The third preprint journal club
- Venue: Waimairi Staff Room (Waimairi 219, our staff room)
- Time: 10-11 AM, 27th March, Tuesday
- Topic: TraitMap: harnessing continuous personalised feedback via smartphone sensors to disrupt and change addictive behaviours by Munro GM, and Bullen, C
- Discussant: Priya Mohan
- Link to the preprint article that you can read or download: Click to access the article
- Link to writing a review of the article: Click to access the document to write a review, you have to be member of the group to write a review
- to join the preprint journal club, visit: Visit the UC Preprint review group
- Summary:
Background: Mental and substance use disorders (M/SUDs) are the leading cause of non-fatal illness worldwide, incurring substantial social and economic costs. The limited impact of interventions to treat people with M/SUD has prompted a clinical shift toward more personality-informed approaches. Within psychiatry, evidence shows key personality traits can be used as endophenotypes for M/SUDs. In mobile health (mHealth) research, applications (apps) that detect risk behaviours and send users personalised feedback are likely to counter many of the harms associated with substance use. Aims: To develop and test ‘TraitMap’, a novel mHealth system that combines self-report measures, continuous biomedical monitoring, and personalised feedback to support complex self-care in people with M/SUDs. Fully realised, TraitMap will detect drug cravings and personalise intervention to disrupt substance-related risk behaviours. Methods: A 3-stage project involving 1) collection and analysis of multi-stakeholder feedback via online surveys, 2) design and evaluation of a prototype mobile app tailored to people with M/SUDs, 3) a pilot trial to assess the impact of TraitMap on drug cravings and associated harms that will underpin the future design of a larger randomised controlled trial. Contribution: Unlike previous studies, this project will be developed using ResearchKit, an app development platform specifically tailored to medical research needs. Findings from world-leading medical research units at Stanford, Johns Hopkins, and Oxford show ResearchKit counters many of the methodological limitations and data loss that typically characterise Internet trials. By contrast, the highly automated data collection features of ResearchKit will enable the study to streamline informed consent, prompt continued user participation, and collect infinitely richer data sets.