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

Wearable Devices and Sensors (889) Fitness Trackers and Smart Pedometers/Accelerometers (561)
Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study

Authors of this article:

Ralph Maddison1, 2 Author Orcid Image ;   Luke Gemming2 Author Orcid Image ;   Javier Monedero3 Author Orcid Image ;   Linda Bolger3 Author Orcid Image ;   Sarahjane Belton3 Author Orcid Image ;   Johann Issartel3 Author Orcid Image ;   Samantha Marsh2 Author Orcid Image ;   Artur Direito2 Author Orcid Image ;   Madeleine Solenhill4 Author Orcid Image ;   Jinfeng Zhao5 Author Orcid Image ;   Daniel John Exeter5 Author Orcid Image ;   Harshvardhan Vathsangam6 Author Orcid Image ;   Jonathan Charles Rawstorn1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (14) Metrics

Journals

  1. Mowforth O, Davies B, Kotter M. The Use of Smart Technology in an Online Community of Patients With Degenerative Cervical Myelopathy. JMIR Formative Research 2019;3(2):e11364 View
  2. Nittas V, Lun P, Ehrler F, Puhan M, Mütsch M. Electronic Patient-Generated Health Data to Facilitate Disease Prevention and Health Promotion: Scoping Review. Journal of Medical Internet Research 2019;21(10):e13320 View
  3. Zhao J, Mackay L, Chang K, Mavoa S, Stewart T, Ikeda E, Donnellan N, Smith M. Visualising Combined Time Use Patterns of Children’s Activities and Their Association with Weight Status and Neighbourhood Context. International Journal of Environmental Research and Public Health 2019;16(5):897 View
  4. Hardy J, Veinot T, Yan X, Berrocal V, Clarke P, Goodspeed R, Gomez-Lopez I, Romero D, Vydiswaran V. User acceptance of location-tracking technologies in health research: Implications for study design and data quality. Journal of Biomedical Informatics 2018;79:7 View
  5. Kim S, Kim J, Park J, Shin M, Choi M. Predicting Energy Expenditure During Gradient Walking With a Foot Monitoring Device: Model-Based Approach. JMIR mHealth and uHealth 2019;7(10):e12335 View
  6. Silva A, Simões P, Queirós A, Rodrigues M, Rocha N. Mobile Apps to Quantify Aspects of Physical Activity: a Systematic Review on its Reliability and Validity. Journal of Medical Systems 2020;44(2) View
  7. Direito A, Tooley M, Hinbarji M, Albatal R, Jiang Y, Whittaker R, Maddison R. Tailored Daily Activity: An Adaptive Physical Activity Smartphone Intervention. Telemedicine and e-Health 2020;26(4):426 View
  8. Goodspeed R, Yan X, Hardy J, Vydiswaran V, Berrocal V, Clarke P, Romero D, Gomez-Lopez I, Veinot T. Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study. JMIR mHealth and uHealth 2018;6(8):e168 View
  9. Stålesen J, Westergren T, Herman Hansen B, Berntsen S. A Mapping Review of Physical Activity Recordings Derived From Smartphone Accelerometers. Journal of Physical Activity and Health 2020;17(11):1184 View
  10. Argent R, Hetherington-Rauth M, Stang J, Tarp J, Ortega F, Molina-Garcia P, Schumann M, Bloch W, Cheng S, Grøntved A, Brønd J, Ekelund U, Sardinha L, Caulfield B. Recommendations for Determining the Validity of Consumer Wearables and Smartphones for the Estimation of Energy Expenditure: Expert Statement and Checklist of the INTERLIVE Network. Sports Medicine 2022;52(8):1817 View
  11. Prado R, Knebel M, Ribeiro E, Teixeira I, Sasaki J, Araújo L, Guerra P, Florindo A. Smartphone apps for tracking physical activity and sedentary behavior: A criterion validity review. Revista Brasileira de Atividade Física & Saúde 2022;27:1 View
  12. Koh K, Hyder A, Karale Y, Kamel Boulos M. Big Geospatial Data or Geospatial Big Data? A Systematic Narrative Review on the Use of Spatial Data Infrastructures for Big Geospatial Sensing Data in Public Health. Remote Sensing 2022;14(13):2996 View
  13. Pontin F, Lomax N, Clarke G, Morris M. Characterisation of Temporal Patterns in Step Count Behaviour from Smartphone App Data: An Unsupervised Machine Learning Approach. International Journal of Environmental Research and Public Health 2021;18(21):11476 View
  14. Das S, Nandi D, Neogi B. Design Analysis of Prosthetic Unilateral Transtibial Lower Limb with Gait Coordination. Prosthesis 2023;5(2):575 View