Published on in Vol 5, No 6 (2017): June

Mobile Device Accuracy for Step Counting Across Age Groups

Mobile Device Accuracy for Step Counting Across Age Groups

Mobile Device Accuracy for Step Counting Across Age Groups

Journals

  1. Chao D, Lin T, Ma W. Enhanced Self-Efficacy and Behavioral Changes Among Patients With Diabetes: Cloud-Based Mobile Health Platform and Mobile App Service. JMIR Diabetes 2019;4(2):e11017 View
  2. Henriksen A, Haugen Mikalsen M, Woldaregay A, Muzny M, Hartvigsen G, Hopstock L, Grimsgaard S. Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables. Journal of Medical Internet Research 2018;20(3):e110 View
  3. Cox S, Lane A, Volchenboum S. Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials. JCO Clinical Cancer Informatics 2018;(2):1 View
  4. Collins J, Yang H, Trentadue T, Gong Y, Losina E, Parmenter B. Validation of the Fitbit Charge 2 compared to the ActiGraph GT3X+ in older adults with knee osteoarthritis in free-living conditions. PLOS ONE 2019;14(1):e0211231 View
  5. Ortiz‐Rubio P, Oladunjoye A, Agus M, Steil G. Adjusting Insulin Delivery to Activity (AIDA) clinical trial: Effects of activity‐based insulin profiles on glucose control in children with type 1 diabetes. Pediatric Diabetes 2018;19(8):1451 View
  6. Park S, Toth L, Crouter S, Springer C, Marcotte R, Bassett D. Effect of Monitor Placement on the Daily Step Counts of Wrist and Hip Activity Monitors. Journal for the Measurement of Physical Behaviour 2020;3(2):164 View
  7. Park S, Toth L, Hibbing P, Springer C, Kaplan A, Feyerabend M, Crouter S, Bassett D. Dominant vs. Non-Dominant Wrist Placement of Activity Monitors: Impact on Steps per Day. Journal for the Measurement of Physical Behaviour 2019;2(2):118 View
  8. Navalta J, Montes J, Bodell N, Aguilar C, Lujan A, Guzman G, Kam B, Manning J, DeBeliso M. Wearable Device Validity in Determining Step Count During Hiking and Trail Running. Journal for the Measurement of Physical Behaviour 2018;1(2):86 View
  9. Feehan L, Geldman J, Sayre E, Park C, Ezzat A, Yoo J, Hamilton C, Li L. Accuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data. JMIR mHealth and uHealth 2018;6(8):e10527 View
  10. Bunn J, Jones C, Oliviera A, Webster M. Assessment of step accuracy using the Consumer Technology Association standard. Journal of Sports Sciences 2019;37(3):244 View
  11. Wu J, Ho T, Chang Y, Hsu C, Tsai C, Lai F, Lin M. Wearable-Based Mobile Health App in Gastric Cancer Patients for Postoperative Physical Activity Monitoring: Focus Group Study. JMIR mHealth and uHealth 2019;7(4):e11989 View
  12. Liang Z, Chapa-Martell M. Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors. JMIR mHealth and uHealth 2019;7(6):e13384 View
  13. Kennedy A, Hales S. Tools Clinicians Can Use to Help Get Patients Active. Current Sports Medicine Reports 2018;17(8):271 View
  14. Seewald N, Smith S, Lee A, Klasnja P, Murphy S. Practical Considerations for Data Collection and Management in Mobile Health Micro-randomized Trials. Statistics in Biosciences 2019;11(2):355 View
  15. Fuller D, Colwell E, Low J, Orychock K, Tobin M, Simango B, Buote R, Van Heerden D, Luan H, Cullen K, Slade L, Taylor N. Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review. JMIR mHealth and uHealth 2020;8(9):e18694 View
  16. Piccinini F, Martinelli G, Carbonaro A. Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements. Sensors 2020;20(21):6293 View
  17. Krittanawong C, Rogers A, Johnson K, Wang Z, Turakhia M, Halperin J, Narayan S. Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nature Reviews Cardiology 2021;18(2):75 View
