20 datasets found
  1. CDC PLACES: Local Data for Better Health, County Data and Census Tract Data...

    • zenodo.org
    csv, zip
    Updated Jan 30, 2025
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    Will Fitzgerald; Will Fitzgerald; Gretchen Gehrke; Gretchen Gehrke (2025). CDC PLACES: Local Data for Better Health, County Data and Census Tract Data 2024 release [Dataset]. http://doi.org/10.5281/zenodo.14774046
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    csv, zipAvailable download formats
    Dataset updated
    Jan 30, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Will Fitzgerald; Will Fitzgerald; Gretchen Gehrke; Gretchen Gehrke
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    From the CDC Places page on ArcGIS:

    PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation. The original 500 Cities Project provided city- and census tract-level estimates for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States.

    This service includes 40 measures for chronic disease related health outcomes (12), prevention measures (7), health risk behaviors (4), disability (7), health status (3), and health-related social needs (7). Data were provided by CDC Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2022 or 2021), Census Bureau 2020 census population data or annual population estimates for county vintage 2022, and American Community Survey (ACS) 2018-2022 estimates.
    • The health outcomes include arthritis, current asthma, high blood pressure, cancer (non-skin) or melanoma, high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, depression, obesity, all teeth lost, and stroke.
    • The prevention measures are lack of health insurance, routine checkup within the past year, visited dentist or dental clinic in the past year, taking medicine to control high blood pressure, cholesterol screening, mammography use for women, and colorectal cancer screening.
    • The health risk behaviors are binge drinking, current cigarette smoking, physical inactivity, and short sleep duration.
    • The disability measures are six disability types (hearing, vision, cognitive, mobility, self-care, and independent living) and any disability.
    • The health status measures are frequent mental distress, frequent physical distress, and poor or fair health.
    • The health-related social needs measures are social isolation, food stamps, food insecurity, housing insecurity, utility services threat, transportation barriers, and lack of social and emotional support.
  2. f

    Data from: Participant characteristics.

    • datasetcatalog.nlm.nih.gov
    Updated Jan 7, 2025
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    Abe, Takashi; Kokubo, Toshio; Suzuki, Yoko; Kanbayashi, Takashi; Fukusumi, Shoji; Takahara, Isamu; Suzuki, Chihiro; Yanagisawa, Masashi (2025). Participant characteristics. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001287225
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    Dataset updated
    Jan 7, 2025
    Authors
    Abe, Takashi; Kokubo, Toshio; Suzuki, Yoko; Kanbayashi, Takashi; Fukusumi, Shoji; Takahara, Isamu; Suzuki, Chihiro; Yanagisawa, Masashi
    Description

    In remote areas, visiting a laboratory for sleep testing is inconvenient. We, therefore, developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as feasible as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (the first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the discrepancy of different places or different nights. No significant differences were found between the laboratories other than the percentage of total sleep time in stage N3. Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements: the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as total sleep time, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R latency, showed poor reliabilities (<0.5) based on Koo and Li’s criteria. In contrast, the percentage of stage N3 showed moderate (0.5–0.75) or good (0.75–0.9) reliabilities. As almost all sleep variables showed no difference and same level of test-retest reliability between the Mobile Sleep Lab and Human Sleep Lab, the Mobile Sleep Lab might be suitable for conducting polysomnography as a conventional sleep laboratory. The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders. Practical application of the Mobile Sleep Lab can transform sleep medicine in remote areas.

  3. Characteristics of participants with or without daytime somnolence.

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jun 1, 2023
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    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret (2023). Characteristics of participants with or without daytime somnolence. [Dataset]. http://doi.org/10.1371/journal.pone.0281379.t001
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    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of participants with or without daytime somnolence.

