A cross-lagged panel network analysis of loneliness, internet use, and depression among Chinese college students
Displacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged netw...
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| Veröffentlicht in: | Journal of affective disorders Jg. 381; S. 372 - 380 |
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| Sprache: | Englisch |
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15.07.2025
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| Abstract | Displacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression.
The participants (N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics.
Contemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time.
These findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use.
•Loneliness in 2020 leads to increased depressive symptoms and slightly higher internet use in 2021.•Internet use shows mixed effects, reducing suicide risk but slightly increasing other depressive symptoms in 2021.•Cross-lagged panel network analysis reveals loneliness as the central driver. |
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| AbstractList | AbstractBackgroundDisplacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression. MethodsThe participants ( N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics. ResultsContemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time. ConclusionsThese findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use. Displacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression.BACKGROUNDDisplacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression.The participants (N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics.METHODSThe participants (N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics.Contemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time.RESULTSContemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time.These findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use.CONCLUSIONSThese findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use. Displacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression. The participants (N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics. Contemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time. These findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use. •Loneliness in 2020 leads to increased depressive symptoms and slightly higher internet use in 2021.•Internet use shows mixed effects, reducing suicide risk but slightly increasing other depressive symptoms in 2021.•Cross-lagged panel network analysis reveals loneliness as the central driver. Displacement theory proposes that internet use can lead to heightened feelings of loneliness and depression, whereas the stimulation theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged network analyses to facilitate causal inferences in understanding the relations between Internet use, loneliness and depression. The participants (N = 25,249) who were recruited from 22 colleges in Guangdong province, China completed the survey in 2020 and 2021. This study examined the interactions between Internet use, loneliness and depressive symptoms (PHQ-9) among this participants. All analyses adjusted for demographics. Contemporaneous networks revealed strong connections within the depression cluster and negligible associations between loneliness and internet use. Key bridging nodes were loneliness, guilt, anhedonia, and concentration problems, which linked constructs across the network. Temporal analyses did not support either the displacement or stimulation hypotheses, showing that higher loneliness predicted future depressive symptoms such as sad mood and anhedonia, as well as marginally higher internet use. Internet use exhibited mixed effects, weakly reducing suicide risk while marginally exacerbating depressive symptoms over time. These findings underscore the importance of loneliness as a central driver and highlight the nuanced role of internet use. |
| Author | Chen, Yujing Tang, Siyao Luo, Cong Liu, Mingzhu Chen, Rongning Zhao, Jingbo Li, Xueguo |
| Author_xml | – sequence: 1 givenname: Siyao surname: Tang fullname: Tang, Siyao organization: Mental Health Center, School of Public Health, Southern Medical University, Guangzhou 510515, China – sequence: 2 givenname: Cong surname: Luo fullname: Luo, Cong organization: Mental Health Center, Guangdong Baiyun University, Guangzhou, China – sequence: 3 givenname: Yujing surname: Chen fullname: Chen, Yujing organization: Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, Guangdong 510515, China – sequence: 4 givenname: Mingzhu surname: Liu fullname: Liu, Mingzhu organization: School of Traditional Chinese Medicine, Southern Medical University, Guangzhou 510515, China – sequence: 5 givenname: Rongning surname: Chen fullname: Chen, Rongning organization: Mental Health Center, School of Public Health, Southern Medical University, Guangzhou 510515, China – sequence: 6 givenname: Xueguo surname: Li fullname: Li, Xueguo organization: Mental Health Center, School of Public Health, Southern Medical University, Guangzhou 510515, China – sequence: 7 givenname: Jingbo surname: Zhao fullname: Zhao, Jingbo email: mingtian@smu.edu.cn organization: Mental Health Center, School of Public Health, Southern Medical University, Guangzhou 510515, China |
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| Keywords | Loneliness Depressive symptoms Internet use Cross-lagged panel network design |
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| SubjectTerms | Adolescent Adult China - epidemiology Cross-lagged panel network design Cross-Sectional Studies Depression - epidemiology Depression - psychology Depressive symptoms Female Humans Internet - statistics & numerical data Internet use Internet Use - statistics & numerical data Loneliness Loneliness - psychology Male Psychiatric/Mental Health Students - psychology Students - statistics & numerical data Surveys and Questionnaires Universities Young Adult |
| Title | A cross-lagged panel network analysis of loneliness, internet use, and depression among Chinese college students |
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