If we build it, will they come? Issues of engagement with digital health interventions for trauma recovery

Abstract

Exposure to traumatic events is extremely common with nearly 75% reported to have experienced one or more traumatic events worldwide. A significant number of those exposed will develop posttraumatic stress disorder (PTSD) along with depression, anxiety, and substance use disorders. Globally, trauma-related mental health disorders are the leading cause of global disability burden, and many of these disorders are caused, or worsened, by exposure to wars, natural and human-caused disasters, and other traumatic events. Significant barriers to treatment exist including logistical, geographical, financial, stigma, and other attitudinal challenges. One opportune approach to overcoming these barriers is the provision of mental health interventions via technology that can be readily standardized for wide dissemination of evidence-based care. However, engagement with technology-based interventions is a concern and limited participation and high attrition rates are common. This may be especially true for trauma survivors who often experience symptoms of avoidance and hyperarousal. Engagement is regarded as an essential component of intervention efficacy and has been demonstrated to be associated with more positive clinical outcomes, yet theoretically based research in this area is sparse. This review focuses on the complex issue of engagement with digital health interventions (DHIs). Specifically, we review the conceptualization and measurement of engagement, predictors of engagement, and importantly, the relationship of engagement with intervention effectiveness. Finally, a theoretically based model of engagement is proposed that considers the unique challenges of trauma recovery. This review is not intended to provide a systematic or exhaustive set of recommendations, rather it is intended to highlight the challenges of engagement research including its definition, measurement, and modeling. Future engagement research that includes valid and reliable measures of engagement will enable consistent exploration of engagement predictors that can then inform methods for increasing engagement and, ultimately, intervention effectiveness.

Engagement with a cognitive behavioural therapy mobile phone app predicts changes in mental health and wellbeing: MoodMission

“…To measure the real-world effects of using MHapps outside of clinical trials, some have suggested investigating and quantifying positive relationships between app usage and mental health outcomes (Carpenter et al, 2016;Yeager & Benight, 2018). This can help avoid "digital placebo effects," which occur when merely the installation of a MHapp can bias individuals' responses towards favourable mental health outcomes (Torous & Firth, 2016).…”

A comparative study of engagement in mobile and wearable health monitoring for bipolar disorder

“…Monitoring symptoms is also a prerequisite for measuring mood instability in BP and for delivering interventions that address needs of specific individuals in the moment . Many mobile and wearable technologies are available for individuals to self‐monitor their symptoms, but engagement is a common problem . This study evaluated engagement strategies for individuals with BP to monitor symptoms longitudinally.…”

“…Smartphone apps require that users actively record their symptoms, a burden that may decrease engagement with users preferring more automated ways to record symptoms . They also involve subjective measurements, which may lead to less accurate or less biologically relevant markers of symptoms . Sensors and wearable devices allow symptoms to monitored passively and objectively .…”

“…mobile and wearable technologies are available for individuals to self-monitor their symptoms, [7][8][9][10][11][12] but engagement is a common problem. [13][14][15] This study evaluated engagement strategies for individuals with BP to monitor symptoms longitudinally.…”

“…7 They also involve subjective measurements, which may lead to less accurate or less biologically relevant markers of symptoms. 6,14,16 Sensors and wearable devices allow symptoms to monitored passively and objectively. For example, MONARCA, 19 PRIORI, 20 and Bi-Affect 21 use patterns of speech and behavior from recorded calls, keystrokes, number of phone calls, and duration of phone calls to predict mood.…”