This paper is in the following e-collection/theme issue:

Fitness Trackers and Smart Pedometers/Accelerometers (506) mHealth for Wellness, Behavior Change and Prevention (2455)

Published on in Vol 5 , No 10 (2017) :October

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study

Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study

Authors of this article:

Orianna DeMasi 1, 2 Author Orcid Image ;   Sidney Feygin 3 Author Orcid Image ;   Aluma Dembo 4 Author Orcid Image ;   Adrian Aguilera 5, 6 Author Orcid Image ;   Benjamin Recht 2 Author Orcid Image
Article Authors Cited by (24) Tweetations (32) Metrics

Journals

  1. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  2. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  3. Hagemeier N, Carlson T, Roberts C, Thomas M. A Longitudinal Analysis of First Professional Year Pharmacy Student Well-being. American Journal of Pharmaceutical Education 2020;84(7):ajpe7735 View
  4. Garcia-Moreno F, Bermudez-Edo M, Garrido J, Rodríguez-García E, Pérez-Mármol J, Rodríguez-Fórtiz M. A Microservices e-Health System for Ecological Frailty Assessment Using Wearables. Sensors 2020;20(12):3427 View
  5. Sarda A, Munuswamy S, Sarda S, Subramanian V. Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study. JMIR mHealth and uHealth 2019;7(1):e11041 View
  6. Montag C, Baumeister H, Kannen C, Sariyska R, Meßner E, Brand M. Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology. J 2019;2(2):102 View
  7. Haga S, Shaw R, Kneifel C, Bond S, Ginsburg G. Promoting Wellness Through Mobile Health Technology in a College Student Population: Protocol Development and Pilot Study. JMIR Research Protocols 2020;9(4):e16474 View
  8. Giunti G, Guisado Fernández E, Dorronzoro Zubiete E, Rivera Romero O. Supply and Demand in mHealth Apps for Persons With Multiple Sclerosis: Systematic Search in App Stores and Scoping Literature Review. JMIR mHealth and uHealth 2018;6(5):e10512 View
  9. Düking P, Achtzehn S, Holmberg H, Sperlich B. Integrated Framework of Load Monitoring by a Combination of Smartphone Applications, Wearables and Point-of-Care Testing Provides Feedback that Allows Individual Responsive Adjustments to Activities of Daily Living. Sensors 2018;18(5):1632 View
  10. Robbins R, Seixas A, Walton Masters L, Chanko N, Diaby F, Vieira D, Jean-Louis G. Sleep Tracking: a Systematic Review of the Research Using Commercially Available Technology. Current Sleep Medicine Reports 2019;5(3):156 View
  11. de Vries L, Baselmans B, Bartels M. Smartphone-Based Ecological Momentary Assessment of Well-Being: A Systematic Review and Recommendations for Future Studies. Journal of Happiness Studies 2021;22(5):2361 View
  12. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  13. Vigoureux T, Lee S. Individual and joint associations of daily sleep and stress with daily well-being in hospital nurses: an ecological momentary assessment and actigraphy study. Journal of Behavioral Medicine 2021;44(3):320 View
  14. Liu J, Goetz J, Sen S, Tewari A. Learning From Others Without Sacrificing Privacy: Simulation Comparing Centralized and Federated Machine Learning on Mobile Health Data. JMIR mHealth and uHealth 2021;9(3):e23728 View
  15. Lee S. Naturally Occurring Consecutive Sleep Loss and Day-to-Day Trajectories of Affective and Physical Well-Being. Annals of Behavioral Medicine 2022;56(4):393 View
  16. Rehman U, Park S, Lee S. Secure Health Fog: A Novel Framework for Personalized Recommendations Based on Adaptive Model Tuning. IEEE Access 2021;9:108373 View
  17. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  18. Bilal A, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A, Papadopoulos F. Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open 2022;12(4):e059033 View
  19. Antezana G, Venning A, Smith D, Bidargaddi N. Understanding what we know so far about young people's engagement with wellbeing apps. A scoping review and narrative synthesis. DIGITAL HEALTH 2022;8:205520762211441 View
  20. Alamoudi D, Breeze E, Crawley E, Nabney I. The Feasibility of Using Smartphone Sensors to Track Insomnia, Depression, and Anxiety in Adults and Young Adults: Narrative Review. JMIR mHealth and uHealth 2023;11:e44123 View
  21. Lee K, Lee T, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas M, Wright A, Demiris G, Ritchie C, Pickering C, Nicholas Dionne-Odom J. Using digital phenotyping to understand health-related outcomes: A scoping review. International Journal of Medical Informatics 2023;174:105061 View
  22. Spengler L, Gößwein E, Kranefeld I, Liebherr M, Kracht F, Schramm D, Gennat M. From Modeling to Optimizing Sustainable Public Transport: A New Methodological Approach. Sustainability 2023;15(10):8171 View

Books/Policy Documents

  1. Thapa S, Tay L, Hou D. Examining and Exploring the Shifting Nature of Occupational Stress and Well-Being. View