Evidence map›Paper›PMID 38335018›Full record

ArticleJMIR research protocols2024

The Effectiveness of Digital Health Lifestyle Interventions on People With Prediabetes: Protocol for a Systematic Review, Meta-Analysis, and Meta-Regression.

Tanja Fredensborg Holm, Flemming Witt Udsen, Kristine Færch, Morten Hasselstrøm Jensen, Bernt Johan von Scholten, Ole Kristian Hejlesen, Stine Hangaard

Abstract read
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Tanja Fredensborg HolmDepartment of Health Science and Technology, Aalborg University, Gistrup, Denmark.ORCID https://orcid.org/0009-0007-1725-5922
Flemming Witt UdsenDepartment of Health Science and Technology, Aalborg University, Gistrup, Denmark.ORCID https://orcid.org/0000-0003-2293-9169
Kristine FærchData Science, Novo Nordisk A/S, Søborg, Denmark.ORCID https://orcid.org/0000-0002-6127-0448
Morten Hasselstrøm JensenDepartment of Health Science and Technology, Aalborg University, Gistrup, Denmark.ORCID https://orcid.org/0000-0002-6649-8644
Bernt Johan von ScholtenData Science, Novo Nordisk A/S, Søborg, Denmark.ORCID https://orcid.org/0000-0002-1489-0636
Ole Kristian HejlesenDepartment of Health Science and Technology, Aalborg University, Gistrup, Denmark.ORCID https://orcid.org/0000-0003-3578-8750
Stine HangaardDepartment of Health Science and Technology, Aalborg University, Gistrup, Denmark.ORCID https://orcid.org/0000-0003-0395-3563

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere has been an increasing interest in the use of digital health lifestyle interventions for people with prediabetes, as these interventions may offer a scalable approach to preventing type 2 diabetes. Previous systematic reviews on digital health lifestyle interventions for people with prediabetes had limitations, such as a narrow focus on certain types of interventions, a lack of statistical pooling, and no broader subgroup analysis of intervention characteristics. The identified limitations observed in previous systematic reviews substantiate the necessity of conducting a comprehensive review to address these gaps within the field. This will enable a comprehensive understanding of the effectiveness of digital health lifestyle interventions for people with prediabetes.

objectiveThe objective of this systematic review, meta-analysis, and meta-regression is to systematically investigate the effectiveness of digital health lifestyle interventions on prediabetes-related outcomes in comparison with any comparator without a digital component among adults with prediabetes.

methodsThis systematic review will include randomized controlled trials that investigate the effectiveness of digital health lifestyle interventions on adults (aged 18 years or older) with prediabetes and compare the digital interventions with nondigital interventions. The primary outcome will be change in body weight (kg). Secondary outcomes include, among others, change in glycemic status, markers of cardiometabolic health, feasibility outcomes, and incidence of type 2 diabetes. Embase, PubMed, CINAHL, and CENTRAL (Cochrane Central Register of Controlled Trials) will be systematically searched. The data items to be extracted include study characteristics, participant characteristics, intervention characteristics, and relevant outcomes. To estimate the overall effect size, a meta-analysis will be conducted using the mean difference. Additionally, if feasible, meta-regression on study, intervention, and participant characteristics will be performed. The Cochrane risk of bias tool will be applied to assess study quality, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of evidence.

resultsThe results are projected to yield an overall estimate of the effectiveness of digital health lifestyle interventions on adults with prediabetes and elucidate the characteristics that contribute to their effectiveness.

conclusionsThe insights gained from this study may help clarify the potential of digital health lifestyle interventions for people with prediabetes and guide the decision-making regarding future intervention components.

trial registrationPROSPERO CRD42023426919; http://tinyurl.com/d3enrw9j. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50340.

Indexed as

digital healtheffectivenesslifestyle interventionmeta-analysismeta-regressionprediabetic statesystematic reviewtype 2 diabetes preventionweight loss

Identifiers

PMID38335018
PMCPMC10891485

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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 OpenQuestion graph.