The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Mobile Text Messaging for Health: A Systematic Review of Reviews
Open accessAbstract
The aim of this systematic review of reviews is to identify mobile text-messaging interventions designed for health improvement and behavior change and to derive recommendations for practice. We have compiled and reviewed existing systematic research reviews and meta-analyses to organize and summarize the text-messaging intervention evidence base, identify best-practice recommendations based on findings from multiple reviews, and explore implications for future research. Our review found that the majority of published text-messaging interventions were effective when addressing diabetes self-management, weight loss, physical activity, smoking cessation, and medication adherence for antiretroviral therapy. However, we found limited evidence across the population of studies and reviews to inform recommended intervention characteristics. Although strong evidence supports the value of integrating text-messaging interventions into public health practice, additional research is needed to establish longer-term intervention effects, identify recommended intervention characteristics, and explore issues of cost-effectiveness.
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In the citing paper
“However, while a significant number of studies focused on chronic disease prevention, particularly cardiovascular disease prevention by utilization of SMS text messaging, or on diabetes management [19], there are limited studies on BP outcomes [24].”
In the citing paper
“With a global penetration rate of 96% [28], mobile phones are ubiquitous and avidly used”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [28]
In the citing paper
“A series of systematic reviews on the use of text messaging for different health disorders have shown that text messaging is an effective and acceptable health care intervention [28,29].”
From Conversational Agents in Health Care: Scoping Review and Conceptual Analysis (2020) · cites this paper as [28]
In the citing paper
“This technology demonstrates high efficacy as part of the treatment of conditions ranging from hypertension to substance abuse [24-26]”
In the citing paper
“However, previous syntheses either focused on a specific health issue [9,10] or were constrained by small sample sizes and a lack of moderator analyses [3,4], leaving what factors account for variance in ESs unanswered.”
From The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review (2019) · cites this paper as [3]
In the citing paper
“included more studies than previous reviews ([3,4,10]”
From The Comparative Effectiveness of Mobile Phone Interventions in Improving Health Outcomes: Meta-Analytic Review (2019) · cites this paper as [3]
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Metadata
- Authors
- Hall, A. K., Cole-Lewis, H., & Bernhardt, J. M.
- Affiliations
- University of Washington (US)
- Columbia University (US)
- The University of Texas at Austin (US)
- Venue
- Annual Review of Public Health
- Publisher
- Annual Reviews
- Published
- 2015-03-18
- Volume
- 36
- Issue
- 1
- Pages
- 393-415
- ISSN
- 0163-7525 (ISSN-L)
- 1545-2093
- PMC
- PMC4406229
- OpenAlex
- W2118907952
- Cited by
- 815
- References
- 46
- Citations by year
- 2026: 32 · 2025: 52 · 2024: 65 · 2023: 67 · 2022: 89 · 2021: 94 · 2020: 94 · 2019: 93 · 2018: 84 · 2017: 85 · 2016: 42 · 2015: 17
- FWCI
- 13.65
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Public Health (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- University of Washington (T15LM007442)
- National Institutes of Health
- U.S. National Library of Medicine (T15LM007079, T15LM007442)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Social Sciences › Health › Social Media in Health Education
- Keywords
- text messaging, health behavior change, diabetes self-management, weight loss, physical activity, smoking cessation, medication adherence, antiretroviral therapy, public health interventions, cost-effectiveness
- MeSH terms
- Health Promotion, Humans, Program Evaluation, Cell Phone, Medication Adherence, Text Messaging, Weight Reduction Programs
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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