RESUMEN
STUDY OBJECTIVES: Harmonizing and aggregating data across studies enables pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data. METHODS: We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items, (2) group items into domains, (3) harmonize items, and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five US cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation. RESULTS: We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of satisfaction, alertness/sleepiness, timing, efficiency, duration, insomnia, and sleep apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g. timing, total sleep time, and efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g. wake-up time and duration) and more heterogeneous (e.g. time in bed and bedtime) across samples. CONCLUSIONS: Our process can guide researchers and cohort stewards toward effective sleep harmonization and provide a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.