  4. d

    Low Barrier Shelters Dashboard

    • catalog.data.gov
    • gimi9.com
    • +2more
    Updated Feb 4, 2025
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    City of Washington, DC (2025). Low Barrier Shelters Dashboard [Dataset]. https://catalog.data.gov/dataset/low-barrier-shelters-dashboard
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    Dataset updated
    Feb 4, 2025
    Dataset provided by
    City of Washington, DC
    Description

    Emergency shelter is available for adults who are experiencing homelessness. The Emergency Shelter program provides beds on a first-come, first-served basis, to anyone who does not have a safe place to sleep. Shelters provide a warm and safe place to sleep as well as on-site assessment and case management. Programs offer various onsite services for those accessing shelter. Low barrier shelters are operated by non-profit organizations under contract with the Department of Human Services. Shelter capacity is expanded during the winter months for residents who are at risk of hypothermia.

  5. f

    Sociodemographic characteristics.

    • figshare.com
    xls
    Updated Aug 19, 2024
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    Nitai Roy; Kallol Deb Paul; Sumaiya Sultana Tamanna; Anup Kumar Paul; Moneerah Mohammad Almerab; Mohammed A. Mamun (2024). Sociodemographic characteristics. [Dataset]. http://doi.org/10.1371/journal.pone.0307895.t001
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    xlsAvailable download formats
    Dataset updated
    Aug 19, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Nitai Roy; Kallol Deb Paul; Sumaiya Sultana Tamanna; Anup Kumar Paul; Moneerah Mohammad Almerab; Mohammed A. Mamun
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundConstruction workers are a population that is at risk for mental illnesses such as depression, anxiety, and even suicide due to the high stress and physical demands of their work. This study aimed to determine the prevalence and risk factors for depression, anxiety, and stress among Bangladeshi construction workers.MethodsFrom February 2022 to June 2022, community-based cross-sectional research was conducted among construction workers. Survey data was gathered using interviewer administered questionnaires with 502 participants from the construction sites. Data were collected based on the information related to socio-demographics, lifestyle, occupation, health hazards, and mental health (i.e., depression, anxiety, and stress). The results were interpreted using the chi-square test and logistic regression utilizing SPSS statistical software.ResultsThe study revealed the prevalence rates of depression, anxiety, and stress among construction workers to be 17.9%, 30.3%, and 12%, respectively. Key findings indicate that construction workers who maintained a healthy sleep duration were 64% less likely to be depressed compared to those with poor sleep (AOR = 0.36; 95% CI: 0.21–0.61, p

  6. e

    Circadian rhythm and chronic sleep deprivation effects on human performance...

    • b2find.eudat.eu
    Updated Nov 2, 2023
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    (2023). Circadian rhythm and chronic sleep deprivation effects on human performance - eye-tracking experiment. - Dataset - B2FIND [Dataset]. https://b2find.eudat.eu/dataset/40f506c9-bab2-596f-b309-46c7ff87d60c
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    Dataset updated
    Nov 2, 2023
    Description

