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Record W4323035330 · doi:10.5770/cgj.26.627

The Relationship Between Physical Activity and Limited Range of Motion in the Older Bedridden Patients

2023· article· en· W4323035330 on OpenAlexvenueno aff
Chiaki Murata, Hideki Kataoka, Hideki Aoki, Shunpei Nakashima, Koichi Nakagawa, Kyo Goto, Junichiro Yamashita, Seima Okita, Ayumi Takahashi, Yuichiro Honda, Junya Sakamoto, Minoru Okita

Bibliographic record

VenueCanadian Geriatrics Journal · 2023
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMedicineRange of motionPhysical medicine and rehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Background The purpose of this study is to examine the association between physical activity and contracture in older patients confined to bed in long-term care (LTC) facilities. Methods Patients wore ActiGraph GT3X+ for 8 hours on their wrists, and vector magnitude (VM) counts were obtained as the amount of activity. The passive range of motion (ROM) of joints was measured. The severity of ROM restriction classi-fied, as the tertile value of the reference ROM of each joint, was scored 1–3 points. Spearman’s rank correlation coef-ficients (Rs) were used to measure the association between the VM counts per day and ROM restrictions. Results The sample comprised 128 patients with a mean (SD) age of 84.8 (8.8) years. The mean (SD) of VM was 84574.6 (115195.2) per day. ROM restriction was observed in most joints and movement directions. ROMs in all joints and move-ment directions, except wrist flexion and hip abduction, were significantly correlated with VM. Furthermore, the VM and ROM severity scores showed a significant negative correlation (Rs = -0.582, p < .0001). Conclusions A significant correlation between the physical activity and ROM restrictions indicates that a decrease in the amount of physical activity could be one of the causes of contracture.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.012
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.344
Teacher spread0.289 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Geriatrics JournalSame topicBalance, Gait, and Falls PreventionFrench-language works237,207