Patient B was a 72-year-old woman diagnosed with severe Alzheimer's disease. She had lived in the institution since September 2019. She needed assistance and close monitoring in performing daily activities, for example changing clothes, and communication. Kabochan was selected for her as she could do only limited interaction while sitting.
For one of the cases, data were collected at an institution for older persons and another from a psychiatric hospital in a prefecture in western Japan. The institution for older persons provided daily and long-term nursing care services, particularly ensuring personal hygiene maintenance such as bathing, exercise, meals, and activities of daily living. The healthcare staff provided daily healthcare activities that started in the morning and continued until evening. The other institution is a private hospital for patients with mental health problems such as dementia and schizophrenia. In these settings, both the Pepper and Kabochan were regularly used for physical exercises and recreational activities.
Patient A and Patient B were patients in one of these settings and were familiar with the healthcare robots. They were able and willing to participate and familiar with the two robots, Pepper and Kabochan.
Data were collected in November 2019. Digital video recordings were made during similar interactions in two separate transactive care situations. Research assistants were trained to observe and note the interactions among the older persons, Pepper and Kabochan, and the intermediary. During the data collection period, the researchers recorded field notes regarding significant situational events between the older person, Pepper, Kabochan, and the nurse intermediary. The field notes also included researcher reflections during the interactions. Observation notes and recorded dialogues were transcribed and translated into English. Those data supported with recorded scenes (pictures) were analyzed and interpreted carefully by reading and rereading the transcriptions and carefully watching and listening to audiovisual recordings. The significant data were highlighted and grouped into the same identified thematic categories. (Please see Figure 3 for the examples of data used for analysis).
The first situation was between Patient A and Pepper. Pepper is a humanoid robot manufactured by SoftBank Robotics, with applications made by Xing Company, Japan (Tanioka et al.,). This clinical examination utilized the "Kenko Okoku" Talk application for Pepper to enhance human-robot interaction through improved communication between older people and humanoid robots (Miyagawa et al.,). This Pepper has been used for communication purposes and could identify humans and respond to conversations.
The second situation was among Patient B, Kabochan with Pechat, and the nurse as an intermediary. The intermediary facilitated the conversation between Patient B and Kabochan by repeating and supporting the question and answer for Patient B when she indicated that she did not hear the words uttered by Kabochan.
Data were generated from two nursing care activities involving conversations and observations recorded through sophisticated audio-video technologies. These observations recorded interactions among older persons with mental health conditions, healthcare robots (Pepper and Kabochan), and a nurse as an intermediary. Osaka has described the role of an intermediary as a nurse or healthcare provider who is "the critical component of a responsive management program" (p. 267).
A qualitative thematic analysis following Lambert and Lambert was used to analyze and interpret the data. Lambert and Lambert described a process of thematic analysis as an approach in qualitative descriptive study, in which interviews, written descriptions, or observational recordings are used as data. In the qualitative descriptive analysis, a window to the experiential occasion is not moderated by adhering to established criteria such as saturation point and the number of patients, but by the philosophical underpinnings appreciated as the guiding principles for robust analysis and interpretation of data. In essence, a descriptive statement ultimately answers the research question describing the phenomenon being studied (Sandelowski,).
The framework for the analysis and interpretation was grounded from TRETON (Tanioka,). TRETON clarifies the shared engagement that occurs in nursing situations involving healthcare robots as partners. Generated data using observational and conversational dialogue during a transactive relational engagement between healthcare robots, older persons with mental health conditions, and the nurse as intermediary were analyzed and interpreted. Interpretation of the data emphasized the dialogical engagement context, as described and explained by Vaismoradi et al.. The presentation of the findings was done through a straightforward descriptive statement in which the organization of the results greatly depended on the researchers' understanding of the descriptions and how the data were extracted (Lambert & Lambert,).
Trustworthiness and rigor were established through triangulation and detailed transcription (Gunawan,). Triangulation was conducted by generating data from multiple sources, including digital-video recordings, photographs, conversational dialogue, observation, and reflections field notes, which were also transcribed. Detailed descriptions of the patients, the settings, and the data collection process were presented. Audit trails were done by the researchers supporting the derived themes. Additionally, findings, discussions, and conclusions were confirmed to fit the data gathered. The research team consisted of five experienced scholars and researchers with two doctoral students as co-researchers. No conflicting relationships between the research team members and with patients were found to influence the findings.
The study was approved by the Ethics Committee of the Tokushima University Hospital (# 3046) and the Mifune Hospital Clinical Research Ethics Review Committee (#201180502). Patients and their responsible family members approved their participation, including being audiotaped and having excerpts from transcripts and altered photos from the audio-video recordings used in the research reports. Photographs taken during the data generation were blurred to protect identities.