Evidence map›Paper›PMID 42783180›Full record

ArticleBiosensors2026

Wearable-Derived Evening Motion Entropy and Morning Light Exposure Are Associated with Depressive Symptoms in University Students.

Guanxiang Ding, Qizhi Zhao, Linxin Zou, Yuezhou Zhang, Xianghong Zhao, Hao Li, Zhengxiang Yu

Abstract read
In one paragraph

Article in Biosensors, 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

7 authors.

Guanxiang DingSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Qizhi ZhaoSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.ORCID 0009-0007-2350-3750
Linxin ZouSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Yuezhou ZhangDepartment of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London WC2R 2LS, UK.ORCID 0000-0003-4817-3835
Xianghong ZhaoSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.ORCID 0000-0001-6504-1418
Hao LiSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.
Zhengxiang YuSchool of Information Science and Engineering, NingboTech University, Ningbo 315100, China.

Funding

2023 Ningbo Key Research Plan No. 2023Z040Major Technological Innovation Projects of Ningbo High tech Zone No. 2022BCX050002National Natural Science Foundation of China No. 61972350Ningbo 2025 Major Project of Science and Technology Innovation No. 2021Z109Ningbo Natural Science Foundation project No. 2024J215Ningbo Public Welfare Research Program No. 2023S108Ningbo Science and Technology Innovation Yongjiang 2035 Key Technology Breakthrough Plan No. 2024Z261Zhejiang Provincial Natural Science Foundation No. LY23F020013
6 · The paper itself

Abstract

Depressive symptoms are prevalent in university students, yet scalable screening is constrained by self-report and intermittent assessment. Wearable monitoring of motion and light offers an objective alternative, though their combined screening characterization is limited. Undergraduate volunteers wore a self-designed wristband for seven days, recording triaxial acceleration and ambient light at 1 min resolution. Depressive symptoms were assessed via nine-item Patient Health Questionnaire (PHQ-9) and grouped as Dep (≥5) or HC (<5). Motion and light rhythm features were extracted from 24 h profiles and predefined windows; logistic regression (LR) and support vector machine (SVM) compared motion-only, light-only, and joint sets, interpreted with Shapley additive explanations (SHAP). After preprocessing, 152 participants provided valid activity data (HC = 132, Dep = 20) and 218 valid light data (HC = 186, Dep = 32). The Dep group showed delayed activity timing, lower daily light exposure, and greater evening motion irregularity, with differences most prominent during 18:00-22:00 for motion entropy and variance. The joint motion-light SVM performed best (accuracy 85.2%, recall 60.0%, F1 Score 46.2%, Area Under Curve 0.892). SHAP highlighted evening motion entropy and variance, morning light exposure, and rhythm-related wavelet features. These findings indicate that wearable motion and light rhythms are associated with depressive symptoms; evening motion entropy and morning light exposure may aid campus risk screening as interpretable markers, although the cross-sectional design and PHQ-9-based grouping preclude diagnostic or causal conclusions.

Indexed as

DepressionWearable Electronic DevicesAdultEntropyFemaleHumansLightMaleMotionStudentsUniversitiesYoung Adultcircadian rhythm disruptiondepressive symptomslight exposuremachine learningwearable sensing

Identifiers

PMID42783180
PMCPMC13604703

What OpenQuestion holds

Textmetadata
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Registered trials

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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.