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This dataset contains behavioural, psychophysiological, and demographic data collected from 193 preschool children aged 3-5 years to predict the risk of anxiety disorders. The data was originally collected for the research paper "Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach" published in the Journal of Child Psychology and Psychiatry. This dataset can be used to develop machine learning models to quantify anxiety risk in young children.
Training Data.xlsx: Includes 130 randomly selected participants, which is about 70% of the full dataset. Testing Data.xlsx: Has the remaining 30% of participants, which is 63 children.
If you use this dataset in your research, please credit the data has been made publicly available through the Harvard Dataverse repository (Carpenter, 2016). Details on the study methodology and cohort are published in Carpenter et al., 2019 in the Journal of Child Psychology and Psychiatry. This dataset can be accessed at https://doi.org/10.7910/DVN/N42LWG and used under a CC0 1.0 Universal license that supports open scientific collaboration.
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This is an uncertainty number, for more information see Uncertainty in data on kolada.se. Residents aged 16-84 who are bothered by anxiety and/or anxiety are an excise estimate from a sample survey. This means that the estimate is not necessarily representative of the population as a whole. However, with the point estimate ± the uncertainty number, one can most likely say that the true mean is within the range. Data is available according to gender breakdown.
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This is a development key figure, see questions and answers on kolada.se for more information. Share (%) population aged 16-84 with severe anxiety, anxiety or anxiety. The results are taken from the national survey Health on equal terms (National Institute of Public Health). The data refers to year T-2 to year T. Data is available according to gender breakdown.
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The Improving Access to Psychological Therapies (IAPT) data set is a regular return of data generated by providers of NHS-commissioned IAPT services in England (including services provided by independent organisations). It was mandated as a monthly return from 1 April 2012 and collects details of all people accessing these services. Submissions are received by the Health and Social Care Information Centre as record level anonymised data from patient-administration systems. This publication comprises national- and provider-level data quality measures of some key data items in the IAPT dataset: postcode; birthdate; gender; ethnicity; general medical practice code; NHS number; religious belief; sexual orientation; presence of a long-term physical health condition; provisional diagnosis; source of referral; Patient Health Questionnaire (PHQ 9) score; Generalised Anxiety Disorder (GAD) score; Work and Social Adjustment Scale score; appointment type; therapy type; disability. These measures are provided as both counts and percentages of all eligible IAPT records and will be of interest to stakeholders (e.g. the Department of Health and service commissioners), data providers and users of our statistics (e.g. mental health organisations including charities, and service users and their representatives). Read more about the IAPT dataset The Information Standards Notice for the IAPT data set can be found on the Information Standards Board website. Note that amendments to the construction of some data quality measures, agreed with the Department of Health, have been implemented in this release. These revisions affect the following measures: DQM26 Patient Health Questionnaire (PHQ 9) Score; DQM27 Generalised Anxiety Disorder (GAD 7) Score; DQM28 Work and Social Adjustment Scale Score; DQM29 Appointment Type; DQM30 Therapy type. The rules for the Data Quality Measures can be found in the following link and any revisions will be documented on the following page: www.hscic.gov.uk/iapt General Notes The Health and Social Care Information Centre does not presently have permission to hold person-identifiable data. It is therefore only been possible to validate person-identifiable data items such as NHS number, Postcode and Date of Birth by using data items which have been derived from these items (as specified in the validation rules).
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Research data for the MA Thesis: Nutrition related health status and associated factors in juvenile male and female on remand detainees at entry into Pollsmoor correctional service facility in the Western Cape.Design/Methodology: In this cross-sectional comparative study of 67 male and 52 female young on-remand detainees the following was assessed during 2016-2017: socio-demographic information, hunger score (CDC FANTA project hunger score), meal pattern, food choices (Non-quantified FFQ), understanding of healthy eating, physical activity (GPAQv2), risk taking behaviours, body shape perception, symptoms of depression and anxiety (K10 tool) and specific anthropometric measures including weight, height, triceps-skinfold, mid-upper arm circumference and handgrip strength. BMI and the corrected arm muscle area were calculated.The pdf of the thesis is accessible at http://hdl.handle.net/11427/33202 Approval was granted by UCT HREC, National department of correctional services South Africa and permission was granted to publish information.
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Mean heart rate (beats/minute) by anxiety group in women and men.
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The statistics from the Consumers and Environment survey are broken down by women and men. The statistics describe how consumption in different markets affects the environment and where consumers perceive it as the easiest and most difficult to make choices with environmental concerns. The 2019 report has included a matter of climate anxiety. In other words, if consumers experience anxiety or guilt in connection with the purchase of products in different areas of consumption. Our hope is that the report can be used as an inspiration for measures aimed at limiting the environmental impact of consumption, as well as for the prioritisation and follow-up of such measures.
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ObjectivesThe aim of this study was to investigate the effects of aroma foot massage on blood pressure, anxiety, and health-related quality of life (QOL) in Japanese community-dwelling men and women using a crossover randomized controlled trial.MethodsFifty-seven eligible participants (5 men and 52 women) aged 27 to 72 were randomly divided into 2 intervention groups (group A: n = 29; group B: n = 28) to participate in aroma foot massages 12 times during the 4-week intervention period. Systolic and diastolic blood pressure (SBP and DBP, respectively), heart rate, state anxiety, and health-related QOL were measured at the baseline, 4-week follow-up, and 8-week follow-up. The effects of the aroma foot massage intervention on these factors and the proportion of participants with anxiety were analyzed using a linear mixed-effect model for a crossover design adjusted for participant and period effects. Furthermore, the relationship between the changes in SBP and state anxiety among participants with relieved anxiety was assessed using a linear regression model.ResultsAroma foot massage significantly decreased the mean SBP (p = 0.02), DBP (p = 0.006), and state anxiety (p = 0.003) as well as the proportion of participants with anxiety (p = 0.003). Although it was not statistically significant (p = 0.088), aroma foot massage also increased the score of mental health-related QOL. The change in SBP had a significant and positive correlation with the change in state anxiety (p = 0.01) among participants with relieved anxiety.ConclusionThe self-administered aroma foot massage intervention significantly decreased the mean SBP and DBP as well as the state anxiety score, and tended to increase the mental health-related QOL scores. The results suggest that aroma foot massage may be an easy and effective way to improve mental health and blood pressure.Trial RegistrationUniversity Hospital Medical Information Network 000014260
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Estimated marginal means for depression and anxiety.
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Relative Risk (RR) and 95% Confidence Intervals (95% CIs).
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https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
This dataset contains behavioural, psychophysiological, and demographic data collected from 193 preschool children aged 3-5 years to predict the risk of anxiety disorders. The data was originally collected for the research paper "Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach" published in the Journal of Child Psychology and Psychiatry. This dataset can be used to develop machine learning models to quantify anxiety risk in young children.
Training Data.xlsx: Includes 130 randomly selected participants, which is about 70% of the full dataset. Testing Data.xlsx: Has the remaining 30% of participants, which is 63 children.
If you use this dataset in your research, please credit the data has been made publicly available through the Harvard Dataverse repository (Carpenter, 2016). Details on the study methodology and cohort are published in Carpenter et al., 2019 in the Journal of Child Psychology and Psychiatry. This dataset can be accessed at https://doi.org/10.7910/DVN/N42LWG and used under a CC0 1.0 Universal license that supports open scientific collaboration.