# In-Depth Co-Design of Mental Health Monitoring Technologies by People with Lived Experience
## 4 Discussion
### Discussion in Light of the Wider Literature
#### 4.2.2 Why Co-Design Digital Mental Health Monitoring Interventions?  
There are numerous reasons why shared decision-making is still an emerging model of care within mental health care contexts, including risk aversion [62], the need for greater training and guidance, and the still-emergent approaches transitioning from research into practice that encompass the value of informal care network engagements within care processes [63].  
To be genuinely time-saving and treatment-enhancing for clinicians, DMHIs need to be integrated into clinical workflows in a way that is relevant and has utility in meeting practice expectations, while encouraging the consideration of individual needs and, in turn, enabling more humanizing and accurate reporting of service users' needs when (re)developing models of care. For consumers, this has the potential to enable better self-advocacy within services. In the long term, these approaches may enable in-clinic monitoring processes that are subsidized by Medicare, to potentially minimize GP visits and streamline medication adjustments.  
2.3 What the Existing Literature Tells Us about Consumer Engagement with Mental Health Monitoring Technologies  
Consumer attitudes towards technology adoption represent a widely developed area of inquiry within digital health consumer research. It consists of a number of theories, such as the theory of planned behavior or TPB [64], the technology acceptance model [65], and the unified theory of technology acceptance or UTAUT [66], which synthesized previous technology adoption theories to highlight the influence of key contextual factors, such as performance expectations, perceptions about ease of use, and usability expectations, as well as social influence and facilitating conditions, such as the task-technology fit [37].  
Studies that explore consumer engagement with digital mental health interventions [7; 8; 18; 23; 25; 67; 68] indicate that these interdependencies are mediated by consumer demographic characteristics, such as gender (females tend to be more anxious about privacy, more risk-averse, and more concerned about ease of use, meaning that trust and access are more important considerations for them), age (young people tend to be more comfortable when adopting new technologies than older people), technology usage experience, education, and the consumer's current employment, housing context, and medical history (since depressive symptoms and low mood have been identified as particular barriers for engagement, along with tiredness and the severity of symptoms) [7]. Mental health literacy is a sensitive determinant of mental health service engagement. Negative past experiences with mental health services, for example, when a consumer believes that treatment has been forced upon them, may significantly hamper trust, whereas positive past experiences with mental health services and a positive perception of mental health help-seeking practice greatly facilitate engagement [7]. Other privacy considerations relate to the potential stigma of mental illness, factors that traditionally inform consumer trust in digital services, such as ease of use, will generally require additional privacy safeguards such as the discreet naming of mental health mobile apps and the sensitive wording of text message reminders, since these online tools may be used in public [18].