both conditions, an overnight food and fluid fast was followed by an initial multi-frequency bioimpedance assessment (InBody 770). Participants then ingested 11 mL/kg of water (water condition) or did not (control condition) during a 5-minute period. Thereafter, bioimpedance assessments were performed every 10 minutes for one hour with participants remaining upright throughout. Linear mixed effects models were used to examine the influence of condition and time on raw bioimpedance, body fluids, and body composition. Water consumption increased impedance of the arms but not trunk or legs. However, drift in leg impedance was observed, with decreasing values over time in both conditions. No effects of condition on body fluids were detected, but total body water and intracellular water decreased by ~0.5 kg over time in both conditions. Correspondingly, lean body mass did not differ between conditions but decreased over the measurement duration. The increase in body mass in the water condition was detected exclusively as fat mass, with final fat mass values ~1.3 kg higher than baseline and also higher than the control condition. Acute water ingestion and prolonged standing exert practically meaningful effects on relevant bioimpedance variables quantified by a modern, vertical multi-frequency analyzer. These findings have implications for pre-assessment standardization, methodological reporting, and interpretation of assessments