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Record W4413845637 · doi:10.2196/81630

How You Sleep May Impact How You Walk: A Pilot Cohort Study (Preprint)

2025· article· en· W4413845637 on OpenAlexvenueno aff
Francesca Marino, Minzae Kim, Phillip H Hwang, Yorghos Tripodis, Michael L. Alosco, Jesse Mez, Ann C. McKee, Rhoda Au, Robert J. Thomas, Ludy C. Shih

Bibliographic record

VenueJMIR Aging · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintSleep (system call)CohortMedicineComputer scienceInternal medicineWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Background Higher step width variability while walking is associated with poor physical function and falls. Sleep is an established modifiable risk factor for both gait and physical function impairments, but it remains to be examined whether sleep is also related to step width variability. Objective This study aimed to evaluate the cross-sectional associations between objectively measured sleep quality, using cardiopulmonary coupling spectrograms, and step width variability during a preferred walking condition among middle-aged and older adults. Methods This study included 72 adults (mean age 71, SD 8.3 y; n=37, 51% female; n=65, 90% non-Hispanic White) who had ≥2 nights of objectively measured sleep (cardiopulmonary coupling via SleepImage ring) and completed a 10-m walk at preferred speed while wearing inertial sensors (APDM Mobility Lab). Sleep measures included sleep duration, efficiency, fragmentation, stability, apnea-hypopnea index, percentage of time oxygen saturation level <90%, oxygen desaturation index, and respiratory disturbance index. Additional derived sleep variables were explored using least absolute shrinkage and selection operator models. Step width variability was defined by the asymmetry of lateral step variability and categorized as medial (≤−7.5 cm), minimal (within ±7.5 cm; reference), or lateral displacement (≥7.5 cm). Multinomial logistic regression models adjusted for age, sex, race, education, BMI, and usual gait speed evaluated cross-sectional associations between sleep and step width variability categories. Results We found that a 1% higher sleep fragmentation was associated with a 6% higher probability of step width variability ≥7.5 cm (95% CI 1.01‐1.11), while a 1% higher sleep stability was associated with a 5% lower probability of variability ≥7.5 cm (95% CI 0.91‐0.99), compared to minimal variability. From the least absolute shrinkage and selection operator models, we found that a 1% higher sleep quality index, a 1% higher rapid eye movement sleep, a 1-second shorter apnea duration, and a 1-beat per minute slower mean heart rate were also associated with a lower probability of lateral compared to minimal displacement. Conclusions Poor sleep quality was associated with higher step width variability among middle-aged and older adults. This suggests that sleep may be a modifiable risk factor for maintaining postural stability while walking among middle-aged and older adults. Future studies are needed to examine whether intervening in these sleep measures also lowers the risk of falls.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.384
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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