    Circadian rhythms and restricted sleep length affect cognitive functions and, consequently, the performance of day to day activities. To date, no more than a few studies have explored the consequences of these factors on oculomotor behaviour. In this study, eye tracking data have been recorded from 24 participants performing a modified spatial cueing task under two paired conditions. The first condition concerned the time of day variations, as the task was conducted at 10am, 2pm, 6pm, and 10pm. The second condition was the sleep restriction - the subjects participated in the study twice: after one week of unrestricted sleep and after one week of chronic partial sleep deprivation. The task comprised congruent trials with target stimuli preceded with congruent directional cues (60%), incongruent trials with target stimuli preceded with incongruent directional cues (15%), and stimuli without cues (25%). Targets and cues were presented in six possible locations. Participants were instructed to direct their attention and gaze from fixation point to targets only if they were preceded by a cue. The aim of this study was to verify if chronic sleep deficit and circadian rhythm affect the number of errors in performance of saccadic task and whether this impact vary according to the type of error The eye-tracking data are in txt format (archived to 'rar'). Overall size of the data files is about 15 GB. Data are log files form Smart Eye Pro eye tracking system. Detailed information about all recorded parameters are included in User Manual for the Smart Eye Pro system, please see manual available in this project record. Twenty four paid volunteers participated in this experiment (12 females, mean age 22.7 ± 1.6 years). Participants met the experiment requirements: right-handedness, right-eyed, normal or corrected-to-normal vision, no physical or psychiatric disorders. They were all non-smokers and drug-free. The experimental task was performed in two conditions: rested wakefulness (RW – after a week with unrestricted, fully restorative sleep) and chronic sleep deficit (SD, after seven days of sleep curtailment by 30%). The order of experimental sessions (RW and SD) was counterbalanced across all participants. The sessions were separated by at least two weeks in order to minimize the residual effects of sleep deficit on performance. All the participants performed an experimental task four times during the day: at approximately 10:00, 14:00, 18:00 and 22:00. A semi constant routine protocol was applied: room temperature and light intensity were kept constant, caloric intake and the level of motor activity were controlled. The participants spent approximately 14 hours in a controlled laboratory environment. During experimental days, they were allowed to engage in non-strenuous activities (eg. reading, watching videos, conversation). Caffeine intake was banned; alcohol consumption during the preceding week was restricted. A modified spatial cueing task (Posner, 1980) was prepared using E-Prime 2.0 (©Psychology Software Tools) and presented on a 17-inch screen located approximately 80cm from participants' eyes. Targets and cues were presented in six possible locations at 8° and 2° of visual angle in x-axis and 5° and 1° in y-axis of visual angle respectively. Leftwards and rightwards target locations were distributed equally, whereas middle target locations were weighted by 50% vs. 25% of upper and 25% of lower locations. The task comprised congruent trials with target stimuli preceded by congruent directional cues (60%), incongruent trials with target stimuli preceded by incongruent directional cues (15%), and stimuli without cues (25%). The total number of stimuli in the task was 500 in each measurement. Targets were presented for 500ms and cues for 200ms. The intertrial interval was varied in the range of 800 - 3500ms with average of 2200ms. Time interval between cue and target varied between 200 and 700ms with an average of 450ms. The participants were instructed to direct their attention and gaze from fixation point to targets only if they were preceded by a cue. The task lasted about 35 minutes. One week prior to the first experimental day, participants were extensively trained on the experimental task, in order to avoid the influence of a learning process on the number of errors. Eye position was monitored with Smart Eye Pro (Smart Eye AB, Göteborg, Sweden). Posner, M. I. Orienting of attention. Q. J. Exp. Psychol., 1980, 32: 3-25.

  7. z

    Data from: Mobile Sleep Lab: Comparison of polysomnographic parameters with...

    • zenodo.org
    bin, csv
    Updated Oct 30, 2024
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    Chihiro Suzuki; Chihiro Suzuki; Yoko Suzuki; Yoko Suzuki; Takashi Abe; Takashi Abe; Takashi Kanbayashi; Takashi Kanbayashi; Shoji Fukusumi; Shoji Fukusumi; Toshio Kokubo; Isamu Takahara; Masashi Yanagisawa; Masashi Yanagisawa; Toshio Kokubo; Isamu Takahara (2024). Mobile Sleep Lab: Comparison of polysomnographic parameters with a conventional sleep laboratory [Dataset]. http://doi.org/10.5281/zenodo.12742345
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    bin, csvAvailable download formats
    Dataset updated
    Oct 30, 2024
    Dataset provided by
    Zenodo
    Authors
    Chihiro Suzuki; Chihiro Suzuki; Yoko Suzuki; Yoko Suzuki; Takashi Abe; Takashi Abe; Takashi Kanbayashi; Takashi Kanbayashi; Shoji Fukusumi; Shoji Fukusumi; Toshio Kokubo; Isamu Takahara; Masashi Yanagisawa; Masashi Yanagisawa; Toshio Kokubo; Isamu Takahara
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jul 24, 2024
    Description

    In remote areas, visiting a laboratory for sleep testing is inconvenient. We, therefore, developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as feasible as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (the first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the discrepancy of different places or different nights. No significant differences were found between the laboratories other than the percentage of total sleep time in stage N3. Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements: the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as total sleep time, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R latency, showed poor reliabilities (<0.5) based on Koo and Li’s criteria. In contrast, the percentage of stage N3 showed moderate (0.5–0.75) or good (0.75–0.9) reliabilities. As almost all sleep variables showed no difference and same level of test-retest reliability between the Mobile Sleep Lab and Human Sleep Lab, the Mobile Sleep Lab might be suitable for conducting polysomnography as a conventional sleep laboratory. The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders. Practical application of the Mobile Sleep Lab can transform sleep medicine in remote areas.

