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

Big Data Analytics for Elder Care (28) Registered Report (704) Tools, Programs and Algorithms (338) Participatory Medicine & E-Patients (841) Digital Biomarkers and Digital Phenotyping (241) Aging with Chronic Disease (141) Epidemiologic Studies and Surveys in Elder Care (13) Bioinformatics Methods and Algorithms (15) Information and Patient Education on Healthy Aging (43)

Published on in Vol 5, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35696, first published .
A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

Authors of this article:

Karina Louise Skov Husted1, 2 Author Orcid Image ;   Andreas Brink-Kjær3 Author Orcid Image ;   Mathilde Fogelstrøm1 Author Orcid Image ;   Pernille Hulst1 Author Orcid Image ;   Akita Bleibach1 Author Orcid Image ;   Kaj-Åge Henneberg4 Author Orcid Image ;   Helge Bjarup Dissing Sørensen3 Author Orcid Image ;   Flemming Dela1, 5 Author Orcid Image ;   Jens Christian Brings Jacobsen6 Author Orcid Image ;   Jørn Wulff Helge1 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Bentlage E, Nyamadi J, Dubbeldam R. The Importance of Activating Factors in Physical Activity Interventions for Older Adults Using Information and Communication Technologies: Systematic Review. JMIR mHealth and uHealth 2023;11:e42968 View
  2. Coradduzza D, Sedda S, Cruciani S, De Miglio M, Ventura C, Nivoli A, Maioli M. Age-Related Cognitive Decline, Focus on Microbiome: A Systematic Review and Meta-Analysis. International Journal of Molecular Sciences 2023;24(18):13680 View
  3. Warner B, Ratner E, Datta A, Lendasse A. A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans. Aging 2024;16(17):12414 View
  4. Yoon D, Kim J, Lee S. A study on the development of a fitness age prediction model: the national fitness award cohort study 2017–2021. BMC Public Health 2024;24(1) View
  5. Jeong C, Leiby J, Kim D, Choe E. Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study. JMIR Aging 2025;8:e64473 View
  6. Espíndola-Fernández D, Posada-Cano A, Aristizábal-Ocampo D, Gallo-Villegas J. Medición de la edad biológica con biomarcadores: una revisión de alcance. Medicina 2024;46(1):178 View
  7. Pryimakov O, Sawczuk M, Korobeynikov G, Prysiazhniuk S, Skrypko A, Mazurok N. Structure of intra- and intergroup relationships among key parameters of physical development, physical fitness, and biological age in students aged 17–20. Physical Education of Students 2025;29(3):233 View

Conference Proceedings

  1. Zahra A, Saputra A, Sarwinda D, Bustamam A, Barinda A, Marliau R. 2023 Eighth International Conference on Informatics and Computing (ICIC). Construction Formula for Biological Age Estimation using Principal Component Analysis in Imbalanced Dataset View
  2. Collie A, Garcia E, Warner B, Ratner E, Douglas C. 2025 IEEE Conference on Artificial Intelligence (CAI). A Novel Modeling Focused Missing Data Imputation Framework View