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Journal of Medical Internet Research

Citing this Article

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Published on 13.06.13 in Vol 15, No 6 (2013): June

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

Works citing "Comparison of Physical Activity Measures Using Mobile Phone-Based CalFit and Actigraph"

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.2470):

(note that this is only a small subset of citations)

  1. Sullivan AN, Lachman ME. Behavior Change with Fitness Technology in Sedentary Adults: A Review of the Evidence for Increasing Physical Activity. Frontiers in Public Health 2017;4
    CrossRef
  2. Dunton GF, Dzubur E, Intille S. Feasibility and Performance Test of a Real-Time Sensor-Informed Context-Sensitive Ecological Momentary Assessment to Capture Physical Activity. Journal of Medical Internet Research 2016;18(6):e106
    CrossRef
  3. Donaire-Gonzalez D, Valentín A, de Nazelle A, Ambros A, Carrasco-Turigas G, Seto E, Jerrett M, Nieuwenhuijsen MJ. Benefits of Mobile Phone Technology for Personal Environmental Monitoring. JMIR mHealth and uHealth 2016;4(4):e126
    CrossRef
  4. Seto E, Hua J, Wu L, Shia V, Eom S, Wang M, Li Y, Yao L. Models of Individual Dietary Behavior Based on Smartphone Data: The Influence of Routine, Physical Activity, Emotion, and Food Environment. PLOS ONE 2016;11(4):e0153085
    CrossRef
  5. Aranki D, Kurillo G, Yan P, Liebovitz DM, Bajcsy R. Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned. IEEE Transactions on Affective Computing 2016;7(3):206
    CrossRef
  6. Li P, Wang Y, Tian Y, Zhou T, Li J. An Automatic User-adapted Physical Activity Classification Method Using Smartphones. IEEE Transactions on Biomedical Engineering 2016;:1
    CrossRef
  7. Pande A, Mohapatra P, Nicorici A, Han JJ. Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy. JMIR Rehabilitation and Assistive Technologies 2016;3(2):e7
    CrossRef
  8. Su JG, Jerrett M, Meng Y, Pickett M, Ritz B. Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment. Science of The Total Environment 2015;506-507:518
    CrossRef
  9. Pah AR, Rasmussen-Torvik LJ, Goel S, Greenland P, Kho AN. Big Data: What Is It and What Does It Mean for Cardiovascular Research and Prevention Policy. Current Cardiovascular Risk Reports 2015;9(1)
    CrossRef
  10. Mimura K, Kishino H, Karino G, Nitta E, Senoo A, Ikegami K, Kunikata T, Yamanouchi H, Nakamura S, Sato K, Koshiba M. Potential of a smartphone as a stress-free sensor of daily human behaviour. Behavioural Brain Research 2015;276:181
    CrossRef
  11. Nieuwenhuijsen MJ, Donaire-Gonzalez D, Rivas I, de Castro M, Cirach M, Hoek G, Seto E, Jerrett M, Sunyer J. Variability in and Agreement between Modeled and Personal Continuously Measured Black Carbon Levels Using Novel Smartphone and Sensor Technologies. Environmental Science & Technology 2015;49(5):2977
    CrossRef
  12. Nieuwenhuijsen M, Donaire-Gonzalez D, Foraster M, Martinez D, Cisneros A. Using Personal Sensors to Assess the Exposome and Acute Health Effects. International Journal of Environmental Research and Public Health 2014;11(8):7805
    CrossRef
  13. Bort-Roig J, Gilson ND, Puig-Ribera A, Contreras RS, Trost SG. Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review. Sports Medicine 2014;44(5):671
    CrossRef
  14. Barrett MA, Humblet O, Hiatt RA, Adler NE. Big Data and Disease Prevention:From Quantified Self to Quantified Communities. Big Data 2013;1(3):168
    CrossRef