Abstract
Background: Gaps exist between developers, commissioners and end users in terms of the perceived desirability of different features and functionality for mobile apps. Aim: We present lessons learned and recommendations from working on a large project with various stakeholders to develop a mobile app for chronic obstructive pulmonary disease (COPD) patients. Methods: We adopted a user-centred, participatory approach to app development. Following a series of focus groups and interviews to capture requirements, we developed a prototype app, designed to enable daily symptom recording (experience sampling). The prototype was tested in a usability study applying the Think Aloud protocol on people with COPD. It was then released via the Android app store and experience sampling data and event data were captured to gather further usability data in the wild. Results: Five people with COPD took part in the usability study. Identified themes include familiarity with technology, appropriate levels for feeding back information, and usability issues such as manual dexterity. Thirty-seven participants used the app over a four-month period (median age 47 years). The symptoms most correlated to perceived wellbeing were ‘tiredness' (r =.61, P < .001) and breathlessness' (r =.59, P < .001). Conclusions: Design implications for COPD apps include the need for clearly labelled features (rather than relying on colours/symbols which require experience using smartphones), providing weather information, and using the same terminology as healthcare professionals (rather than simply lay terms). Target users, researchers and developers should be involved at every stage of app development, using an iterative approach to build a prototype app, which should then be tested in controlled settings as well as `in the wild' over longer time periods.
Original language | English |
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Journal | JMIR Human Factors |
Publication status | Accepted/In press - 3 Feb 2020 |
Keywords
- Chronic Obstructive Pulmonary Disease
- application design
- Experience Sampling Method
- mHealth
- symptom tracking