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Daily self-reported and automatically generated smartphone-based sleep measurements in patients with newly diagnosed bipolar disorder, unaffected first-degree relatives and healthy control individuals.
BMJ Mental Health ( IF 5.2 ) Pub Date : 2020-11-01 , DOI: 10.1136/ebmental-2020-300148
Sharleny Stanislaus 1, 2 , Maj Vinberg 3 , Sigurd Melbye 2, 3 , Mads Frost 4 , Jonas Busk 5 , Jakob Eyvind Bardram 5 , Maria Faurholt-Jepsen 3 , Lars Vedel Kessing 2, 3
Affiliation  

Objectives (1) To investigate daily smartphone-based self-reported and automatically generated sleep measurements, respectively, against validated rating scales; (2) to investigate if daily smartphone-based self-reported sleep measurements reflected automatically generated sleep measurements and (3) to investigate the differences in smartphone-based sleep measurements between patients with bipolar disorder (BD), unaffected first-degree relatives (UR) and healthy control individuals (HC). Methods We included 203 patients with BD, 54 UR and 109 HC in this study. To investigate whether smartphone-based sleep calculated from self-reported bedtime, wake-up time and screen on/off time reflected validated rating scales, we used the Pittsburgh Sleep Quality Index (PSQI) and sleep items on the Hamilton Depression Rating Scale 17-item (HAMD-17) and the Young Mania Rating Scale (YMRS). Findings (1) Self-reported smartphone-based sleep was associated with the PSQI and sleep items of the HAMD and the YMRS. (2) Automatically generated smartphone-based sleep measurements were associated with daily self-reports of hours slept between 12:00 midnight and 06:00. (3) According to smartphone-based sleep, patients with BD slept less between 12:00 midnight and 06:00, with more interruption and daily variability compared with HC. However, differences in automatically generated smartphone-based sleep were not statistically significant. Conclusion Smartphone-based data may represent measurements of sleep patterns that discriminate between patients with BD and HC and potentially between UR and HC. Clinical implication Detecting sleep disturbances and daily variability in sleep duration using smartphones may be helpful for both patients and clinicians for monitoring illness activity. Trial registration number clinicaltrials.gov ([NCT02888262][1]). [1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02888262&atom=%2Febmental%2F23%2F4%2F146.atom

中文翻译:

对新诊断的双相情感障碍患者、未受影响的一级亲属和健康对照个体进行每日自我报告和自动生成的基于智能手机的睡眠测量。

目标 (1) 根据经过验证的评分量表,分别调查基于智能手机的每日自我报告和自动生成的睡眠测量结果;(2) 调查基于智能手机的每日自我报告睡眠测量是否反映了自动生成的睡眠测量,以及 (3) 调查双相情感障碍 (BD) 患者、未受影响的一级亲属 (UR) 之间基于智能手机的睡眠测量的差异)和健康对照个体(HC)。方法 本研究纳入了 203 名 BD 患者、54 名 UR 患者和 109 名 HC 患者。为了调查根据自我报告的就寝时间、起床时间和屏幕开/关时间计算出的基于智能手机的睡眠是否反映了经过验证的评分量表,我们使用了匹兹堡睡眠质量指数 (PSQI) 和汉密尔顿抑郁评分量表上的睡眠项目 17-项目(HAMD-17)和青年狂热评定量表(YMRS)。研究结果 (1) 自我报告的基于智能手机的睡眠与 HAMD 和 YMRS 的 PSQI 和睡眠项目相关。(2) 自动生成的基于智能手机的睡眠测量结果与每天午夜 12:00 至 06:00 之间睡眠时间的自我报告相关。(3) 根据基于智能手机的睡眠数据,与 HC 相比,BD 患者在午夜 12:00 至 06:00 之间睡眠较少,睡眠中断和每日变化较多。然而,基于智能手机自动生成的睡眠差异没有统计学意义。结论 基于智能手机的数据可能代表了区分 BD 和 HC 患者以及可能区分 UR 和 HC 患者的睡眠模式测量结果。临床意义使用智能手机检测睡眠障碍和睡眠持续时间的每日变化可能有助于患者和临床医生监测疾病活动。试验注册号 ClinicalTrials.gov ([NCT02888262][1])。[1]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT02888262&atom=%2Febmental%2F23%2F4%2F146.atom
更新日期:2020-10-30
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