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      The Alarm Clock Against the Sun: Trends in Google Trends Search Activity Across the Transitions to and from Daylight Saving Time


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          The human circadian timing system depends on the light/dark cycle as its main cue to synchronize with the environment, and thus with solar time. However, human activities depend also on social time, i.e. the set of time conventions and restrictions dictated by society, including Daylight Saving Time (DST), which adds an hour to any degree of desynchrony between social and solar time. Here, we used Google Trends as a data source to analyze diurnal variation, if any, and the daily peak in the relative search volume of 26 Google search queries in relation to the transitions to/from DST in Italy from 2015 to 2020. Our search queries of interest fell into three categories: sleep/health-related, medication and random non sleep/health-related. After initial rhythm and phase analysis, 11 words were selected to compare the average phase of the 15 days before and after the transition to/from DST. We observed an average phase advance after the transition to DST, and a phase delay after the transition to civil time, ranging from 25 to 60 minutes. Advances or delays shorter than 60 minutes, which were primarily observed in the sleep/health-related category, may suggest that search timing for these queries is at least partially driven by the endogenous circadian rhythm. Finally, a significant trend in phase anticipation over the years was observed for virtually all words. This is most likely related to an increase in age, and thus in earlier chronotypes, amongst Google users.

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          Most cited references21

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          Transcriptional architecture of the mammalian circadian clock

          Next-generation sequencing approaches have yielded new insights into circadian function. Here, Takahashi reviews genome-wide analyses of the clock transcriptional feedback loop in mammals, which reveal a global circadian regulation of transcription factor occupancy, RNA polymerase II recruitment and initiation, nascent transcription and chromatin remodelling.
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            The Use of Google Trends in Health Care Research: A Systematic Review

            Background Google Trends is a novel, freely accessible tool that allows users to interact with Internet search data, which may provide deep insights into population behavior and health-related phenomena. However, there is limited knowledge about its potential uses and limitations. We therefore systematically reviewed health care literature using Google Trends to classify articles by topic and study aim; evaluate the methodology and validation of the tool; and address limitations for its use in research. Methods and Findings PRISMA guidelines were followed. Two independent reviewers systematically identified studies utilizing Google Trends for health care research from MEDLINE and PubMed. Seventy studies met our inclusion criteria. Google Trends publications increased seven-fold from 2009 to 2013. Studies were classified into four topic domains: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Among surveillance studies, 92% were validated against a reference standard data source, and 80% of studies using correlation had a correlation statistic ≥0.70. Overall, 67% of articles provided a rationale for their search input. However, only 7% of articles were reproducible based on complete documentation of search strategy. We present a checklist to facilitate appropriate methodological documentation for future studies. A limitation of the study is the challenge of classifying heterogeneous studies utilizing a novel data source. Conclusion Google Trends is being used to study health phenomena in a variety of topic domains in myriad ways. However, poor documentation of methods precludes the reproducibility of the findings. Such documentation would enable other researchers to determine the consistency of results provided by Google Trends for a well-specified query over time. Furthermore, greater transparency can improve its reliability as a research tool.
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              Epidemiology of the human circadian clock.

              Humans show large inter-individual differences in organising their behaviour within the 24-h day-this is most obvious in their preferred timing of sleep and wakefulness. Sleep and wake times show a near-Gaussian distribution in a given population, with extreme early types waking up when extreme late types fall asleep. This distribution is predominantly based on differences in an individuals' circadian clock. The relationship between the circadian system and different "chronotypes" is formally and genetically well established in experimental studies in organisms ranging from unicells to mammals. To investigate the epidemiology of the human circadian clock, we developed a simple questionnaire (Munich ChronoType Questionnaire, MCTQ) to assess chronotype. So far, more than 55,000 people have completed the MCTQ, which has been validated with respect to the Horne-Østberg morningness-eveningness questionnaire (MEQ), objective measures of activity and rest (sleep-logs and actimetry), and physiological parameters. As a result of this large survey, we established an algorithm which optimises chronotype assessment by incorporating the information on timing of sleep and wakefulness for both work and free days. The timing and duration of sleep are generally independent. However, when the two are analysed separately for work and free days, sleep duration strongly depends on chronotype. In addition, chronotype is both age- and sex-dependent.

                Author and article information

                J Circadian Rhythms
                J Circadian Rhythms
                Journal of Circadian Rhythms
                Ubiquity Press
                29 November 2023
                : 21
                : 3
                [1 ]Department of Medicine, University of Padova, Padova, Italy
                [2 ]Chronobiology Section, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK
                [3 ]Department of Biology, University of Padova, Padova, Italy
                [4 ]Department of agricultural-nutritional, environmental and animal sciences, University of Udine, Udine, Italy
                [5 ]Institute of Neuroscience, National Research Council (CNR), Padova, Italy
                [6 ]Department of Biomedical Sciences, University of Padova, Padova, Italy
                Author notes
                CORRESPONDING AUTHOR: Sara Montagnese Department of Medicine, University of Padova, Padova, Italy; Chronobiology Section, Faculty of Health and Medical Sciences, University of Surrey, Guildford, UK sara.montagnese@ 123456unipd.it
                Author information
                Copyright: © 2023 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

                : 08 June 2023
                : 06 November 2023
                Funded by: STARS@UNIPD 2019;
                The study and authors LZ, GG and AB were supported by a STARS@UNIPD 2019 Consolidator Grant to author SM.
                Short Paper

                Cell biology
                google trends,relative search volume,daylight saving time,circadian clock,social clock


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