ArticleJMIR research protocols2020
A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.
Article in JMIR research protocols, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Development of a New Biological Age Technology Applicable in Public Health Promotion Interventions.
Evaluation and Validation of Biological Age as Health Technology
Validity and Reliability of a Clinical Method for Assessment of Cardiorespiratory Fitness Using Seismocardiography
Who cites it
5 citing papers in PubMed.
- Multidimensional modeling of biological aging: integrating gait, eye movement, rest-state functional connectivity, and plasma biomarkers in non-dementia older adults.The journal of prevention of Alzheimer's disease · 2026Article
- Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies.Frontiers in aging neuroscience · 2025Review
- A biological age model based on physical examination data to predict mortality in a Chinese population.iScience · 2024Article
- Living in poverty and accelerated biological aging: evidence from population-representative sample of U.S. adults.BMC public health · 2024Article
- A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.JMIR aging · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundActions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span.
objectiveThis interdisciplinary study intends to develop a biological age model and explore its usefulness.
methodsThe model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course.
resultsWe completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021.
conclusionsWe expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale.
trial registrationClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/19209.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.