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Record W3200387216 · doi:10.2196/29928

mHealth Interventions for Lifestyle and Risk Factor Modification in Coronary Heart Disease: Randomized Controlled Trial

2021· article· en· W3200387216 on OpenAlexvenueno aff
Jang‐Whan Bae, Seoung-Il Woo, Joongyub Lee, Sang‐Don Park, Sung Woo Kwon, Seong Huan Choi, Gwang-Seok Yoon, Mi‐Sook Kim, Seung-Sik Hwang, Won Kyung Lee

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialPsychological interventionConventional PCImHealthShort Message ServiceBlood pressurePopulationPhysical therapyRisk factorInternal medicineMyocardial infarctionNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Self-management of lifestyle and cardiovascular disease risk factors is challenging in older patients with coronary heart disease (CHD). SMS text messaging could be a potential support tool for self-management and the most affordable and accessible method through a mobile phone. High-quality evidence had been lacking, and previous studies evaluated the effects of SMS text messaging on the subjective measures of short-term outcomes. Recently, a large-sized randomized controlled trial in Australia reported promising findings on the objective measures upon 6-month follow-up. However, an examination of the effectiveness of such interventions in an Asian population with unique demographic characteristics would be worthwhile. OBJECTIVE: This study examined the effectiveness of a 1-way SMS text messaging program to modify the lifestyle and cardiovascular disease risk factors of patients who underwent the first percutaneous coronary intervention (PCI). METHODS: A parallel, single-blinded, 1:1 random allocation clinical trial was conducted with 879 patients treated through PCI. They were recruited during hospital admission from April 2017 to May 2020 at 2 university hospitals in the Republic of Korea. In addition to standard care, the intervention group received access to a supporting website and 4 SMS text messages per week for 6 months regarding a healthy diet, physical activity, smoking cessation, and cardiovascular health. Random allocation upon study enrollment and SMS text messaging after hospital discharge were performed automatically using a computer program. The coprimary outcomes were low-density-lipoprotein cholesterol (LDL-C), systolic blood pressure (SBP), and BMI. The secondary outcomes were change in lifestyle and adherence to the recommended health behaviors. RESULTS: Of the eligible population, 440 and 439 patients who underwent PCI were assigned to the intervention and control groups, respectively. The 1-way SMS text messaging program significantly enhanced physical activity (P=.02), healthy diet (P<.01), and medication adherence (P<.04) among patients with CHD. Hence, more people were likely to control their cardiovascular disease risk factors per the recommendations. The intervention group was more likely to control all 5 risk factors by 62% (relative risk 1.62, 95% CI 1.05-2.50) per the recommendations. On the other hand, physiological measures of the primary outcomes, including LDL-C levels, SBP, and BMI, were not significant. Most participants found the SMS text messaging program useful and helpful in motivating lifestyle changes. CONCLUSIONS: Lifestyle-focused SMS text messages were effective in the self-management of a healthy diet, exercise, and medication adherence, but their influence on the physiological measures was not significant. One-way SMS text messages can be used as an affordable adjuvant method for lifestyle modification to help prevent the recurrence of cardiovascular disease. TRIAL REGISTRATION: Clinical Research Information Service (CRiS) KCT0005087; https://cris.nih.go.kr/cris/search/detailSearch.do/19282.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.001

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.

Opus teacher head0.103
GPT teacher head0.486
Teacher spread0.383 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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

Citations107
Published2021
Admission routes1
Has abstractyes

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