Impact Ambivalence: How People with Eating Disorders Get Trapped in the Perpetual Cycle of Digital Food Content Engagement
November 10, 2023 Β· Declared Dead Β· + Add venue
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Ryuhaerang Choi, Subin Park, Sujin Han, Jennifer G. Kim, Sung-Ju Lee
arXiv ID
2311.05920
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.MM
Citations
0
Last Checked
5 months ago
Abstract
Digital food content could impact viewers' dietary health, with individuals with eating disorders being particularly sensitive to it. However, a comprehensive understanding of why and how these individuals interact with such content is lacking. To fill this void, we conducted exploratory (N=23) and in-depth studies (N=22) with individuals with eating disorders to understand their motivations and practices of consuming digital food content. We reveal that participants engaged with digital food content for both disorder-driven and recovery-supporting motivations, leading to conflicting outcomes. This impact ambivalence, the coexistence of recovery-supporting benefits and disorder-exacerbating risks, sustained a cycle of quitting, prompted by awareness of harm, and returning, motivated by anticipated benefits. We interpret these dynamics within dual systems theory and highlight how recognizing such ambivalence can inform the design of interventions that foster healthier digital food content engagement and mitigate post-engagement harmful effects.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted