Abstract
BACKGROUND: Conversational user interfaces, or chatbots, are becoming more popular in the realm of digital health and well-being. While many studies focus on measuring the cause or effect of a digital intervention on people's health and well-being (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 well-being chatbot called ChatPal, which is based on the concept of positive psychology. The aim of this research is to analyze 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 app's features. METHODS: Log data from ChatPal was analyzed 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 older than 18 years used the app with most users being female (n=387, 67%). User interactions peaked around breakfast, lunchtime, and early evening. Clustering revealed 3 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<.001) across each group. While all conversations within the chatbot were accessed at least once by users, the "treat yourself like a friend" conversation was the most popular, which was accessed by 29% (n=168) of users. However, only 11.7% (n=68) of users repeated this exercise more than once. Analysis of transitions between conversations revealed strong links between "treat yourself like a friend," "soothing touch," and "thoughts diary" among others. Association rule mining confirmed these 3 conversations as having the strongest linkages and suggested other associations between the co-use of chatbot features. 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 app's features, which can be used to further develop the app by considering the features most accessed by users.
| Original language | English |
|---|---|
| Article number | e43052 |
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | JMIR mHealth and uHealth |
| Volume | 11 |
| Early online date | 6 Jul 2023 |
| DOIs | |
| Publication status | Published online - 6 Jul 2023 |
Bibliographical note
Publisher Copyright:©Frederick Booth, Courtney Potts, Raymond Bond, Maurice Mulvenna, Catrine Kostenius, Indika Dhanapala, Alex Vakaloudis, Brian Cahill, Lauri Kuosmanen, Edel Ennis. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 06.07.2023.
Funding Information:
The authors would like to thank all the clients, participants, project members, supporters, and researchers at Ulster University, University of Eastern Finland, Norrbotten Association of Local Municipalities, Region Norrbotten, Luleå University of Technology, NHS Western Isles, Action Mental Health, and Munster Technological University for their involvement in this research. The ChatPal project is supported by Interreg Northern Periphery and Arctic Programme under grant number 345.
Publisher Copyright:
©Frederick Booth, Courtney Potts, Raymond Bond, Maurice Mulvenna, Catrine Kostenius, Indika Dhanapala, Alex Vakaloudis, Brian Cahill, Lauri Kuosmanen, Edel Ennis.
Keywords
- mental wellbeing
- positive psychology
- data analysis
- conversational user interfaces
- event log analysis
- ecological momentary assessment
- user behavior
- conversational agents
- digital interventions
- mental well-being
- user data
- digital health application
- conversational user interface
- digital intervention
- user interface
- conversational agent
- mobile health app
- health care
