The Potential of a Robot Presence in Close Relationship to Influence Human Responses to Experimental Pain
Bibliographic record
Abstract
Pain management is a critical challenge in healthcare, often exacerbated by loneliness and emotional distress. This study investigated the potential of a communication robot, Moffuly, to reduce pain perception and influence hormonal responses in a controlled experimental setting. Nineteen healthy participants underwent heat pain stimulation under two conditions: with and without robotic interaction. Pain levels were assessed using the Short-form McGill Pain Questionnaire and the Visual Analogue Scale, while mood and mental states were evaluated through established questionnaires including the Profile of Mood States, Hospital Anxiety and Depression Scale, and Self-Rating Depression Scale. Hormonal changes, including cortisol, growth hormone, oxytocin, estradiol, and dehydroepiandrosterone-sulfate, were measured from blood samples collected at key time points. The results demonstrated significant reductions in subjective pain and improvements in mood following robotic interaction. These effects were accompanied by favorable hormonal changes, including increased oxytocin and decreased cortisol and growth hormone levels. The findings suggest that robotic interaction may serve as an innovative approach to pain management by addressing both physiological and psychological factors. This study highlights the potential of robotics to complement traditional therapies in alleviating pain and enhancing emotional well-being. By mitigating emotional distress and loneliness, robotic interventions may enhance existing pain therapies and offer innovative solutions for resource-limited healthcare systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".