The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
H_pe for mHealth: More “y” or “o” on the horizon?
Open accessAbstract
Objective Efforts in the domain of mobile health, or mHealth, have been criticized for the unfettered proliferation of pilots and a lack of a rigorous evidence base to support these strategies. In this letter, we present the response of a group of researchers in the mHealth community to the recent calls for evidence issued by global health and funding agencies. We support our conclusions through a summary of the numerous ongoing mHealth studies listed in the US federal clinical trial registry. Methods We conducted a search on the US federal clinicaltrials.gov database using the keywords “mHealth”, “mobile” or “cell AND phone” to obtain 1678 results of studies. We manually inspected each result to check if it fit the purview of an mHealth study. Studies that were terminated or withdrawn prior to submission were excluded. Results We identified 215 unique mHealth studies that were registered in the clinicaltrials.gov database, of which 8.4% (n = 18) were observational in nature while the remaining 91.6% (n = 197) were interventional. Of the 215 studies, 81.8% (n = 176) studies used a classical randomized trial design and 40 new studies were added to the database between May and November 2012 alone. Based on these results, we posit that the field is entering a new ‘era’ where a body of rigorous evaluation of mHealth strategies is rapidly accumulating. Conclusions The transition into an era of evidence-based mHealth supports our position that innovation in this domain can be evaluated with the same rigor as other public health strategies, attenuating some of the hype previously associated with mHealth.
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“Equipping frontline health workers with mobile phone applications may enhance the delivery of maternal, neonatal, and child health services as well as the accuracy of data capture and recording [16, 26, 35, 36].”
From Qualitative Assessment of the Feasibility, Usability, and Acceptability of a Mobile Client Data App for Community-Based Maternal, Neonatal, and Child Care in Rural Ghana (2016) · cites this paper as [36]
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Eysenbach, G., & CONSORT-EHEALTH Group (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1923 · Cited by 1,863
Mobile phone-based interventions for smoking cessation
Whittaker, R., McRobbie, H., Bullen, C., Rodgers, A., & Gu, Y. (2016) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006611.pub4 · Cited by 1,079
Horvath, T., Azman, H., Kennedy, G. E., & Rutherford, G. W. (2012) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd009756 · Cited by 428
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Research works printed but not linked: 2
- Office of Behavioral and Social Sciences Research, mHealth – Workshop on “mHealth Evidence”. <http://obssr.od.nih.gov/scientific_areas/methodology/mhealth/mhealth-workshop.aspx>, 2011 (accessed 02.06.12).
- Horvath, 2012, Mobile phone text messaging to help patients with HIV infection take their antiretroviral medications every day, Cochrane Database Syst. Rev., CD009756
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Die digitale Zukunft der Pflege – Chancen und Risiken
Göttelmann, P., Meier, M., Maurer, A., Staudacher, D., & Spirig, R. (2017) · Springer eBooks · doi:10.1007/978-3-658-13644-4_1 · Cited by 1
Prentiss, T. C., Tamler, I., Plum, A., Parke, D. M., Zervos, J., Tanniru, M. R., Sears, M. H., White, M., Wisdom, K., & Kaljee, L. (2017) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2017.396 · Cited by 1
“gov database, with 40 alone being added during a six-month period [26].”
BackgroundMethodA Systematic Review of Mobile Health in Education From 2000 to 2016
Altınpulluk, H. (2018) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2018070101 · Cited by 1
Ramadhan Akhmad, T., & Rachbini Didik, J. (2021) · Dinasti International Journal of Management Science · doi:10.31933/dijms.v3i2.1048 · Cited by 3
m ‐Health Care Models and Applications
Istepanian, R. S. H., & Woodward, B. (2016) · Wiley eBooks · doi:10.1002/9781119302889.ch5
Methodological Issues of using Placebos in Interventions Based on Digital Technology
Farr, W. J., Male, I., Green, D., Morris, C., Gage, H., Bailey, S. E. R., Speller, S., Colville, V., Jackson, M., Bremner, S. A., et al. (2017) · Journal of Mobile Technology in Medicine · doi:10.7309/jmtm.6.2.7
Acceptance of a Neuropaediatric Exergame Rehabilitation System with Severe Cerebral Palsy
Farr, W. J., Poole, M., Thursfield, C., & Male, I. (2017) · Annual Symposium on Computer-Human Interaction in Play · doi:10.1145/3130859.3131317
“ Contact with a therapist who individualizes programs of therapy For therapists and researchers utilizing virtual systems, the impact of these systems may be more ”hype than hope” [9] especially as many systems are trialled initially in geriatric populations e.”
Backgroundsystematic review of mobile health adoption factors for Iraqi Healthcare Institutions
TahaYaseen, A., Ahmad, A. R., & Gunasekaran, S. S. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns1.6424
Schoenberg, N. E., Sherman, D. L., Pfammatter, A. F., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21524027
Schoenberg, N. E., Sherman, D. L., Pfammatter, A. F., et al. (2022) · Figshare · doi:10.6084/m9.figshare.21524030
Mukasa, S., & Sangwa, S. (2026) · American Journal of Engineering and Technology Management · doi:10.11648/j.ajetm.20261102.12
Metadata
- Authors
- Labrique, A., Vasudevan, L., Chang, L. W., & Mehl, G.
- Affiliations
- Johns Hopkins University (US)
- World Health Organization (CH)
- Corresponding author
- Labrique, Alain
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2013-05
- Volume
- 82
- Issue
- 5
- Pages
- 467-469
- ISSN
- 1386-5056 (ISSN-L)
- PMC
- PMC3849805
- OpenAlex
- W2025562154
- Semantic Scholar
- a635a86475437c0f9f794a64e0e8db034051b2c8
- Cited by
- 111
- References
- 3
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 7 · 2021: 3 · 2020: 9 · 2019: 12 · 2018: 13 · 2017: 18 · 2016: 14 · 2015: 9 · 2014: 11 · 2013: 6
- FWCI
- 14.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (K23MH086338)
- 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 › Mobile Learning in Education
- Keywords
- mobile health, evidence-based medicine, clinical trial registries, ClinicalTrials.gov, randomized controlled trials, observational studies, research evaluation, evidence base
- MeSH terms
- Humans, Quality of Health Care, Outcome Assessment, Health Care, Telemedicine, Biomedical Technology, Quality Improvement
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