Conversational, Longitudinal, Ecological Assessment (CLEA): Exploring a new AI-driven method for qualitative data collection in a behavioural health context

In this paper, we present conversational longitudinal ecological assessment (CLEA), a novel conversational AI-enabled method for collecting ecologically valid, temporally sensitive qualitative health data via mobile instant messaging. We report findings from an exploratory deployment of an instantiation of CLEA within a 12-week community-based weight management programme, delivered by a charity partner in an area of deprivation. Using WhatsApp, we deployed our CLEA chat-agent to conduct twice-weekly conversational data collection sessions with participants, to elicit data about their experience of the programme and associated behaviour change. This was followed by in-person semi-structured interviews (N = 9) to examine user experiences and perceptions of interacting with the chat-agent. Participants reported that WhatsApps familiarity supported accessibility and sustained engagement, while the conversational format encouraged reflection directed towards the research focus. Responding to chat-agent prompts required cognitive effort, leading some participants to defer engagement until they had adequate time and mental space; however, this reflective demand was largely experienced as beneficial within the programme context. The AIs quasi-human interactional qualities fostered a sense of support while reducing social judgement, enabling more candid disclosure. Together, these findings demonstrate initial feasibility and acceptability of CLEA for longitudinal qualitative data collection in an underserved population, and illustrate its capacity to elicit meaningful, contextually grounded insights consistently over time, that can be used in the formative stage of digital health intervention development. The study highlights both the opportunities and trade-offs of conversational AI for qualitative data collection, including design implications for health researchers looking to implement or extend the method. Finally, we position CLEA in relation to other longitudinal methods of health data elicitation. Author summaryDeveloping effective interventions for health behaviours such as healthy eating and physical activity requires methods that can capture the complex, individual factors shaping peoples everyday experiences, including stress and motivation. Because such factors often fluctuate over time, longitudinal approaches are needed to understand how experiences and behaviours unfold in real-world contexts. For such methods to be effective, they must also be acceptable, engaging, and accessible--particularly for underserved or disadvantaged populations who are disproportionately affected by health-related conditions such as obesity. In this study, we introduce conversational longitudinal ecological assessment (CLEA), a digital health method that uses conversational AI technology to collect ecologically valid qualitative data over time through an accessible communication platform. We demonstrate the feasibility, acceptability, and utility of CLEA through a real-world deployment investigating an underserved groups experience of a community-based weight management programme. To support other health researchers, we position CLEA in relation to existing longitudinal methods and highlight the key design considerations that shape engagement, data quality, and participant experience.