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The adoption of multisensor wearables presents the opportunity of longitudinal monitoring of sleep in large populations. Personalized yet device-agnostic algorithms can sidestep laborious human annotations and objectify cross-cohort comparisons. We developed and tested a heart rate-based algorithm that captures inter- and intra-individual sleep differences in free-living conditions and does not require human input. We evaluated it on four study cohorts using different research- and consumer-grade devices for over 2000 nights. Recording periods included both 24 h free-living and conventional lab-based night-only data. We compared our optimized method against polysomnography, sleep diaries and sleep periods produced through a state-of-the-art acceleration based method. Against sleep diaries, the algorithm yielded a mean squared error of 0.04–0.06 and a total sleep time (TST) deviation of $$-$$ - 2.70 (± 5.74) and 12.80 (± 3.89) minutes, respectively. When evaluated with PSG lab studies, the MSE ranged between 0.06 and 0.11 yielding a time deviation between $$-$$ - 29.07 and $$-$$ - 55.04 minutes. These results showcase the value of this open-source, device-agnostic algorithm for the reliable inference of sleep in free-living conditions and in the absence of annotations.

Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However, the largest experiments conducted to date, have had only hundreds of participants. In this work, we processed the data of the recently published Multi-Ethnic Study of Atherosclerosis (MESA) Sleep study to have both PSG and actigraphy data synchronized. We propose the adoption of this publicly available large dataset, which is at least one order of magnitude larger than any other dataset, to systematically compare existing methods for the detection of sleep-wake stages, thus fostering the creation of new algorithms. We also implemented and compared state-of-the-art methods to score sleep-wake stages, which range from the widely used traditional algorithms to recent machine learning approaches. We identified among the traditional algorithms, two approaches that perform better than the algorithm implemented by the actigraphy device used in the MESA Sleep experiments. The performance, in regards to accuracy and F1 score of the machine learning algorithms, was also superior to the device’s native algorithm and comparable to human annotation. Future research in developing new sleep-wake scoring algorithms, in particular, machine learning approaches, will be highly facilitated by the cohort used here. We exemplify this potential by showing that two particular deep-learning architectures, CNN and LSTM, among the many recently created, can achieve accuracy scores significantly higher than other methods for the same tasks.Other Information Published in: npj Digital Medicine License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41746-019-0126-9

Venezuela is going through the worst economical, political and social crisis in its modern history. Basic products like food or medicine are scarce and hyperinflation is combined with economic depression. This situation is creating an unprecedented refugee and migrant crisis in the region. Governments and international agencies have not been able to consistently leverage reliable information using traditional methods. Therefore, to organize and deploy any kind of humanitarian response, it is crucial to evaluate new methodologies to measure the number and location of Venezuelan refugees and migrants across Latin America. In this paper, we propose to use Facebook's advertising platform as an additional data source for monitoring the ongoing crisis. We estimate and validate national and sub-national numbers of refugees and migrants and break-down their socio-economic profiles to further understand the complexity of the phenomenon. Although limitations exist, we believe that the presented methodology can be of value for real-time assessment of refugee and migrant crises world-wide.

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This paper presents the results of task 3 of the ShARe/CLEF eHealth Evaluation Lab 2014. This evaluation lab focuses on improving access to medical information on the web. The task objective was to investigate the eect of using additional information such as a related discharge summary and external resources such as medical ontologies on the eectiveness of information retrieval systems, in a monolingual (Task 3a) and in a multilingual (Task 3b) context. The participants were allowed to submit up to seven runs for each language (English, Czech, French, German), one mandatory run using no additional information or external resources, and three each using or not using discharge summaries.

International audience