Early Development of a Virtual Coach for Healthy Coping Interventions in Type 2 Diabetes Mellitus: Validation Study
Bibliographic record
Abstract
BACKGROUND: Mobile health solutions aimed at monitoring tasks among people with diabetes mellitus (DM) have been broadly applied. However, virtual coaches (VCs), embedded or not in mobile health, are considered valuable means of improving patients' health-related quality of life and ensuring adherence to self-care recommendations in diabetes management. Despite the growing need for effective, healthy coping digital interventions to support patients' self-care and self-management, the design of psychological digital interventions that are acceptable, usable, and engaging for the target users still represents the main challenge, especially from a psychosocial perspective. OBJECTIVE: This study primarily aims to test VC interventions based on psychoeducational and counseling approaches to support and promote healthy coping behaviors in adults with DM. As a preliminary study, university students have participated in it and have played the standardized patients' (SPs) role with the aim of improving the quality of the intervention protocol in terms of user acceptability, experience, and engagement. The accuracy of users' role-playing is further analyzed. METHODS: This preliminary study is based on the Obesity-Related Behavioral Intervention Trial model, with a specific focus on its early phases. The healthy coping intervention protocol was initially designed together with a team of psychologists following the main guidelines and recommendations for psychoeducational interventions for healthy coping in the context of DM. The protocol was refined with the support of 3 experts in the design of behavioral intervention technologies for mental health and well-being, who role-played 3 SPs' profiles receiving the virtual coaching intervention in a Wizard of Oz setting via WhatsApp. A refined version of the healthy coping protocol was then iteratively tested with a sample of 18 university students (mean age 23.61, SD 1.975 years) in a slightly different Wizard of Oz evaluation setting. Participants provided quantitative and qualitative postintervention feedback by reporting their experiences with the VC. Clustering techniques on the logged interactions and dialogs between the VC and users were collected and analyzed to identify additional refinements for future VC development. RESULTS: Both quantitative and qualitative analyses showed that the digital healthy coping intervention was perceived as supportive, motivating, and able to trigger self-reflection on coping strategies. Analyses of the logged dialogs showed that most of the participants accurately played the SPs' profile assigned, confirming the validity and usefulness of this testing approach in preliminary assessments of behavioral digital interventions and protocols. CONCLUSIONS: This study outlined an original approach to the early development and iterative testing of digital healthy coping interventions for type 2 DM. Indeed, the intervention was well-accepted and proved its effectiveness in the definition and refinement of the initial protocol and of the user experience with a VC before directly involving real patients in its subsequent use and testing.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".