Your browser doesn't support javascript.
loading
Predicting stress in first-year college students using sleep data from wearable devices.
Bloomfield, Laura S P; Fudolig, Mikaela I; Kim, Julia; Llorin, Jordan; Lovato, Juniper L; McGinnis, Ellen W; McGinnis, Ryan S; Price, Matt; Ricketts, Taylor H; Dodds, Peter Sheridan; Stanton, Kathryn; Danforth, Christopher M.
Afiliación
  • Bloomfield LSP; Gund Institute for Environment, University of Vermont, Burlington, Vermont, United States of America.
  • Fudolig MI; Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America.
  • Kim J; Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.
  • Llorin J; Department of Mathematics & Statistics, University of Vermont, Burlington, Vermont, United States of America.
  • Lovato JL; Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.
  • McGinnis EW; Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.
  • McGinnis RS; Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.
  • Price M; Vermont Complex Systems Center, University of Vermont, Burlington, Vermont, United States of America.
  • Ricketts TH; Department of Social Science and Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America.
  • Dodds PS; Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America.
  • Stanton K; Center for Remote Patient and Participant Monitoring, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America.
  • Danforth CM; Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America.
PLOS Digit Health ; 3(4): e0000473, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38602898
ABSTRACT
Consumer wearables have been successful at measuring sleep and may be useful in predicting changes in mental health measures such as stress. A key challenge remains in quantifying the relationship between sleep measures associated with physiologic stress and a user's experience of stress. Students from a public university enrolled in the Lived Experiences Measured Using Rings Study (LEMURS) provided continuous biometric data and answered weekly surveys during their first semester of college between October-December 2022. We analyzed weekly associations between estimated sleep measures and perceived stress for participants (N = 525). Through mixed-effects regression models, we identified consistent associations between perceived stress scores and average nightly total sleep time (TST), resting heart rate (RHR), heart rate variability (HRV), and respiratory rate (ARR). These effects persisted after controlling for gender and week of the semester. Specifically, for every additional hour of TST, the odds of experiencing moderate-to-high stress decreased by 0.617 or by 38.3% (p<0.01). For each 1 beat per minute increase in RHR, the odds of experiencing moderate-to-high stress increased by 1.036 or by 3.6% (p<0.01). For each 1 millisecond increase in HRV, the odds of experiencing moderate-to-high stress decreased by 0.988 or by 1.2% (p<0.05). For each additional breath per minute increase in ARR, the odds of experiencing moderate-to-high stress increased by 1.230 or by 23.0% (p<0.01). Consistent with previous research, participants who did not identify as male (i.e., female, nonbinary, and transgender participants) had significantly higher self-reported stress throughout the study. The week of the semester was also a significant predictor of stress. Sleep data from wearable devices may help us understand and to better predict stress, a strong signal of the ongoing mental health epidemic among college students.
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: PLOS Digit Health Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos