Evidence map›Paper›PMID 33656444›Full record

SynthesisJMIR mHealth and uHealth2021

Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis.

Adewale Samuel Akinosun, Rob Polson, Yohanca Diaz-Skeete, Johannes Hendrikus De Kock, Lucia Carragher, Stephen Leslie, Mark Grindle, Trish Gorely

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers, 15 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
93citing papers in PubMed, 15 pooled it
12.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

93 citing papers in PubMed, 15 syntheses or guidelines pooled it, 159 citations in OpenAlex.

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  4. Digital versus nondigital behavioral interventions on cardiovascular risk reduction: systematic review and meta-analysis.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2025
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33 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 2 countries.

Adewale Samuel AkinosunDepartment of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.ORCID 0000-0002-3592-8090
Rob PolsonHighland Health Sciences Library, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.ORCID 0000-0003-3901-2167
Yohanca Diaz-SkeeteSchool of Health and Science, Dundalk Institute of Technology, Dundalk, Ireland.ORCID 0000-0002-5566-8572
Johannes Hendrikus De KockDepartment of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.ORCID 0000-0002-2468-5572
Lucia CarragherSchool of Health and Science, Dundalk Institute of Technology, Dundalk, Ireland.ORCID 0000-0003-4523-3003
Stephen LeslieCardiology Unit, Raigmore Hospital, NHS Highlands, Inverness, United Kingdom.ORCID 0000-0002-1403-4733
Mark GrindleDepartment of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.ORCID 0000-0002-3237-7369
Trish GorelyDepartment of Nursing and Midwifery, Institute of Health Research and Innovation, Centre for Health Science, University of the Highlands and Islands, Inverness, United Kingdom.ORCID 0000-0001-7367-0679
University of the Highlands and Islands · GBDundalk Institute of Technology · IERaigmore Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundApproximately 50% of cardiovascular disease (CVD) cases are attributable to lifestyle risk factors. Despite widespread education, personal knowledge, and efficacy, many individuals fail to adequately modify these risk factors, even after a cardiovascular event. Digital technology interventions have been suggested as a viable equivalent and potential alternative to conventional cardiac rehabilitation care centers. However, little is known about the clinical effectiveness of these technologies in bringing about behavioral changes in patients with CVD at an individual level.

objectiveThe aim of this study is to identify and measure the effectiveness of digital technology (eg, mobile phones, the internet, software applications, wearables, etc) interventions in randomized controlled trials (RCTs) and determine which behavior change constructs are effective at achieving risk factor modification in patients with CVD.

methodsThis study is a systematic review and meta-analysis of RCTs designed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) statement standard. Mixed data from studies extracted from selected research databases and filtered for RCTs only were analyzed using quantitative methods. Outcome hypothesis testing was set at 95% CI and P=.05 for statistical significance.

resultsDigital interventions were delivered using devices such as cell phones, smartphones, personal computers, and wearables coupled with technologies such as the internet, SMS, software applications, and mobile sensors. Behavioral change constructs such as cognition, follow-up, goal setting, record keeping, perceived benefit, persuasion, socialization, personalization, rewards and incentives, support, and self-management were used. The meta-analyzed effect estimates (mean difference [MD]; standard mean difference [SMD]; and risk ratio [RR]) calculated for outcomes showed benefits in total cholesterol SMD at -0.29 [-0.44, -0.15], P<.001; high-density lipoprotein SMD at -0.09 [-0.19, 0.00], P=.05; low-density lipoprotein SMD at -0.18 [-0.33, -0.04], P=.01; physical activity (PA) SMD at 0.23 [0.11, 0.36], P<.001; physical inactivity (sedentary) RR at 0.54 [0.39, 0.75], P<.001; and diet (food intake) RR at 0.79 [0.66, 0.94], P=.007. Initial effect estimates showed no significant benefit in body mass index (BMI) MD at -0.37 [-1.20, 0.46], P=.38; diastolic blood pressure (BP) SMD at -0.06 [-0.20, 0.08], P=.43; systolic BP SMD at -0.03 [-0.18, 0.13], P=.74; Hemoglobin A

conclusionsDigital interventions may improve healthy behavioral factors (PA, healthy diet, and medication adherence) and are even more potent when used to treat multiple behavioral outcomes (eg, medication adherence plus). However, they did not appear to reduce unhealthy behavioral factors (smoking, alcohol intake, and unhealthy diet) and clinical outcomes (BMI, triglycerides, diastolic and systolic BP, and HbA

Indexed as

Cardiovascular DiseasesBlood PressureDigital TechnologyExerciseHumansRandomized Controlled Trials as TopicRisk Factorsbehaviorcardiac rehabilitationcardiovascular diseasesdigital technologieseHealthmeta-analysismHealthmobile phonerisk factorssystematic reviewtelehealth

Identifiers

PMID33656444
PMCPMC7970167
OpenAlexW3107353531

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.