Validity of Bioelectric Impedance in Relation to Dual-Energy X-Ray Absorptiometry for Measuring Baseline and Change in Body Composition After an Exercise Program in Stroke
Bibliographic record
Abstract
ABSTRACT: Vanzella, LM, Lawand, R, Shuaib, M, Oh, P, Corbett, D, and Marzolini, S. Validity of bioelectric impedance in relation to dual-energy x-ray absorptiometry for measuring baseline and change in body composition after an exercise program in stroke. J Strength Cond Res 36(12): 3273-3279, 2022-Exercise is an important strategy to improve fat-free mass (FFM) and reduce percent fat mass (FM%). However, no study has reported on a valid, cost-effective method to measure changes in body composition after stroke. The purpose of the study is to determine the level of agreement between bioelectrical impedance analysis (BIA) and dual-energy x-ray absorptiometry (DXA) for assessing baseline and change in FFM and FM% after an exercise training intervention for individuals with mobility deficits after stroke. Fat-free mass and FM% were measured by BIA and DXA at the beginning and after 6 months of participation in an exercise program for individuals with mobility deficits after stroke. Forty-two subjects after stroke were included. Overall, Bland-Altman plots revealed that BIA overestimated the baseline FFM and FM% by only -0.4 ± 1.8 kg and -2.8 ± 1.8%, respectively. BIA underestimated changes in FFM by 0.33 ± 0.45 kg and overestimated changes in FM% by -0.40 ± 0.68%. The 95% CI of the mean bias for baseline FFM was -7.1 to 6.3 kg and -11.8 to 6.0% for FM%, demonstrating good agreement. The 95% CI for the change in FFM was -3.8 to 4.5 kg and -5.0 to 4.2% for FM%, which reflected good agreement. BIA is a good tool for assessing qualitative baseline and change in FFM and FM%. Body composition is important for the prescription and evaluation of rehabilitation programs designed for individuals after stroke. Our results provide clinicians and researchers with a better understanding of the utility of BIA to measure body composition at baseline and in response to exercise interventions in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".