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Record W4391883917 · doi:10.1177/10547738241231626

Exploring the Interrelationships Between Physical Function, Functional Exercise Capacity, and Exercise Self-Efficacy in Persons Living with HIV

2024· article· en· W4391883917 on OpenAlexaff
Kathleen M. Nokes, Dudu G Sokhela, Penelope M. Orton, William Ellery Samuels, J. Craig Phillips, Kimberly Adams Tufts, Joseph Perazzo, Puangtip Chaiphibalsarisdi, Carmen Portillo, Rebecca Schnall, Mary Jane Hamilton, Carol Dawson‐Rose, Allison R. Webel

Bibliographic record

VenueClinical Nursing Research · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Nursing ResearchNational Institutes of Health
KeywordsMedicineAnthropometryAnalysis of variancePhysical therapyUnivariate analysisWaistMultivariate analysisMultivariate analysis of varianceInternal medicineBody mass index

Abstract

fetched live from OpenAlex

While physical activity can mitigate the metabolic effects of HIV disease and HIV medications, many HIV-infected persons report low levels of physical activity. Purpose: To determine if there were differences between the subjective and objective assessments of physical activity while controlling for sociodemographic, anthropometric, and clinical characteristics. Setting/sample: A total of 810 participants across eight sites located in three countries. Measures: Subjective instruments were the two subscales of Self-efficacy for Exercise Behaviors Scale: Making Time for Exercise and Resisting Relapse and Patient-Reported Outcomes Measurement Information System, which measured physical function. The objective measure of functional exercise capacity was the 6-minute Walk Test. Analysis: Both univariate and multivariant analyses were used. Results: Physical function was significantly associated with Making Time for Exercise (β = 1.76, p = .039) but not with Resisting Relapse (β = 1.16, p = .168). Age (β = −1.88, p = .001), being employed (β = 16.19, p < .001) and race (βs = 13.84–31.98, p < .001), hip–waist ratio (β = −2.18, p < .001), and comorbidities (β = 7.31, p < .001) were significant predictors of physical functioning. The model predicting physical function accounted for a large amount of variance (adjusted R 2 = .938). The patterns of results predicting functional exercise capacity were similar. Making Time for Exercise self-efficacy scores significantly predicted functional exercise capacity (β = 0.14, p = .029), and Resisting Relapse scores again did not (β = −0.10, p = .120). Among the covariates, age (β = −0.16, p < .001), gender (β = −0.43, p < .001), education (β = 0.08, p = .026), and hip–waist ratio (β = 0.09, p = .034) were significant. This model did not account for much of the overall variance in the data (adjusted R 2 = .081). We found a modest significant relationship between physical function and functional exercise capacity ( r = 0.27). Conclusions: Making Time for Exercise Self-efficacy was more significant than Resisting Relapse for both physical function and functional exercise capacity. Interventions to promote achievement of physical activity need to use multiple measurement strategies.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.315
GPT teacher head0.461
Teacher spread0.146 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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