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Record W2979686435 · doi:10.1136/openhrt-2019-001017

Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): systematic review and individual patient data meta-analysis

2019· review· en· W2979686435 on OpenAlex
Sheikh Mohammed Shariful Islam, Kirsten Bobrow, Ralph Maddison, Robyn Whittaker, Leila Anne Pfaeffli Dale, Andreas Lechner, Scott A. Lear, Zubin J. Eapen, Louis Niessen, Karla Santo, Sandrine Stepien, Julie Redfern, Anthony Rodgers, Clara K Chow

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOpen Heart · 2019
Typereview
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsSt. Paul's Hospital
FundersNIHR Oxford Biomedical Research CentreNational Health and Medical Research CouncilMedical Research CouncilNational Institute for Health and Care ResearchGeorge Institute for Global HealthUniversity of AucklandUniversity of Cape TownSimon Fraser UniversityInternational Centre for Diarrhoeal Disease Research, BangladeshUniversity of SydneyHigh Blood Pressure Research Council of AustraliaDeakin UniversityUniversity of Oxford
KeywordsMeta-analysisPsychological interventionBlood pressureMedicineBody mass indexSubgroup analysisText messagingRandom effects modelSystematic reviewInternal medicineMEDLINEComputer scienceWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

Background: A variety of small mobile phone text-messaging interventions have indicated improvement in risk factors for cardiovascular disease (CVD). Yet the extent of this improvement and whether it impacts multiple risk factors together is uncertain. We aimed to conduct a systematic review and individual patient data (IPD) meta-analysis to investigate the effects of text-messaging interventions for CVD prevention. Methods: Electronic databases were searched to identify trials investigating a text-messaging intervention focusing on CVD prevention with the potential to modify at least two CVD risk factors in adults. The main outcome was blood pressure (BP). We conducted standard and IPD meta-analysis on pooled data. We accounted for clustering of patients within studies and the primary analysis used random-effects models. Sensitivity and subgroup analyses were performed. Results: Nine trials were included in the systematic review involving 3779 participants and 5 (n=2612) contributed data to the IPD meta-analysis. Standard meta-analysis showed that the weighted mean differences are as follows: systolic blood pressure (SBP), -4.13 mm Hg (95% CI -11.07 to 2.81, p<0.0001); diastolic blood pressure (DBP), -1.11 mm Hg (-1.91 to -0.31, p=0.002); and body mass index (BMI), -0.32 (-0.49 to -0.16, p=0.000). In the IPD meta-analysis, the mean difference are as follows: SBP, -1.3 mm Hg (-5.4 to 2.7, p=0.5236); DBP, -0.8 mm Hg (-2.5 to 1.0, p=0.3912); and BMI, -0.2 (-0.8 to 0.4, p=0.5200) in the random-effects model. The impact on other risk factors is described, but there were insufficient data to conduct meta-analyses. Conclusion: Mobile phone text-messaging interventions have modest impacts on BP and BMI. Simultaneous but small impacts on multiple risk factors are likely to be clinically relevant and improve outcome, but there are currently insufficient data in pooled analyses to examine the extent to which simultaneous reduction in multiple risk factors occurs. PROSPERO registration number: CRD42016033236.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0160.005
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.370
GPT teacher head0.533
Teacher spread0.163 · 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