Search

BACKGROUND In recent years, advances in technology have led to an influx of mental health apps, in particular the development of mental health and wellbeing chatbots which have already shown promise in terms of their efficacy, availability and accessibility. The ChatPal chatbot was developed to promote positive mental wellbeing amongst 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 thoughts diaries. OBJECTIVE The primary objective of this study is to establish if using a multilingual mental health and wellbeing chatbot (ChatPal) has an effect on mental wellbeing. Secondary objectives include characterising individuals whose wellbeing improved or declined, and applying thematic analysis to the user feedback. METHODS Participants were recruited to use the chatbot over a 12-week trial period. ChatPal was trialled in five regions; Northern Ireland, Scotland, Republic of Ireland, Sweden and Finland. Outcome measures included the Short Warwick Edinburgh Mental Wellbeing Scale, World Health Organisation-Five Well-Being Index, and Satisfaction with Life Scale, which were recorded at baseline, midpoint and end-point. Written feedback was collected from participants throughout the trial and subjected to qualitative analysis to identify themes. RESULTS A total of 348 people were recruited to the trial (75% female, 25% male) aged between 18-73 (mean age 30). The wellbeing scores of participants improved from baseline to midpoint and from baseline to end-point, however improvement in scores were not statistically significant on the Short Warwick Edinburgh Mental Wellbeing Scale (P=.42), World Health Organisation-Five Well-Being Index (P=.52), or Satisfaction with Life Scale (P=.81). Individuals that did show improvements in wellbeing (n=16) interacted more with the chatbot and were significantly younger compared (average 27 years old) to those whose wellbeing declined over the trial. Three themes were identified from user feedback including ‘positive experiences’, ‘mixed/ neutral experiences’ and ‘negative experiences’. Positive experiences mentioned enjoying exercises provided by the app, while most of the mixed/ neutral or negative experiences mentioned liking the chatbot overall, but that there were some barriers, such as technical or performance errors which needed to be overcome. CONCLUSIONS This paper further supports the need for blended service offerings. Marginal improvements in mental wellbeing were seen in those who used ChatPal (albeit non significant), so we propose that the chatbot could be used along with different service offerings to complement other digital or face-to-face services.

BACKGROUND Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and wellbeing. Whilst many studies focus on measuring the cause of effect of a digital intervention on people's health and wellbeing (outcomes), there is a need to understand how users really engage and use a digital intervention in the real world. OBJECTIVE In this study we examine the user logs of a mental wellbeing chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyse the log data from the chatbot to provide insight into usage patterns, the different types of users using clustering, and associations between the usage of the application’s features. METHODS Log data from ChatPal was analysed to explore usage. A number of user characteristics including user tenure, unique days, mood logs recorded, conversations accessed, and total number of interactions were used with k-means clustering to identify user archetypes. Association rule mining was used to explore links between conversations. RESULTS ChatPal log data revealed 579 individuals over the age of 18 used the application with most users being female (67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed three groups including ‘abandoning users’ (n=473), ‘sporadic users’ (n=93) and ‘frequent transient users’ (n=13). Each cluster had distinct usage characteristics, and the features were significantly different (P CONCLUSIONS This study has provided insight into the types of people using the ChatPal chatbot, patterns of use and associations between the usage of the application’s features which can be used to further develop the application by considering the features most accessed by users. CLINICALTRIAL

n/a