Evidence mapPaperPMID 30942695Full record

SynthesisJMIR mHealth and uHealth2019

The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review.

Qinghua Yang, Stephanie K Van Stee

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 6 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

62 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  15. Effects of a Smartphone-Based Mindfulness Intervention on Momentary Stress Among First Responders: a Micro-Randomized Trial.Prevention science : the official journal of the Society for Prevention Research · 2026
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  18. Review
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2 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

2 authors.

Qinghua YangDepartment of Communication Studies, Bob Schieffer College of Communication, Texas Christian University, Fort Worth, TX, United States.ORCID 0000-0003-1358-8096
Stephanie K Van SteeDepartment of Communication and Media, University of Missouri-St. Louis, St. Louis, MO, United States.ORCID 0000-0003-3233-8535

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs mobile technology continues expanding, researchers have been using mobile phones to conduct health interventions (mobile health-mHealth-interventions). The multiple features of mobile phones offer great opportunities to disseminate large-scale, cost-efficient, and tailored messages to participants. However, the interventions to date have shown mixed results, with a large variance of effect sizes (Cohen d=-0.62 to 1.65).

objectiveThe study aimed to generate cumulative knowledge that informs mHealth intervention research. The aims were twofold: (1) to calculate an overall effect magnitude for mHealth interventions compared with alternative interventions or conditions, and (2) to analyze potential moderators of mHealth interventions' comparative efficacy.

methodsComprehensive searches of the Communication & Mass Media Complete, PsycINFO, Web of Knowledge, Academic Search Premier, PubMed and MEDLINE databases were conducted to identify potentially eligible studies in peer-reviewed journals, conference proceedings, and dissertations and theses. Search queries were formulated using a combination of search terms: "intervention" (Title or Abstract) AND "health" (Title or Abstract) AND "*phone*" OR "black-berr*" (OR mHealth OR "application*" OR app* OR mobile OR cellular OR "short messag*" OR palm* OR iPhone* OR MP3* OR MP4* OR iPod*) (Title or Abstract). Cohen d was computed as the basic unit of analysis, and the variance-weighted analysis was implemented to compute the overall effect size under a random-effects model. Analysis of variance-like and meta-regression models were conducted to analyze categorical and continuous moderators, respectively.

resultsThe search resulted in 3424 potential studies, the abstracts (and full text, as necessary) of which were reviewed for relevance. Studies were screened in multiple stages using explicit inclusion and exclusion criteria, and citations were evaluated for inclusion of qualified studies. A total of 64 studies were included in the current meta-analysis. Results showed that mHealth interventions are relatively more effective than comparison interventions or conditions, with a small but significant overall weighted effect size (Cohen d=0.31). In addition, the effects of interventions are moderated by theoretical paradigm, 3 engagement types (ie, changing personal environment, reinforcement tracking, social presentation), mobile use type, intervention channel, and length of follow-up.

conclusionsTo the best of our knowledge, this is the most comprehensive meta-analysis to date that examined the overall effectiveness of mHealth interventions across health topics and is the first study that statistically tested moderators. Our findings not only shed light on intervention design using mobile phones, but also provide new directions for research in health communication and promotion using new media. Future research scholarship is needed to examine the effectiveness of mHealth interventions across various health issues, especially those that have not yet been investigated (eg, substance use, sexual health), engaging participants using social features on mobile phones, and designing tailored mHealth interventions for diverse subpopulations to maximize effects.

Indexed as

HumansMobile ApplicationsOutcome Assessment, Health CareTechnology Assessment, BiomedicalTelemedicineintervention studymeta-analysismHealthmobile phones

Identifiers

PMID30942695
PMCPMC6468337

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.