Review: Economic evidence of preventive interventions for anxiety disorders in children and adolescents - a systematic review

BackgroundAnxiety disorders are common in children and youth. Also, in prevention, be it universal, selective or indicated, economic evaluation supports decision-making in the allocation of scarce resources. This review identified and summarised the existing evidence of economic evaluations for the prevention of anxiety disorders in children and adolescents.MethodsA systematic search was conducted on the EBSCO, Scopus, Web of Science, ProQuest, Cochrane and PubMed databases. We included studies that focused on children and adolescents under 18 years of age, aimed to prevent anxiety disorders and presented an incremental analysis of costs and effectiveness. A registered checklist was used that assessed the quality of the included articles.ResultsThe search yielded 1697 articles. Five articles were included in this review. Three were RCT-based, and two were model-based studies. Out of five included interventions, one was a universal school-based intervention, two selective interventions and two indicated interventions. Universal school-based prevention of anxiety was not cost-effective compared with usual teaching. Selective parent training and indicative child- and parent-focused CBT prevention were likely cost-effective compared with usual care or doing nothing.ConclusionParent education and cognitive behaviour therapy interventions can be cautiously interpreted as being a cost-effective way of preventing anxiety in children and adolescents. However, the evidence is weakly related to cost-effectiveness as there are only a few studies, with relatively small sample sizes and short follow-ups.</p

Chatbots to Support Mental Wellbeing of People Living in Rural Areas: Can User Groups Contribute to Co-design?

Digital technologies such as chatbots can be used in the field of mental health. In particular, chatbots can be used to support citizens living in sparsely populated areas who face problems such as poor access to mental health services, lack of 24/7 support, barriers to engagement, lack of age appropriate support and reductions in health budgets. The aim of this study was to establish if user groups can design content for a chatbot to support the mental wellbeing of individuals in rural areas. University students and staff, mental health professionals and mental health service users (N = 78 total) were recruited to workshops across Northern Ireland, Ireland, Scotland, Finland and Sweden. The findings revealed that participants wanted a positive chatbot that was able to listen, support, inform and build a rapport with users. Gamification could be used within the chatbot to increase user engagement and retention. Content within the chatbot could include validated mental health scales and appropriate response triggers, such as signposting to external resources should the user disclose potentially harmful information or suicidal intent. Overall, the workshop participants identified user needs which can be transformed into chatbot requirements. Responsible design of mental healthcare chatbots should consider what users want or need, but also what chatbot features artificial intelligence can competently facilitate and which features mental health professionals would endorse

Can Chatbots Help Support a Person’s Mental Health?:Perceptions and Views from Mental Healthcare Professionals and Experts

Godkänd;2022;Nivå 0;2022-02-04 (johcin)</p

Insights and lessons learned from trialling a mental health chatbot in the wild

This study reports on the development and 'in the wild' trialling of a chatbot (ChatPal) which promotes good mental wellbeing. A stakeholder-centered approach for design was adopted where end users, mental health professionals and service users were involved in the design which was centered around positive psychology. In the wild usage of the chatbot was investigated from Jul-20-Mar-21. Exploratory analyses of usage metrics were carried out using the event log data. User tenure, unique usage days, total chatbot interactions and average daily interactions were used in K-means clustering to identify user archetypes. The chatbot was used by a variety of age groups (18-65+) and genders, mainly those living in Ireland. K-means clustering identified three clusters: sporadic users (n=4), frequent transient users (n=38) and abandoning users (n=169) each with distinct usage characteristics. This study highlights the importance of event log data analysis for making improvements to the mental health chatbot.</p

A multilingual digital mental health and wellbeing chatbot (ChatPal): pre-post multicenter intervention study

Background: In recent years, advances in technology have led to an influx of mental health apps, in particular the development of mental health and well-being chatbots, which have already shown promise in terms of their efficacy, availability, and accessibility. The ChatPal chatbot was developed to promote positive mental well-being among citizens living in rural areas. ChatPal is a multilingual chatbot, available in English, Scottish Gaelic, Swedish, and Finnish, containing psychoeducational content and exercises such as mindfulness and breathing, mood logging, gratitude, and thought diaries. Objective: The primary objective of this study is to evaluate a multilingual mental health and well-being chatbot (ChatPal) to establish if it has an effect on mental well-being. Secondary objectives include investigating the characteristics of individuals that showed improvements in well-being along with those with worsening well-being and applying thematic analysis to user feedback. Methods: A pre-post intervention study was conducted where participants were recruited to use the intervention (ChatPal) for a 12-week period. Recruitment took place across 5 regions: Northern Ireland, Scotland, the Republic of Ireland, Sweden, and Finland. Outcome measures included the Short Warwick-Edinburgh Mental Well-Being Scale, the World Health Organization-Five Well-Being Index, and the Satisfaction with Life Scale, which were evaluated at baseline, midpoint, and end point. Written feedback was collected from participants and subjected to qualitative analysis to identify themes. Results: A total of 348 people were recruited to the study (n=254, 73% female; n=94, 27% male) aged between 18 and 73 (mean 30) years. The well-being scores of participants improved from baseline to midpoint and from baseline to end point; however, improvement in scores was not statistically significant on the Short Warwick-Edinburgh Mental Well-Being Scale (P=.42), the World Health Organization-Five Well-Being Index (P=.52), or the Satisfaction With Life Scale (P=.81). Individuals that had improved well-being scores (n=16) interacted more with the chatbot and were significantly younger compared to those whose well-being declined over the study (P=.03). Three themes were identified from user feedback, including “positive experiences,” “mixed or neutral experiences,” and “negative experiences.” Positive experiences included enjoying exercises provided by the chatbot, while most of the mixed, neutral, or negative experiences mentioned liking the chatbot overall, but there were some barriers, such as technical or performance errors, that needed to be overcome. Conclusions: Marginal improvements in mental well-being were seen in those who used ChatPal, albeit nonsignificant. We propose that the chatbot could be used along with other service offerings to complement different digital or face-to-face services, although further research should be carried out to confirm the effectiveness of this approach. Nonetheless, this paper highlights the need for blended service offerings in mental health care.Validerad;2023;Nivå 2;2023-08-09 (hanlid)</p

Chatpal Chatbot dialogue data set

The scripts used in the ChatPal chatbot are freely available as an output from the ChatPal project. The datasets contain the chatbot utterances in English, Swedish, Finnish and Scottish Gaelic. Any replies collected from users through the ChatPal chatbot are not included in these data. Datasets are available in csv format and contain Unicode character encodings (UTF-8). Disclaimer: The datasets are open access, should be used appropriately and can be repurposed. However, the ChatPal project team are not responsible for how you chose to use the data or repurpose the content