  8. n

    Data from: Secrets of the night: roost sites and sleep disturbance factors...

    • data.niaid.nih.gov
    • datadryad.org
    zip
    Updated Sep 5, 2022
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    Joachim Siekiera; Łukasz Jankowiak; Piotr Profus; Tim Sparks; Piotr Tryjanowski (2022). Secrets of the night: roost sites and sleep disturbance factors during the autumn migration of first-year white stork Ciconia ciconia [Dataset]. http://doi.org/10.5061/dryad.5x69p8d6m
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    zipAvailable download formats
    Dataset updated
    Sep 5, 2022
    Dataset provided by
    Czech University of Life Sciences Prague
    University of Cambridge
    Polish Academy of Sciences
    University of Szczecin
    Authors
    Joachim Siekiera; Łukasz Jankowiak; Piotr Profus; Tim Sparks; Piotr Tryjanowski
    Description

    The migration phase of birds is divided into two main states: stopovers and actual migratory flights. For soaring birds, such as storks, nighttime is especially important to conserve energy and to start the next day in favourable weather conditions. Although there is a large number of recent studies on white stork Ciconia ciconia, for example using advanced technologies such as GPS technology, the nocturnal behaviour of the species is still an enigma. Thus, we GSM-GPS-tagged 90 immature storks and checked their nocturnal behaviour, especially roost disturbance, during their first autumn migration from breeding grounds in southern Poland to wintering places in Africa. Storks roosted on three types of site: on buildings, on the ground, or in trees. Birds that roosted on the ground had a higher probability of nighttime disturbance than those that used trees or buildings. The probability of disturbance at night was also related to longitude and latitude; the most easterly birds and those at the start of the migration route were disturbed more often during the night. Furthermore, and interestingly, environmental conditions at roosts were also significantly related to the probability of disturbance; birds roosting at tree sites with higher NDVI (greener) and with higher levels of artificial light both had a lower probability of disturbance. A possible explanation of this could be related to lower potential predatory pressure at night. We found that after long flights birds were disturbed more often at night, and that disturbed birds migrated further the next day. For a better understanding of the nocturnal behaviour of storks, as well as of other migratory birds, the use of modern technological tools with greater precision is recommended.

  9. f

    Adjusted ORs (95%CI) of poor sleep quality in relation to the joint effects...

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Dec 19, 2019
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    Lu, Tao; Hu, Qian-Sheng; Stamatakis, Katherine A.; Vaughn, Michael; Wu, Qi-Zhen; Lawrence, Wayne R.; Dong, Guang-Hui; Liu, Ru-Qing; Liu, Echu; He, Zhi-Zhou; Bloom, Michael S.; Yang, Mingan; Qian, Zhengmin (2019). Adjusted ORs (95%CI) of poor sleep quality in relation to the joint effects of positive depression screening and ethnicity among Han and Manchu study participants residing in rural areas of northern China, from 2012 to 2013 a. [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000115547
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    Dataset updated
    Dec 19, 2019
    Authors
    Lu, Tao; Hu, Qian-Sheng; Stamatakis, Katherine A.; Vaughn, Michael; Wu, Qi-Zhen; Lawrence, Wayne R.; Dong, Guang-Hui; Liu, Ru-Qing; Liu, Echu; He, Zhi-Zhou; Bloom, Michael S.; Yang, Mingan; Qian, Zhengmin
    Description

    Adjusted ORs (95%CI) of poor sleep quality in relation to the joint effects of positive depression screening and ethnicity among Han and Manchu study participants residing in rural areas of northern China, from 2012 to 2013 a.

  10. f

    Table 1_Lateralized differences in power spectra across different frequency...

