Using polysomnography over multiple weeks to characterize an individual’s habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities. Agreement in bed and wake time estimates across modalities was high (rho = 0.81–0.92) and were adrift of one another for an average of 4 min, providing redundant sleep measurement. On the ~23% of nights where discrepancies between modalities exceeded 1 h, k-means clustering revealed three patterns, each consistently expressed within a given individual. The three corresponding groups that emerged differed systematically in age, sleep timing, time in bed, and peri-sleep phone usage. Hence, contrary to being problematic, discrepant data across measurement modalities facilitated the identification of stable interindividual differences in sleep behavior, underscoring its utility to characterizing population sleep and peri-sleep behavior.

在多个星期内使用多导睡眠图来表征个人的习惯性睡眠行为，虽然准确，但很难升级。作为替代方案，我们整合了来自消费者睡眠追踪器的睡眠测量、基于智能手机的生态瞬时评估以及 198 名参与者为期 2 个月的用户电话交互。所有三种模式的用户留存率平均超过 80%。不同方式的入睡和起床时间估计的一致性很高（rho = 0.81–0.92），并且平均相差 4 分钟，提供了冗余的睡眠测量。在约 23% 的模式之间的差异超过 1 小时的夜晚，k 均值聚类揭示了三种模式，每种模式都在给定的个体中一致表达。出现的三个相应组在年龄、睡眠时间、卧床时间、和睡前电话使用。因此，与存在问题相反，测量模式之间的差异数据有助于识别睡眠行为中稳定的个体间差异，强调其在表征人群睡眠和睡前行为方面的效用。