Published on in Vol 7, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12084, first published .
Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

Correlates of Stress in the College Environment Uncovered by the Application of Penalized Generalized Estimating Equations to Mobile Sensing Data

Journals

  1. Goodday S, Friend S. Unlocking stress and forecasting its consequences with digital technology. npj Digital Medicine 2019;2(1) View
  2. Acikmese Y, Alptekin S. Prediction of stress levels with LSTM and passive mobile sensors. Procedia Computer Science 2019;159:658 View
  3. Huckins J, daSilva A, Wang W, Hedlund E, Rogers C, Nepal S, Wu J, Obuchi M, Murphy E, Meyer M, Wagner D, Holtzheimer P, Campbell A. Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study. Journal of Medical Internet Research 2020;22(6):e20185 View
  4. Pryss R, John D, Schlee W, Schlotz W, Schobel J, Kraft R, Spiliopoulou M, Langguth B, Reichert M, O'Rourke T, Peters H, Pieh C, Lahmann C, Probst T. Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study. JMIR mHealth and uHealth 2019;7(10):e13978 View
  5. Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161 View
  6. Chong K, Woo B. Emerging wearable technology applications in gastroenterology: A review of the literature. World Journal of Gastroenterology 2021;27(12):1149 View
  7. Madrid-García A, León-Mateos L, Pato E, Jover J, Fernández-Gutiérrez B, Abasolo L, Menasalvas E, Rodríguez-Rodríguez L. Predictors of health-related quality of life in musculoskeletal disease patients: a longitudinal analysis. Therapeutic Advances in Musculoskeletal Disease 2021;13:1759720X2110340 View
  8. Vidal Bustamante C, Coombs G, Rahimi-Eichi H, Mair P, Onnela J, Baker J, Buckner R. Fluctuations in behavior and affect in college students measured using deep phenotyping. Scientific Reports 2022;12(1) View
  9. Castro R, Ribeiro-Alves M, Oliveira C, Romero C, Perazzo H, Simjanoski M, Kapciznki F, Balanzá-Martínez V, De Boni R. What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review. Frontiers in Public Health 2022;9 View
  10. Xiang Y, Li S, Zhang P. An exploration in remote blood pressure management: Application of daily routine pattern based on mobile data in health management. Fundamental Research 2022;2(1):154 View
  11. Zhang H, Ibrahim A, Parsia B, Poliakoff E, Harper S. Passive social sensing with smartphones: a systematic review. Computing 2023;105(1):29 View
  12. Jacobson N, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1) View
  13. Deng J, Chen B, Fu C, Du J. Exploration of Campus Environmental Health Issues and Individual Disparities in Environmental Perceptions Based on Daily Activity Path. Buildings 2023;13(10):2544 View

Books/Policy Documents

  1. Bediou B, Wac K. Encyclopedia of Child and Adolescent Health. View