    • datasetcatalog.nlm.nih.gov
    • frontiersin.figshare.com
    Updated Jan 21, 2025
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    Ye, Jing; Shi, Hong; Zhu, Yifeng; Gao, Wentao; Gao, Mingjie; Huang, Daijin; Huang, Jiao; Lv, Yunhui; Chen, Weijia; Wang, Yongbo (2025). Table 1_Lateralized differences in power spectra across different frequency bands during NREM sleep in patients with primary insomnia.xlsx [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001356894
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    Dataset updated
    Jan 21, 2025
    Authors
    Ye, Jing; Shi, Hong; Zhu, Yifeng; Gao, Wentao; Gao, Mingjie; Huang, Daijin; Huang, Jiao; Lv, Yunhui; Chen, Weijia; Wang, Yongbo
    Description

    ObjectiveTo compare the electroencephalogram power spectrum of patients with primary insomnia and good sleep controls in multiple brain areas and different frequency bands during non-rapid eye movement sleep.Methods48 primary insomnias and 30 age-and gender-matched good sleep controls were recorded overnight with polysomnography. Power spectral analysis was performed in six brain areas (F3, F4, C3, C4, O1 and O2) and across seven frequency bands (delta, sigma, alpha, theta, beta1, beta2, and gamma) during non-rapid eye movement sleep between primary insomnias and good sleep controls.ResultsIn primary insomnias, there were significant differences in frequency bands and areas. Compared to good sleep controls, delta power was lower in primary insomnias, while beta1, beta2, and gamma were higher. Beta2 power was substantially higher in all areas, sigma power was significantly higher on the right side, and gamma power was considerably higher on the left side in primary insomnias. The Beta1 power was positively correlated the number of awakenings (r = 0.3291, p = 0.02) in primary insomnias on the right side.ConclusionThis study marked the first specialized comparison of power spectral analysis during non-rapid eye movement sleep in different areas and across different frequency bands. The result suggested that primary insomnias had reduced deep sleep (lower delta power) and hyperarousal state (higher beta 2 power). Primary insomnia was associated with significant fragmented sleep, and an increase in beta1 power was related to the number of awakenings.SignificanceThese findings revealed the hemispheric lateralization characteristics of power spectral disturbances during non-rapid eye movement sleep in primary insomnias and provided valuable insights for selecting electrode placements in future power spectral analyses of primary insomnias.

  11. f

    Characteristics of participants with academic success or failure.

    • plos.figshare.com
    xls
    Updated Jun 2, 2023
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    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret (2023). Characteristics of participants with academic success or failure. [Dataset]. http://doi.org/10.1371/journal.pone.0281379.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of participants with academic success or failure.

  12. f

    Path diagram.

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret (2023). Path diagram. [Dataset]. http://doi.org/10.1371/journal.pone.0281379.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Path diagram.

  13. f

    Characteristics of the measurement model.

    • plos.figshare.com
    xls
    Updated Jun 10, 2023
    + more versions
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    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret (2023). Characteristics of the measurement model. [Dataset]. http://doi.org/10.1371/journal.pone.0281379.t003
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    xlsAvailable download formats
    Dataset updated
    Jun 10, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Daniel Pérez-Chada; Sergio Arias Bioch; Daniel Schönfeld; David Gozal; Santiago Perez-Lloret
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Characteristics of the measurement model.

  14. f

    Table_2_Plasticity and Susceptibility of Brain Morphometry Alterations to...

