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Record W4412493950 · doi:10.1080/17483107.2025.2531242

Reliability of propulsion technique and physiological parameters during wheelchair ergometer tests and its use in assessing the effects of different hand rim types

2025· article· en· W4412493950 on OpenAlexaff
Rowie J. F. Janssen, Melle van Dilgt, Kim van Hutten, Riemer J. K. Vegter, Sonja de Groot, Monique Berger

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsSprintWheelchairIntraclass correlationReliability (semiconductor)PropulsionPhysical medicine and rehabilitationPhysical therapyCycle ergometerSimulationEngineeringMedicineComputer sciencePower (physics)MathematicsReproducibilityStatisticsHeart rateInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: Wheelchair optimisation in clinical settings often relies on expert opinion from static wheelchair seating posture. A wheelchair ergometer provides biomechanical and physiological insights during propulsion. This study assessed the reliability of propulsion technique and physiological parameters in submaximal and sprint tests and used these values to compare two hand rim types. MATERIALS AND METHODS: Nineteen non-wheelchair users completed two exercise blocks per hand rim type (Gekko vs. conventional) on a wheelchair ergometer. Each block included a 4-min submaximal test and a 30 s sprint. The intraclass correlation coefficient (ICC), standard error of measurement (SEM) and smallest detectable change (SDC) were calculated to assess reliability for one to four exercise blocks. Hand rim differences were analysed using a mixed-effects model. RESULTS: Submaximal propulsion technique showed good to excellent reliability (ICC = 0.75-1.00), while physiological variables and sprint propulsion technique had moderate to excellent reliability (ICC = 0.5-0.95). SDCs ranged from 12 to 29% (submaximal) and 13 to 25% (sprint), except for negative power variables (25-70%). Averaging the two, three or four tests reduced SDCs by 29%, 42% and 50%, respectively, compared to one test. The Gekko rim outperformed the conventional rim in negative power (submaximal) and in distance, velocity and power (sprints). CONCLUSIONS: The submaximal test demonstrated better reliability and lower SDCs than the sprint test. The Gekko rim performed better at group level but not for every individual. Using SDCs instead of group results enhances clinical relevance. Future research should validate these findings in wheelchair users to support evidence-based wheelchair setup recommendations. Implications for rehabilitationThe study showed that submaximal propulsion technique has good-to-excellent reliability, while submaximal physiological parameters and sprint propulsion technique showed moderate-to-excellent reliability. Smallest detectable change (SDC) values varied, with higher values seen in negative power and physiological parameters, and generally higher SDCs in the sprint test.Although group-level statistics showed significant differences in propulsion technique (favouring the Gekko hand rim), individual analysis using SDC cut-offs revealed variations among participants. This highlights that wheelchair adaptations should be evaluated at an individual level, as group results may not capture important differences that matter for specific users.To detect the most meaningful individual differences, it is recommended to average the results of four tests. However, since the most significant reduction in SDCs occurs between one and two tests, at least two tests should be conducted when time or physical capacity is limited.The findings are not only relevant for comparing hand rims. SDC thresholds can be used to assess a wide range of individual factors, such as propulsion technique, fitness or wheelchair features (e.g., seat height). These thresholds can help tailor interventions based on the unique needs of each wheelchair user.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.354
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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