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An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes

Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on...

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Autores principales: Fareed, Naleef, Swoboda, Christine, Wang, Yiting, Strouse, Robert, Hoseus, Jenelle, Baker, Carrie, Joseph, Joshua J, Venkatesh, Kartik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603563/
https://www.ncbi.nlm.nih.gov/pubmed/37824196
http://dx.doi.org/10.2196/46654
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author Fareed, Naleef
Swoboda, Christine
Wang, Yiting
Strouse, Robert
Hoseus, Jenelle
Baker, Carrie
Joseph, Joshua J
Venkatesh, Kartik
author_facet Fareed, Naleef
Swoboda, Christine
Wang, Yiting
Strouse, Robert
Hoseus, Jenelle
Baker, Carrie
Joseph, Joshua J
Venkatesh, Kartik
author_sort Fareed, Naleef
collection PubMed
description Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts.
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spelling pubmed-106035632023-10-28 An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes Fareed, Naleef Swoboda, Christine Wang, Yiting Strouse, Robert Hoseus, Jenelle Baker, Carrie Joseph, Joshua J Venkatesh, Kartik JMIR Diabetes Viewpoint Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts. JMIR Publications 2023-10-12 /pmc/articles/PMC10603563/ /pubmed/37824196 http://dx.doi.org/10.2196/46654 Text en ©Naleef Fareed, Christine Swoboda, Yiting Wang, Robert Strouse, Jenelle Hoseus, Carrie Baker, Joshua J Joseph, Kartik Venkatesh. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 12.10.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Diabetes, is properly cited. The complete bibliographic information, a link to the original publication on https://diabetes.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Viewpoint
Fareed, Naleef
Swoboda, Christine
Wang, Yiting
Strouse, Robert
Hoseus, Jenelle
Baker, Carrie
Joseph, Joshua J
Venkatesh, Kartik
An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title_full An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title_fullStr An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title_full_unstemmed An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title_short An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions—Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes
title_sort evidence-based framework for creating inclusive and personalized mhealth solutions—designing a solution for medicaid-eligible pregnant individuals with uncontrolled type 2 diabetes
topic Viewpoint
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603563/
https://www.ncbi.nlm.nih.gov/pubmed/37824196
http://dx.doi.org/10.2196/46654
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