Globally, stigma associated with mental, neurological and substance use (MNS) disorders is rampant and a barrier to good health and overall well-being of people with these conditions. Person-centred digital approaches such as participatory video may reduce stigma, but evidence on their effectiveness in Africa is absent.

To evaluate the effectiveness of participatory video in reducing mental health-related stigma in a resource-limited setting.

We evaluated the effectiveness of using participatory video and face-to-face interaction between people with MNS disorders and a target audience in lowering stigma among 420 people living in Kilifi, Kenya. Changes in knowledge, attitudes and behaviour (KAB) were measured by comparing baseline scores with scores immediately after watching the participatory videos and 4 months after the intervention. Sociodemographic correlates of stigma scores were examined using multivariable linear regression models.

Compared with baseline, KAB scores significantly improved at both time points, suggesting reduced stigma levels. At 4 months, the changes in scores were: knowledge (β = 0.20, 95% CI 0.16–0.25; P < 0.01), liberal attitude (β = 1.08, 95% CI 0.98–1.17; P < 0.01), sympathetic attitude (β = 0.52, 95% CI 0.42–0.62; P < 0.01), tolerant attitude (β = 0.72, 95% CI 0.61–0.83; P < 0.01) and behaviour (β = 0.37, 95% CI 0.31–0.43; P < 0.01). Sociodemographic variables were significantly correlated with KAB scores; the correlations were not consistent across the domains.

Participatory video is a feasible and effective strategy in improving knowledge, attitudes and intended behaviour in a resource-limited setting. Further studies are required to understand the mechanisms through which it lowers stigma and to examine long-term sustainability and the effectiveness of multicomponent interventions.

In low-resource settings, e-mental health may substantially increase access to evidence-based interventions for common mental disorders. We conducted a systematic literature search to identify randomised trials examining the effects of digital interventions with or without therapeutic guidance compared to control conditions in individuals with anxiety and/or depression symptoms in low- and middle-income countries (LMICs).

The main outcome was the reduction in symptoms at the post-test. Secondary outcomes included improvements in quality of life and longer-term effects (≥20 weeks post-randomisation). The effect size Hedges’ g was calculated using the random effects model.

A total of 21 studies (23 comparisons) with 5.296 participants were included. Digital interventions were more effective than controls in reducing symptoms of common mental disorders at the post-test (g = −0.89, 95% confidence interval [CI] −1.26 to −0.52, p < 0.001; NNT = 2.91). These significant effects were confirmed when examining depressive (g = −0.77, 95% CI −1.11; −0.44) and anxiety symptoms separately (g = −1.02, 95% CI −1.53 to −0.52) and across all other sensitivity analyses. Digital interventions also resulted in a small but significant effect in improving quality of life (g = 0.32, 95% CI 0.19 to 0.45) at the post-test. Over the longer term, the effects were smaller but remained significant for all examined outcomes. Heterogeneity was moderate to high in all analyses. Subgroup and meta-regression analyses did not result in significant outcomes in any of the examined variables (e.g., guided vs. unguided interventions).

Digital interventions, with or without guidance, may effectively bridge the gap between treatment supply and demand in LMICs. Nevertheless, more studies are needed to draw firm conclusions regarding the magnitude of the effects of digital interventions.

Digital mental health interventions, such as chatbots that promote mental health and wellbeing are a promising way to deliver low-threshold support 24/7 for those in need. According to current knowledge about the topic, health care professionals should participate in the design and development processes for digital interventions.

The aim of this presentation is to describe the interdisciplinary content development process of the ChatPal chatbot.

The content development process started in co-operation with mental health professionals and potential users to identify requirements. Content was created, evaluated and tested in international, multi-disciplinary group workshops, and online tools were used to allow the collaboration. Initial conversational scripts were drafted in English, and translated into Finnish, Swedish and Scottish Gaelic.

A multilingual chatbot was developed and the conversation scripts were structured and stored using a spreadsheet. The conversation scripts will be made freely available online in due course using this structured approach to formatting chatbot dialogue content. It will allow repurposing the content as well as facilitating studies that wish to assess the design of conversation scripts for mental health chatbots. Conversation design process also highlighted some challenges in turning empathetic and supportive conversations to short utterances suitable for a chatbot.

The ChatPal chatbot is now available in four languages. As literature about the topic is still scarce, it is important to describe and document the content development processes of mental health chatbots. Future work will develop a conversational UX toolkit that would allow health professionals to design chatbot scripts using design guidelines.

No significant relationships.

Digital interventions for anxiety disorders are a promising solution to address barriers to evidence-based treatment access. Precise and powerful estimates of digital intervention effectiveness for anxiety disorders are necessary for further adoption in practice. The present systematic review and meta-analysis examined the effectiveness of digital interventions across all anxiety disorders and specific to each disorder v. wait-list and care-as-usual controls.

A systematic search of bibliographic databases identified 15 030 abstracts from inception to 1 January 2020. Forty-seven randomized controlled trials (53 comparisons; 4958 participants) contributed to the meta-analysis. Subgroup analyses were conducted by an anxiety disorder, risk of bias, treatment support, recruitment, location and treatment adherence.

A large, pooled effect size of g = 0.80 [95% Confidence Interval: 0.68–0.93] was found in favor of digital interventions. Moderate to large pooled effect sizes favoring digital interventions were found for generalized anxiety disorder (g = 0.62), mixed anxiety samples (g = 0.68), panic disorder with or without agoraphobia (g = 1.08) and social anxiety disorder (g = 0.76) subgroups. No subgroups were significantly different or related to the pooled effect size. Notably, the effects of guided interventions (g = 0.84) and unguided interventions (g = 0.64) were not significantly different. Supplemental analysis comparing digital and face-to-face interventions (9 comparisons; 683 participants) found no significant difference in effect [g = 0.14 favoring digital interventions; Confidence Interval: −0.01 to 0.30].

The precise and powerful estimates found further justify the application of digital interventions for anxiety disorders in place of wait-list or usual care.