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396 results for “Physical health”
Physical Health of Adults during Covid-19
<h1>Background</h1> <p>This dataset is one of the studies of the <a href="https://www.corona-health.net/en/">Corona Health project</a>. It addresses how the physical health and habits of adults during the global pandemic changed over time. It consists of two questionnaires, a baseline questionnaire and a bi-weekly follow up questionnaire to track the behaviour in the last 14 days. The whole datasets contains more than <strong>1800 users</strong> (98 % of them German) and a total of <strong>7000 questionnaires</strong>. For some users, it also has GPS and app usage data.</p> <h1>Files</h1> <ul> <li>rki_heart_baseline.csv -> The baseline questionnaire, containing demographic data as well</li> <li>rki_heart_followup.csv -> The follow-up questionnaire</li> <li>answersheets.csv -> The unprocessed answersheets from both, baseline and follow-up questionnaires in a raw format.</li> <li>codebook.xlsx -> The Codebook that describes the three files in detail</li> </ul> <h1>More information</h1> <ul> <li> <p><strong>Key Facts</strong></p> <ul> <li>No. of questionnaires: 1805 Baseline + 5895 Follow-up</li> <li>n Tracking Consent GPS (ratio): 1366 (75%)</li> <li>n Tracking Consent App Usage (ratio): 101 (5.6%)</li> </ul> </li> <li> <p><strong>Sociodemographics</strong></p> <ul> <li>Age, mean (SD): 41.7 (15.1)</li> <li>Gender Ratio: <ul> <li>Male: 36%</li> <li>Female: 64%</li> <li>Diverse: 0%</li> </ul> </li> <li>Body Mass Index, mean (SD): 26.7 (6.23)</li> <li>Users located in Germany (ratio): 98.7%</li> </ul> </li> <li> <p><strong>Lifestyle habits at baseline</strong></p> <ul> <li>Daily Smokers (ratio): 292 (16.2%)</li> <li>Daily fruit consumers before lockdown (ratio): 533 (29.5%)</li> <li>Daily fruit consumers after lockdown (ratio): 538 (29.8%)</li> <li>Daily vegetable consumers before lockdown (ratio): 562 (31.1%)</li> <li>Daily vegetable consumers after lockdown (ratio): 559 (31.0%)</li> </ul> </li> <li> <p><strong>Cardiovascular Health at baseline</strong></p> <ul> <li>History of hypertension (ratio): 454 (25.2%)</li> <li>History of diabetes mellitus (ratio): 113 (6.3%)</li> <li>History of hyperlipidemia (ratio): 461 (25.5%)</li> </ul> </li> </ul> <p>For a more detailed description of the dataset, please go on <a href="https://github.com/joa24jm/CH-Heart" target="_blank" rel="noopener">GitHub/joa24jm/ch-heart.</a> There, you can also find a link to our publication.</p>
Supplementary Data from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>These data are used to conduct the analysis in, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied</em> Statistics. This is purely for archival purposes to facilitate access to and replication of the aforementioned analysis. Data were obtained from the following sources:</p> <ol> <li> U.S. Emissions Data [<a href="https://ampd.epa.gov/ampd">U.S. EPA, Air markets program data (AMPD)</a>] <ul> <li>AMPD_Unit_with_Sulfur_Content_and_Regulations_with_Facility_Attributes.csv</li> </ul> </li> <li> US Census 2016 American Community Survey [<a href="https://www.census.gov/programs-surveys/acs">US Census Bureau ACS</a>] <ul> <li>Census_2016_TxZCTA.RDS</li> <li><em>Note: data were obtained using the r package ‘<a href="https://walker-data.com/tidycensus/">tidycensus</a>’.</em></li> </ul> </li> <li> Daymet Annual Climate Summaries [<a href="https://daac.ornl.gov/DAYMET/guides/Daymet_V4_Annual_Climatology.html">Daymet Version 4</a>] <ul> <li>daymet_v4_prcp_annttl_na_2016.nc</li> <li>daymet_v4_tmax_annavg_na_2016.nc</li> <li>daymet_v4_tmin_annavg_na_2016.nc</li> <li>daymet_v4_vp_annavg_na_2016.nc</li> </ul> </li> <li> SO<sub>4</sub> and Black Carbon Concentrations [<a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5/#V4.NA.03">Randall Martin Atmospheric Composition Analysis Group, North American Regional Estimates, version V4.NA.02</a>] <ul> <li>GWRwSPEC_BC_NA_201601_201612.nc</li> <li>GWRwSPEC_SO4_NA_201601_201612.nc</li> </ul> </li> <li> HyADS