Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment
Abstract
Background: In time, we may be able to detect the early onset of symptoms of depression and even predict relapse using behavioural data gathered through mobile technologies. However, barriers to adoption exist and under-standing the importance of these factors to users is vital to ensure maximum adoption.Method: In a discrete choice experiment, people with a history of depression (N = 171) were asked to select their preferred technology from a series of vignettes containing four characteristics: privacy, clinical support, estab-lished benefit and device accuracy (i.e., ability to detect symptoms), with different levels.
Mixed logit models were used to establish what was most likely to affect adoption. Sub-group analyses explored effects of age, gender, education, technology acceptance and familiarity, and nationality.Results: Higher level of privacy, greater clinical support, increased perceived benefit and better device accuracy were important.
Accuracy was the most important, with only modest compromises willing to be made to increase other factors such as privacy. Established benefit was the least valued of the attributes with participants happy with technology that had possible but unknown benefits.
Preferences were moderated by technology acceptance, age, nationality, and educational background.Conclusion: For people with a history of depression, adoption of technology may be driven by the desire for accurate detection of symptoms. However, people with lower technology acceptance and educational attain-ment, those who were younger, and specific nationalities may be willing to compromise on some accuracy for more privacy and clinical support.
These preferences should help shape design of mHealth tools.