Network Analysis of Time Use and Depressive Symptoms Among Emerging Adults: Findings From the Guizhou Population Health Cohort Study
To date, the relationship between diverse time use behaviors and depression status among emerging adults have not been disentangled in the literature. Therefore, if and how the time displacement mechanism activates depressive symptoms among emerging adults remains unclear. To fill this gap in the li...
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Veröffentlicht in: | Frontiers in psychiatry 2022-04, Vol.13, p.809745-809745 |
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Zusammenfassung: | To date, the relationship between diverse time use behaviors and depression status among emerging adults have not been disentangled in the literature. Therefore, if and how the time displacement mechanism activates depressive symptoms among emerging adults remains unclear.
To fill this gap in the literature, we employed a network analysis to make estimations. The emerging adult sample (
= 1,811) was collected by the Guizhou Population Health Cohort Study. Time use behaviors were measured by an adaption of the self-administered International Physical Activity Questionnaire, and depressive symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9).
The results revealed that the time displacement mechanism of emerging adults differed from that of adolescents. Sleep duration was not crowded out by other activities, while the time spent on computer use was found to be negatively related to time spent on heavy work activities. Moreover, computer use behavior triggered three depressive symptoms ("Anhedonia," "Guilt," and "Motor"), but inhibited "Suicide." The results of the directed acyclic graph revealed that females and heavy drinkers were at risk of depression.
The study sample was confined to only one province, which may limit its generalizability. The cross-sectional design impeded the ability to draw causal inferences.
Our results enhance the current understanding of the internal mechanism of how time use behaviors influence depressive symptoms among emerging adults. |
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ISSN: | 1664-0640 1664-0640 |
DOI: | 10.3389/fpsyt.2022.809745 |