Coal-Attributed PM2.5 Concentrations [<a href="https://doi.org/10.1097/EDE.0000000000001024">Henneman et al. (2019)</a>] <ul> <li>HyADS_grids_pm25_byunit_2016.fst</li> <li>HyADS_grids_pm25_total_2016.fst</li> </ul> </li> <li> Mexico Emissions Data [<a href="https://www.epa.gov/air-emissions-modeling/2014-2016-version-7-air-emissions-modeling-platforms">National Emissions Inventory Collaborative, 2016v1 emissions modeling platform</a>] <ul> <li>Mexico_2016_point_interpolated_02mar2018_v0.csv</li> </ul> </li> <li> North American Regional Reanalysis Meteorological Data [<a href="https://psl.noaa.gov/data/gridded/data.narr.monolevel.html">NOAA</a>] <ul> <li>rhum.2m.mon.mean.nc</li> <li>uwnd.10m.mon.mean.nc</li> <li>vwnd.10m.mon.mean.nc</li> </ul> </li> <li> Cigarette smoking data [<a href="https://doi.org/10.1186/1478-7954-12-5">Dwyer-Lindgren et al. (2014)</a>] <ul> <li>smokedatwithfips_1996-2012.csv</li> </ul> </li> <li> Synthetic pediatric asthma data [<em>Note:<strong> synthetic data!</strong> Simulated to match the format, but not the observations, from the <a href="https://www.dshs.texas.gov/texas-health-care-information-collection">Texas Health Care Information Collection (THCIC), Texas DSHS</a></em>] <ul> <li>synth-ped-asthma-data.csv</li> </ul> </li> <li> Texas state shape file [<a href="https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html">US Census</a>] <ul> <li>texas-state-sf.RDS</li> </ul> </li> <li> US ZIPcode-to-county data crosswalk [<a href="https://mcdc.missouri.edu/applications/geocorr2014.html">Missouri Census Data Center</a>] <ul> <li>tx-zip-to-county.csv</li> </ul> </li> </ol> <p>Code and supplementary material from this analysis, as well as more detailed data descriptions, are available at: <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a></p>
Mental health, physical health, training load and subjective performance during the COVID-19 pandemic – a Swiss elite athletes' cohort study
<p>Dataset of Swiss elite athletes (n=203) participating in a repeated online survey evaluating mental and physical health factors, as well as training and performance related metrics. After the first survey during the first lockdown between April and May 2020, there were monthly follow-up surveys over a 6-month period.</p>
Family Type, Mental and Physical Health of in-school Female Adolescents in Ado-Ekiti, Nigeria
<p>This dataset is based on a cross-sectional survey conducted in Ado-Ekiti, Nigeria among in-school female adolescents. The general objective of the study was to examine the influence of family type on the physical and mental health of in-school female adolescents. The data were obtained in 2017 from in-school female adolescents aged 10-18 years old. The respondents were randomly selected from four purposively selected secondary schools. Two schools were selected from the government-owned public schools and two from schools owned by individuals or private organizations. Nigeria operates a six-year secondary education; three years at the junior secondary school (JSS 1-3) and three years at the senior secondary (SS 1-3). The population for this study excluded students in SS3 because they had just concluded their terminal examination at the time of this study. Using the formula proposed by (Krejcie & Morgan, 1970) for deriving a small sample when the population is known, a sample size of 383 was derived from a population of 1656 female students in the four schools. With 10% added to adjust for non-response, a total of 421 students were involved in the study. The number of respondents allocated to each school was proportional to the size of the female students' population in each school. In each school, respondents were assigned to classes proportional to the size, and the particular respondents were identified using a random number. Data were collected with a self-administered structured questionnaire which was piloted before the actual survey. The data is saved in