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Record W1971804436 · doi:10.1016/j.pmrj.2010.04.026

The Effects of a Brief Relaxation Program on Symptom Distress and Heart Rate Variability in Cancer Patients

2010· article· en· W1971804436 on OpenAlexaboutno aff
Arash Asher, J. Lynn Palmer, Rajesh Yadav, Syed Wamique Yusuf, Benedict Konzen, Éduardo Bruera, Ying Guo

Bibliographic record

VenuePM&R · 2010
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
FundersEuropean Society of Cardiology
KeywordsHeart rate variabilityMedicineRelaxation (psychology)DistressRelaxation TherapyPhysical therapyHeart rateAutonomic nervous systemCardiologyInternal medicineClinical psychologyBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine whether a 15-minute, one-time guided relaxation program for cancer patients could improve symptom distress as measured by the Edmonton Symptom Assessment System (ESAS). In addition, we were interested in characterizing the changes of the autonomic nervous system, as demonstrated by heart rate variability (HRV) high-frequency (HF) spectral analysis, before and after this relaxation program. DESIGN: Nonrandomized pilot study. SETTING: Comprehensive cancer center. METHODS: Twenty cancer patients underwent a 15-minute relaxation program. The ESAS and a 5-minute HRV recording were completed before and after the relaxation program. MAIN OUTCOME MEASURES: The differences between the pre- and post-summed ESAS score and HRV values were compared by a paired t-test. RESULTS: The summed ESAS scores were significantly lower after the relaxation program (P<.01), with an average 31% decrease in total score. However, no differences were found in HRV HF power. There was no correlation between the change in HRV HF and change in symptom distress, as measured by ESAS. CONCLUSIONS: A brief guided relaxation program can significantly improve symptoms as measured by ESAS. More research is required to understand the effects of relaxation on HF HRV power.

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)0.002
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 score0.397
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.005
GPT teacher head0.263
Teacher spread0.259 · 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

Citations8
Published2010
Admission routes1
Has abstractyes

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