Evidence map›Paper›PMID 41852810›Full record

ReviewResearch (Washington, D.C.)2026

Behavioromics: A New Paradigm of Big Data-Powered Insights for Proactive Health.

Jingman Shi, Junlong Li, Huanhuan Huang, Teng Teng, Hao Wu

Abstract readReview
In one paragraph

Review in Research (Washington, D.C.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jingman ShiCenter for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P. R. China.ORCID https://orcid.org/0009-0004-3048-1973
Junlong LiCenter for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P. R. China.ORCID https://orcid.org/0009-0008-3610-3729
Huanhuan HuangCenter for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P. R. China.ORCID https://orcid.org/0000-0003-0845-7526
Teng TengDepartment of Psychiatry, the First Affiliated Hospital of Chongqing Medical University, Key Laboratory of Major Brain Disease and Aging Research (Ministry of Education), Psychiatric Center of Chongqing Medical University the First Affiliated Hospital, Chongqing 400014, P. R. China.ORCID https://orcid.org/0009-0003-3993-0491
Hao WuCenter for Big Data and Intelligent Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P. R. China.ORCID https://orcid.org/0009-0004-3048-1973

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Behavior plays a critical role in health and disease. Although molecular omics have advanced the understanding of biological mechanisms, they alone cannot explain the role of behavior in health, and traditional behavioral measurement technologies have struggled to capture the dynamic, multidimensional, and heterogeneous nature of real-world behaviors. With the development of digital technologies such as wearable devices, smartphone sensors, and ecological momentary assessment, behavioromics has emerged as a new research paradigm with a holistic systems perspective, integrating continuous, multimodal behavioral data with molecular, physiological, and environmental information. By organizing behavioral measurement, modeling, and interpretation within a unified analytical framework, behavioromics reveals dynamic behavioral patterns, supports mechanism-informed inference, and identifies actionable targets for early intervention. Despite ongoing challenges in data heterogeneity, causal interpretation, and ethical governance, behavioromics holds promise for reshaping disease prediction, early detection, and precision intervention, and for advancing proactive health through earlier, behavior-centered prevention.

Identifiers

PMID41852810
PMCPMC12992924

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.