Investigating the influences of social overload and task complexity on user engagement decrease

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human-robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions-categorized as accurate performance or inaccurate performance due to high/low task load-are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human-robot collaboration.

Social Media Overload as a Predictor of Depressive Symptoms Under the COVID-19 Infodemic: A Cross-Sectional Survey From Chinese University Students

Objectives: People's mental health and digital usage have attracted widespread attention during the COVID-19 pandemic. This study aimed to investigate how social media overload influenced depressive symptoms under the COVID-19 infodemic and the role of risk perception and social media fatigue. Methods: A questionnaire survey was conducted on 644 college students during the COVID-19 lockdown in Shanghai, and data analysis was conducted using the PROCESS4.0 tool. Results: The findings showed that in the COVID-19 information epidemic: 1) both information overload and communication overload were significantly and positively associated with depressive symptoms; 2) risk perception of COVID-19, and social media fatigue mediated this association separately; 3) and there was a chain mediating relationship between communication overload and depressive symptoms. Conclusion: Social media overload was positively associated with depressive symptoms among college students under the COVID-19 infodemic by increasing risk perception and social media fatigue. The findings sparked further thinking on how the public should correctly use social media for risk communication during public health emergencies.

Can Patients Co-Create Value in an Online Healthcare Platform? An Examination of Value Co-Creation

With the development of online healthcare services, patients could receive support and create value with other users on online healthcare platforms. However, little research has been conducted on the internal mechanisms of patient value co-creation from the perspective of online healthcare platforms. To analyze patient value co-creation in online healthcare platforms, this study explores the underlying mechanisms of patient value co-creation among patients. The results show that value co-creation includes patient citizenship behavior and participation behavior. Information quality, peer communication, and system quality influence functional experiences and emotional experiences. In addition, functional experiences and emotional experiences could influence patient value co-creation. This study clarifies the mechanism of value co-creation among patients and provides insight into value co-creation in online healthcare platforms.