New England Geriatric Research Education and Clinical Center, VA Boston Healthcare System, Boston, MA, USA.
Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Sleep. 2024 Apr 12;47(4). doi: 10.1093/sleep/zsae034.
The 3P and 4P models represent illness severity over the course of insomnia disorder. The 3P model suggests that illness severity is worst during acute onset. The 4P model suggests that illness severity crescendos with chronicity. The present analysis from an archival dataset assesses illness severity with new onset illness (i.e. from good sleep [GS] to acute insomnia [AI] to chronic insomnia [CI]). Illness severity is quantified in terms of total wake time (TWT).
GSs (N = 934) were followed up to 1 year with digital sleep diaries, and classified as GS, AI, or CI. Data for CIs were anchored to the first of 14 days with insomnia so that day-to-day TWT was represented prior to and following AI onset. A similar graphic (+/-acute onset) was constructed for number of days per week with insomnia. GS data were temporally matched to CI data. Segmented linear mixed regression models were applied to examine the change in slopes in the AI-to-CI period compared to GS-to-AI period.
Twenty-three individuals transitioned to AI and then CI. Average TWT rose during the first 2 weeks of AI onset (b = 1.8, SE = 0.57, p = 0.001) and was then stable for 3 months (b = -0.02, SE = 0.04, p = 0.53). Average number of affected days was stable from AI to CI (b = 0.0005, SE = 0.002, p = 0.81). That is, while there was week-to-week variability in the number of days affected, no linear trend was evident.
In our sample of CIs, primarily with middle insomnia, the average severity and number of affected days were worst with the onset of AI (worst is first) and stable thereafter.
3P 和 4P 模型代表失眠障碍过程中的疾病严重程度。3P 模型表明,疾病严重程度在急性发作时最严重。4P 模型表明,疾病严重程度随着慢性化而加剧。本研究从档案数据集中评估新发疾病的疾病严重程度(即从良好睡眠[GS]到急性失眠[AI]到慢性失眠[CI])。疾病严重程度用总清醒时间(TWT)来量化。
使用数字睡眠日记对 GS(N=934)进行为期 1 年的随访,并分为 GS、AI 或 CI。CI 的数据以失眠的第 14 天为锚定点,以便代表 AI 发作前后的每日 TWT。类似的图形(+/-急性发作)构建了每周失眠天数。GS 数据与 CI 数据在时间上匹配。应用分段线性混合回归模型来检查 AI 到 CI 期间与 GS 到 AI 期间斜率的变化。
23 人转为 AI 后再转为 CI。AI 发作的前两周内 TWT 升高(b=1.8,SE=0.57,p=0.001),然后在 3 个月内稳定(b=-0.02,SE=0.04,p=0.53)。AI 到 CI 期间受影响天数的平均值保持稳定(b=0.0005,SE=0.002,p=0.81)。也就是说,虽然受影响天数存在每周的变化,但没有明显的线性趋势。
在我们的 CI 样本中,主要是中间失眠,AI 发作时平均严重程度和受影响天数最严重(先恶化后稳定)。