Stata format. </p> <p>A scale for measuring self-reported mental well was designed using questions adapted from Ross, Mirowsky, & Goldsteen (1990) and (Langton & Berger, 2011). The scale reliability coefficient was 0.85. The students were asked to state how often they experience 22 conditions such as feeling sad, discouraged, lonely, hopeless, worthless, wishing you were dead, having trouble concentrating, having difficulty sleeping, crying, and worried among others. </p> <p>Physical health was measured using a scale generated with questions adapted from (Langton & Berger, 2011; Ross et al., 1990). The students were asked to state the frequency of experiencing seven conditions in the month before the survey: feeling sick, tired, dizzy, having chest pain, a headache, muscle or joint pain, and stomach ache. The response options were every day, more than two times, two times, once, and never, graded 1-5.</p> <p>Family type was categorised in four ways: 1) two-parent and single parent. Two parents comprised of couples who were legally married and those who were living together in a consensual union whereas single parents comprised never married, widowed, divorced, and separated mother or father. 2) two-parent (monogamous), two-parent (polygynous), and single parent 3) two-parent, single father, and single mother 4) two-parent, never married, widowed, divorced, and separated</p>
A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool
<p>Datasets relating to the Wellcome Open Research publication of the same name.</p>
COVID-19, physical activity, and health
<p>Dataset related to the project on COVID-19, physical activity, and health</p> <p>This dataset includes:</p> <p><strong>1) A codebook (including the name of the main variables)</strong></p> <p>--> "code_book_covid.xlsx"</p> <p><strong>2) The anonymous data set</strong></p>
Targeting Physical Health in Schizophrenia: Physical Activity Can Enhance Life Randomized Control Trial
ClinicalTrials.gov study NCT04173572. IPD Sharing: YES. Countries: 1. Publications: 0.
Toxic Sentence Classification Dataset with labels of categories such as religion, mental health, race, sex, body image, disability, physical abuse, and politics
<p>The dataset has a collection of various toxic sentences belonging to different categories. It was collected from various sources. It indicates which category each sentence belongs to. The values of the category columns are binary 1 or 0 indicating whether the sentence belongs to that particular category or not. Each sentence belongs to only 1 category. </p> <p> </p> <p>Columns:<br>1.comment_text: Contains toxic sentences that are insensitive and offensive, focusing on various categories.<br>2.mental_health: Binary value 1 indicates that the sentence focuses on mental health.<br>3.Race:Binary value 1 indicates that the sentence is racist.<br>4.sex:Binary value 1 indicates that the sentence focuses on sexuality.<br>5.body_image:Binary value 1 indicates that the sentence focuses on body image.<br>6.disability:Binary value 1 indicates that the sentence focuses on physical disability and related issues.<br>7.religion:Binary value 1 indicates that the sentence can be triggering to people who are extremely religious.<br>8.physical_abuse:Binary value 1 indicates that the sentence focuses on physical abuse issues.<br>9.politics:Binary value 1 indicates that the sentence focuses on political issues.</p>
Dolutegravir in Real Life: self-reported mental and physical health outcomes after transitioning from efavirenz- to dolutegravir-based antiretroviral therapy in a prospective cohort study in Lesotho
<p>Pseudonymized dataset for the accepted manuscript "<em>Dolutegravir in Real Life: self-reported mental and physical health outcomes after transitioning from efavirenz- to dolutegravir-based antiretroviral therapy in a prospective cohort study in Lesotho</em>"</p>
Results from, "Causal health impacts of power plant emission controls under modeled and uncertain physical process interference."
