Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

ACM Transactions on Computing for Healthcare, Journal Year: 2025, Volume and Issue: unknown

Published: March 11, 2025

The interplay between mood and eating episodes has been extensively researched within the fields of nutrition, psychology, behavioral science, revealing a connection two. Previous studies have relied on questionnaires mobile phone self-reports to investigate relationship eating. In more recent work, sensor data utilized characterize both behavior independently, particularly in context food diaries health applications. However, current literature exhibits several limitations: lack investigation into generalization inference models trained with from various everyday life situations specific contexts like eating; an absence using explore intersection inadequate examination model personalization techniques limited label settings, common challenge (i.e., far fewer negative reports compared positive or neutral reports). this study, we examined two separate datasets different studies: i) Mexico (N \({}_{MEX}\) = 84, 1843 mood-while-eating distribution positive: 51.7%, neutral: 38.6% negative: 9.8%) 2019, ii) eight countries \({}_{MUL}\) 678, 329K reports, including 24K 83%, 14.9%, 2.2%) 2020, which contain passive smartphone sensing self-report data. Our results indicate that generic experience decline performance contexts, such as during eating, highlighting issue sub-context shifts sensing. Moreover, discovered population-level (non-personalized) hybrid (partially personalized) modeling fall short commonly used three-class task (positive, neutral, negative). Additionally, found user-level posed challenges for majority participants due insufficient labels class. To overcome these limitations, implemented novel community-based approach, building set users similar target user. findings demonstrate can be inferred accuracies 63.8% (with F1-score 62.5) MEX dataset 88.3% 85.7) MUL models, surpassing those achieved traditional methods.

Language: Английский

Students’ Burnout Symptoms Detection Using Smartwatch Wearable Devices: A Systematic Literature Review

Published: May 8, 2025

(1) Background: The current uses of smartwatch wearable devices have expanded, not only being a part everyday routine life but also playing dynamic role in the early detection many behavioral patterns users. Furthermore, modern era, there is an increasing trend mental disturbances even adolescence, phenomenon that continues into academic life. Taking account situation, objective this systematic literature review emphasizes AI symptom burnout student population. (2) Methods: A was designed based on PRISMA guidelines. general extracted aspect to exploit all related research evidence about effectiveness (3) Results: reviewed studies document importance physiological monitoring and AI-driven predictive models, with collaboration self-reported scales assessing well-being. It reported stress most frequently studied burnout-related symptom. Meanwhile, heart rate (HR) variability (HRV) are commonly used biomarkers can be monitor evaluate detection. (4) Conclusions: Despite promising potential these technologies, several challenges limitations must addressed enhance their reliability.

Language: Английский