An Embodied Conversational Agent in an eHealth Self-management Intervention for Chronic Obstructive Pulmonary Disease and Chronic Heart Failure: Exploratory Study in a Real-life Setting
Bibliographic record
Abstract
BACKGROUND: Embodied conversational agents (ECAs) have the potential to stimulate actual use of eHealth apps. An ECA's design influences the user's perception during short interactions, but daily life evaluations of ECAs in health care are scarce. OBJECTIVE: This is an exploratory, long-term study on the design of ECAs for eHealth. The study investigates how patients perceive the design of the ECA over time with regard to the ECA's characteristics (friendliness, trustworthiness, involvement, expertise, and authority), small talk interaction, and likeliness of following the agent's advice. METHODS: We developed an ECA within an eHealth self-management intervention for patients with both chronic obstructive pulmonary disease (COPD) and chronic heart failure (CHF), which we offered for 4 months. Patients rated 5 agent characteristics and likeliness of following the agent's advice before use and after 3 and 9 weeks of use. The amount of patients' small talk interaction was assessed by log data. Lastly, individual semistructured interviews were used to triangulate results. RESULTS: Eleven patients (7 male and 4 female) with COPD and CHF participated (median age 70 years). Patients' perceptions of the agent characteristics did not change over time (P>.05 for all characteristics) and only 1 participant finished all small talk dialogues. After 3 weeks of use, the patients were less likely to follow the agent's advice (P=.01). The agent's messages were perceived as nonpersonalized and the feedback as inappropriate, affecting the agent's perceived reliability. CONCLUSIONS: This exploratory study provides first insights into ECA design for eHealth. The first impression of an ECA's design seems to remain during long-term use. To investigate future added value of ECAs in eHealth, perceived reliability should be improved by managing users' expectations of the ECA's capabilities and creating ECA designs fitting individual needs. TRIAL REGISTRATION: Netherlands Trial Register NL6480; https://www.trialregister.nl/trial/6480.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".