Are web-based stress management interventions effective as an indirect treatment for depression? An individual participant data meta-analysis of six randomised trials.

Question: Depression is highly prevalent and associated with numerous adverse consequences for both individuals and society. Due to low uptake of direct treatment, interventions that target related, but less stigmatising problems, such as perceived stress, have emerged as a new research paradigm.This individual participant data (IPD) meta-analysis examines if a web-based stress management intervention can be used as an 'indirect' treatment of depression.

The Evaluation of the GET.ON Nationwide Web-Only Treatment Service for Depression- and Stress-Related Symptoms: Naturalistic Trial.

Background: GET.ON (HelloBetter) treatment interventions have been shown to be efficacious in multiple randomized controlled trials.

Objective: This study evaluated the effectiveness of 2 GET.

Digital intervention (Renewed) to support symptom management, wellbeing, and quality of life among cancer survivors in primary care: a randomised controlled trial.

Background: Many cancer survivors following primary treatment have prolonged poor quality of life.

Aim: To determine the effectiveness of a bespoke digital intervention to support cancer survivors.

Design And Setting: This was a pragmatic parallel open randomised trial in UK general practices (ISRCTN:96374224).

Predicting effects of a digital stress intervention for patients with depressive symptoms: Development and validation of meta-analytic prognostic models using individual participant data.

Objective: Digital stress interventions could be helpful as an "indirect" treatment for depression, but it remains unclear for whom this is a viable option. In this study, we developed models predicting individualized benefits of a digital stress intervention on depressive symptoms at 6-month follow-up.

Method: Data of = 1,525 patients with depressive symptoms (Center for Epidemiological Studies' Depression Scale, CES-D ≥ 16) from = 6 randomized trials (digital stress intervention vs.

Personalised depression forecasting using mobile sensor data and ecological momentary assessment.

Introduction: Digital health interventions are an effective way to treat depression, but it is still largely unclear how patients' individual symptoms evolve dynamically during such treatments. Data-driven forecasts of depressive symptoms would allow to greatly improve the personalisation of treatments. In current forecasting approaches, models are often trained on an entire population, resulting in a general model that works overall, but does not translate well to each individual in clinically heterogeneous, real-world populations.