Abstract
Study selection and analysis Bayesian one-stage models were used to estimate pooled effects on depressive symptom severity, minimally important improvement and reliable deterioration. The dose– response relationship was examined using multilevel additive models, and IPD network meta-analysis was employed to estimate the effect of guidance.
Findings In total, N=1235 patients suffering from clinical-level depression from K=6 randomised trials were included. Moderate-to-large effects were found on depressive symptom severity at 7 weeks post-intervention (d=−0.65; 95% credibility interval (CrI): −0.84 to −0.48) as measured with the Center for Epidemiological Studies’ Depression Scale. Effects were sustained at 3-month follow-up (d=−0.74; 95% CrI: −1.01 to −0.48). Post-intervention symptom severity was linearly related to the number of completed sessions. The incremental impact of guidance was estimated at d=−0.25 (95% CrI: −1.30 to 0.82), with a 35% posterior probability that guided and unguided formats produce equivalent effects.
Conclusions Our results indicate that web-based stress management can serve as an indirect treatment, yielding effects comparable with direct interventions for depression. Further research is needed to determine if such formats can indeed increase the utilisation of evidence-based treatment, and to corroborate the favourable effects for human guidance.
| Original language | English |
|---|---|
| Pages (from-to) | 1-8 |
| Number of pages | 8 |
| Journal | BMJ Mental Health |
| Volume | 27 |
| Issue number | 1 |
| Early online date | 13 Feb 2024 |
| DOIs | |
| Publication status | Published - Feb 2024 |
| Externally published | Yes |
Funding
Funding The European Union (EFRE) funded the six RCTs included as part of the Innovation Incubator at Leuphana University (EFRE: ZW6-80119999, CCI 2007DE161PR001). MH is supported by a fellowship of the Bavarian Research Institute for Digital Transformation (BIDT), an institute of the Bavarian Academy of Sciences and Humanities. We acknowledge support by the German Research Foundation (DFG). The European Union (EFRE) funded the six RCTs included as part of the Innovation Incubator at Leuphana University (EFRE: ZW6-80119999, CCI 2007DE161PR001). MH is supported by a fellowship of the Bavarian Research Institute for Digital Transformation (BIDT), an institute of the Bavarian Academy of Sciences and Humanities. We acknowledge support by the German Research Foundation (DFG).
| Funders | Funder number |
|---|---|
| Bayerisches Forschungsinstitut für Digitale Transformation | |
| Deutsche Forschungsgemeinschaft | |
| European Commission | |
| Bayerische Akademie der Wissenschaften | |
| European Regional Development Fund | |
| Leuphana University | ZW6-80119999, CCI 2007DE161PR001 |
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