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Record W2761311486 · doi:10.17863/cam.22596

Self-monitoring of blood pressure in hypertension: A systematic review and individual patient data meta-analysis.

2017· review· en· W2761311486 on OpenAlexfundno aff
Katherine L. Tucker, James P Sheppard, Richard Stevens, Hayden B. Bosworth, Alfred A. Bové, Emma P Bray, Kenneth Earle, Johnson George, Marshall Godwin, Beverly B. Green, Paul L. Hebert, Richard Hobbs, Ilkka Kantola, Sally Kerry, Alfonso Leiva, David J. Magid, Jonathan Mant, Karen L. Margolis, Brian McKinstry, Mary Ann McLaughlin, Stefano Omboni, Olugbenga Ogedegbe, Gianfranco Parati, Nashat Qamar, Bahman P. Tabaei, Juha Varis, Willem J. Verberk, Bonnie J. Wakefield, Richard J. McManus

Bibliographic record

VenueMonash University Research Portal (Monash University) · 2017
Typereview
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
FundersNIHR School for Primary Care ResearchNIHR Oxford Biomedical Research CentreGenentechPfizerLung Foundation AustraliaHeart and Stroke Foundation of CanadaNational Institute for Health and Care ResearchMedical Research CouncilSanofi
KeywordsMedicineBlood pressureAmbulatory blood pressureMeta-analysisCochrane LibraryMEDLINERandomized controlled trialAmbulatoryInternal medicineSystematic reviewEmergency medicinePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Self-monitoring of blood pressure (BP) appears to reduce BP in hypertension but important questions remain regarding effective implementation and which groups may benefit most. This individual patient data (IPD) meta-analysis was performed to better understand the effectiveness of BP self-monitoring to lower BP and control hypertension. METHODS AND FINDINGS: Medline, Embase, and the Cochrane Library were searched for randomised trials comparing self-monitoring to no self-monitoring in hypertensive patients (June 2016). Two reviewers independently assessed articles for eligibility and the authors of eligible trials were approached requesting IPD. Of 2,846 articles in the initial search, 36 were eligible. IPD were provided from 25 trials, including 1 unpublished study. Data for the primary outcomes-change in mean clinic or ambulatory BP and proportion controlled below target at 12 months-were available from 15/19 possible studies (7,138/8,292 [86%] of randomised participants). Overall, self-monitoring was associated with reduced clinic systolic blood pressure (sBP) compared to usual care at 12 months (-3.2 mmHg, [95% CI -4.9, -1.6 mmHg]). However, this effect was strongly influenced by the intensity of co-intervention ranging from no effect with self-monitoring alone (-1.0 mmHg [-3.3, 1.2]), to a 6.1 mmHg (-9.0, -3.2) reduction when monitoring was combined with intensive support. Self-monitoring was most effective in those with fewer antihypertensive medications and higher baseline sBP up to 170 mmHg. No differences in efficacy were seen by sex or by most comorbidities. Ambulatory BP data at 12 months were available from 4 trials (1,478 patients), which assessed self-monitoring with little or no co-intervention. There was no association between self-monitoring and either lower clinic or ambulatory sBP in this group (clinic -0.2 mmHg [-2.2, 1.8]; ambulatory 1.1 mmHg [-0.3, 2.5]). Results for diastolic blood pressure (dBP) were similar. The main limitation of this work was that significant heterogeneity remained. This was at least in part due to different inclusion criteria, self-monitoring regimes, and target BPs in included studies. CONCLUSIONS: Self-monitoring alone is not associated with lower BP or better control, but in conjunction with co-interventions (including systematic medication titration by doctors, pharmacists, or patients; education; or lifestyle counselling) leads to clinically significant BP reduction which persists for at least 12 months. The implementation of self-monitoring in hypertension should be accompanied by such co-interventions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0110.002
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.005
Research integrity0.0010.002
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 head0.435
GPT teacher head0.390
Teacher spread0.045 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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