    • frontiersin.figshare.com
    doc
    Updated Jun 3, 2023
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    Xi-Jian Dai; Jian Jiang; Zhiqiang Zhang; Xiao Nie; Bi-Xia Liu; Li Pei; Honghan Gong; Jianping Hu; Guangming Lu; Yang Zhan (2023). Table_2_Plasticity and Susceptibility of Brain Morphometry Alterations to Insufficient Sleep.DOC [Dataset]. http://doi.org/10.3389/fpsyt.2018.00266.s003
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    docAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    Frontiers
    Authors
    Xi-Jian Dai; Jian Jiang; Zhiqiang Zhang; Xiao Nie; Bi-Xia Liu; Li Pei; Honghan Gong; Jianping Hu; Guangming Lu; Yang Zhan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Background: Insufficient sleep is common in daily life and can lead to cognitive impairment. Sleep disturbance also exists in neuropsychiatric diseases. However, whether and how acute and chronic sleep loss affect brain morphology remain largely unknown.Methods: We used voxel-based morphology method to study the brain structural changes during sleep deprivation (SD) at six time points of rested wakefulness, 20, 24, 32, 36 h SD, and after one night sleep in 22 healthy subjects, and in 39 patients with chronic primary insomnia relative to 39 status-matched good sleepers. Attention network and spatial memory tests were performed at each SD time point in the SD Procedure. The longitudinal data were analyzed using one-way repeated measures ANOVA, and post-hoc analysis was used to determine the between-group differences.Results: Acute SD is associated with widespread gray matter volume (GMV) changes in the thalamus, cerebellum, insula and parietal cortex. Insomnia is associated with increased GMV in temporal cortex, insula and cerebellum. Acute SD is associated with brain atrophy and as SD hours prolong more areas show reduced GMV, and after one night sleep the brain atrophy is restored and replaced by increased GMV in brain areas. SD has accumulative negative effects on attention and working memory.Conclusions: Acute SD and insomnia exhibit distinct morphological changes of GMV. SD has accumulative negative effects on brain morphology and advanced cognitive function. The altered GMV may provide neurobiological basis for attention and memory impairments following sleep loss.Statement of significanceSleep is less frequently studied using imaging techniques than neurological and psychiatric disorders. Whether and how acute and chronic sleep loss affect brain morphology remain largely unknown. We used voxel-based morphology method to study brain structural changes in healthy subjects over multiple time points during sleep deprivation (SD) status and in patients with chronic insomnia. We found that prolonged acute SD together with one night sleep recovery exhibits accumulative atrophic effect and recovering plasticity on brain morphology, in line with behavioral changes on attentional tasks. Furthermore, acute SD and chronic insomnia exhibit distinct morphological changes of gray matter volume (GMV) but they also share overlapping GMV changes. The altered GMV may provide structural basis for attention and memory impairments following sleep loss.

  15. f

    Long-Lasting Novelty-Induced Neuronal Reverberation during Slow-Wave Sleep...

    • plos.figshare.com
    application/cdfv2
    Updated May 31, 2023
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    Sidarta Ribeiro; Damien Gervasoni; Ernesto S Soares; Yi Zhou; Shih-Chieh Lin; Janaina Pantoja; Michael Lavine; Miguel A. L Nicolelis (2023). Long-Lasting Novelty-Induced Neuronal Reverberation during Slow-Wave Sleep in Multiple Forebrain Areas [Dataset]. http://doi.org/10.1371/journal.pbio.0020024
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    application/cdfv2Available download formats
    Dataset updated
    May 31, 2023
    Dataset provided by
    PLOS Biology
    Authors
    Sidarta Ribeiro; Damien Gervasoni; Ernesto S Soares; Yi Zhou; Shih-Chieh Lin; Janaina Pantoja; Michael Lavine; Miguel A. L Nicolelis
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The discovery of experience-dependent brain reactivation during both slow-wave (SW) and rapid eye-movement (REM) sleep led to the notion that the consolidation of recently acquired memory traces requires neural replay during sleep. To date, however, several observations continue to undermine this hypothesis. To address some of these objections, we investigated the effects of a transient novel experience on the long-term evolution of ongoing neuronal activity in the rat forebrain. We observed that spatiotemporal patterns of neuronal ensemble activity originally produced by the tactile exploration of novel objects recurred for up to 48 h in the cerebral cortex, hippocampus, putamen, and thalamus. This novelty-induced recurrence was characterized by low but significant correlations values. Nearly identical results were found for neuronal activity sampled when animals were moving between objects without touching them. In contrast, negligible recurrence was observed for neuronal patterns obtained when animals explored a familiar environment. While the reverberation of past patterns of neuronal activity was strongest during SW sleep, waking was correlated with a decrease of neuronal reverberation. REM sleep showed more variable results across animals. In contrast with data from hippocampal place cells, we found no evidence of time compression or expansion of neuronal reverberation in any of the sampled forebrain areas. Our results indicate that persistent experience-dependent neuronal reverberation is a general property of multiple forebrain structures. It does not consist of an exact replay of previous activity, but instead it defines a mild and consistent bias towards salient neural ensemble firing patterns. These results are compatible with a slow and progressive process of memory consolidation, reflecting novelty-related neuronal ensemble relationships that seem to be context- rather than stimulus-specific. Based on our current and previous results, we propose that the two major phases of sleep play distinct and complementary roles in memory consolidation: pretranscriptional recall during SW sleep and transcriptional storage during REM sleep.

