Optimal Timing for Initiation of Biofeedback-Assisted Relaxation Training in Hospitalized Coronary Heart Disease Patients With Sleep Disturbances
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".