Université Paris Descartes, APHP, Hôtel Dieu, Centre du Sommeil et de la Vigilance et EA 7330 VIFASOM, Paris, France.
Université Paris Descartes, APHP, Hôtel Dieu, Centre du Sommeil et de la Vigilance et EA 7330 VIFASOM, Paris, France.
Sleep Med. 2018 Dec;52:88-91. doi: 10.1016/j.sleep.2018.07.024. Epub 2018 Aug 31.
OBJECTIVE/BACKGROUND: It has been shown that actigraphy may have a discriminant function (DS) for the diagnosis of narcolepsy type 1 patients (NT1), based on a combination of nighttime and daytime parameters. Here, we aimed to test those findings using another actigraph model with a different clinical sample as control (ie, primary insomniacs, PI), carrying out a secondary analysis of previously collected data.
PATIENTS/METHODS: The study sample consisted of 13 NT1 (nine females; mean age 39.38 ± 11.48), 13 PI (nine females; mean age 38.69 ± 10.72) and 13 Healthy Controls (HC) (nine females; mean age 38 ± 10.77). Participants wore the Actiwatch AW64 (Cambridge Neurotechnology Ltd, Cambridge, UK) around the non-dominant wrist for seven consecutive days.
Significant differences between groups were observed with a higher number of episodes of wakefulness (wake bouts, WB) in PI than HC, a higher fragmentation index (FI) in NT1 than HC and PI, a higher duration of the longest nap (LNAP) in NT1 than HC and PI and higher DS in PI and NT1 than HC. A new DS (NDS), with LNAP and FI as independent variables, was proposed; which was higher in NT1 than HC and PI.
The present study confirms that actigraphy discriminates NT1 from HC. However, considering PI, a new discriminant function NDS which takes into account LNAP and FI is better for this actigraph model.
目的/背景:已经证明,基于夜间和日间参数的组合,活动记录仪可能对 1 型发作性睡病(NT1)患者具有判别功能(DS)。在这里,我们旨在使用另一种活动记录仪模型(即原发性失眠症患者,PI)作为对照来测试这些发现,对以前收集的数据进行二次分析。
患者/方法:研究样本包括 13 名 NT1(9 名女性;平均年龄 39.38±11.48)、13 名 PI(9 名女性;平均年龄 38.69±10.72)和 13 名健康对照(HC)(9 名女性;平均年龄 38±10.77)。参与者在非优势手腕上佩戴 Actiwatch AW64(剑桥神经技术有限公司,剑桥,英国)连续七天。
观察到组间存在显著差异,PI 中的觉醒次数(觉醒次数,WB)高于 HC,NT1 中的碎片化指数(FI)高于 HC 和 PI,NT1 中的最长午睡持续时间(LNAP)高于 HC 和 PI,PI 和 NT1 中的 DS 高于 HC。提出了一个新的 DS(NDS),以 LNAP 和 FI 为自变量;NT1 高于 HC 和 PI。
本研究证实,活动记录仪可区分 NT1 和 HC。然而,考虑到 PI,考虑到 LNAP 和 FI 的新判别函数 NDS 更适合这种活动记录仪模型。