Latent Profile Analysis of Patients' Acceptance of Companion Robots in Unaccompanied Wards: A Cross-sectional Study
This cross-sectional study of 425 inpatients in China utilized latent profile analysis to identify three distinct acceptance profiles for companion robots in unaccompanied wards, revealing that older age, lower socioeconomic status, insufficient social support, low self-efficacy, and negative robot perceptions are key predictors of low acceptance, thereby highlighting the need for stratified health education and optimized robot design.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In many hospitals across China, a new kind of care is taking shape. For decades, the standard model relied on family members staying by a patient's bedside around the clock to help with eating, cleaning, and monitoring. But as the population ages and families become smaller, there are fewer people available to fill these roles. To solve this, some hospitals have introduced "no-accompanying wards." In these units, professional nursing teams take over the full responsibility of daily care, allowing family members to visit without the burden of providing 24-hour physical assistance. While this eases the strain on families, it places a heavy load on the nursing staff, who must now manage constant monitoring and basic tasks for every patient. To bridge this gap, hospitals are beginning to test companion robots. These machines are designed to handle routine duties like checking vital signs, offering reminders, and providing a friendly voice to talk to, acting as a digital partner to the human care team.
The critical question for this new system is not whether the robots work, but whether the patients will accept them. A recent study conducted in a large hospital in Sichuan Province sought to understand exactly how patients feel about sharing their recovery space with a machine. The researchers focused on 425 patients who were staying in a no-accompanying ward. Instead of treating all patients as a single group with the same average opinion, the team used a method that looks for hidden patterns within the crowd. They asked patients to rate their feelings about the robots on various aspects, such as how useful they seemed, how much they trusted the machine, and how easy it would be to use. By analyzing these responses, the researchers discovered that the patients did not fall into one uniform category. Instead, they naturally sorted themselves into three distinct groups based on their attitudes.
The first group, making up nearly half of the patients, was the "active acceptance" group. These 203 individuals were enthusiastic about the idea. They believed the robots would be useful, found them easy to imagine using, and felt a sense of trust in the technology. They saw the machines as helpful tools that could improve their hospital experience. The second group, comprising about one-third of the patients, was the "cautious acceptance" group. These 139 people were open to the idea but held back. They recognized the potential benefits but remained unsure about the practical details and were hesitant to fully trust the robots with their care. The third and smallest group, representing roughly one-fifth of the patients, was the "wait-and-see" group. These 83 individuals were skeptical. They had low confidence in the robots' ability to help, felt uncertain about using them, and preferred to hold off on adoption until they saw more proof of safety and reliability.
The study went further to understand who fell into which group. The researchers found that age, education, and income were strong predictors of a patient's stance. Older patients, those with less formal education, and those with lower household incomes were significantly more likely to belong to the skeptical or cautious groups. Conversely, younger patients with higher education levels and better incomes tended to be the active adopters. The study also looked at psychological factors. Patients who felt they had strong support from medical staff and family, and who felt confident in their own ability to handle new tasks, were much more likely to accept the robots. On the other hand, patients who already felt anxious about technology or worried that robots could not provide genuine human care were the ones most likely to reject the idea.
These findings suggest that simply placing a robot in a hospital room is not enough to ensure it will be used effectively. The research indicates that a "one-size-fits-all" approach to introducing this technology will likely fail because the patients are not all the same. The study highlights that the most vulnerable groups—older adults with less education and those who feel they lack support—are the ones most likely to resist the technology. To make these robots work, hospitals may need to tailor their approach. For the hesitant patients, the focus should be on building confidence through hands-on training and clear demonstrations of safety features, rather than just explaining the technology. For the enthusiastic group, they can be encouraged to help others feel more comfortable. Ultimately, the success of these digital companions depends on matching the technology to the specific needs and fears of the people they are meant to serve, ensuring that the human element of care remains central even as machines take on more tasks.
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