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Record W2469603690 · doi:10.1177/0193945916654907

Body Composition Outcomes of a Qigong Intervention Among Community-Dwelling Aging Adults

2016· article· en· W2469603690 on OpenAlexfundno aff
Mei-Ying Chang, Hsiao‐Yu Chen

Bibliographic record

VenueWestern Journal of Nursing Research · 2016
Typearticle
Languageen
FieldMedicine
TopicBiofield Effects and Biophysics
Canadian institutionsnot available
FundersUniversity of British Columbia
KeywordsLean body massBody mass indexMedicineFat massPhysical therapyIntervention (counseling)Composition (language)GerontologyBody weightInternal medicineNursing

Abstract

fetched live from OpenAlex

Aging causes various changes in body composition, which are critical implications for health and physical functioning in aging adults. The aim of this study was to explore the body composition outcomes of a qigong intervention among community-dwelling aging adults. This was a quasi-experimental study in which 90 participants were recruited. Forty-eight participants (experimental group) attended a 30-min qigong program 3 times per week for 12 weeks, whereas 42 participants (control group) continued performing their usual daily activities. The experimental group achieved a greater reduction in the fat mass percentage at the posttest, and exhibited increased fat-free mass, lean body mass percentage, and lean body mass to fat mass ratio compared with the controls. No difference between the two groups in body mass index, fat mass, and lean body mass was observed. These results indicated that the qigong intervention showed beneficial outcomes of body composition among community-dwelling aging adults.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.077
GPT teacher head0.427
Teacher spread0.350 · 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 designNon-randomized trial
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

Citations13
Published2016
Admission routes1
Has abstractyes

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