本研究采用两时点的纵向研究设计,基于6764名老年人被试,探究互联网使用对老年人抑郁症状的影响以及社会网络（朋友网络和家庭网络）的中介作用和城乡因素的调节作用。研究发现：（1）互联网使用显著负向预测老年人的抑郁症状;（2）朋友网络在两者之间发挥中介作用,且朋友网络与家庭网络还在其间发挥链式中介作用;（3）城乡因素显著调节互联网使用对朋友网络的影响。在城市老年人中,朋友网络的独立中介效应以及朋友网络和家庭网络的链式中介效应成立;农村老年人无法通过互联网使用改善社会网络,进而减轻其抑郁症状。研究结论对于老年人抑郁症状的早期预防和心理干预具有积极意义。
Abstract
The high penetration rate of internet technology among older adults provides an opportunity to explore the influence of internet use on older adults’ depression symptoms. Existing studies have focused on internet use among adolescents, and relatively few studies have explored the association between internet use and depression symptoms among older adults. Based on the main effects model and buffering model of social networks, this study considers internet use as an online social network for older adults and hypothesizes that internet use is negatively associated with depression symptoms among older adults.
The mechanisms underlying how internet use affects depression symptoms are limited. Internet is an online social network, and the “network gain effect theory” believes that online social networks can increase offline social networks in real life, which furtherly reduce depression symptoms. Thus, social networks may play a mediating role in the relationships between internet use and depression symptoms. However, existing studies mainly focus on the overall social network of the older adults, such as the mediating role of social capital, and failed to explore the differences in the mediating role of different types of social networks. Because family and friend social networks are the important types of offline social networks, this study hypothesizes that family and friend social networks play mediating effects in the associations between internet use and depression symptoms. Besides, according to the pattern of different sequences, the effects of internet use on family and friend social networks may be sequential. This study therefore hypothesizes that friends and family social networks play a chain mediating effect. In addition, as the long-time of urban and rural division in China, the effects of internet use on family and friend social networks may also demonstrate the disparities in rural and urban areas. This study hypothesizes that urban-rural disparity moderates the effects of internet use on social networks.
The China Longitudinal Aging Social Survey (CLASS) in the 2016 and 2018 waves were employed. After excluding samples with missing data of key variables, a number of 6,764 older samples were selected. SPSS 23.0 and PROCESS were used to analyze data. First, this study examined the common method deviation analysis. Second, the descriptive results and the correlation and regression analyses between key variables were reported. Third, this study explored mediating effects of family and friend social networks. Finally, the moderating effects of urban-rural disparity were tested.
The results were as follows. First, internet use was significantly associated with fewer depression symptoms. Second, friend social network played a mediating effect on the associations between internet use and depression symptoms, while the mediating effects of the family social network were not significant. In addition, friend and family social networks played a chain mediating effect. In terms of the moderating effects of urban-rural disparity, internet use was positively associated with a larger friend social network among urban older adults. However, no significant moderating effect was found in terms of the influence of internet use on family social networks. Furthermore, friend social networks, as well as the chain association of friend and family social networks played mediating effects among urban older adults, but for rural older adults, no significant mediating effects of friends and family social networks were found.
Practical contributions are proposed in this study. First, the problem of the "digital divide" should be bridged and measures should be taken to promote internet use among older adults, especially for rural older adults. Second, friend social networks are an important target for depression intervention, and social activities should be carried out to provide an opportunity to expand friend social network. Third, when intervening on depression symptoms based on older adults’ social networks, it is important to consider the urban-rural disparity. The intervention on urban older adults’ friend social networks could benefit to alleviate depression symptoms. For rural older adults, other effective methods should be considered to promote their friends and family social networks.
关键词
互联网使用 /
抑郁症状 /
家庭网络 /
朋友网络 /
城乡因素
Key words
Internet use /
depression symptoms /
family social network /
friend social network /
urban-rural disparity
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 丁轶飞, 陈兰双, 张镇. (2022). 老年人社交网络量表在中国的信效度检验. 中国临床心理学杂志, 30(4), 909-914.
[2] 杜鹏, 汪斌. (2020). 互联网使用如何影响中国老年人生活满意度? 人口研究, 44(4), 3-17.
[3] 费孝通. (2006). 乡土中国. 上海人民出版社.
[4] 牛更枫, 史晓涵, 田媛, 孙晓军, 雷玉菊. (2021). 社交网站使用与老年人抑郁:线上社会资本和孤独感的作用. 中国临床心理学杂志, 29(5), 1055-1059.
[5] 牛更枫, 孙晓军, 周宗奎, 田媛, 刘庆奇, 连帅磊. (2016). 青少年社交网站使用对自我概念清晰性的影响: 社会比较的中介作用. 心理科学, 39(1), 97-102.
[6] 荣健, 戈艳红, 孟娜娜, 谢婷婷, 丁宏. (2020). 2010~2019年中国老年人抑郁症患病率的Meta分析. 中国循证医学杂志, 20(1), 26-31.