  16. f

    The effects of sleep loss on young drivers’ performance: A systematic review...

    • plos.figshare.com
    docx
    Updated May 30, 2023
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    Shamsi Shekari Soleimanloo; Melanie J. White; Veronica Garcia-Hansen; Simon S. Smith (2023). The effects of sleep loss on young drivers’ performance: A systematic review [Dataset]. http://doi.org/10.1371/journal.pone.0184002
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    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Shamsi Shekari Soleimanloo; Melanie J. White; Veronica Garcia-Hansen; Simon S. Smith
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Young drivers (18–24 years) are over-represented in sleep-related crashes (comprising one in five fatal crashes in developed countries) primarily due to decreased sleep opportunity, lower tolerance for sleep loss, and ongoing maturation of brain areas associated with driving-related decision making. Impaired driving performance is the proximal reason for most car crashes. There is still a limited body of evidence examining the effects of sleep loss on young drivers’ performance, with discrepancies in the methodologies used, and in the definition of outcomes. This study aimed to identify the direction and magnitude of the effects of sleep loss on young drivers’ performance, and to appraise the quality of current evidence via a systematic review. Based on the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA) approach, 16 eligible studies were selected for review, and their findings summarised. Next, critical elements of these studies were identified, and the Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines augmented to rate those elements. Using those criteria, the quality of individual papers was calculated and the overall body of evidence for each driving outcome were assigned a quality ranking (from ‘very low’ to ‘high-quality’). Two metrics, the standard deviation of lateral position and number of line crossings, were commonly reported outcomes (although in an overall ‘low-quality’ body of evidence), with significant impairments after sleep loss identified in 50% of studies. While speed-related outcomes and crash events (also with very low- quality evidence) both increased under chronic sleep loss, discrepant findings were reported under conditions of acute total sleep deprivation. It is crucial to obtain more reliable data about the effects of sleep loss on young drivers’ performance by using higher quality experimental designs, adopting common protocols, and the use of consistent metrics and reporting of findings based on GRADE criteria and the PRISMA statement. Key words: Young drivers, sleep loss, driving performance, PRISMA, the GRADE, systematic review.

  17. f

    Subjective sleep quality (mean ± 1 SD) of elderly participants (N = 1992)...

    • plos.figshare.com
    xls
    Updated Dec 5, 2024
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    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally (2024). Subjective sleep quality (mean ± 1 SD) of elderly participants (N = 1992) inhabiting the rural areas of the Sambalpur district and comparison thereof. [Dataset]. http://doi.org/10.1371/journal.pone.0314770.t005
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    xlsAvailable download formats
    Dataset updated
    Dec 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sambalpur
    Description

    Subjective sleep quality (mean ± 1 SD) of elderly participants (N = 1992) inhabiting the rural areas of the Sambalpur district and comparison thereof.

  18. f

    Gender and age-wise distribution of sleep quality in elderly participants (N...

    • plos.figshare.com
    xls
    Updated Dec 5, 2024
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    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally (2024). Gender and age-wise distribution of sleep quality in elderly participants (N = 1992) of rural areas of Sambalpur district. [Dataset]. http://doi.org/10.1371/journal.pone.0314770.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Dec 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sambalpur
    Description

    Gender and age-wise distribution of sleep quality in elderly participants (N = 1992) of rural areas of Sambalpur district.

