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

mHealth for Wellness, Behavior Change and Prevention (2873) Accident and Injury Prevention (42) Automobile and Road Safety (16)

Published on in Vol 6, No 4 (2018): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9290, first published .
Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

Measuring Risky Driving Behavior Using an mHealth Smartphone App: Development and Evaluation of gForce

Authors of this article:

Raisa Z Freidlin1 Author Orcid Image ;   Amisha D Dave2 Author Orcid Image ;   Benjamin G Espey1 Author Orcid Image ;   Sean T Stanley1 Author Orcid Image ;   Marcial A Garmendia1 Author Orcid Image ;   Randall Pursley1 Author Orcid Image ;   Johnathon P Ehsani3 Author Orcid Image ;   Bruce G Simons-Morton4 Author Orcid Image ;   Thomas J Pohida1 Author Orcid Image
Article Authors Cited by (12) Tweetations (6) Metrics

Journals

  1. Grimberg E, Botzer A, Musicant O. Smartphones vs. in-vehicle data acquisition systems as tools for naturalistic driving studies: A comparative review. Safety Science 2020;131:104917 View
  2. Abbas Q, Alsheddy A. Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis. Sensors 2020;21(1):56 View
  3. Kozak K, Seidel A, Matvieieva N, Neupetsch C, Teicher U, Lemme G, Ben Achour A, Barth M, Ihlenfeldt S, Drossel W. Unique Device Identification–Based Linkage of Hierarchically Accessible Data Domains in Prospective Surgical Hospital Data Ecosystems: User-Centered Design Approach. JMIR Medical Informatics 2023;11:e41614 View
  4. Lazuras L, Rowe R, Ypsilanti A, Smythe I, Poulter D, Reidy J. Driving self-regulation and risky driving outcomes. Transportation Research Part F: Traffic Psychology and Behaviour 2022;91:461 View
  5. Singh H, Kathuria A. Profiling drivers to assess safe and eco-driving behavior – A systematic review of naturalistic driving studies. Accident Analysis & Prevention 2021;161:106349 View
  6. Ehsani J, Weast R, Chirles T, Hellinger A, Shields W, Yenokyan G, Igusa T. Evaluating a smartphone application to increase the quantity and improve the quality of supervised practice driving. Injury Prevention 2021;27(6):587 View
  7. Toups R, Chirles T, Ehsani J, Michael J, Bernstein J, Calamia M, Parsons T, Carr D, Keller J, Albert S. Driving Performance in Older Adults: Current Measures, Findings, and Implications for Roadway Safety. Innovation in Aging 2022;6(1) View
  8. Lazuras L, Rowe R, Ypsilanti A, Smythe I, Poulter D, Reidy J. Driving Self-Regulation and Risky Driving Outcomes. SSRN Electronic Journal 2022 View
  9. Sun T, Hu H, Cai R, Yu T, Yu F. Correlation Analysis of Drivers’ Natural Driving Behavior Based on Kernel Density Estimation. SAE International Journal of Sustainable Transportation, Energy, Environment, & Policy 2023;4(2) View
  10. Zhang C, Ma Y, Chen S, Zhang J, Xing G. Exploring the occupational fatigue risk of short-haul truck drivers: Effects of sleep pattern, driving task, and time-on-task on driving behavior and eye-motion metrics. Transportation Research Part F: Traffic Psychology and Behaviour 2024;100:37 View
  11. Chen H, Möller H, Senserrick T, Rogers K, Cullen P, Ivers R. Young drivers’ early access to their own car and crash risk into early adulthood: Findings from the DRIVE study. Accident Analysis & Prevention 2024;199:107516 View
  12. Katayama A, Abe T, Hase A, Miyatake N. Relationship between Driving Ability and Physical Fitness Factors in Older Adults: A Multiple Linear Regression Analysis. International Journal of Environmental Research and Public Health 2024;21(6):660 View