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JMIR Mhealth Uhealth. 2018 Apr 23;6(4):e95. doi: 10.2196/mhealth.9502.

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

Author information

1
Department of Neurobiology, Karolinska Institutet, Solna, Sweden.
2
Department of General Practice, The University of Melbourne, Carlton, Australia.
3
Computing and Information Systems, The University of Melbourne, Parkville, Australia.
4
Centre for Design Innovation, Swinburne University of Technology, Hawthorn, Australia.
#
Contributed equally

Abstract

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.

KEYWORDS:

decision support techniques; depression; ehealth; mental health; primary health care; risk; user-centered design

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