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Record W1970439385 · doi:10.1097/jcn.0b013e318297c41b

Optimal Timing for Initiation of Biofeedback-Assisted Relaxation Training in Hospitalized Coronary Heart Disease Patients With Sleep Disturbances

2013· article· en· W1970439385 on OpenAlex
Lina Wang, Hong Tao, Yue Zhao, Xiu-rong Jiang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Cardiovascular Nursing · 2013
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsEmergent BioSolutions (Canada)
Fundersnot available
KeywordsMedicineRelaxation (psychology)Pittsburgh Sleep Quality IndexBiofeedbackPhysical therapyMorningAnalysis of varianceRelaxation TherapyAnxietySleep disorderRepeated measures designStatistical significanceSleep qualityInternal medicineInsomniaPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical studies have shown that biofeedback-assisted relaxation positively influences the treatment outcomes of sleep disturbance. However, there are only few studies reporting the timing of relaxation training initiation, and the relationships between the timing of initiation and the effectiveness of relaxation remain unclear. OBJECTIVES: The aim of this study was to determine the optimal timing for initiating nurse-led biofeedback-assisted relaxation on hospitalized coronary heart disease patients with sleep disturbance. METHODS: An experimental pretest and repeated posttest design was used to compare the effectiveness of nurse-led biofeedback-assisted relaxation. A total of 128 patients with coronary heart disease were randomly assigned to 1 of 4 groups: morning group, night group, morning-night group, or control group. Outcome measures included self-report of sleep-related indicators, the scores of the Pittsburgh Sleep Quality Index (PSQI) and the Zung's Self-rating Anxiety Scale (SAS), and the dosage of sleep medication used. A 2-way analysis of variance and a simple effect test were used to analyze the differences among the 4 groups. RESULTS: No significant differences could be detected at baseline. Compared with the control group, the nurse-led biofeedback-assisted relaxation yielded a greater benefit for patients in the 3 intervention groups. Group and time factors (pretest-protest) could explain the variation in the effectiveness of this program (main effect P < .01). There were statistical differences among the groups: patients in the night group (FSOL = 33.15, P < .001; FTST = 17.99, P < .001; FSE = 10.26, P = .002; FPSQI = 27.38, P < .001; FSAS = 54.39, P < .001, respectively) and in the morning-night group (FSOL = 33.62, P < .001; FTST = 34.13, P < .001; FSE = 24.04, P < .001; FPSQI = 31.26, P < .001; FSAS = 73.93, P < .001, respectively) had slightly shorter sleep latency, experienced fewer awakenings, reported higher sleep quality, and used significantly fewer sleep medications than the morning group did (F = 32.97, P < .001). CONCLUSIONS: The timing of the initiation of nurse-led biofeedback-assisted relaxation was 1 of the factors affecting the effectiveness of relaxation. Relaxation training either at night or in the morning-night combination could effectively enhance sleep quality and decrease the need for of sleep medications in hospitalized patients with sleep disturbance.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.338

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 head0.017
GPT teacher head0.259
Teacher spread0.241 · 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