The social robot will see you now: patient acceptability of social robots in managing heart failure
Bibliographic record
Abstract
Abstract Background Social robots (SRs) are artificial agents embodied with human or animal features that can be embedded with technology to facilitate the remote monitoring of patients’ physiological and psychological health, aid with activities of daily living, provide rehabilitation services, and offer companionship. SRs may offer opportunities for improving the management of heart failure (HF), as these patients experience fluctuating and unpredictable functional impairment, and many are elderly and live alone (or with aging caregivers), with limited social support. It is not yet known whether patients with HF would be accepting of such technologies, which would ultimately influence their willingness to use SRs. Acceptability data are warranted to optimize the successful implementation of future social robotic interventions for patients with HF. Purpose The aim of this early-phase study was to quantify patients’ acceptability of SRs and to identify the sociodemographic and clinical characteristics (i.e., NYHA class) that are linked to patients’ acceptability. Methods Patients diagnosed with HF (NYHA class II, III, and IV) were recruited from a large cardiac teaching hospital. After viewing three videos profiling SRs, patients provided sociodemographic and clinical information, ranked their desired SR capabilities, and completed the validated Unified Theory of Acceptance and Use of Technology (UTAUT) 7-point Likert self-report questionnaire. Descriptive statistics were used to describe the sample, levels of acceptance, and desired capabilities of SRs. Pearson correlations and analysis of variance were used to determine associations between sociodemographic characteristics, NYHA class, and acceptance based on the UTAUT scale. Results The sample consisted of 81 patients with HF (M age= 65 years; 32% female; 87% white; 69.1% married or common-law; 25.9% rural residence; 79% NYHA class II and 21% NYHA class III). Scores on the UTAUT indicated moderate acceptance of SRs (M=4.5/7; SD=1.9); 42.1% of patients indicated that they would use an SR if it were available, whereas 21% noted they would not; 44.4% reported that a SR would help to improve their health, whereas 22.2% believed that an SR would not lead to improvements in their health. Acceptance rates did not differ significantly by age, sex, ethnicity, education, marital status, remoteness, or NYHA class. The most desired capabilities of SRs were monitoring blood pressure, heart rhythm, and vital signs. The least desired were administering IV medications, performing nasal or oral swabs, or assisting with eating, bathing, or dressing. Conclusion The adoption of SRs in patients’ homes to support HF-management is a potentially acceptable option for patients with HF. Acceptance rates are not linked to key sociodemographic factors; it is possible that other pertinent clinical, psychosocial, or environmental factors are more important drivers of SR acceptance, but this remains to be tested.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".