<p>Relevance, clarity and depth sample data scoring.</p>

Samuel Downes presents data from a 12-week exploratory deployment of a conversational AI method for qualitative health data collection. The dataset likely contains participant responses from WhatsApp-based chat-agent sessions and follow-up interviews within a community-based weight management programme in an area of deprivation. It was last updated on 2026-05-27 and is shared under a CC-BY-4.0 license.

Use Cases

• Analyzing participant engagement and reflection patterns based on conversational data collection sessions.
• Studying the perceived acceptability and feasibility of AI chat-agents based on user experience interview data.
• Investigating the trade-offs between candid disclosure and cognitive effort in AI-mediated health data elicitation.

Strengths

• Data originates from a structured 12-week community-based programme deployment.
• Includes qualitative data from both AI chat-agent sessions and in-person semi-structured interviews (N=9).
• Shared under a permissive CC-BY-4.0 license for reuse.

Limitations

• Dataset is very small at 11.5 KB, indicating limited scope.
• Row count and column-level documentation are unknown, requiring manual inspection after download.
• Data may reflect geographic and temporal bias inherent to the single study context.

Provenance

Source

Samuel Downes via figshare

Collection Method

Data collected via a conversational AI chat-agent on WhatsApp and follow-up semi-structured interviews.

Time Range

Data collection occurred during a 12-week programme; specific years are not stated.

Freshness

Last updated 2026-05-27 17:33:19; freshness should be verified.

Geography

Study conducted in an area of deprivation; specific location is not stated.