Abstract
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 (PCONCLUSIONS: 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.
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 (PCONCLUSIONS: 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.
| Original language | English |
|---|---|
| Journal | JMIR mHealth and uHealth |
| Publication status | Accepted/In press - 23 Jan 2023 |
Keywords
- mental wellbeing
- positive psychology
- data analysis
- conversational user interfaces
- event log analysis
- ecological momentary assessment
- user behavior
- conversational agents
- digital interventions
