Evidence map›Paper›PMID 40852149›Full record

ArticleFrontiers in psychiatry2025

Smartphone sensor-based depression detection in campus environments: a proof-of-concept study with small-sample behavioral analysis.

Yichen Bai, Yueze Liu, Yang Zhang, Amr Tolba

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Yichen Bai *School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Yueze Liu *School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Yang ZhangCyberspace Administration of Lanzhou University, Lanzhou, China.
Amr TolbaComputer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Depression is a rising global health issue, particularly among adolescents, with university students facing distinct mental health challenges. Methods: This proof-of-concept study explores smartphone sensor-based depression detection in Chinese university campus settings using a small sample of 12 participants. We utilized data from accelerometers, gyroscopes, and light sensors to establish associations between smartphone-derived behavioral patterns and PHQ-9 scores, a standard depression measure. A customized data processing scheme tailored to campus life enabled the extraction of 18 feature sequences reflecting depressive symptoms. Feature selection was conducted using Pearson correlation, and model validation was performed using leave-one-out cross-validation with common classification algorithms. Results: The results yielded accuracy rates between 73.11% and 88.24%. Findings showed negative correlations between PHQ-9 scores and dietary regularity, bedtime, and physical activity levels. Discussion: This pioneering study highlights smartphone sensors' potential for early depression detection in Chinese higher education, supporting non-invasive mental health interventions.

Indexed as

daily mobile behavior analysisdepression detectionfeature engineeringmachine learningsmall data samplessmartphone sensors

Identifiers

PMID40852149
PMCPMC12368363

What OpenQuestion holds

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LicenceCC BY
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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.