The Right Tool for the Task: Body-Weight Supported Treadmill or Total Body Recumbent Stepper for Mobility-Adapted Cardiopulmonary Exercise Testing in Multiple Sclerosis Patients with Disability
Bibliographic record
Abstract
ABSTRACT Objective Cardiopulmonary Exercise Testing (CPET) is challenging among persons with mobility disability. We sought the optimal adapted device to achieve a maximal CPET. Design Randomized crossover trial, within-subjects, repeated measures design Setting Primary Care and Referral Center Participants Clinic-referred persons with multiple sclerosis (PwMS) (n=10) with three-month stability, no exercise obstruction, MoCa>24, ability to walk with or without assistance, and sex- and age-matched (±3 years) Controls (n=7) recruited by convenience sampling Interventions CPET on body weight-supported treadmill (BWST) and total body recumbent stepper (TBRS) Main Outcome Measures Standard aerobic metrics (V̇O 2max , % normative values for V̇O 2max [%V̇O 2max ], heart rate maximum [HR max ], age-predicted HR max , and Respiratory Exchange Ratio) Results PwMS achieved similar V̇O 2max (mL·min -1 ·kg -1 ) on the TBRS and BWST (26.53±8.7 vs. 24.24±7.8) while Controls obtained higher values on BWST than TBRS (40.27±7.6 vs. 34.32±7.1, p <0.001). PwMS more consistently achieved criteria for maximum CPET using TBRS. During the preliminary investigation of the MS subgroup with a higher mobility disability, CPET using BWST exaggerated already low CPET metrics. Conclusions Although Controls achieved higher CPET values on BWST, V̇O 2max between devices were similar among PwMS. Only when using BWST, PwMS V̇O 2max and %V̇O 2max were lower than Controls, likely because of leg fatigue and weakness. Using TBRS permits persons with mobility disability to achieve more criteria for a maximum CPET. Our results suggest that CPET using BWST, being reliant on the lower body, likely disadvantages PwMS, especially those with mobility disability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".