Effects of Guided Imagery on Blood Pressure in Pregnant Women with Hypertension: A Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Hypertension occurs in nearly 10 percent of pregnancies, and is associated with higher risk of infant and maternal morbidity and mortality than in normal pregnancies. Previous studies have suggested that relaxation therapies reduce blood pressure in nonpregnant adults. The objectives of this pilot randomized trial were to provide preliminary evidence of whether relaxation by means of guided imagery would reduce blood pressure in hypertensive pregnant women, and to assess the feasibility of a larger trial. METHODS: A total of 69 pregnant women with hypertension were randomized to periods of guided imagery or of quiet rest, twice daily for 4 weeks or until delivery, whichever came first. Daytime ambulatory mean arterial pressure, systolic and diastolic blood pressure, and anxiety were measured weekly for up to 4 weeks. RESULTS: Women allocated to guided imagery had lower mean arterial pressure elevations over time than those allocated to quiet rest (guided imagery: M = 1.58 mmHg, SD = 7.63; quiet rest: M = 5.93 mmHg, SD = 6.55; t = 2.36, p = 0.02). However, when adjusted for baseline mean arterial pressure and gestation, the effect was not significant (p = 0.14). Numbers of women prescribed antihypertensive medication postrandomization were similar (guided imagery: n = 16; quiet rest: n = 13, χ(2) = 0.74, p = 0.46). There was also no evidence of an effect on women's anxiety. Nearly 90 percent (n = 26) of the guided imagery group indicated that they would use it again. CONCLUSIONS: Further rigorous study is warranted to determine effects of guided imagery on maternal blood pressure and perinatal health outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".