Analyzing program data and promotional approaches to inform best practices from a mobile phone-based reproductive health message program in Afghanistan Article Swipe
YOU?
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· 2022
· Open Access
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· DOI: https://doi.org/10.1177/20552076221089801
Objective Digital health technologies have contributed to the adoption of beneficial reproductive, maternal, newborn, and child health (RMNCH) behaviors through social and behavior change programming, including in hard-to-reach settings. On-demand digital health interventions rely on promotions to build awareness and increase use among target audiences. There is little research on preferred content and use of promotional approaches for RMNCH digital health activities. Methods We conducted a retrospective descriptive analysis of Mobile for Reproductive Health (m4RH) data in Afghanistan to assess the use and changes in call volume via the 2-3-4 platform by promotional approaches over 23 months between October 2017 and August 2019. Results There were 103,859 completed messages (CM) heard. Most callers reporting demographics were under 18 years, with roughly even distribution by gender. The number of CMs listened to across all menus increased with time. The basic m4RH family planning menu was most popular, with callers most frequently selecting information on intrauterine contraceptive devices. Nine types of promotional approaches were implemented. Compared against call volume, SMS blast promotion was the most productive promotional approach, radio broadcasts had modest increases, and social media and interpersonal communication demonstrated no clear change. Conclusions m4RH use increased over time, particularly among younger people. The number of promotional approaches used does not appear as important as the type of approach used to generate program awareness. Mass media communications, including SMS blast promotions and radio broadcasts, may be the most effective strategies. Deeper program data analysis can guide tailoring of message content and promotional approaches to reach target audiences with the RMNCH content they most value.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/20552076221089801
- OA Status
- gold
- Cited By
- 2
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224257425
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224257425Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/20552076221089801Digital Object Identifier
- Title
-
Analyzing program data and promotional approaches to inform best practices from a mobile phone-based reproductive health message program in AfghanistanWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-01Full publication date if available
- Authors
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Lara Lorenzetti, Kate F. Plourde, Sayed Haroon Rastagar, Arzoo S. Afzali, Ahmad Shah Sultani, Abdul Khaleq Khalil, Abdul Waheed Adeeb, Shafiqullah Hemat, Catherine S. ToddList of authors in order
- Landing page
-
https://doi.org/10.1177/20552076221089801Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1177/20552076221089801Direct OA link when available
- Concepts
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Reproductive health, Social media, Psychological intervention, Promotion (chess), Short Message Service, Health promotion, Phone, Interpersonal communication, Health communication, Mass media, Mobile phone, Medicine, Internet privacy, Advertising, Psychology, Public health, Computer science, Population, Business, Environmental health, Nursing, World Wide Web, Telecommunications, Social psychology, Political science, Philosophy, Politics, Linguistics, Communication, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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