Correct citation for this article:
G. M. Munro1, C. Bullen1
TraitMap: harnessing continuous personalised feedback via smartphone
sensors to disrupt and change addictive behaviours
1 National Institute for Health Innovation,
School of Population Health, The University of Auckland
e: gm093@aucklanduni.ac.nz,
w: www.nihi.auckland.ac.nz
tel: +64 9 373 7599 (ext 89137)
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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.
Keywords: addiction, behaviour change, feedback, intervention, mobile
health, personalisation