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Record W4399717539 · doi:10.37036/ahnj.v10i1.532

The Effect of a Combination of Warm Water Foot Baths and Lavender Essential Oil Aromatherapy on Sleep Quality in Adult Hypertension

2024· article· en· W4399717539 on OpenAlexaff
Novita Fajriyah, Susanti Susanti, Rina Budi Kristiani, Hana Dodik Pramiasti, Li Tang

Bibliographic record

VenueAdi Husada Nursing Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicPublic Health and Nutrition
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsAromatherapyMedicineLavenderBlood pressureEssential hypertensionLavender oilSleep qualityEssential oilSleep (system call)AnesthesiaPhysical therapyTraditional medicineInternal medicineInsomniaChemistryPharmacologyAlternative medicineFood science

Abstract

fetched live from OpenAlex

Hypertension is one of the deadliest diseases in the world that can attack anyone, both young and old. Hypertension sufferers have poorer sleep quality compared to those with normal blood pressure. This research is a quantitative research using a Quasi-Experimental Design. This research aimed to determine the effect of a combination of warm water foot baths and lavender essential oil aromatherapy on sleep quality in adults with hypertension. Wilcoxon test results (p = 0.004), which means that there is an effect of combination therapy with warm water foot baths and lavender essential oil aromatherapy on the sleep quality of adults with hypertension. The feet have many thermoreceptors and a high density of capillaries. Soaking your feet in warm water can help prepare the body for sleep naturally and quickly trigger peripheral vasodilation. There are two main ways to apply aromatherapy: topically and through inhalation. Lavender essential oil is said to activate the limbic system, especially the hippocampus and amygdala. Combining warm foot baths with aromatherapy would be a rational approach to the management of sleep disorders in adult patients with hypertension.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.325
Teacher spread0.312 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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