<p>This repository contains results from the paper, "<a href="https://arxiv.org/abs/2306.05665">Causal health impacts of power plant emission controls under modeled and uncertain physical process interference</a>," by Wikle and Zigler (2024), to appear in <em>Annals of Applied Statistics</em>. The storage of these results helps facilitate access to and replication of the analysis in the paper. The results include output from the:</p> <ol> <li>Sulfate analysis <ul> <li>tx-2016-100k-int-mx.RDS</li> </ul> </li> <li>Asthma analysis <ul> <li>asthma-pois-cut.RDS</li> <li>asthma-pois-plugin.RDS</li> <li>asthma-bart-cut.RDS</li> <li>asthma-bart-plugin.RDS</li> </ul> </li> <li>Medicare analysis <ul> <li>medicare-pois-cut.RDS</li> <li>medicare-pois-plugin.RDS</li> <li>medicare-bart-cut.RDS</li> <li>medicare-bart-plugin.RDS</li> </ul> </li> <li>Simulation study <ul> <li>simstudy-cm1-lm.RDS</li> <li>simstudy-cm1-bart.RDS</li> <li>simstudy-cm2-lm.RDS</li> <li>simstudy-cm2-bart.RDS</li> <li>simstudy-cm3-lm.RDS</li> <li>simstudy-cm3-bart.RDS</li> <li>simstudy-pm1-pois.RDS</li> <li>simstudy-pm1-bart.RDS</li> <li>simstudy-pm2-pois.RDS</li> <li>simstudy-pm2-bart.RDS</li> <li>simstudy-pm3-pois.RDS</li> <li>simstudy-pm3-bart.RDS</li> </ul> </li> <li>Log-linear BART sensitivity analysis <ul> <li>sensitivity-m100.RDS</li> <li>sensitivity-m200.RDS</li> <li>sensitivity-m300.RDS</li> <li>sensitivity-m400.RDS</li> <li>sensitivity-power05.RDS</li> <li>sensitivity-power1.RDS</li> <li>sensitivity-power15.RDS</li> <li>sensitivity-power2.RDS</li> <li>sensitivity-power25.RDS</li> <li>sensitivity-power3.RDS</li> <li>sensitivity-power4.RDS</li> <li>sensitivity-power5.RDS</li> </ul> </li> </ol> <p>A description of these results can be found at <a href="https://github.com/nbwikle/estimating-interference">https://github.com/nbwikle/estimating-interference</a>, along with the R code used to generate these (and other results, such as figures) found in the manuscript and supplementary material.</p>
Linking 23 physical activity intensity levels to health-related quality of life in 10-year-old children
<p>This anonymized data is a subset of the ASK study: <br>Resaland, G.K., Moe, V.F., Aadland, E., et al. Active Smarter Kids (ASK): Rationale and design of a cluster-randomized controlled trial investigating the effects of daily physical activity on children’s academic performance and risk factors for non-communicable diseases.BMC Public Health 15, 709 (2015). https://doi.org/10.1186/s12889-015-2049-y</p> <p>The ASK study was approved by the Regional Committees for Medical and Health Research Ethics in Norway (reference number: 2013/1893) and registered at www.Clinicaltrials.gov with ID NCT02132494. </p> <p>The selected cross-sectional baseline data in this file are used in the following unpublished paper: <br>Stai, M., Aadland, E., Andersen, J.R. (Year). *Linking 23 physical activity intensity levels to health-related quality of life in 10-year-old children.</p> <p>The data is stored in Excel. <br><br>Explanations: <br>- #NULL! = missing data<br>- Sex (0 = Girl, 1= Boy) <br>- Overweight or obesity (0 = No, 1 = Yes)<br>- All other data has labeling that is self-explanatory. </p> <p>Corresponding Author: <br>John Roger Andersen, johnra@hvl.no </p> <p> </p>
Targeting Physical Activity to Improve Cardiovascular Health in Type 2 Diabetes
ClinicalTrials.gov study NCT02473926. IPD Sharing: NO. Countries: 1. Publications: 1.
Physical Health in Midlife: Influences of Adversity and Relationships Over Time
ClinicalTrials.gov study NCT01853332. IPD Sharing: Not stated. Countries: 1. Publications: 1.
An Observational Study to Evaluate Physical Activity, Bleeding Incidence and Health Related Quality of Life, in Participants With Haemophilia A Without Inhibitors Receiving Standard of Care Treatment
ClinicalTrials.gov study NCT04165135. IPD Sharing: NO. Countries: 1. Publications: 1.
Health Education Materials With/Out a Physical Activity Program for Patients Who Have Undergone Treatment for High-Risk Stage II or Stage III Colon Cancer
ClinicalTrials.gov study NCT00819208. IPD Sharing: NO. Countries: 6. Publications: 5.
The Effects of Altering Physical Activity on Cardiometabolic Health
ClinicalTrials.gov study NCT01879059. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health
ClinicalTrials.gov study NCT03771417. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Computerized Health Education to Promote Physical Activity
ClinicalTrials.gov study NCT00242658. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Thera-Band Elastic Band-Assisted Progressive Resistance Training on Physical Health in Diabetes Patients with Frailty Syndrome
ClinicalTrials.gov study NCT06658106. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluating the Effectiveness of the GILL eHealth Intervention to Improve Physical Health and Lifestyle Behaviours in Patients With Severe Mental Illness
ClinicalTrials.gov study NCT05533749. IPD Sharing: YES. Countries: 1. Publications: 1.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.