Digital phenotyping offers a powerful approach for forecasting risk for suicidal ideation or attempts over time. This method may serve as an especially useful strategy for understanding associations between sleep disorder symptoms and suicidal ideation and behavior. Sleep disorder symptoms predict suicidal ideation and behavior in cross-sectional research among active-duty military personnel, but few studies have examined longitudinal associations between sleep disorder symptoms and suicidal thoughts and behaviors in service members. In this study, we will use digital phenotyping to intensively assess Marines for 28 days through a combination of active and passive assessment strategies.

Methods

Marines with suicidal ideation or a suicide attempt in the past month will be recruited from Camp Lejeune, NC and provided with a Fitbit device and receive ecological momentary assessments (EMA) of suicidal urges several times throughout the day. Using dynamic multilevel models, we will explore the impact of sleep disorder symptoms on next-day suicide urges, as well as mediators of these effects.

Clinical implications

This study has the potential to inform optimal strategies to assess suicide risk in treatment. These findings will inform the development and implementation of real-time interventions to reduce risk for suicide among military personnel.

中文翻译：

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数字表型改善治疗中自杀冲动的预测：研究方案

数字表型提供了一种强大的方法来预测随着时间的推移自杀意念或尝试的风险。这种方法可以作为一种特别有用的策略来理解睡眠障碍症状与自杀意念和行为之间的关联。睡眠障碍症状在现役军人的横断面研究中预测自杀意念和行为，但很少有研究检查睡眠障碍症状与军人自杀意念和行为之间的纵向关联。在这项研究中，我们将通过主动和被动评估策略相结合，使用数字表型对海军陆战队进行为期 28 天的密集评估。

方法

过去一个月有自杀念头或自杀未遂的海军陆战队员将从北卡罗来纳州 Lejeune 营招募，并配备 Fitbit 设备，并在一天中多次接受自杀冲动的生态瞬时评估 (EMA)。使用动态多层次模型，我们将探讨睡眠障碍症状对第二天自杀冲动的影响，以及这些影响的中介因素。

临床意义

这项研究有可能为评估治疗中自杀风险的最佳策略提供信息。这些发现将为实时干预的开发和实施提供信息，以降低军事人员的自杀风险。