  19. f

    Normality test of global PSQI score and seven components of elderly subjects...

    • plos.figshare.com
    xls
    Updated Dec 5, 2024
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    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally (2024). Normality test of global PSQI score and seven components of elderly subjects (N = 1992) of rural areas of Sambalpur district. [Dataset]. http://doi.org/10.1371/journal.pone.0314770.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Dec 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Sarojini Minz; Monalisa Mohapatra; Uma Charan Pati; Pritipadma Sahu; Raghunath Satpathy; Rupashree Brahma Kumari; Pradosh Kumar Acharya; Nirupama Sahoo; Sujit Kumar Jally
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Sambalpur
    Description

    Normality test of global PSQI score and seven components of elderly subjects (N = 1992) of rural areas of Sambalpur district.

  20. f

    a. Urban and rural characteristics in demographic parameters of pregnant...

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jul 28, 2025
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    Mugdha Deshpande; Neha Kajale; Nikhil Shah; Ketan Gondhalekar; Vivek Patwardhan; Anagha Pai Raiturker; Sanjay Gupte; Leena Patankar; Jasmin Bhawra; Anuradha Khadilkar; Tarun Reddy Katapally (2025). a. Urban and rural characteristics in demographic parameters of pregnant women in the MAI Cohort. b. Urban and rural differences in physical activity, sleep quality, prenatal distress, and dietary intake of pregnant women across trimesters in MAI Cohort. [Dataset]. http://doi.org/10.1371/journal.pone.0328081.t001
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    xlsAvailable download formats
    Dataset updated
    Jul 28, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Mugdha Deshpande; Neha Kajale; Nikhil Shah; Ketan Gondhalekar; Vivek Patwardhan; Anagha Pai Raiturker; Sanjay Gupte; Leena Patankar; Jasmin Bhawra; Anuradha Khadilkar; Tarun Reddy Katapally
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    a. Urban and rural characteristics in demographic parameters of pregnant women in the MAI Cohort. b. Urban and rural differences in physical activity, sleep quality, prenatal distress, and dietary intake of pregnant women across trimesters in MAI Cohort.

  21. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Will Fitzgerald; Will Fitzgerald; Gretchen Gehrke; Gretchen Gehrke (2025). CDC PLACES: Local Data for Better Health, County Data and Census Tract Data 2024 release [Dataset]. http://doi.org/10.5281/zenodo.14774046
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CDC PLACES: Local Data for Better Health, County Data and Census Tract Data 2024 release

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csv, zipAvailable download formats
Dataset updated
Jan 30, 2025
Dataset provided by
Zenodohttp://zenodo.org/
Authors
Will Fitzgerald; Will Fitzgerald; Gretchen Gehrke; Gretchen Gehrke
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

From the CDC Places page on ArcGIS:

PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation. The original 500 Cities Project provided city- and census tract-level estimates for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States.

This service includes 40 measures for chronic disease related health outcomes (12), prevention measures (7), health risk behaviors (4), disability (7), health status (3), and health-related social needs (7). Data were provided by CDC Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2022 or 2021), Census Bureau 2020 census population data or annual population estimates for county vintage 2022, and American Community Survey (ACS) 2018-2022 estimates.
  • The health outcomes include arthritis, current asthma, high blood pressure, cancer (non-skin) or melanoma, high cholesterol, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease, diagnosed diabetes, depression, obesity, all teeth lost, and stroke.
  • The prevention measures are lack of health insurance, routine checkup within the past year, visited dentist or dental clinic in the past year, taking medicine to control high blood pressure, cholesterol screening, mammography use for women, and colorectal cancer screening.
  • The health risk behaviors are binge drinking, current cigarette smoking, physical inactivity, and short sleep duration.
  • The disability measures are six disability types (hearing, vision, cognitive, mobility, self-care, and independent living) and any disability.
  • The health status measures are frequent mental distress, frequent physical distress, and poor or fair health.
  • The health-related social needs measures are social isolation, food stamps, food insecurity, housing insecurity, utility services threat, transportation barriers, and lack of social and emotional support.
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