Cohort profile: TheSmartSleep Study, Denmark Triangulation of evidence from survey, clinical and tracking data

Abstract

Purpose: The SmartSleep Study is established to comprehensively assess the impact of night-time smartphone use on sleep patterns and health. An innovative combination of large-scale repeated survey information, high-resolution sensor-driven smartphone data, in-depth clinical examination and registry linkage allow for detailed investigations into multisystem physiological dysregulation and long-term health consequences associated with night-time smartphone use and sleep impairment. Participants: The SmartSleep Study consists of three interconnected data samples, which combined include 30,673 individuals with information on smartphone use, sleep and health. Subsamples of the study population also include high-resolution tracking data (n=5,927) collected via a customized app and deep clinical phenotypic data (n=245). A total of 7,208 participants will be followed in nationwide health registries with full data coverage and long-term follow-up. Findings to date: We highlight previous findings on the relation between smartphone use and sleep in the SmartSleep Study, and we evaluate the interventional potential of the citizen science approach used in one of the data samples. We also present new results from an analysis in which we utilize 803,000 data-points from the high-resolution tracking data to identify clusters of temporal trajectories of night-time smartphone use that characterize distinct use patterns. Based on these objective tracking data, we characterize four clusters of night-time smartphone use. Future plans: The unprecedented size and coverage of the SmartSleep Study allow for a comprehensive documentation of smartphone activity during the entire sleep span. The study will be expanded by linkage to nationwide registers, which will allow for further investigations into the long-term health and social consequences of night-time smartphone use. We also plan new rounds of data collection in the coming years.

Self-reported and tracked nighttime smartphone use and their association with overweight and cardiometabolic risk markers

“…1 . The samples are described in detail elsewhere 47 . Briefly, the three interconnected samples employ different recruitment strategies but share overlapping measurement methods to study various aspects of nighttime smartphone use, sleep, and health.…”

“…We used data from the Population Sample to investigate associations between self-reported and latent clusters of tracked nighttime smartphone use and BMI. The data collection has been described elsewhere 47 . Participants were asked to download the SmartSleep app (GitHub repository: https://github.com/smartsleepku ), track their nighttime smartphone use for up to 14 nights, and complete a detailed survey integrated into the app.…”

“…Utilizing 803,000 data points in 5927 individuals with high-resolution smartphone tracking data, we used non-parametric Functional Data Analysis 49 to identify latent clusters of nights with screen activity that characterized distinct night usage patterns . The development of these night clusters is described elsewhere 47 . In brief, four night clusters were identified: (1) non-use, defined as no smartphone activity during the self-reported sleep period, (2) sleep onset use, defined as smartphone activity during the sleep period mainly confined to the period right after sleep onset (the beginning of the sleep period), (3) sleep offset use, defined as smartphone activity during the sleep period mainly confined to the period right before sleep offset (the end of the sleep period), and (4) continuous use, defined as continuous smartphone activity throughout the sleep period.…”

“…The same issue with representativeness is also a concern in the Citizen Science Sample, as it is a self-selected sample 22 . We have previously performed sociodemographic comparisons between each study sample in the SmartSleep Study and the general adult population to address the concern 47 . We found that more females, middle-aged individuals, and individuals with a higher educational level were more likely to participate in the SmartSleep Study.…”

Using Large Cohort Data to Strengthen Information-Based Public Health Policies: An Appeal to Portuguese Authorities

“…The Nordic countries are an example to follow, where national registries and the use of a single unique identifier enable the development of large cohort studies involving millions of participants, [1][2][3] with adequate censoring for death and migration, based on individual-level data collected for administrative purposes, either individually or per groups of countries. 4 In Denmark, for instance, researchers can access anonymized raw data, use the Statistics Denmark's online research machine to analyze large datasets and transfer the results of the analysis afterwards.…”