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

Medicine 2.0'13 (London) (23) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Medicine 2.0'13 London (Full Paper of Conference Presentation) (30)

Published on in Vol 2, No 2 (2013): Jul-Dec

Live Long and Prosper: Potentials of Low-Cost Consumer Devices for the Prevention of Cardiovascular Diseases

Live Long and Prosper: Potentials of Low-Cost Consumer Devices for the Prevention of Cardiovascular Diseases

Live Long and Prosper: Potentials of Low-Cost Consumer Devices for the Prevention of Cardiovascular Diseases

Authors of this article:

Jochen Meyer1 ;   Andreas Hein2
Article Authors Cited by (19) Tweetations Metrics

Journals

  1. Meng Y, Speier W, Shufelt C, Joung S, E Van Eyk J, Bairey Merz C, Lopez M, Spiegel B, Arnold C. A Machine Learning Approach to Classifying Self-Reported Health Status in a Cohort of Patients With Heart Disease Using Activity Tracker Data. IEEE Journal of Biomedical and Health Informatics 2020;24(3):878 View
  2. Speier W, Dzubur E, Zide M, Shufelt C, Joung S, Van Eyk J, Bairey Merz C, Lopez M, Spiegel B, Arnold C. Evaluating utility and compliance in a patient-based eHealth study using continuous-time heart rate and activity trackers. Journal of the American Medical Informatics Association 2018;25(10):1386 View
  3. Hawkins J, Charles J, Edwards M, Hallingberg B, McConnon L, Edwards R, Jago R, Kelson M, Morgan K, Murphy S, Oliver E, Simpson S, Moore G. Acceptability and Feasibility of Implementing Accelorometry-Based Activity Monitors and a Linked Web Portal in an Exercise Referral Scheme: Feasibility Randomized Controlled Trial. Journal of Medical Internet Research 2019;21(3):e12374 View
  4. Sawani S, Siddiqui A, Azam S, Humayun K, Ahmed A, Habib A, Naz S, Tufail M, Iqbal R. Lifestyle changes and glycemic control in type 1 diabetes mellitus: a trial protocol with factorial design approach. Trials 2020;21(1) View
  5. Austin C, Hokanson J, McGinnis P, Patrick S. The Relationship between Running Power and Running Economy in Well-Trained Distance Runners. Sports 2018;6(4):142 View
  6. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of Commercially Available Wearable Activity Trackers Among Adults Aged Over 50 and With Chronic Illness: A Mixed-Methods Evaluation. JMIR mHealth and uHealth 2016;4(1):e7 View
  7. Apovian C, Garvey W, Ryan D. Challenging obesity: Patient, provider, and expert perspectives on the roles of available and emerging nonsurgical therapies. Obesity 2015;23(S2) View
  8. Meyer J, Boll S. Digital Health Devices for Everyone!. IEEE Pervasive Computing 2014;13(2):10 View
  9. WOODMAN J, CROUTER S, BASSETT D, FITZHUGH E, BOYER W. Accuracy of Consumer Monitors for Estimating Energy Expenditure and Activity Type. Medicine & Science in Sports & Exercise 2017;49(2):371 View
  10. Zelenović J, Zelenović V. Managing Consumers and Employees through Digital Services. Sustainability 2022;14(14):8824 View
  11. Rao K, Speier W, Meng Y, Wang J, Ramesh N, Xie F, Su Y, Nowell W, Curtis J, Arnold C. Machine Learning Approaches to Classify Self-Reported Rheumatoid Arthritis Health Scores Using Activity Tracker Data: Longitudinal Observational Study. JMIR Formative Research 2023;7:e43107 View
  12. Wróbel-Lachowska M, Dominiak J, Woźniak M, Bartłomiejczyk N, Diethei D, Wysokińska A, Niess J, Grudzień K, Woźniak P, Romanowski A. ‘That’s when I put it on’: stakeholder perspectives in large-scale remote health monitoring for older adults. Personal and Ubiquitous Computing 2023;27(6):2193 View

Books/Policy Documents

  1. Cosoli G, Scalise L. Sensors. View
  2. Meyer J, Fortmann J, Wasmann M, Heuten W. MultiMedia Modeling. View
  3. Hellmers S, Fudickar S, Büse C, Dasenbrock L, Heinks A, Bauer J, Hein A. Ambient Assisted Living. View
  4. Ferriere M. Prescription des Activités Physiques. View