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

Wearables and MHealth Viewpoints (83) Fitness Trackers and Smart Pedometers/Accelerometers (606) Evaluation and Research Methodology for mHealth (219) Fitness Trackers and Smart Pedometers/Accelerometers (606) Wearable Devices and Sensors (980) mHealth for Data Collection and Research (1072) Mobile Health (mhealth) (3137) mHealth for Data Collection and Research (1072) Evaluation and Research Methodology for mHealth (219) Mobile Health (mhealth) (3137) Wearable Devices and Sensors (980)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45103, first published .
The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program

The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program

The Importance of Data Quality Control in Using Fitbit Device Data From the All of Us Research Program

Authors of this article:

Lauren Lederer1 Author Orcid Image ;   Amanda Breton2 Author Orcid Image ;   Hayoung Jeong1 Author Orcid Image ;   Hiral Master3 Author Orcid Image ;   Ali R Roghanizad1 Author Orcid Image ;   Jessilyn Dunn1 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Daniore P, Nittas V, Haag C, Bernard J, Gonzenbach R, von Wyl V. From wearable sensor data to digital biomarker development: ten lessons learned and a framework proposal. npj Digital Medicine 2024;7(1) View
  2. Morris A, Douch S, Popnikolova T, McGinley C, Matcham F, Sonuga-Barke E, Downs J. A framework for remotely enabled co-design with young people: its development and application with neurodiverse children and their caregivers. Frontiers in Psychiatry 2024;15 View
  3. Lee J, Kong S, Shin S, Lee G, Kim H, Shim Y, Cho J, Kang D, Park H. Wearable Device–Based Intervention for Promoting Patient Physical Activity After Lung Cancer Surgery. JAMA Network Open 2024;7(9):e2434180 View
  4. Guzmán‐Vélez E, Rivera‐Hernández A, Fabrega S, Oliveira G, Martínez J, Baena A, Picard G, Lopera F, Arnold S, Taylor J, Quiroz Y. Relationship between physical activity and biomarkers of pathology and neuroinflammation in preclinical autosomal‐dominant Alzheimer's disease. Alzheimer's & Dementia: Translational Research & Clinical Interventions 2024;10(4) View
  5. Liu J, Borsari B, Li Y, Liu S, Gao Y, Xin X, Lou S, Jensen M, Garrido-Martín D, Verplaetse T, Ash G, Zhang J, Girgenti M, Roberts W, Gerstein M. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell 2025;188(2):515 View
  6. Jeong H, Roghanizad A, Master H, Kim J, Kouame A, Harris P, Basford M, Marginean K, Dunn J. Data from the All of Us research program reinforces existence of activity inequality. npj Digital Medicine 2025;8(1) View
  7. Doherty I, Wells M. Sleep disparities in the United States: Comparison of logistic and linear regression with stratification by race. Sleep Epidemiology 2025;5:100106 View
  8. Zhang Y, Stewart C, Ranjan Y, Conde P, Sankesara H, Rashid Z, Sun S, Dobson R, Folarin A. Large-scale digital phenotyping: Identifying depression and anxiety indicators in a general UK population with over 10,000 participants. Journal of Affective Disorders 2025;375:412 View

Conference Proceedings

  1. Roomkham S. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. 'If It Doesn't Work for Me as a Researcher, Let Alone Others' : Understanding Wellbeing Through an Autoethnography of Wearable and Non-Wearable Technologies View