Scoping review and evaluation of SMS/text messaging platforms for mHealth projects or clinical interventions
Open accessAbstract
Objectives Mobile technology supporting text messaging interventions (TMIs) continues to evolve, presenting challenges for researchers and healthcare professionals who need to choose software solutions to best meet their program needs. The objective of this review was to systematically identify and compare text messaging platforms and to summarize their advantages and disadvantages as described in peer-reviewed literature.Methods A scoping review was conducted using four steps: 1) identify currently available platforms through online searches and in mHealth repositories; 2) expand evaluation criteria of an mHealth mobile messaging toolkit and integrate prior user experiences as researchers; 3) evaluate each platform's functions and features based on the expanded criteria and a vendor survey; and 4) assess the documentation of platform use in the peer-review literature. Platforms meeting inclusion criteria were assessed independently by three reviewers and discussed until consensus was reached. The PRISMA guidelines were followed to report findings.Results Of the 1041 potentially relevant search results, 27 platforms met inclusion criteria. Most were excluded because they were not platforms (e.g., guides, toolkits, reports, or SMS gateways). Of the 27 platforms, only 12 were identified in existing mHealth repositories, 10 from Google searches, while five were found in both. The expanded evaluation criteria included 22 items. Results indicate no uniform presentation of platform features and functions, often making these difficult to discern. Fourteen of the platforms were reported as open source, 10 focused on health care and 16 were tailored to meet needs of low resource settings (not mutually exclusive). Fifteen platforms had do-it-yourself setup (programming not required) while the remainder required coding/programming skills or setups could be built to specification by the vendor. Frequently described features included data security and access to the platform via cloud-based systems. Pay structures and reported targeted end-users varied. Peer-reviewed publications listed only 6 of the 27 platforms across 21 publications. The majority of these articles reported the name of the platform used but did not describe advantages or disadvantages.Conclusions Searching for and comparing mHealth platforms for TMIs remains a challenge. The results of this review can serve as a resource for researchers and healthcare professionals wanting to integrate TMIs into health interventions. Steps to identify, compare and assess advantages and disadvantages are outlined for consideration. Expanded evaluation criteria can be used by future researchers. Continued and more comprehensive platform tools should be integrated into mHealth repositories. Detailed descriptions of platform advantages and disadvantages are needed when mHealth researchers publish findings to expand the body of research on TMI tools for healthcare. Standardized descriptions and features are recommended for vendor sites.
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Dimagi's CommCare is an offline-first, open-source mobile data collection and service delivery platform that helps CHWs in every phase of effective COVID-19 response.
“Dimagi’s CommCare is an offline-first, open-source mobile data collection and service delivery platform that helps CHWs in every phase of effective COVID-19 response - from screening and contact tracing to patient monitoring and post-care support [10].”
From Equipping community health workers with digital tools for pandemic response in LMICs (2021) · cites this paper as [10]
The Results (Platform Evaluation Criteria Development; page unavailable) describe CommCare as a mobile tool for community health workers’ electronic data capture and clinic-based data management. They say a project could be developed offline but required connectivity to download it to a phone and upload data; they do not establish that CommCare is open-source or support the claimed COVID-19 response uses. Searches for COVID-19, contact tracing, screening, monitoring, and post-care support found no passage linking those uses to CommCare. (LLM, conf=0.90)
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- Authors
- Iribarren, S. J., Brown, W., Giguere, R., Stone, P., Schnall, R., Staggers, N., & Carballo-Diéguez, A.
- Affiliations
- University of Washington (US)
- San Francisco General Hospital (US)
- New York State Psychiatric Institute (US)
- Columbia University (US)
- University of Utah (US)
- Corresponding author
- Iribarren, Sarah J.
- ORCID
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 101
- Pages
- 28-40
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC5398898
- OpenAlex
- W2584804976
- Cited by
- 62
- References
- 38
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 4 · 2022: 9 · 2021: 10 · 2020: 12 · 2019: 6 · 2018: 9 · 2017: 5
- FWCI
- 14.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R03MH103957, P30MH062246, P30MH043520)
- National Institute of Nursing Research (T32NR014205)
- U.S. National Library of Medicine (R01LM012355, T15LM007079)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Keywords
- mobile health, text messaging intervention, platform comparison, open source software, resource-limited settings, data security, cloud systems, health interventions, reporting guidelines
- MeSH terms
- Evaluation Studies as Topic, Humans, Telemedicine, Cell Phone, Text Messaging, Early Medical Intervention
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.
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