[7] 沈费伟, 曹子薇. (2023). 从数字鸿沟到数字包容: 老年人参与数字乡村建设的策略选择. 西北农林科技大学学报(社会科学版), 23(1), 21-29.
[8] 史珈铭, 刘晓婷. (2022). 社会隔离对老年人认知功能的影响: 有调节的链式中介效应. 心理科学, 45(5), 1182-1189.
[9] 唐丹, 张琨, 亓心茹. (2022). 互联网使用对老年人社会网络及孤独感的影响: 基于用途的分析. 人口研究, 46(3), 88-101.
[10] 汪国华. (2006). 从熟人社会到陌生人社会: 城市离婚率趋高的社会学透视. 新疆社会科学, 5, 99-104.
[11] 韦宏耀, 钟涨宝. (2016). 代际交换、孝道文化与结构制约: 子女赡养行为的实证分析. 南京农业大学学报(社会科学版), 16(1), 144-155.
[12] 谢立黎, 杨璐, 胡波, 王飞. (2022). 社交软件使用对中老年人社会网络的影响. 人口研究, 46(5), 91-103.
[13] 熊婕, 周宗奎, 陈武, 游志麒, 翟紫艳. (2012). 大学生手机成瘾倾向量表的编制.中国心理卫生杂志, 26(3), 222-225.
[14] 张文娟, 刘瑞平. (2016). 中国老年人社会隔离的影响因素分析. 人口研究, 40(5), 75-91.
[15] 周爱保, 刘沛汝, 张彦驰, 尹玉龙. (2015). 老年人的朋友参照效应. 心理学报, 47(9), 1143-1151.
[16] 周浩, 龙立荣. (2004). 共同方法偏差的统计检验与控制方法. 心理科学进展, 12(6), 942-950.
[17] 周榕, 李光勤, 王娟. (2020). 代际居住距离对独居老人孤独感的影响研究——基于2661名城市独居老人的经验分析. 西北人口, 41(6), 102-114.
[18] Carstensen, L. L. (1992). Social and emotional patterns in adulthood: Support for socioemotional selectivity theory. Psychology and Aging, 7(3), 331-338.
[19] Chang Q. S., Sha F., Chan C. H., & Yip, P. S. F. (2018). Validation of an abbreviated version of the Lubben Social Network Scale (“LSNS-6”) and its associations with suicidality among older adults in China. PLoS ONE, 13(8), Article e0201612.
[20] Chen E. R. C., Wood D., & Ysseldyk R. (2022). Online social networking and mental health among older adults: A scoping review. Canadian Journal on Aging, 41(1), 26-39.
[21] Chen, Y. R. R., & Schulz, P. J. (2016). The effect of information communication technology interventions on reducing social isolation in the elderly: A systematic review. Journal of Medical Internet Research, 18(1), Article e18.
[22] Cong, Z., & Silverstein, M. (2008). Intergenerational time-for-money exchanges in rural China: Does reciprocity reduce depressive symptoms of older grandparents. Research in Human Development, 5(1), 6-25.
[23] Cotten S. R., Ford G., Ford S., & Hale T. M. (2014). Internet use and depression among retired older adults in the united states: A longitudinal analysis. The Journals of Gerontology Series B, 69(5), 763-771.
[24] Hayes A. F.(2018). Introduction to mediation, moderation, and conditional process analysis: a regression-Based approach. Guilford Press.
[25] Kahn R. L.,& Antonucci, T. C. (1980). Convoys over the life course: Attachment, roles, and social support. In P. B. Baltes, & O. G. Grim (Eds.), Life span development and behavior ( pp. 253-286). Academic Press.
[26] Lam S. S. M., Jivraj S., & Scholes S. (2020). Exploring the relationship between internet use and mental health among older adults in England: Longitudinal observational study. Journal of Medical Internet Research, 22(7), Article e15683.
[27] Lubben J., Blozik E., Gillmann G., Iliffe S., Von Renteln Kruse W., Beck J. C., & Stuck A. E. (2006). Performance of an abbreviated version of the Lubben social network scale among three European community-dwelling older adult populations. The Gerontologist, 46(4), 503-513.
[28] Morris M. E., Adair B., Ozanne E., Kurowski W., Miller K. J., Pearce A. J., & Said C. M. (2014). Smart technologies to enhance social connectedness in older people who live at home. Australasian Journal on Ageing, 33(3), 142-152.
[29] Podsakoff P. M., MacKenzie S. B., Lee J. Y., & Podsakoff N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903.
[30] Radloff, L. S. (1997). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401.
[31] Uchino B. N., Cacioppo J. T., & Kiecolt-Glaser J. K. (1996). The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychological Bulletin, 119(3), 488-531.
[32] Wrzus C., Hänel M., Wagner J., & Neyer F. J. (2013). Social network changes and life events across the life span: A meta-analysis. Psychological Bulletin, 139(1), 53-80.
[33] Zheng, Z. H., & Chen, H. (2020). Age sequences of the elderly' social network and its efficacies on well-being: An urban-rural comparison in China. BMC Geriatrics, 20(1), Article 372.
基金
*本研究得到国家社科基金重大项目（21ZDA103）的资助
PDF(819 KB)
