UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
■这项研究招募了1423名受访者。潜在轮廓分析用于识别BP的亚组,潜在过渡分析用于确定每个亚组的过渡概率。Logistic回归检查了确定的类别和相关因素之间的关联。
■确定了三个BP亚组。在稳定性和变化方面,中度睡前拖延组表现出最高的稳定性(66%),其次是重度睡前拖延组(62.4%),轻度睡前拖延组转为中度睡前拖延的概率为52%。从影响因素来看,更有问题的电话使用(PSU)(OR:1.08;95%CI=1.05-1.12),更多的抑郁(OR:1.17;95%CI=1.06-1.29)和焦虑(OR:1.16;95%CI=1.05-1.28)都是加重从轻度到中度睡眠拖延转变的因素。同样,PSU(OR:1.15;95%CI=1.12-1.19),焦虑(OR:1.10;95%CI=1.06-1.14),和抑郁症(OR:1.10;95%CI=1.06-1.14)增加了严重睡前拖延的风险。自我控制成为对抗BP的保护因素。
■这项研究确定了BP在两个时间点的三个亚组以及每个亚组的转换规则。我们的研究结果表明,血压相对稳定,随着时间的推移有些变化。结果还突出了PSU的重要功能,抑郁症,焦虑,自我控制可以起到预防和干预BP的作用。