Patient preferences for key drivers and facilitators of adoption of mHealth technology to manage depression: A discrete choice experiment.

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 understanding 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, established 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 attainment, 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.

Investigating a psychological model of mental conditions and coping during the COVID-19 pandemic driven by participatory methods.

BACKGROUND: There is evidence of increased mental health problems during the early stages of the COVID-19 pandemic. We aimed to identify the factors that put certain groups of people at greater risk of mental health problems. METHODS: We took a participatory approach, involving people with lived experience of mental health problems and/or carers, to generate a set of risk factors and potential moderators of the effects of COVID on mental health. An online cross-sectional survey was completed by 1464 United Kingdom residents between 24th April and 27th June 2020. The survey had questions on whether respondents were existing mental health service users and or carers, level of depression (PHQ9) and anxiety (GAD7), demographics, threat and coping appraisals, perceived resilience (BRS), and specific coping behaviours (validated as part of this study). The relationship between responses and coping strategies was measured using tetrachoric correlations. Structural equation modelling was used to test the model. RESULTS: A model significantly fit our data (rel χ2 = 2.05, RMSEA = 0.029 95%, CI (0.016, 0.042), CFI = 0.99, TLI = 0.98, SRMR = 0.014). Age and coping appraisal predicted anxiety and depression. Whereas, threat appraisal and ethnicity only predicted anxiety, and resilience only predicted depression. Additionally, specific coping behaviours predicted anxiety and depression, with overlap on distraction. CONCLUSIONS: Some, but not all, risk factors significantly predict anxiety and depression. While there is a relationship between anxiety and depression, different factors may put people at greater risk of one or the other during the pandemic.