Effectiveness and Acceptability of Third-Wave eHealth Treatments: A Meta-Analysis

O’Connor, M., Munnelly, A., Whelan, R., & McHugh, L. (2018). The efficacy and acceptability of third-wave behavioral and cognitive eHealth treatments: A systematic review and meta-analysis of randomized controlled trials. Behavior Therapy, 49(3), 459–475. https://doi.org/10.1016/j.beth.2017.07.007

Summary by: Benedikt Luther

According to a report by the European Parliamentary Research Service, an estimated 84 million people in the European Union (EU) currently suffer from mental health problems (Amand-Eeckhout, 2023). This corresponds to almost one in five EU citizens and is equivalent to the entire population of Germany. Additionally, mental health problems carry negative social and economic consequences, such as job loss, family issues, and decreased social participation, all of which may worsen mental health crises—a vicious cycle. Access to professional mental healthcare is not equally guaranteed for everyone. eHealth applications represent one way for improving access to mental health services, particularly when traditional face-to-face therapy is limited by geographic, economic, or social barriers. However, questions remain regarding how these digital interventions are accepted and how effective they are.

O’Connor et al. (2018) examined in a systematic review and meta-analysis the efficacy and acceptability of digital interventions based on so-called "third-wave" therapies, including for example Acceptance and Commitment Therapy (ACT) and Mindfulness-Based Cognitive Therapy (MBCT). The authors define eHealth broadly as “an innovative method of delivering therapeutic content with the potential to improve access to third-wave behaviural [sic!] and cognitive therapies” (O’Connor et al., 2018, p. 459). The meta-analysis included 21 studies using randomized controlled trials with a total of N = 3,176 participants. The interventions primarily targeted to improve anxiety, depression, and quality of life. The meta-analysis found small to medium positive effects for the reduction of anxiety (g = 0.32, 95 % CI [0.09, 0.56], p = .01) and depression (g = 0.52, 95 % CI [0.26, 0.77], p < .001), compared to inactive control groups. Additionally, quality of life significantly improved under these conditions (g = 0.46, 95 % CI [0.00, 0.92], p = .05) However, compared to active control conditions, the significant effects were smaller in magnitude (O’Connor et al., 2018). The differences in the outcomes anxiety (g = 0.31, 95 % CI [0.07, 0.54], p = .01); depression (g = 0.29, 95 % CI [0.14, 0.44], p < .001); quality-of-life differences were nonsignificant (g = 0.31, 95 % CI [- 0.31, 0.93], p = .33) Furthermore, digital third-wave interventions showed no significant differences in efficacy compared to other established psychotherapeutic treatments, anxiety (g = 0.00, 95 % CI [-0.16, 0.17], p = .97), depression (g = -0.02, 95 % CI [-0.18, 0.15], p = .83), and quality of life (g = 0.22, 95 % CI [-0.21, 0.65], p = .31). This suggests that eHealth programs could serve as viable alternatives. Importantly, there were no significant differences in attrition rate between inactive controls (OR = 1.24, 95 % CI [0.82, 1.86], p = .30), active controls (OR = 1.03, 95 % CI [0.63, 1.66], p = .92), or comparison interventions (OR = 1.03, 95 % CI [0.79, 1.33], p = .84) That indicates good acceptance of digital formats (O’Connor et al., 2018).

Nevertheless, several limitations remain noteworthy. The authors note that access to eHealth may depend on the digital divide (O’Connor et al., 2018). That means that digital offers are not equally accessible to everyone, particularly for vulnerable populations facing socioeconomic disadvantages such as children or individuals dealing with homelessness. Thus, eHealth interventions alone may not fully break the vicious cycle described earlier. That highlights the importance of considering these vulnerable groups explicitly during program design. Additionally, because the meta-analysis drew studies exclusively from peer-reviewed, English-language journals, results are potentially subject to publication bias. It should also be mentioned that the results should not be generalized to children and adolescents, as they were not part of the meta-analysis (O’Connor et al., 2018). Furthermore, it is critical to mention that the study data are relatively old, covering studies published between 2007 and 2015. Given the technological advancements since then, eHealth programs likely have evolved substantially.

To conclude, eHealth interventions are effective and widely accepted by users. Patients do not tend to discontinue these interventions more frequently than face-to-face therapies. However, under certain conditions, eHealth alternatives are not as effective as traditional face-to-face interventions. Nonetheless, eHealth programs offer scalable, costeffective opportunities for broad dissemination (O’Connor et al., 2018). Future initiatives must particularly consider vulnerable groups with lower socioeconomic status and provide targeted support to ensure equity in mental healthcare access.