The Impact of Robot Monitoring Behavior on Distraction and Technology Acceptance in Older Adults

Academic Background

With the intensification of societal aging, the demand for elderly care is increasing. Especially during the COVID-19 pandemic, psychological and physiological health issues faced by older adults due to social isolation have become more prominent. Social robots, as an emerging technology, are considered to play a significant role in elderly care, particularly in monitoring the health and safety of older adults. However, the presence and behavior of robots may interfere with the daily activities of older adults, leading to discomfort or even rejection. Therefore, studying the impact of robot behavior on distraction in older adults and their acceptance of robotic technology holds significant importance.

This study aims to explore the influence of robot behavior during monitoring tasks on older adults’ engagement in daily activities, especially their responses to robot behavior under tasks requiring different cognitive loads. Researchers attempt to assess older adults’ engagement with robots and their disengagement from their own tasks using subjective and objective metrics, further analyzing the relationship between robot acceptance and individual personality traits.

Paper Source

This paper was co-authored by Gianpaolo Maggi, Luca Raggioli, Alessandra Rossi, and Silvia Rossi, who are respectively from the Department of Psychology at Università degli Studi della Campania Luigi Vanvitelli and the Department of Electrical Engineering and Information Technology at the University of Naples Federico II, Italy. The paper was published in 2025 in IEEE Transactions on Affective Computing, one of the authoritative journals in this field.

Research Process and Results

Study Subjects and Experimental Design

The study recruited 18 elderly volunteers aged between 53 and 82 years, with an average age of 60.28 years. All participants passed a cognitive function assessment (Montreal Cognitive Assessment, MoCA) and were free from cognitive decline, neurological diseases, or severe depression.

The experiment used a “within-subjects design,” where each participant needed to interact with a robot in four different daily activity scenarios: watching TV, making coffee, talking on the phone, and working on a computer. These activities gradually increased in cognitive load, and while performing the tasks, the robot would randomly move around the room and approach the participants twice for monitoring (at distances of 2.5m and 1.5m).

Experimental Procedure

1. Pre-study Questionnaire: Participants first completed demographic information and the NEO-PI-3 personality trait questionnaire to assess their personality traits (e.g., neuroticism, extraversion, openness, agreeableness, and conscientiousness).
2. Experimental Phase: Participants were guided into a simulated home environment room where the humanoid robot Pepper moved randomly within the room. While performing the four tasks, the robot approached them for monitoring. Each monitoring session lasted 10 seconds, during which the robot faced the participant.
3. Data Collection: The entire experiment was recorded through external cameras and the robot’s built-in camera, and a clinician scored participants’ behaviors to evaluate their focus on the task and the distraction caused by the robot.
4. Subjective Assessment: After each task, participants rated the level of distraction caused by the robot on a scale from 0 to 100.
5. Objective Assessment: Clinicians assessed participants’ focus on the task and distraction caused by the robot using a 5-point Likert scale.

Experimental Results

1. Task Focus and Distraction: The study found that participants were more easily distracted by the robot during low cognitive load tasks (e.g., watching TV), while showing higher task focus during high cognitive load tasks (e.g., working on a computer). Specific data showed that the task focus score during the TV-watching task was significantly lower than during the coffee-making and computer-working tasks.
2. Differences Between Subjective and Objective Assessments: There was a certain discrepancy between participants’ subjective assessments of distraction and clinicians’ objective evaluations. For example, during the phone call task, participants’ subjective assessment of distraction was positively correlated with the number of gazes directed at the robot (r = 0.489, p = 0.040).
3. Affective Engagement and Distraction: The study found that the number of smiles directed at the robot was negatively correlated with clinicians’ distraction assessments (r = -0.48, p < 0.001), indicating that smiling might be a potential indicator of attention shift.
4. Robot Acceptance and Task Focus: The study found that participants’ perception of the ease of use of the robot was positively correlated with their task focus (r = 0.567, p = 0.014), while trust in the robot was negatively correlated with task focus (r = -0.525, p = 0.025).
5. Impact of Personality Traits: Participants’ agreeableness was positively correlated with the number of smiles (r = 0.511, p = 0.030), while conscientiousness was positively correlated with task focus (r = 0.520, p = 0.027).

Conclusions and Significance

This study reveals the impact of robot monitoring behavior on distraction in older adults and explores the relationship between robot acceptance and individual personality traits. The results show that the presence of robots indeed affects older adults’ focus on tasks, especially during low cognitive load tasks. Additionally, participants’ trust in and perceived ease of use of the robot play an important role in task focus.

This research provides important references for designing more humanized robots in the future. For example, robots should minimize interference during low cognitive load tasks, while being more proactive in providing assistance during high cognitive load tasks. Moreover, the study shows that individual personality traits play an important role in robot acceptance, paving the way for developing personalized robot behavior strategies in the future.

Research Highlights

1. Multi-dimensional Assessment: This study combines subjective and objective assessments to comprehensively analyze the impact of robot behavior on distraction in older adults.
2. Impact of Personality Traits: The study introduces personality traits into the analysis of robot acceptance for the first time, providing theoretical support for personalized robot design.
3. Practical Application Value: The findings provide important references for the practical application of robots in elderly care, especially in how to reduce the interference of robots in the daily activities of older adults.

Summary

This study analyzes the impact of robot monitoring tasks on distraction in older adults and explores the relationship between robot acceptance and individual personality traits. The study finds that the presence of robots indeed affects older adults’ focus on tasks, especially during low cognitive load tasks. Additionally, participants’ trust in and perceived ease of use of the robot play an important role in task focus. This research provides important references for designing more humanized robots in the future, especially in the field of elderly care.