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Accepted for/Published in: JMIR Mental Health

Date Submitted: Nov 18, 2021
Open Peer Review Period: Nov 18, 2021 - Jan 13, 2022
Date Accepted: Mar 5, 2022
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study

Yeager CM, Benight CC

Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study

JMIR Ment Health 2022;9(5):e35048

DOI: 10.2196/35048

PMID: 35499857

PMCID: 9112079

Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study

  • Carolyn M. Yeager; 
  • Charles C. Benight

ABSTRACT

Background:

Worldwide, exposure to potentially traumatic events is extremely common and many will develop posttraumatic stress disorder (PTSD) along with other disorders. Unfortunately, considerable barriers to treatment exist. One promising approach to overcoming treatment barriers are digital mental health interventions (DMHIs). Yet, engagement with DMHIs is a concern and theoretically based research in this area is sparse and often inconclusive.

Objective:

The focus of this study was on the complex issue of DMHI engagement. Based on the social cognitive theoretical (SCT), the conceptualization of engagement and a theoretically based model of predictors and outcomes were investigated using a DMHI for trauma recovery.

Methods:

A 6-week longitudinal study with a national sample of trauma survivors was performed that measured engagement, predictors of engagement, and mediational pathways to symptom reduction while using a trauma recovery DMHI (NT1 = 915, NT2 = 350, NT3 = 168, NT4 = 101).

Results:

Confirmatory factor analysis of the engagement latent construct of duration, frequency, interest, attention, and affect produced an acceptable model fit, (χ² = 8.35, df = 2, P = .015, CFI = .973, RMSEA = .059, 90% CI = [.022, .103]. Using the latent construct, the longitudinal theoretical model demonstrated adequate model fit, CFI = .929, RMSEA = .052, 90% CI [.040, .064] and indicated that engagement self-efficacy (β = .35, P < .001) and outcome expectations (β = .37, P < .001) were significant predictors of engagement (R2 = 39%). The relationship between engagement and outcomes was mediated by both activation self-efficacy (β = .80, P < .001), and trauma coping self-efficacy (β = .40, P < .001), which predicted a reduction in PTSD symptoms (β = -.20, P = .017).

Conclusions:

The results of this study may provide a solid foundation toward formalizing the nascent science of engagement. The engagement conceptualization consisted of general measures of attention, interest, affect, and usage that could be applied to other applications. The longitudinal research model supported two theoretically based predictors of engagement, engagement self-efficacy and outcome expectancies. Two task specific self-efficacies, activation and coping, proved to be significant mediators between engagement and symptom reduction. Taken together, this model can be applied to other DMHIs to understand engagement as well as predictors and mechanisms of action. Ultimately, this could help improve the design and development of engaging and effective trauma recovery DMHIs.


 Citation

Please cite as:

Yeager CM, Benight CC

Engagement, Predictors, and Outcomes of a Trauma Recovery Digital Mental Health Intervention: Longitudinal Study

JMIR Ment Health 2022;9(5):e35048

DOI: 10.2196/35048

PMID: 35499857

PMCID: 9112079

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