Anxiety, emotional abuse, and physical activity as correlates of AI-related affective dependence among adolescents: mediation, moderation, and network analyses.
9 affiliations
- 1Chengdu University, Chengdu, China.
- 2Department of Anesthesiology for Surgery, Xilingol League Central Hospital, Xilin Hot, Inner Mongolia, China.
- 3Heilongjiang University of Chinese Medicine, Harbin, China.
- 4School of Physical Education, Chengdu Vocational University of the Arts, Chengdu, China.
- 5Jishou University, Jishou, China.
- 6Jilin Medical University, Jilin City, China.
- 7Affiliated Hospital of Jilin Medical University, Jilin City, China.
- 8Chengdu Experimental Middle School, Chengdu, China.
- 9Department of Science and Education, Xilingol League Central Hospital, Xilinhot, Inner Mongolia, China.
Abstract
Objective
Drawing on attachment and emotion-regulation perspectives, this cross-sectional study examined the associations among emotional abuse (EA), anxiety, physical activity (PA), and AI-related affective dependence (hereafter, AI dependence) among adolescents.Methods
In November 2025, a total of 1,589 Chinese adolescents completed self-report questionnaires assessing EA, anxiety, PA, and AI dependence. Descriptive analyses, Pearson correlations, mediation and moderated mediation models, and a covariate-adjusted partial-correlation network were conducted. Common method bias was evaluated using both Harman's single-factor test and a CFA-based unmeasured latent method construct (ULMC) approach.Results
EA was positively associated with anxiety and AI dependence and negatively associated with PA. Anxiety statistically accounted for a small but significant proportion of the EA-AI dependence association [indirect effect = 0.034, 95% CI (0.013, 0.055), 16.43% of the total association]. PA moderated the EA-anxiety association; the positive association was weaker among adolescents reporting higher PA. In the network analysis, the EA-anxiety edge was the strongest conditional association, and EA showed the highest node strength, although these findings should be interpreted descriptively rather than causally.Conclusion
The findings suggest that anxiety and PA are meaningful correlates in the association between EA and AI dependence. Because the data are cross-sectional, the results indicate statistical associations rather than temporal or causal mechanisms.
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BibTeX
@article{yang2026anxiety,
title = {{Anxiety, emotional abuse, and physical activity as correlates of AI-related affective dependence among adolescents: mediation, moderation, and network analyses.}},
author = {Jingyu Yang and Jing Wang and Xiaodi Ma and Ning Wang and Ting Xiao and Ye Wu and Liying Lan and Yang Liu and Yanfei Wang},
journal = {Frontiers in public health},
year = {2026},
month = oct,
doi = {10.3389/fpubh.2026.1956596},
url = {https://europepmc.org/article/MED/42903159},
note = {CC BY},
}
RIS
TY - JOUR
TI - Anxiety, emotional abuse, and physical activity as correlates of AI-related affective dependence among adolescents: mediation, moderation, and network analyses.
AU - Jingyu Yang
AU - Jing Wang
AU - Xiaodi Ma
AU - Ning Wang
AU - Ting Xiao
AU - Ye Wu
AU - Liying Lan
AU - Yang Liu
AU - Yanfei Wang
PY - 2026
DA - 2026/10/06
JO - Frontiers in public health
DO - 10.3389/fpubh.2026.1956596
UR - https://europepmc.org/article/MED/42903159
AB - Objective Drawing on attachment and emotion-regulation perspectives, this cross-sectional study examined the associations among emotional abuse (EA), anxiety, physical activity (PA), and AI-related affective dependence (hereafter, AI dependence) among adolescents.Methods In November 2025, a total of 1,589 Chinese adolescents completed self-report questionnaires assessing EA, anxiety, PA, and AI dependence. Descriptive analyses, Pearson correlations, mediation and moderated mediation models, and a covariate-adjusted partial-correlation network were conducted. Common method bias was evaluated using both Harman's single-factor test and a CFA-based unmeasured latent method construct (ULMC) approach.Results EA was positively associated with anxiety and AI dependence and negatively associated with PA. Anxiety statistically accounted for a small but significant proportion of the EA-AI dependence association [indirect effect = 0.034, 95% CI (0.013, 0.055), 16.43% of the total association]. PA moderated the EA-anxiety association; the positive association was weaker among adolescents reporting higher PA. In the network analysis, the EA-anxiety edge was the strongest conditional association, and EA showed the highest node strength, although these findings should be interpreted descriptively rather than causally.Conclusion The findings suggest that anxiety and PA are meaningful correlates in the association between EA and AI dependence. Because the data are cross-sectional, the results indicate statistical associations rather than temporal or causal mechanisms.
SN - 2296-2565
ER -