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Proportionate methods for evaluating a simple digital mental health tool
BACKGROUND: Traditional evaluation methods are not keeping pace with rapid developments in mobile health. More flexible methodologies are needed to evaluate mHealth technologies, particularly simple, self-help tools. One approach is to combine a variety of methods and data to build a comprehensive p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750412/ https://www.ncbi.nlm.nih.gov/pubmed/28993317 http://dx.doi.org/10.1136/eb-2017-102755 |
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author | Davies, E Bethan Craven, Michael P Martin, Jennifer L Simons, Lucy |
author_facet | Davies, E Bethan Craven, Michael P Martin, Jennifer L Simons, Lucy |
author_sort | Davies, E Bethan |
collection | PubMed |
description | BACKGROUND: Traditional evaluation methods are not keeping pace with rapid developments in mobile health. More flexible methodologies are needed to evaluate mHealth technologies, particularly simple, self-help tools. One approach is to combine a variety of methods and data to build a comprehensive picture of how a technology is used and its impact on users. OBJECTIVE: This paper aims to demonstrate how analytical data and user feedback can be triangulated to provide a proportionate and practical approach to the evaluation of a mental well-being smartphone app (In Hand). METHODS: A three-part process was used to collect data: (1) app analytics; (2) an online user survey and (3) interviews with users. FINDINGS: Analytics showed that >50% of user sessions counted as ‘meaningful engagement’. User survey findings (n=108) revealed that In Hand was perceived to be helpful on several dimensions of mental well-being. Interviews (n=8) provided insight into how these self-reported positive effects were understood by users. CONCLUSIONS: This evaluation demonstrates how different methods can be combined to complete a real world, naturalistic evaluation of a self-help digital tool and provide insights into how and why an app is used and its impact on users’ well-being. CLINICAL IMPLICATIONS: This triangulation approach to evaluation provides insight into how well-being apps are used and their perceived impact on users’ mental well-being. This approach is useful for mental healthcare professionals and commissioners who wish to recommend simple digital tools to their patients and evaluate their uptake, use and benefits. |
format | Online Article Text |
id | pubmed-5750412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-57504122018-02-12 Proportionate methods for evaluating a simple digital mental health tool Davies, E Bethan Craven, Michael P Martin, Jennifer L Simons, Lucy Evid Based Ment Health Original Article BACKGROUND: Traditional evaluation methods are not keeping pace with rapid developments in mobile health. More flexible methodologies are needed to evaluate mHealth technologies, particularly simple, self-help tools. One approach is to combine a variety of methods and data to build a comprehensive picture of how a technology is used and its impact on users. OBJECTIVE: This paper aims to demonstrate how analytical data and user feedback can be triangulated to provide a proportionate and practical approach to the evaluation of a mental well-being smartphone app (In Hand). METHODS: A three-part process was used to collect data: (1) app analytics; (2) an online user survey and (3) interviews with users. FINDINGS: Analytics showed that >50% of user sessions counted as ‘meaningful engagement’. User survey findings (n=108) revealed that In Hand was perceived to be helpful on several dimensions of mental well-being. Interviews (n=8) provided insight into how these self-reported positive effects were understood by users. CONCLUSIONS: This evaluation demonstrates how different methods can be combined to complete a real world, naturalistic evaluation of a self-help digital tool and provide insights into how and why an app is used and its impact on users’ well-being. CLINICAL IMPLICATIONS: This triangulation approach to evaluation provides insight into how well-being apps are used and their perceived impact on users’ mental well-being. This approach is useful for mental healthcare professionals and commissioners who wish to recommend simple digital tools to their patients and evaluate their uptake, use and benefits. BMJ Publishing Group 2017-11 2017-10-09 /pmc/articles/PMC5750412/ /pubmed/28993317 http://dx.doi.org/10.1136/eb-2017-102755 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ |
spellingShingle | Original Article Davies, E Bethan Craven, Michael P Martin, Jennifer L Simons, Lucy Proportionate methods for evaluating a simple digital mental health tool |
title | Proportionate methods for evaluating a simple digital mental health tool |
title_full | Proportionate methods for evaluating a simple digital mental health tool |
title_fullStr | Proportionate methods for evaluating a simple digital mental health tool |
title_full_unstemmed | Proportionate methods for evaluating a simple digital mental health tool |
title_short | Proportionate methods for evaluating a simple digital mental health tool |
title_sort | proportionate methods for evaluating a simple digital mental health tool |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5750412/ https://www.ncbi.nlm.nih.gov/pubmed/28993317 http://dx.doi.org/10.1136/eb-2017-102755 |
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