Introduction
Mental and substance use disorders (M/SUDs) inflict the highest costs to
society of all diseases, affecting production levels, legal systems, law
enforcement, and continued damage to users (Whiteford, Ferrari,
Degenhardt, Feigin, & Vos, 2015). Key populations (KPs), most
vulnerable to HIV exposure such as men who have sex with men (MSM),
people who use drugs (PWUD), sex workers, and transgender individuals
are disproportionately burdened due to stigma-induced psychosocial
stress (WHO, 2014). While behavioural interventions support abstinence
during early recovery, 80-95% of people relapse within 12 months
(Brandon et al., 2007). Relapse is especially problematic in KPs because
it exacerbates mental disorders, risk behaviours, and the spreading of
HIV to other groups (Gupta, Kumar, & Garg, 2013). The reason many
behavioural interventions fail in natural contexts is because patients
have difficulty enacting them outside of controlled clinical settings in
which they were first introduced (Boyer, Smelson, Fletcher, Ziedonis, &
Picard, 2010). As a result, patients with M/SUDs neither have the
ability to detect biological and affective changes that trigger drug
cravings or modify their behaviours to decrease health risk (Boyer et
al., 2010).
Current limitations in the diagnosis and treatment of M/SUDs has
prompted a clinical shift toward personality-informed approaches (Trull
& Widiger, 2013). Within psychiatry, growing consensus argues that
dimensional trait models can more effectively diagnose M/SUDs (Krueger
& Markon, 2014; Suzuki, Samuel, Pahlen, & Krueger, 2015). This
consensus draws on clear evidence that key personality traits can act as
endophenotypes for M/SUDs (Belcher, Volkow, Moeller, & Ferré, 2014).
Specifically, high negative emotionality/neuroticism (NEM/N) increases,
whereas high positive emotionality/extraversion (PEM/E) and constraint
(CON) decreases vulnerability (Belcher et al., 2014). Researchers in
mobile health (mHealth) also argue that mobile sensing technology can
disrupt and change habitual behaviours via self-monitoring and feedback
(Hermsen, Frost, Renes, & Kerkhof, 2016). While the long-term impact of
such disruptions remain unclear, mobile applications (apps) offer
clinicians unprecedented potential to increase user engagement whilst
continuously self-monitoring disease outcomes (Jardine, Fisher, &
Carrick, 2015). The fact that mobile apps can detect many of the
affective changes that trigger drug craving and risk behaviours suggest
their particular efficacy in treating M/SUDs (Boyer et al., 2010; Donker
et al., 2013; Litvin, Abrantes, & Brown, 2013).
What these two converging research domains share above an emphasis on
personality-informed intervention is the undying assumption that
personality traits can no longer be defined as fixed dimensions
(Ferguson, 2010). Rather, traits are highly dynamic qualities that morph
considerably in response to spatio-temporal changes (Chapman, Hampson,
& Clarkin, 2014). Health-protective personality changes are thus not
only achievable but indeed desirable and beneficial (Hampson, Goldberg,
Vogt, & Dubanoski, 2006). Findings confirm the timely nature of
personality-informed diagnosis (Suzuki et al., 2015; Trull & Widiger,
2013) and intervention to meet continuous support demands in people with
M/SUD (Donker et al., 2013; Litvin et al., 2013). The following section
examines how such intervention may be achieved.