  18. Macedo L, Richardson J, Battie M, Hancock M, Kwan M, Hladysh G, Zhuo L. Back to living well: community-based management of low back pain: a feasibility study. Pilot and Feasibility Studies 2021;7(1) View
  19. Lunney M, Wiebe N, Kusi-Appiah E, Tonelli A, Lewis R, Ferber R, Tonelli M. Wearable Fitness Trackers to Predict Clinical Deterioration in Maintenance Hemodialysis: A Prospective Cohort Feasibility Study. Kidney Medicine 2021;3(5):768 View
  20. Park S, Marcotte R, Toth L, Paulus P, Lauricella L, Kim A, Crouter S, Springer C, Staudenmayer J, Bassett D. Free-Living Validation and Harmonization of 10 Wearable Step Count Monitors. Translational Journal of the American College of Sports Medicine 2021;6(4) View
  21. Nilsen I, Andersson A, Laurenius A, Osterberg J, Sundbom M, Haenni A. Lower Interstitial Glucose Concentrations but Higher Glucose Variability during Low-Energy Diet Compared to Regular Diet—An Observational Study in Females with Obesity. Nutrients 2021;13(11):3687 View
  22. Goh C, Wang N, Müller A, Yap R, Edney S, Müller-Riemenschneider F. Validation of Smartphones and Different Low-Cost Activity Trackers for Step Counting Under Free-Living Conditions. Journal for the Measurement of Physical Behaviour 2023:1 View
  23. Chrismas B, Majed L, Al-Mohannadi A, Sayegh S. Adherence and retention to the self-managed community-based Step Into Health program in Qatar (2012–2019). Frontiers in Public Health 2022;10 View
  24. Chevance G, Golaszewski N, Tipton E, Hekler E, Buman M, Welk G, Patrick K, Godino J. Accuracy and Precision of Energy Expenditure, Heart Rate, and Steps Measured by Combined-Sensing Fitbits Against Reference Measures: Systematic Review and Meta-analysis. JMIR mHealth and uHealth 2022;10(4):e35626 View
  25. Shi K, Chen Z, Li X, Xiao Z, Sun W, Hu W. Inferring Activity Patterns from Sparse Step Counts Data with Recurrent Neural Networks. ACM Transactions on Computing for Healthcare 2023;4(1):1 View
  26. Burns D, Boyer P, Arrowsmith C, Whyne C. Personalized Activity Recognition with Deep Triplet Embeddings. Sensors 2022;22(14):5222 View
  27. Cheng S, Alison J, Stamatakis E, Dennis S, McKeough Z. Validity and Accuracy of Step Count as an Indicator of a Sedentary Lifestyle in People With Chronic Obstructive Pulmonary Disease. Archives of Physical Medicine and Rehabilitation 2023;104(8):1243 View
  28. Culvenor A, West T, Bruder A, Scholes M, Barton C, Roos E, Oei E, McPhail S, Souza R, Lee J, Patterson B, Girdwood M, Couch J, Crossley K. SUpervised exercise-therapy and Patient Education Rehabilitation (SUPER) versus minimal intervention for young adults at risk of knee osteoarthritis after ACL reconstruction: SUPER-Knee randomised controlled trial protocol. BMJ Open 2023;13(1):e068279 View
  29. Lederer L, Breton A, Jeong H, Master H, Roghanizad A, Dunn J. The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program. JMIR mHealth and uHealth 2023;11:e45103 View
  30. Yuan J, Zhang Y, Liu S, Zhu R. Wearable Leg Movement Monitoring System for High-Precision Real-Time Metabolic Energy Estimation and Motion Recognition. Research 2023;6 View
  31. Perry A, Dooley E, Master H, Spartano N, Brittain E, Pettee Gabriel K. Physical Activity Over the Lifecourse and Cardiovascular Disease. Circulation Research 2023;132(12):1725 View
  32. SMALL S, KHALID S, PRICE A, DOHERTY A. Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty. Medicine & Science in Sports & Exercise 2024;56(5):805 View

Books/Policy Documents

  1. Klebbe R, Steinert A, Buchem I, Müller-Werdan U. Learning and Collaboration Technologies. Ubiquitous and Virtual Environments for Learning and Collaboration. View
  2. Xiao T, Albert M. Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. View