Abstract
BackgroundMany individuals engaging in Internet-based interventions fail to complete these treatments as intended. The processes responsible for treatment adherence in Internet-based interventions are still poorly understood.ObjectiveThe aim of this study was to investigate to what extent adherence in an Internet-based intervention can be predicted by motivational and volitional factors outlined in the health action process approach (HAPA).MethodsThis study investigated motivational and volitional factors included in HAPA in a randomized controlled trial to predict treatment adherence of N=101 individuals with subclinical depression in the intervention group of a depression prevention intervention (GET.ON Mood Enhancer). Adherence was operationalized as the number of completed treatment modules. Using longitudinal structural equation modeling, HAPA variables (motivational, maintenance, and recovery self-efficacy, outcome expectancies, intention, and planning) were assessed at baseline and their associations with adherence 7 weeks later.ResultsPlanning predicted adherence. Better planning was, in turn, associated with higher levels of maintenance self-efficacy, and the latter significantly affected treatment adherence via planning. The other hypothesized direct associations were not significant. In total, the HAPA variables accounted for 14% of variance in treatment adherence.ConclusionsPlanning emerged as the strongest predictor of treatment adherence in highly motivated participants in an Internet-based intervention out of all HAPA variables investigated. Findings are in line with the hypothesis that planning facilitates the translation of good intentions into actions. The findings imply that systematically fostering planning skills and maintenance self-efficacy prior to or during Internet-based interventions would help participants to successfully complete these treatments.Trial RegistrationGerman Clinical Trials Register DRKS00005973; https://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00005973 (Archived by WebCite at http://www.webcitation.org/6uxCy64sy).
Highlights
Internet-based interventions have been shown to prevent the incidence of mental health disorders such as depression or anxiety and reduce associated symptom severity [1,2]
To shed light on these factors and processes in Internet-based interventions, this study explores the intention-behavior gap by assessing motivational and volitional adherence predictors based on the health action process approach (HAPA) model for individuals showing subclinical symptoms of depression but not fulfilling the criteria for a major depressive disorder
Treatment adherence is challenging for many participants using Internet-based interventions: they intend to participate in an intervention, but they either do not start or do not complete it
Summary
Internet-based interventions have been shown to prevent the incidence of mental health disorders such as depression or anxiety and reduce associated symptom severity [1,2]. Objective: The aim of this study was to investigate to what extent adherence in an Internet-based intervention can be predicted by motivational and volitional factors outlined in the health action process approach (HAPA). Methods: This study investigated motivational and volitional factors included in HAPA in a randomized controlled trial to predict treatment adherence of N=101 individuals with subclinical depression in the intervention group of a depression prevention intervention (GET.ON Mood Enhancer). Conclusions: Planning emerged as the strongest predictor of treatment adherence in highly motivated participants in an Internet-based intervention out of all HAPA variables investigated. The findings imply that systematically fostering planning skills and maintenance self-efficacy prior to or during Internet-based interventions would help participants to successfully complete these treatments. Trial Registration: German Clinical Trials Register DRKS00005973; https://www.drks.de/drks_web/navigate.do? navigationId=trial.HTML&TRIAL_ID=DRKS00005973 (Archived by WebCite at http://www.webcitation.org/6uxCy64sy)
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