The DOI matches its Crossref record; its reference list is not available.
Mobile training and support (MOTS) service—using technology to increase Ebola preparedness of remotely-located community health workers (CHWs) in Sierra Leone
Open accessAbstract
BACKGROUND: The Ministry of Health in Sierra Leone has developed and operationalized the national Digital Health Strategy to guide integrated roll out of e-health/mobile health solutions. The goal is that "by 2023 an effective and efficient ICT enabled system supports delivery of quality, accessible, affordable, equitable, and timely healthcare services and moves Sierra Leone closer to achieving universal health coverage". Investing in digital platforms for the education of community health workers (CHWs) in Sierra Leone is a critical strategic approach to strengthening the country's readiness for future Ebola outbreaks. A new national curriculum for this target group is being implemented that is based upon classroom training approaches. In a country where many CHWs are remotely located, the use of technology can be an enabler to reach such individuals with key training content to repeat the most important messages. Here we describe the piloting of a mobile training and support (MOTS) service for CHWs using interactive voice response (IVR) technology in Bo district of Sierra Leone. This training platform delivers voice recorded training content in local languages on the topics of Vaccines and (Ebola) Disease Surveillance & Outbreak Response. METHODS: MOTS was developed in collaboration with the Sierra Leone Ministry of Health & Sanitation. Training content was customized in line with the national training curriculum and case reporting requirements. Local ethical approval was achieved and a test protocol involving recruitment of 125 consenting CHWs was implemented in Bo district of Sierra Leone. Two training modules-one covering vaccination and one covering outbreak response and disease surveillance were delivered to the mobile phones of participants as audio messages in the preferred local language. Knowledge change was assessed largely through pre- and post-quiz assessments also implemented through IVR. RESULTS: Knowledge acquisition was observed in the 123 CHWs completing this pilot assessment. The extent of knowledge acquired was higher with the Vaccine training module when compared to the (Ebola) Disease Surveillance & Outbreak Response module. The technology was readily accepted by this population and their engagement was such that they also provided important elements to be improved prior to further implementation. The order in which training modules are delivered as well as general fatigue of the IVR methodology for participating in the quiz assessments may be of importance and requires further investigation. CONCLUSIONS: Technology should be considered when planning delivery of training to CHWs and can be positioned as a vehicle by which repetitive aspects of important training content can be reinforced without the need for additional classroom presence of the CHW community. Sustainability of such solutions requires cost containment and subsequent software accessibility for authorities in resource limited settings. Transparent partnership and alignment with the Ministry of Health & Sanitation in Sierra Leone from the outset of this project is considered an important element to ensure successful implementation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures








Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The usefulness of the mHealth app for CHWs is centered on the ease of integration into the routine workflow, improved capacity to deliver follow-up services, less paperwork, organization of data, and abolishing the paper-based registers [30-41].”
From Operability, Usefulness, and Task-Technology Fit of an mHealth App for Delivering Primary Health Care Services by Community Health Workers in Underserved Areas of Pakistan and Afghanistan: Qualitative Study (2020) · cites this paper as [38]
Experimental — accuracy not yet measuredIn the citing paper
“Moreover, a digital app has the potential to improve users’ knowledge, skills, and performance [36-38].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 9 · Cited by 29
Research works printed but not linked: 6
- Greenleaf AR, Gibson DG, Khattar C, et al. Building the Evidence Base for Remote Data Collection in Low-and Middle-Income Countries: Comparing Reliability and Accuracy Across Survey Modalities. J Med Internet Res 2017;19:e140.
- Pariyo GW, Greenleaf AR, Gibson DG, et al. Does mobile phone survey method matter? Reliability of computer- assisted telephone interviews and interactive voice response non-communicable diseases risk factor surveys in low and middle income countries. PLoS One 2019;14:e0214450.
- Diedhiou A, Gilroy K, Cox CM, et al. Successful mLearning Pilot in Senegal: Delivering Family Planning Refresher Training Using Interactive Voice Response and SMS. Glob Health Sci Pract 2015;3:305-21
- Yadav D, et al. 2017. Sangoshthi: Empowering Community Health Workers through Peer Learning in Rural India. International World Wide Web Conference Committee (IW3C2). Available online: http://papers. www2017.com.au.s3-website-ap-southeast-2.amazonaws. com/proceedings/p499.pdf
- Bluestone J, Johnson P, Fullerton J, et al. Effective in-service training design and delivery: evidence from an integrative literature review. Hum Resour Health 2013;11:51.
- Ngabo F, Nguimfack J, Nwaigwe F, et al. 2012. Designing and Implementing an Innovative SMS-based alert system (RapidSMS-MCH) to monitor pregnancy and reduce maternal and child deaths in Rwanda. Pan Afr Med J 2012;13:31.
Metadata
- Authors
- Mc Kenna, P., Babughirana, G., Amponsah, M., Egoeh, S. G., Banura, E., Kanwagi, R., & Gray, B.
- Affiliations
- World Vision International (GB)
- Grameen Foundation (US)
- Venue
- mHealth
- Publisher
- AME Publishing Company
- Published
- 2019-09
- Volume
- 5
- Pages
- 35-35
- ISSN
- 2306-9740 (ISSN-L)
- PMC
- PMC6789201
- OpenAlex
- W2973552214
- Cited by
- 29
- References
- 9
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 3 · 2022: 3 · 2021: 9 · 2020: 3
- FWCI
- 4.67
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- mHealth (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- European Commission (HORIZON2020, 115847)
- Innovative Medicines Initiative
- Topics
- Physical Sciences › Computer Science › Information Systems › ICT in Developing Communities
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › General Health Professions › Health and Medical Education
- Keywords
- community health workers, mobile health, interactive voice response, Ebola outbreak response, disease surveillance, remote training, digital health strategy, resource-limited settings, technology acceptance, universal health coverage, local languages, training curriculum
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Mobile training and support (MOTS) service—using technology to increase Ebola preparedness of remotely-located community health workers (CHWs) in Sierra Leone. https://app.cochat.ai/papers/10.21037/mhealth.2019.09.03
- The paper
- Mc Kenna, P., Babughirana, G., Amponsah, M., Egoeh, S. G., Banura, E., Kanwagi, R., & Gray, B. (2019). Mobile training and support (MOTS) service—using technology to increase Ebola preparedness of remotely-located community health workers (CHWs) in Sierra Leone. mHealth. https://doi.org/10.21037/mhealth.2019.09.03