Evidence map›Paper›PMID 30425028›Full record

ArticleJMIR mHealth and uHealth2018

Key Elements of mHealth Interventions to Successfully Increase Physical Activity: Meta-Regression.

Lisa V Eckerstorfer, Norbert K Tanzer, Claudia Vogrincic-Haselbacher, Gayannee Kedia, Hilmar Brohmer, Isabelle Dinslaken, Katja Corcoran

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
48citing papers in PubMed, 7 pooled it
–field-weighted citation impact
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

48 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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  7. The effectiveness of app-based mobile interventions on nutrition behaviours and nutrition-related health outcomes: A systematic review and meta-analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2019
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  9. Using photovoice to explore young women's experiences of behaviour change techniques in physical activity mobile apps.The international journal of behavioral nutrition and physical activity · 2023
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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.

Lisa V EckerstorferInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0002-2096-286X
Norbert K TanzerInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0001-7939-9154
Claudia Vogrincic-HaselbacherInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0002-2316-7618
Gayannee KediaInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0003-0163-0867
Hilmar BrohmerInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0001-7763-4229
Isabelle DinslakenInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0002-4019-2364
Katja CorcoranInstitute of Psychology, University of Graz, Graz, Austria.ORCID http://orcid.org/0000-0003-1838-9141

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile technology gives researchers unimagined opportunities to design new interventions to increase physical activity. Unfortunately, it is still unclear which elements are useful to initiate and maintain behavior change.

objectiveIn this meta-analysis, we investigated randomized controlled trials of physical activity interventions that were delivered via mobile phone. We analyzed which elements contributed to intervention success.

methodsAfter searching four databases and science networks for eligible studies, we entered 50 studies with N=5997 participants into a random-effects meta-analysis, controlling for baseline group differences. We also calculated meta-regressions with the most frequently used behavior change techniques (behavioral goals, general information, self-monitoring, information on where and when, and instructions on how to) as moderators.

resultsWe found a small overall effect of the Hedges g=0.29, (95% CI 0.20 to 0.37) which reduced to g=0.22 after correcting for publication bias. In the moderator analyses, behavioral goals and self-monitoring each led to more intervention success. Interventions that used neither behavioral goals nor self-monitoring had a negligible effect of g=0.01, whereas utilizing either technique increased effectiveness by Δg=0.31, but combining them did not provide additional benefits (Δg=0.36).

conclusionsOverall, mHealth interventions to increase physical activity have a small to moderate effect. However, including behavioral goals or self-monitoring can lead to greater intervention success. More research is needed to look at more behavior change techniques and their interactions. Reporting interventions in trial registrations and articles need to be structured and thorough to gain accurate insights. This can be achieved by basing the design or reporting of interventions on taxonomies of behavior change.

Indexed as

behavior changeexercisemeta-analysismeta-regressionmHealthphysical activity

Identifiers

PMID30425028
PMCPMC6256104

What OpenQuestion 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 OpenQuestion graph.