Commentary on Harder et al. (2020): Ensuring the sustainability of mHealth in low‐ and middle‐income countries—how do we cure ‘pilotitis’?
Open accessAbstract
In resource-challenged contexts, mobile health interventions have the potential to boost capacity for the prevention and treatment of heavy drinking. To ensure that impacts are sustained, researchers should consider the wider technological, human resource and health system needs. Alcohol is one of the leading global risk factors for disease and premature death, but people in low- and middle-income countries (LMIC) experience a greater burden compared with those in higher-income countries 5. Increasing take-up of mobile phones in LMIC populations, where health-care services face heightened resource, capacity and geographical challenges, could therefore provide new opportunities for alcohol intervention delivery at scale, and at relatively low cost. Although to date most research relates to Europe, North America and Australasia, there is evidence that mHealth interventions for mental health disorders are also feasible, acceptable and efficacious in LMIC settings 6. However, beyond the initial positive trial results, there are few instances where these types of digital health innovations have led to long-term impacts in global health systems. Common factors that have slowed down the sustained adoption of mHealth interventions in low-, middle- and high-income countries alike, include the skills and attitudes of care providers; cost and/or lack of reimbursement for implementation; limited interoperability and scalability of digital technology; and patient-related concerns, particularly around data protection and confidentiality 7, 8. However, in LMIC settings, certain obstacles are likely to be amplified, particularly those concerning access to appropriate technology and infrastructure 9. Evidence from initiatives that appear to have bridged this translation gap more successfully in less developed regions suggests that the following factors need to be taken into account if mHealth initiatives are to avoid 'pilotisis'. First, where technology is concerned, simplicity is key. Mobile phone access is relatively high in LMICs (recent data suggest around 80% of Kenyans have access to at least one simple device), but only approximately one in four people own a smartphone 10. As such, low-tech alcohol interventions that can work on basic, cheap phones (such as text messaging or telephone-based therapy, as tested here) may be less likely to exclude socio-economically and/or geographically marginalized populations 11. In rural areas, poor internet and electricity access can present additional barriers, meaning that mHealth initiatives also need to take into account the stability of the national power grid in their implementation plans 12. Secondly, while low-tech interventions can extend the reach of care 6, they often still require real-time input from skilled and experienced health providers to deliver appropriate support to patients. There may also be a need for sufficient numbers of trained local technical operators who can manage and service the mobile devices to be used by the health-care practitioners implementing the mHealth intervention in day-to-day practice 11. As such, it is important that researchers work with local training providers from the outset; for example, by incorporating the relevant skill-set within existing medical curricula, as well as offering accreditation and quality assurance to boost practitioner engagement. Thirdly, and importantly, the sustainability of digital and telehealth initiatives ultimately depends upon them being deployed within a supportive and stable health ecosystem. While mHealth has the potential to boost capacity and access to health-care, it is no panacea for a fundamental lack of leadership, resource and political will to prioritize the prevention and treatment of heavy drinking 13. Others have highlighted the need for more effective country-specific alcohol control policies in sub-Saharan Africa in particular 14. Thus, in order to have a long-term, positive impact on LMIC populations, mHealth advocates need to work with policymakers and service commissioners to ensure that implementation is aligned with a broader set of progressive, evidence-based strategies to reduce alcohol harm. None.
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Very few mHealth apps designed to support CHWs across sub-Saharan Africa are operating at a large scale and beyond isolated research settings.
“While there is an increasing number of mHealth apps designed to support CHWs across SSA, very few are operating at a large scale and beyond isolated research settings [2,23,24].”
