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Effects of an exoskeleton-assisted walking program on bone strength in wheelchair individuals with spinal cord injury: A preliminary study using imaging and serum biomarkers

2023· article· en· W4378648845 on OpenAlexaffabout
Alec Bass, Suzanne N. Morin, Michael Guidea, J. Lam, Rami Hammad, Mylène Aubertin‐Leheudre, Dany H. Gagnon

Bibliographic record

VenuePhysiology · 2023
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalMcGill UniversityUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMedicineSpinal cord injuryBone mineralOsteoporosisTibiaFemurBone densityPopulationSpinal cordWeight-bearingPhysical therapyPhysical medicine and rehabilitationSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Lower extremity weight bearing is a key determinant in maintaining bone density and strength. Approximately 60% of individuals with chronic spinal cord injury use a wheelchair long-term as their primary mode of locomotion—leading to a substantial reduction in lower extremity weight bearing. Unfortunately, this contributes to the development of severe sub-lesional osteoporosis, particularly at the femur and tibia, with fragility fracture rates twice those reported among the general population. Overground exoskeleton-assisted walking programs provide a novel opportunity to increase lower extremity weight bearing and mobility which may strengthen bone and mitigate fracture risks and associated complications. OBJECTIVES: To measure the immediate effects of an exoskeleton assisted walking program on lower-extremity bone strength and serum bone biomarkers in individuals with a spinal cord injury who use a wheelchair as their primary mode of locomotion. METHODS: Ten participants completed a 16-week exoskeleton assisted walking program incorporating 34 individualized 1-hour sessions, progressing from 1 to 3 sessions per week. Outcomes were measured immediately before and after completion of the intervention (±7 days). Mechanical bone properties (density, mineral content, geometry and strength indexes) were assessed using dual-energy X-ray absorptiometry (left hip, left arm, left leg) and peripheral quantitative tomography (25% left femur, 66% left tibia). Bone turnover biomarkers (bone formation = osteocalcin, bone resorption = telopeptide-c collagen) and 25-hydroxyvitamine D were assessed using fasting blood samples. Wilcoxon signed-ranked tests were used to determine pre versus post differences ( p<0.05) in outcomes and standardized effect sizes (ES) were computed. RESULTS: At the femur, statistically significant increases in cortical bone mineral density (ES=0.652) and stress-strain index (ES=0.889), as well as decreases in cortical bone mineral content (ES=0.889), cortical cross-sectional area (ES=0.889) and cortical thickness (ES=0.849) were observed. At the tibia, a statistically significant increase in polar moment of inertia (ES=0.790) was observed. Levels of 25-hydroxyvitamine D increased significantly (ES=0.693). CONCLUSION: The completion of a 16-week exoskeleton-assisted walking program elicits promising bone responses. However, due in part to a lack of statistical power, the clinical significance of these findings as well as the ultimate effect on bone strength and fracture risk remains to be confirmed. Larger clinical trials are needed. Longer intervention durations, as well as multimodality interventions (e.g., functional electrical stimulation, pharmacotherapy, dietary) may ultimately prove to be the most effective. This work was supported by the Fonds de recherche du Québec - Santé (FRQS, grant #252532) and the John R. Evans Leaders Fund of the Canada Foundation for Innovation (grant #36243). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.035
GPT teacher head0.399
Teacher spread0.364 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2023
Admission routes2
Has abstractyes

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