Social robot-based depression screening in older adults: A pilot study
Bibliographic record
Abstract
Background: Depression in older adults is a prevalent issue that can lead to severe consequences including a decline in overall health and even suicide.Early detection and management of depression are crucial for preventing such outcomes.The integration of technology solutions in healthcare represents a promising ap-proach to support prevention, diagnosis, and continuous monitoring of patients.Research aim: This pilot study aims to evaluate the feasibility of depression screening in older adults through interactions facilitated by social robots, focusing on individuals without severe cognitive impair-ments. Methods:The study involved five older adults with a minimum score of 24 on the Montreal Cognitive As-sessment (MoCA), ensuring no significant cognitive impairment.The Geriatric Depression Scale (GDS-15) was used as the screening tool.Participants interacted with a social robot and a healthcare professional in alternating sequences for the administration of the GDS-15.Additional assessments using the Positive and Negative Affect Schedule (PANAS) and the Godspeed questionnaire series were conducted to evaluate emo-tional responses and perceptions towards the social robot.Notably, MoCA, PANAS, and Godspeed were not administered by the social robot.Results: Preliminary data showed that all participants fell within the same depression range when screened by both the social robot and the healthcare professional.The results indicated no adverse effects on partici-pants' emotional states post-interaction with the social robot, as evidenced by PANAS scores.The Godspeed questionnaire revealed that participants generally had a positive perception of the social robot.Conclusions: The findings suggest that social robots can effectively perform depression screening in older adults without severe cognitive impairments.Their use matches the assessment outcomes of healthcare professionals and does not negatively impact emotional states, indicating their potential as a feasible and positively perceived tool for early depression diagnosis and continuous monitoring.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".