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Designing m-Health interventions for precision mental health support
Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341865/ https://www.ncbi.nlm.nih.gov/pubmed/32636358 http://dx.doi.org/10.1038/s41398-020-00895-2 |
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author | Bidargaddi, N. Schrader, G. Klasnja, P. Licinio, J. Murphy, S. |
author_facet | Bidargaddi, N. Schrader, G. Klasnja, P. Licinio, J. Murphy, S. |
author_sort | Bidargaddi, N. |
collection | PubMed |
description | Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers. |
format | Online Article Text |
id | pubmed-7341865 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73418652020-07-09 Designing m-Health interventions for precision mental health support Bidargaddi, N. Schrader, G. Klasnja, P. Licinio, J. Murphy, S. Transl Psychiatry Perspective Mobile health (m-Health) resources are emerging as a significant tool to overcome mental health support access barriers due to their ability to rapidly reach and provide support to individuals in need of mental health support. m-Health provides an approach to adapt and initiate mental health support at precise moments, when they are most likely to be effective for the individual. However, poor adoption of mental health apps in the real world suggests that new approaches to optimising the quality of m-Health interventions are critically needed in order to realise the potential translational benefits for mental health support. The micro-randomised trial is an experimental approach for optimising and adapting m-Health resources. This trial design provides data to construct and optimise m-Health interventions. The data can be used to inform when and what type of m-Health interventions should be initiated, and thus serve to integrate interventions into daily routines with precision. Here, we illustrate this approach in a case study, review implementation issues that need to be considered while conducting an MRT, and provide a checklist for mental health m-Health intervention developers. Nature Publishing Group UK 2020-07-07 /pmc/articles/PMC7341865/ /pubmed/32636358 http://dx.doi.org/10.1038/s41398-020-00895-2 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Perspective Bidargaddi, N. Schrader, G. Klasnja, P. Licinio, J. Murphy, S. Designing m-Health interventions for precision mental health support |
title | Designing m-Health interventions for precision mental health support |
title_full | Designing m-Health interventions for precision mental health support |
title_fullStr | Designing m-Health interventions for precision mental health support |
title_full_unstemmed | Designing m-Health interventions for precision mental health support |
title_short | Designing m-Health interventions for precision mental health support |
title_sort | designing m-health interventions for precision mental health support |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341865/ https://www.ncbi.nlm.nih.gov/pubmed/32636358 http://dx.doi.org/10.1038/s41398-020-00895-2 |
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