A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies

By Liesje Donkin, Helen Christensen, Sharon L. Naismith, Bruce Neal, Ian B Hickie, N. Glozier. Journal of Medical Internet Research, 2011

604 citations recorded by the source corpus.

Background As the popularity of e-therapies grows, so too has the body of literature supporting their effectiveness. However, these interventions are often plagued by high attrition rates and varying levels of user adherence. Understanding the role of adherence may be crucial to understanding how program usage influences the effectiveness of e-therapy interventions. Objective The aim of this study was to systematically review the e-therapy literature to (1) describe the methods used to assess adherence and (2) evaluate the association of adherence with outcome of these interventions. Methods A systematic review of e-therapy interventions was conducted across disease states and behavioral targets. Data were collected on adherence measures, outcomes, and analyses exploring the relationship between adherence measures and outcomes. Results Of 69 studies that reported an adherence measure, only 33 (48%) examined the relationship between adherence and outcomes. The number of logins was the most commonly reported measure of adherence, followed by the number of modules completed. The heterogeneity of adherence and outcome measures limited analysis. However, logins appeared to be the measure of adherence most consistently related to outcomes in physical health interventions, while module completion was found to be most related to outcomes in psychological health interventions. Conclusions There is large variation in the reporting of adherence and the association of adherence with outcomes. A lack of agreement about how best to measure adherence is likely to contribute to the variation in findings. Physical and psychological outcomes seem influenced by different types of adherence. A composite measure encompassing time online, activity completion, and active engagements with the intervention may be the best measure of adherence. Further research is required to establish a consensus for measuring adherence and to understand the role of adherence in influencing outcomes.

Subfields: Applied Psychology, Family Practice, General Health Professions

View Paper: https://www.semanticscholar.org/paper/a9d7e0b7eff52cd4e64998262139b55882d518b0

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Citations in the corpus

Artificially intelligent chatbots in digital mental health interventions: a review (2021, neutral): “reducing depressive symptoms than simply increasing the number of logins or activities completed [35].”
Behavioral Intervention Technologies: Evidence review and recommendations for future research (2010, neutral): “considerably, with logins generally being unrelated or negatively associated with improvement and number of modules typically being positively associated with improvement [67].”
Efficacy of Self-guided Internet-Based Cognitive Behavioral Therapy in the Treatment of Depressive Symptoms: A Meta-analysis of Individual Participant Data (2017, neutral): “The role of treatment adherence in outcomes has been identified by a previous review in the field conducted by Donkinandcolleagues.(43)Theauthorsconcludedthat thenumber of sessions correlatedwith outcomes in the interventions that targeted at depressive symptoms.”
The impact of guidance on Internet-based mental health interventions — A systematic review (2014, positive): “Adherence was operationalized following Donkin et al. (2011) as a) the mean number of modules completed and b) the percentage of persons that completed the whole treatment.”
mHealth: a mechanism to deliver more accessible, more effective mental health care. (2014, neutral): “other technology-based strategies for care delivery such as the Internet delivered interventions (Christensen, Griffiths, & Farrer, 2009; Donkin et al., 2011; Price, Gros, McCauley, Gros, & Ruggiero, 2012).”
Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis (2016, neutral): “…(e.g. tailoring, reminders) are associated with higher engagement [25, 26] and whether engagement is associated with intervention effectiveness [11], it is not possible to synthesise results from these reviews or to draw any conclusions regarding the shape of the function (e.g. linear,…”
Measuring Engagement in eHealth and mHealth Behavior Change Interventions: Viewpoint of Methodologies (2018, positive): “There is also some evidence of predictive validity, with technology usage generally correlating with positive behavior change or health outcomes [81,91,97,98].”
The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions (2014, neutral): “Usage aims are often thought to be related to clinical aims, although the relationship between use and outcome has been mixed [12].”
Integrating Human Support Into Behavioral Intervention Technologies: The Efficiency Model of Support (2017, neutral): “…along with weekly coaching based on supportive accountability show an increased engagement with the programs across a variety of metrics, including logins, time until last use, content read, and interactive tools use (Dennison et al., 2014; Duffecy et al., 2013; Mohr, Duffecy, et al., 2013).”
Effectiveness of online mindfulness-based interventions in improving mental health: A review and meta-analysis of randomised controlled trials. (2016, neutral): “This is a common issue in online psychological interventions and may diminish the effectiveness of an intervention (Christensen, Griffiths, & Farrer, 2009; Donkin et al., 2011; Wangberg, Bergmo, & Johnsen, 2008).”
Persuasive System Design Does Matter: A Systematic Review of Adherence to Web-Based Interventions (2012, neutral): “Research suggests that non-optimal exposure to the intervention lessens the effect of these interventions [18, 19].”