[HTML][HTML] The two fundamental shapes of sleep heart rate dynamics and their connection to mental health in college students

MI Fudolig, LSP Bloomfield, M Price, YM Bird… - Digital …, 2024 - karger.com
Introduction: Wearable devices are rapidly improving our ability to observe health-related
processes for extended durations in an unintrusive manner. In this study, we use wearable …

The State of Digital Biomarkers in Mental Health

EW McGinnis, J Cherian, RS McGinnis - Digital Biomarkers, 2024 - karger.com
Untreated mental health conditions are a leading cause of disability in the world [1, 2],
accounting for more than 30% of years lived with disability [1]. However, the staggering …

Can heart rate sequences from wearable devices predict day-long mental states in higher education students: a signal processing and machine learning case study at …

T Chen - Brain Informatics, 2024 - Springer
The mental health of students in higher education has been a growing concern, with
increasing evidence pointing to heightened risks of develo** mental health condition. This …

[HTML][HTML] Predictors of Anxiety Trajectories in Cohort of First-Year College Students

LSP Bloomfield, MI Fudolig, JN Kim, J Llorin, J Lovato… - JAACAP Open, 2024 - Elsevier
Objective The transition to college is a period of growth and vulnerability for young adult
health and well-being and provides a critical window for potential behavioral interventions …

Collective sleep and activity patterns of college students from wearable devices

MI Fudolig, LSP Bloomfield, M Price, YM Bird… - arxiv preprint arxiv …, 2024 - arxiv.org
To optimize interventions for improving wellness, it is essential to understand habits, which
wearable devices can measure with greater precision. Using high temporal resolution …

[PDF][PDF] Decoding minds: Estimation of stress level in students using machine learning

SS Shahapur, P Chitti, S Patil… - Indian Journal …, 2024 - sciresol.s3.us-east-2.amazonaws …
Objectives: Develop a predictive model to categorize student's stress levels and support
early interventions based on self-reported data, academic performance, and study load. This …

Predicting Sleep Quality in Family Caregivers of Dementia Patients From Diverse Populations Using Wearable Sensor Data

JI Park, SAH Aqajari, AM Rahmani… - CIN: Computers …, 2023 - journals.lww.com
This study aimed to use wearable technology to predict the sleep quality of family caregivers
of people with dementia among underrepresented groups. Caregivers of people with …

Longitudinal Profiles of Heart Rate Variability in First-Year College Students Using Wearables

BC Loftness, J Hidalgo, J Cherian… - 2024 IEEE 20th …, 2024 - ieeexplore.ieee.org
As the mental health of college students continues to worsen, identifying students who need
additional support and timely, targeted intervention is vital for college campuses. Leveraging …