Cites
Evaluating barriers to adopting telemedicine worldwide: A systematic review
Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x16674087 · Cited by 1,735 · see citing works
Naslund, J. A., Aschbrenner, K. A., Araya, R., Marsch, L. A., Unützer, J., Patel, V., & Bartels, S. J. (2017) · The Lancet Psychiatry · doi:10.1016/s2215-0366(17)30096-2 · Cited by 638 · see citing works
Shield, K. D., Manthey, J., Rylett, M., Probst, C., Wettlaufer, A., Parry, C. D. H., & Rehm, J. T. (2020) · The Lancet Public Health · doi:10.1016/s2468-2667(19)30231-2 · Cited by 528 · see citing works
Best practices in scaling digital health in low and middle income countries
Labrique, A. B., Wadhwani, C., Williams, K. A., Lamptey, P., Hesp, C., Luk, R., & Aerts, A. (2018) · Globalization and Health · doi:10.1186/s12992-018-0424-z · Cited by 449 · see citing works
Kaner, E. F., Beyer, F. R., Garnett, C., Crane, D., Brown, J., Muirhead, C., Redmore, J., O'Donnell, A., Newham, J. J., de Vocht, F., et al. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011479.pub2 · Cited by 308 · see citing works
Self-citationKruse, C., Betancourt, J., Ortiz, S., Valdes Luna, S. M., Bamrah, I. K., & Segovia, N. (2019) · Journal of Medical Internet Research · doi:10.2196/13263 · Cited by 299 · see citing works
Riper, H. M., Hoogendoorn, A. W., Cuijpers, P. J., Karyotaki, E., Boumparis, N., Mira, A., Andersson, G., Berman, A. H., Bertholet, N., Bischof, G., et al. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002714 · Cited by 283 · see citing works
Integrating mHealth at point of care in low- and middle-income settings: the system perspective
Wallis, L. A., Blessing, P., Dalwai, M., & Shin, S. D. (2017) · Global Health Action · doi:10.1080/16549716.2017.1327686 · Cited by 154 · see citing works
Fowler, L. A., Holt, S. L., & Joshi, D. (2016) · Addictive Behaviors · doi:10.1016/j.addbeh.2016.06.008 · Cited by 123 · see citing works
Alcohol Control Policies in 46 African Countries: Opportunities for Improvement
Ferreira‐Borges, C., Esser, M. B., Dias, S., Babor, T. F., & Parry, C. D. H. (2015) · Alcohol and Alcoholism · doi:10.1093/alcalc/agv036 · Cited by 91 · see citing works
Internet connectivity seen as having positive impact on life in sub-Saharan Africa
Silver, L., & Johnson, C. (2018) · Cited by 68 · see citing works
Harder, V. S., Musau, A. M., Musyimi, C. W., Ndetei, D. M., & Mutiso, V. N. (2020) · Addiction · doi:10.1111/add.14903 · Cited by 58 · see citing works
Kaner, E., Beyer, F. R., Brown, J., Crane, D., Garnett, C. V., Hickman, M., Muirhead, C. R., Redmore, J., Michie, S., & de Vocht, F. (2015) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd011479 · Cited by 40 · see citing works
Use of ultrasound and mHealth to improve perinatal outcomes in low and middle income countries
Swanson, J. O., Nathan, R., Swanson, D. L., Perez, K., Bresnahan, B. W., Mirza, W. A., & Goldenberg, R. L. (2019) · Seminars in Perinatology · doi:10.1053/j.semperi.2019.03.016 · Cited by 9 · see citing works
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1 of the 15 references couldn’t be matched to a known paper.
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Egermark, M., Blasiak, A., Remus, A., Sapanel, Y., & Ho, D. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200056 · Cited by 38 · see citing works
Achieving the potential of mHealth in medicine requires challenging the ethos of care delivery
Ratanawong, J. P., Naslund, J. A., Mikal, J. P., & Grande, S. W. (2022) · Primary Health Care Research & Development · doi:10.1017/s1463423622000068 · Cited by 23 · see citing works
Ndulue, O. I., & Naslund, J. A. (2024) · Psychiatry Research Communications · doi:10.1016/j.psycom.2024.100183 · Cited by 20 · see citing works
O'Donnell, A. J., Schmidt, C. S., Beyer, F. R., Schrietter, M., Anderson, P., Jané‐Llopis, E., Kaner, E., & Schulte, B. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.11.039 · Cited by 17 · see citing works
Self-citationNkurunziza, J., Nuss, S., Estrada, E. H., Kubwimana, M., Boatin, A. A., Bikorimana, L., Fletcher, R. R., Byiringiro, N., Hedt-Gauthier, B., & Cubaka, V. K. (2026) · JMIR mHealth and uHealth · doi:10.2196/64916 · Cited by 2 · see citing works
Rabiee, P. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_9 · Cited by 2 · see citing works
- Authors
- O'Donnell, A.
- Affiliations
- Newcastle University (GB)
- Corresponding author
- O'Donnell, Amy
- ORCID
- O'Donnell, Amy
- Venue
- Addiction
- Publisher
- Wiley
- Published
- 2020-02-19
- Volume
- 115
- Issue
- 6
- Pages
- 1061-1062
- ISSN
- 0965-2140 (ISSN-L)
- 1360-0443
- OpenAlex
- W3007773707
- Cited by
- 6
- References
- 15
- Citations by year
- 2026: 2 · 2024: 1 · 2022: 2 · 2021: 1
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Addiction (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute for Health and Care Research (NIHR300616)
- 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, heavy drinking, alcoholism, low- and middle-income countries, low-tech interventions, text messaging, health system capacity, scalability, harm reduction, harm reduction services
- MeSH terms
- Developing Countries, Humans, Kenya, Telemedicine, Cell Phone, Motivational Interviewing
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