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

Fitness Trackers and Smart Pedometers/Accelerometers (539) mHealth for Wellness, Behavior Change and Prevention (2728) Innovations and Technology for Physical Activity Education (694) Wearable Devices and Sensors (811) Ubiquitous Health (uHealth) (116)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25385, first published .
Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Associations of Physical Activity Level and Variability With 6-Month Weight Change Among 26,935 Users of Connected Devices: Observational Real-Life Study

Authors of this article:

Douae El Fatouhi1 Author Orcid Image ;   Lidia Delrieu2 Author Orcid Image ;   Catherine Goetzinger3, 4 Author Orcid Image ;   Laurent Malisoux3 Author Orcid Image ;   Aurélie Affret1 Author Orcid Image ;   David Campo5 Author Orcid Image ;   Guy Fagherazzi1, 3 Author Orcid Image
Article Authors Cited by (5) Tweetations (10) Metrics

Journals

  1. Bottaz‐Bosson G, Hamon A, Pépin J, Bailly S, Samson A. Continuous positive airway pressure adherence trajectories in sleep apnea: Clustering with summed discrete Fréchet and dynamic time warping dissimilarities. Statistics in Medicine 2021;40(24):5373 View
  2. Evenson K, Scherer E, Peter K, Cuthbertson C, Eckman S, Jaafar Z. Historical development of accelerometry measures and methods for physical activity and sedentary behavior research worldwide: A scoping review of observational studies of adults. PLOS ONE 2022;17(11):e0276890 View
  3. Bliudzius A, Svaikeviciene K, Puronaite R, Kasiulevicius V. Physical Activity Evaluation Using Activity Trackers for Type 2 Diabetes Prevention in Patients with Prediabetes. International Journal of Environmental Research and Public Health 2022;19(14):8251 View
  4. Dlima S, Shevade S, Menezes S, Ganju A. Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e39618 View
  5. Tang H, Jalil N, Tan C, He L, Zhang S. Why more successful? An analysis of participants’ self-monitoring data in an online weight loss intervention. BMC Public Health 2024;24(1) View