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Randomized trials assess feasibility of online content as treatment-as-usual and its effectiveness in mental health support.
BACKGROUND Treatment-as-usual (TAU) conditions are meant to reflect the support people typically receive in a treatment setting. For digital mental health interventions (DMHIs) implemented online, TAU should therefore reflect online help-seeking as usual. Mental health-related popular online content (e.g., advice TikToks, lived experience vlogs, and self-care infographics) may offer an ecologically valid basis for online TAU. OBJECTIVE Examine if popular online content is feasible as a TAU control in DMHI trials. METHODS In each study, participants were randomized to one of three 10-20-minute interventions. In Study 1 (n = 916), (1) a researcher-created single-session intervention (SSI) for depression and anxiety; (2) unguided web-searching for resources; or (3) a curated library of 120 pieces of annotated popular online content. In Study 2 (n = 431), (1) a single-session SSI for loneliness; (2) a curated library of popular online content related to loneliness; or (3) an attention-matched control. Participants reported treatments’ credibility and expected benefit, as well as their distress (Study 1) and loneliness (Study 2). RESULTS In Study 1, dropout during the treatment was 9.6% for the curated library, 25.9% for unguided help-seeking, and 4.8% for the SSI. Credibility and expected benefit for the curated library did not differ from the SSI’s (d = 0.08, P = .88) and were higher than unguided help-seeking’s (d = 0.23, P = .01). In Study 2, dropout was 4.8% for the curated library and 0.0% for both other conditions. Credibility and expected benefit ratings for the curated library were comparable to the attention-matched control’s (d = 0.00, P > .99) but lower than the SSI’s (d = 0.32, P = .02). Change in distress and loneliness over eight weeks did not differ across conditions (ds < 0.15). CONCLUSIONS Curated libraries of popular online content provide a valuable and fairly realistic TAU benchmark for effectiveness trials of online DMHIs. CLINICALTRIAL study 1: https://osf.io/3dyma and study 2: https://osf.io/nvd79
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Kaveladze et al. (2025) studied this question.
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