Evidence map›Paper›PMID 42712341›Full record

ArticleFrontiers in public health2026

The divergent impacts of digital engagement on depression: a longitudinal study of competitive resource mechanisms.

Jidong Hu, Lei Liu, Yang Zou

Abstract read
In one paragraph

Article in Frontiers in public health, 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
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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

3 authors.

Jidong Hu *School of Marxism, Henan Normal University, Xinxiang, Henan, China.
Lei Liu *Graduate School of Education, Dalian University of Technology, Dalian, Liaoning, China.
Yang ZouGraduate School of Education, Dalian University of Technology, Dalian, Liaoning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital engagement has become an important factor influencing mental health, yet the complex effects of different usage patterns still require deeper clarification. This study distinguishes digital engagement into instrumental-oriented digital engagement (IE) and affect-oriented digital engagement (AE), and utilizes panel data on the working-age population from the China Family Panel Studies (CFPS) from 2016 to 2022 to examine their associations with depression and their underlying competitive resource mechanisms. The results indicate that IE is associated with higher depression levels, whereas AE is associated with lower depression levels. Mechanism analysis reveals that both types of digital engagement contribute to enhancing psychological mastery resources, but also consume physiological recovery resources such as sleep and exercise. Among them, the physiological loss associated with IE is more likely to offset its psychological gains, whereas AE is more likely to perform an emotional recovery function under conventional intensity levels. Further analysis shows that the association between IE and depression is stronger among populations with high social role stress, and the association between AE and depression exhibits nonlinear characteristics. The study suggests that different digital engagement patterns exhibit asymmetric and competitive resource characteristics. Public health interventions should not simply emphasize reducing digital use, but rather guide individuals to maintain a dynamic balance between instrumental digital loads and moderate emotional recovery.

Indexed as

DepressionChinaDigital MediaFemaleHumansLongitudinal Studiesdepressiondigital engagementemotional compensationphysiological costresource conservation

Identifiers

PMID42712341
PMCPMC13549851

What Socratic holds

Textmetadata
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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 Socratic graph.