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Research article
Eveline Muika Kabongo
Stellenbosch University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6651-9097
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Introduction Despite the growing global application of mobile health (mHealth) technology in maternal and child health, the contextual factors and mechanisms by which interventional outcomes are generated have not been subjected to extensive review. In this study, we sought to identify context, mechanisms and outcome elements from implementation and evaluation studies of mHealth interventions to formulate theories or models explicating how mHealth interventions work (or not) both for health care providers and for pregnant women and new mothers.
Method An electronic search of six online databases (Medline, Pubmed, Google Scholar, Scopus, Academic Search Premier and Health Systems Evidence) was performed. Using appropriate MeSH terms and selection procedure, 32 articles were considered for analysis. A theory-driven approach, narrative synthesis, was applied to synthesise the data. Thematic content analysis was used to delineate the elements of the intervention, including its context, actors, mechanism and outcomes. Retroduction was applied to link these elements using a realist evaluation heuristic to form generative theories.
Results Mechanisms that promote the implementation of mHealth by community health workers/health care providers include motivation, perceived skill and knowledge improvement, improved self-efficacy, improved confidence, improved relationship between community health workers and clients, perceived support of community health workers, perceived ease of use and usefulness of mHealth, For pregnant women and new mothers, mechanisms that trigger the uptake of mHealth and use of maternal and child health services included: perceived service satisfaction, perceived knowledge acquisition, support and confidence, improved self-efficacy, encouragement, empowerment and motivation. Information overload was identified as a potential negative mechanism for the uptake of maternal and child health services.
Conclusion The models developed in this study provide a detailed understanding of the implementation and uptake of mHealth interventions and how they improve maternal and child health services in low and middle income countries. These models provide a foundation for the ‘white box’ or theory-driven evaluation of mHealth intervention and can improve the rollout and implementation where required.
Keywords
mHealth, mobile phone, maternal health, child health, maternal health services, low- and middle-income countries, theory-driven
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CURRENT STATUS: Published
View the published article at BMC Pregnancy and Childbirth.
No community comments so far
Editorial decision: Major revision
On 24 Jan, 2021
Review #1 received
Received 23 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 29 Dec, 2020
Editor assigned
On 24 Nov, 2020
Submission checks complete
On 24 Nov, 2020
Editor invited
On 24 Nov, 2020
No comments provided
Editorial decision: Major revision
On 14 Oct, 2020
Review #2 received
Received 05 Oct, 2020
Reviewer #2 agreed
On 14 Sep, 2020
Review #1 received
Received 14 Aug, 2020
Reviewer #1 agreed
On 24 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Editor assigned
On 09 Jul, 2020
Editor invited
On 08 Jul, 2020
Submission checks complete
On 06 Jul, 2020
Version 1
Posted 30 Dec, 2019
No comments provided
Editorial decision: Major revision
On 10 Jun, 2020
Review #2 received
Received 29 May, 2020
Reviewer #2 agreed
On 06 May, 2020
Review #1 received
Received 09 Mar, 2020
Reviewer #1 agreed
On 25 Feb, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Editor assigned
On 25 Oct, 2019
Submission checks complete
On 24 Oct, 2019
Editor invited
On 24 Oct, 2019
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A new preprint design is coming!
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See the published version of this preprint at BMC Pregnancy and Childbirth.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Eveline Muika Kabongo
Stellenbosch University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6651-9097
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Pregnancy and Childbirth.
No community comments so far
Editorial decision: Major revision
On 24 Jan, 2021
Review #1 received
Received 23 Jan, 2021
Reviewer #1 agreed
On 03 Jan, 2021
Reviewers invited
Invitations sent on 29 Dec, 2020
Editor assigned
On 24 Nov, 2020
Submission checks complete
On 24 Nov, 2020
Editor invited
On 24 Nov, 2020
No comments provided
Editorial decision: Major revision
On 14 Oct, 2020
Review #2 received
Received 05 Oct, 2020
Reviewer #2 agreed
On 14 Sep, 2020
Review #1 received
Received 14 Aug, 2020
Reviewer #1 agreed
On 24 Jul, 2020
Reviewers invited
Invitations sent on 21 Jul, 2020
Editor assigned
On 09 Jul, 2020
Editor invited
On 08 Jul, 2020
Submission checks complete
On 06 Jul, 2020
Version 1
Posted 30 Dec, 2019
No comments provided
Editorial decision: Major revision
On 10 Jun, 2020
Review #2 received
Received 29 May, 2020
Reviewer #2 agreed
On 06 May, 2020
Review #1 received
Received 09 Mar, 2020
Reviewer #1 agreed
On 25 Feb, 2020
Reviewers invited
Invitations sent on 13 Jan, 2020
Editor assigned
On 25 Oct, 2019
Submission checks complete
On 24 Oct, 2019
Editor invited
On 24 Oct, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Pregnancy and Childbirth.
Introduction Despite the growing global application of mobile health (mHealth) technology in maternal and child health, the contextual factors and mechanisms by which interventional outcomes are generated have not been subjected to extensive review. In this study, we sought to identify context, mechanisms and outcome elements from implementation and evaluation studies of mHealth interventions to formulate theories or models explicating how mHealth interventions work (or not) both for health care providers and for pregnant women and new mothers.
Method An electronic search of six online databases (Medline, Pubmed, Google Scholar, Scopus, Academic Search Premier and Health Systems Evidence) was performed. Using appropriate MeSH terms and selection procedure, 32 articles were considered for analysis. A theory-driven approach, narrative synthesis, was applied to synthesise the data. Thematic content analysis was used to delineate the elements of the intervention, including its context, actors, mechanism and outcomes. Retroduction was applied to link these elements using a realist evaluation heuristic to form generative theories.
Results Mechanisms that promote the implementation of mHealth by community health workers/health care providers include motivation, perceived skill and knowledge improvement, improved self-efficacy, improved confidence, improved relationship between community health workers and clients, perceived support of community health workers, perceived ease of use and usefulness of mHealth, For pregnant women and new mothers, mechanisms that trigger the uptake of mHealth and use of maternal and child health services included: perceived service satisfaction, perceived knowledge acquisition, support and confidence, improved self-efficacy, encouragement, empowerment and motivation. Information overload was identified as a potential negative mechanism for the uptake of maternal and child health services.
Conclusion The models developed in this study provide a detailed understanding of the implementation and uptake of mHealth interventions and how they improve maternal and child health services in low and middle income countries. These models provide a foundation for the ‘white box’ or theory-driven evaluation of mHealth intervention and can improve the rollout and implementation where required.
Figure 1
Figure 2
Figure 3
Figure 4
This is a list of supplementary files associated with this preprint. Click to download.
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