How do substance use and mental health work for me? Towards personalized communication in the treatment of addiction and common mental health problems with ecological momentary assessment and network models

This dissertation investigates how data-informed tools could make psychotherapy more personalized and effective, particularly for people with addiction and co-occurring mental health problems. Ecological momentary assessment (EMA), where patients answer questions on their smartphones several times a day for multiple days, is used to generate personalized feedback to help patients and therapists collaboratively understand individual mental health and addiction patterns.
Simulations showed that, while EMA data can be used to estimate personalized networks graphing how different factors influence each other in an individual’s life, detecting smaller effects is difficult and false negatives are likely. This suggests that simpler feedback focused on fewer variables may be more reliable.
A case study with a person experiencing cannabis addiction and psychosis suggested that EMA-based feedback can support insight and improve communication during interventions. A pilot randomized controlled trial in young adults with cannabis use problems found that while standard therapy decreased cannabis use, adding personalized feedback helped to maintain these improvements over time. Participants valued discussing the personalized feedback with the practitioners, though completing EMA was sometimes demanding.
Additionally, a study of over 1200 cannabis users revealed gender differences in how cannabis use problems relate to mental health problems, suggesting that subgroup-based tailoring of interventions may also be beneficial. The dissertation also discusses how EMA could be integrated with cognitive bias modification interventions to further strengthen addiction treatment.
Overall, EMA-based personalized feedback and personalized networks show promise for improving therapy outcomes, thus, larger studies are needed to replicate clinical usefulness, clarify underlying mechanisms, and improve methodologies to obtain personalized networks.