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Record W2610810959 · doi:10.25011/cim.v39i6.27501

Potential beneficial effects of foot bathing on cardiac rhythm

2016· article· en· W2610810959 on OpenAlexvenueno aff
Duygu Aydın, Siti Sugih Hartiningsih, Melike Gokce Izgi, Sevgi Bay, Kubra Unlu, Meryem Ozlem Tatar, Ayse Melike Alparslan, Mohammed Ozeri, Şenol Dane

Bibliographic record

VenueClinical and investigative medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsnot available
Fundersnot available
KeywordsBathingMedicineFoot (prosody)Heart rate variabilityHeart ratePopulationBalance (ability)CardiologyHeart RhythmPhysical therapyPhysical medicine and rehabilitationAnesthesiaInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE: Foot bathing therapy is a simple technique that induces sensations of comfort and relaxation. The aim of this study was to examine the effect of foot bathing therapy on heart rate variability (HRV) parameters in a healthy population. METHODS: Participants were twenty healthy female subjects (median age=20.67 years, SD=1.04). The recording ECG was applied for 5 minutes before and for 5 minutes after foot bathing therapy of 10 minutes. Subjects rested for 10 minutes without recording ECG in order to stabilize autonomic parameters. The digital signals were then transferred to a laptop and analyzed using LabChart® software (MLS310/7 HRV Module). RESULTS: Almost all HRV parameters increased and heart (pulse) rate and LF/HF ratio decreased after foot bathing therapy compared with before foot bathing therapy. CONCLUSIONS: These results indicate for the first time in humans that foot bathing might induce a state of balance between sympathetic and parasympathetic systems and might be helpful to prevent possible cardiac arrhythmias.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.165
GPT teacher head0.448
Teacher spread0.283 · 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
Published2016
Admission routes1
Has abstractyes

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