Cargando…

Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design

BACKGROUND: Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way...

Descripción completa

Detalles Bibliográficos
Autores principales: Wachtler, Caroline, Coe, Amy, Davidson, Sandra, Fletcher, Susan, Mendoza, Antonette, Sterling, Leon, Gunn, Jane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938570/
https://www.ncbi.nlm.nih.gov/pubmed/29685864
http://dx.doi.org/10.2196/mhealth.9502
_version_ 1783320808079228928
author Wachtler, Caroline
Coe, Amy
Davidson, Sandra
Fletcher, Susan
Mendoza, Antonette
Sterling, Leon
Gunn, Jane
author_facet Wachtler, Caroline
Coe, Amy
Davidson, Sandra
Fletcher, Susan
Mendoza, Antonette
Sterling, Leon
Gunn, Jane
author_sort Wachtler, Caroline
collection PubMed
description BACKGROUND: Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way that will enhance their uptake in routine clinical practice remains challenging; however, mobile apps show promise in this respect. To increase the likelihood that an app-delivered clinical prediction tool can be successfully incorporated into clinical practice, it is important to involve end users in the app design process. OBJECTIVE: The aim of the study was to maximize patient engagement in an app designed to improve treatment allocation for depression. METHODS: An iterative, user-centered design process was employed. Qualitative data were collected via 2 focus groups with a community sample (n=17) and 7 semistructured interviews with people with depressive symptoms. The results of the focus groups and interviews were used by the computer engineering team to modify subsequent protoypes of the app. RESULTS: Iterative development resulted in 3 prototypes and a final app. The areas requiring the most substantial changes following end-user input were related to the iconography used and the way that feedback was provided. In particular, communicating risk of future depressive symptoms proved difficult; these messages were consistently misinterpreted and negatively viewed and were ultimately removed. All participants felt positively about seeing their results summarized after completion of the clinical prediction tool, but there was a need for a personalized treatment recommendation made in conjunction with a consultation with a health professional. CONCLUSIONS: User-centered design led to valuable improvements in the content and design of an app designed to improve allocation of and engagement in depression treatment. Iterative design allowed us to develop a tool that allows users to feel hope, engage in self-reflection, and motivate them to treatment. The tool is currently being evaluated in a randomized controlled trial.
format Online
Article
Text
id pubmed-5938570
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-59385702018-05-09 Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design Wachtler, Caroline Coe, Amy Davidson, Sandra Fletcher, Susan Mendoza, Antonette Sterling, Leon Gunn, Jane JMIR Mhealth Uhealth Original Paper BACKGROUND: Around the world, depression is both under- and overtreated. The diamond clinical prediction tool was developed to assist with appropriate treatment allocation by estimating the 3-month prognosis among people with current depressive symptoms. Delivering clinical prediction tools in a way that will enhance their uptake in routine clinical practice remains challenging; however, mobile apps show promise in this respect. To increase the likelihood that an app-delivered clinical prediction tool can be successfully incorporated into clinical practice, it is important to involve end users in the app design process. OBJECTIVE: The aim of the study was to maximize patient engagement in an app designed to improve treatment allocation for depression. METHODS: An iterative, user-centered design process was employed. Qualitative data were collected via 2 focus groups with a community sample (n=17) and 7 semistructured interviews with people with depressive symptoms. The results of the focus groups and interviews were used by the computer engineering team to modify subsequent protoypes of the app. RESULTS: Iterative development resulted in 3 prototypes and a final app. The areas requiring the most substantial changes following end-user input were related to the iconography used and the way that feedback was provided. In particular, communicating risk of future depressive symptoms proved difficult; these messages were consistently misinterpreted and negatively viewed and were ultimately removed. All participants felt positively about seeing their results summarized after completion of the clinical prediction tool, but there was a need for a personalized treatment recommendation made in conjunction with a consultation with a health professional. CONCLUSIONS: User-centered design led to valuable improvements in the content and design of an app designed to improve allocation of and engagement in depression treatment. Iterative design allowed us to develop a tool that allows users to feel hope, engage in self-reflection, and motivate them to treatment. The tool is currently being evaluated in a randomized controlled trial. JMIR Publications 2018-04-23 /pmc/articles/PMC5938570/ /pubmed/29685864 http://dx.doi.org/10.2196/mhealth.9502 Text en ©Caroline Wachtler, Amy Coe, Sandra Davidson, Susan Fletcher, Antonette Mendoza, Leon Sterling, Jane Gunn. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 23.04.2018. 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 mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Wachtler, Caroline
Coe, Amy
Davidson, Sandra
Fletcher, Susan
Mendoza, Antonette
Sterling, Leon
Gunn, Jane
Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title_full Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title_fullStr Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title_full_unstemmed Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title_short Development of a Mobile Clinical Prediction Tool to Estimate Future Depression Severity and Guide Treatment in Primary Care: User-Centered Design
title_sort development of a mobile clinical prediction tool to estimate future depression severity and guide treatment in primary care: user-centered design
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5938570/
https://www.ncbi.nlm.nih.gov/pubmed/29685864
http://dx.doi.org/10.2196/mhealth.9502
work_keys_str_mv AT wachtlercaroline developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT coeamy developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT davidsonsandra developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT fletchersusan developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT mendozaantonette developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT sterlingleon developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign
AT gunnjane developmentofamobileclinicalpredictiontooltoestimatefuturedepressionseverityandguidetreatmentinprimarycareusercentereddesign