Ethical considerations in using sensors to remotely assess pediatric health behaviors.
Alexandra M PsihogiosSara King-DowlingJonathan A MitchellMeghan E McGradyAriel A WilliamsonPublished in: The American psychologist (2024)
Sensors, including accelerometer-based and electronic adherence monitoring devices, have transformed health data collection. Sensors allow for unobtrusive, real-time sampling of health behaviors that relate to psychological health, including sleep, physical activity, and medication-taking. These technical strengths have captured scholarly attention, with far less discussion about the level of human touch involved in implementing sensors. Researchers face several subjective decision points when collecting health data via sensors, with these decisions posing ethical concerns for users and the public at large. Using examples from pediatric sleep, physical activity, and medication adherence research, we pose critical ethical questions, practical dilemmas, and guidance for implementing health-based sensors. We focus on youth given that they are often deemed the ideal population for digital health approaches but have unique technology-related vulnerabilities and preferences. Ethical considerations are organized according to Belmont principles of respect for persons (e.g., when sensor-based data are valued above the subjective lived experiences of youth and their families), beneficence (e.g., with sensor data management and sharing), and justice (e.g., with sensor access and acceptability among minoritized pediatric populations). Recommendations include the need to increase transparency about the extent of subjective decision making with sensor data management. Without greater attention to the human factors involved in sensor research, ethical risks could outweigh the scientific promise of sensors, thereby negating their potential role in improving child health and care. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Keyphrases
- healthcare
- physical activity
- mental health
- public health
- decision making
- health information
- electronic health record
- sleep quality
- big data
- low cost
- endothelial cells
- human health
- body mass index
- young adults
- quality improvement
- adipose tissue
- working memory
- skeletal muscle
- insulin resistance
- chronic pain
- mental illness
- deep learning
- artificial intelligence
- clinical practice
- weight loss
- childhood cancer