Latent Profiles and Transitions of Bedtime Procrastination Among Chinese College Students: The Predictive Roles of Anxiety, Depression, Problematic Smartphone Use and Self-Control

Lan Hong,1,* Huihui Xu,2,* Jiaqi Zheng,2 Xiujian Lin,2 Lijun Wang,2 Chengjia Zhao,3 Xiaolian Tu,4 Jingjing Zhang,5 Ke Zhao,1,6 Guohua Zhang6,7,* 1Lishui Second People’s Hospital Affiliated to Wenzhou Medical University, Lishui, 323000, People’s Republic of China; 2School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 3School of Education, Renmin University of China, Beijing, 100872, People’s Republic of China; 4School of Renji, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 5School of Nursing, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 6Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China; 7Institute Of Aging, Key Laboratory Of Alzheimer’s Disease Of Zhejiang Province, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guohua Zhang, Zhejiang Provincial Clinical Research Center for Mental Disorders, The Affiliated Wenzhou Kangning Hospital, Wenzhou Medical University, Wenzhou, 325035, People’s Republic of China, Email zghcnu@wmu.edu.cn Ke Zhao, Lishui Second People’s Hospital Affiliated to Wenzhou Medical University, Lishui, 323000, People’s Republic of China, Email cocozk1986@163.comBackground: Bedtime procrastination (BP) has become an important factor affecting individual well-being. This study aimed to assess the stability and changes in BP and examine risk and protective factors.Methods: The study recruited 1423 respondents. Latent profile analysis was used to identify subgroups of BP and latent transition analysis to determine transition probabilities for each subgroup. Logistic regression examined associations between identified classes and related factors.Results: Three subgroups of BP were identified. In terms of stability and changes, the moderate bedtime procrastination group showed the highest stability (66%), followed by the severe bedtime procrastination group (62.4%), and the mild bedtime procrastination group had a 52% probability of switching to moderate bedtime procrastination. In terms of influencing factors, more problematic phone use (PSU) (OR: 1.08; 95% CI = 1.05– 1.12), more depression (OR: 1.17; 95% CI = 1.06– 1.29) and anxiety (OR: 1.16; 95% CI = 1.05– 1.28) are all factors that aggravate the transition from mild to moderate sleep procrastination. Similarly, PSU (OR: 1.15; 95% CI = 1.12– 1.19), anxiety (OR: 1.10; 95% CI = 1.06– 1.14), and depression (OR: 1.10; 95% CI = 1.06– 1.14) increased the risk of severe bedtime procrastination. Self-control emerged as a protective factor against BP.Conclusion: This study identified three subgroups of BP at two time points and the rule of transition for each subgroup. Our findings indicate that BP were relatively stable, with some changes over time. The results also highlight the important function that PSU, depression, anxiety, and self-control can play in preventing and intervening in BP.Keywords: bedtime procrastination, latent profile analysis, latent transition analysis, problematic smartphone use, depression, anxiety, self-control