Sentences with phrase «types of health data»

If you have apps or devices that integrate with HealthKit, they can automatically add data to the different types of Health Data, just as your iPhone is constantly updating the step - tracking data with data from its own sensors.

Not exact matches

A recent study in Sweden titled «The relation between office type and workplace conflict: A gender and noise perspective» and published in the Journal of Environmental Psychology, looked at the data of 5,229 employees who participated in the Swedish Longitudinal Occupational Survey of Health.

The EU executive will also propose a law to reduce national restrictions on where some types of commercial and health data can be stored, saying unnecessary data localization rules hamper technological innovation.

Data from a number of other studies, including the Nurses» Health Study and the Health Professionals Follow - up Study also report greater risk of type 2 diabetes, high blood pressure, heart disease and metabolic syndrome, which is related to diabetes and cardiovascular problems, for consumers of artificially sweetened beverages.

The data for family income in figures 4 and 5 don't include certain types of government transfers or the value of health insurance contributions from employers or (in the case of Medicare and Medicaid) government.

Mary Edwards, who leads Accenture's work with federal civilian health agencies, explained to MobiHealthNews during an interview that this type of data increasingly is collected by consumer devices, like Fitbits, and is rarely a part of a clinical or claims record.

- retirement savings and income - Pre-59 1/2 72t Calculations (avoiding penalty tax)- college savings and 529 plan illustrations - college cost and tuition data - Coverdell education savings - risk profile questionnaires and quizes - model portfolio illustrations - asset allocation and portfolio optimization - portfolio management and value tracking - 401 (k) retirement savings - Cost of waiting to save - Effect of Taxes and Inflation - Estate Tax Estimator - Finding Money for your savings goals - Health Savings Account (HSA) illustrations - Historical Hypothetical Portfolio Performance - Impact of Inflation - Life Insurance Needs Analysis - IRA Eligibility (all types of IRAs)- IRA Savings and Goal Analysis - IRA Required Minimum Distributions (RMDs)- IRA to Roth Conversion - Long Term Care Insurance - Lumpsum Distributions vs. Rollover Distributions - Model Portfolio Creation and Comparisons - Mortgage Amortization - Net Unrealized Appreciation of Employer Stock - Net Worth Estimator - New Value Calculator - Pension / Defined Benefit Income estimates - Portfolio Allocation Rebalancing - Portfolio Optimization and «Advice» - Portfolio Return Calculations - Paycheck Tax Savings - Required Minimum Distribution calculations - Retirement Budget and Expense Planning - Retirement Income Analyzer - Retirement Savings Estimator - Risk Tolerance Profile - Roth 401k - Roth Conversion - Roth v. IRA illustrations - Short Term Savings goals - Social Security benefit estimates - Stretch IRA / Legacy IRA illustrations - Tax Free Yield calculations

- retirement savings and income - Pre-59 1/2 72t Calculations (avoiding penalty tax)- college savings and 529 plan illustrations - college cost and tuition data - Coverdell education savings - risk profile questionnaires and quizes - model portfolio illustrations - asset allocation and portfolio optimization - portfolio management and value tracking - 401 (k) retirement savings - Cost of waiting to save - Effect of Taxes and Inflation - Estate Tax Estimator - Finding Money for your savings goals - Health Savings Account (HSA) illustrations - Historical Hypothetical Portfolio Performance - Impact of Inflation - Life Insurance Needs Analysis - IRA Eligibility (all types of IRAs)- IRA Savings and Goal Analysis - IRA Required Minimum Distributions (RMDs)- IRA to Roth Conversion - Long Term Care Insurance - Lumpsum Distributions vs. Rollover Distributions - Model Portfolio Creation and Comparisons - Mortgage Amortization - Net Unrealized Appreciation of Employer Stock - Net Worth Estimator - New Value Calculator - Pension / Defined Benefit Income estimates - Portfolio Allocation Rebalancing - Portfolio Optimization and «Advice» - Portfolio Return Calculations - Paycheck Tax Savings - Required Minimum Distribution calculations - Retirement Budget and Expense Planning - Retirement Income Analyzer - Retirement Savings Estimator - Risk Tolerance Profile - Roth Conversion - Roth v. IRA illustrations - Short Term Savings goals - Social Security benefit estimates - Stretch IRA / Legacy IRA illustrations - Tax Free Yield calculations

We calculated these transition probabilities using data from the longitudinal National Health and Nutrition Evaluation Survey, which assessed a cohort of women in 1987 and the same women again in 1992.25 Several limitations of these data affect our model: 1) because this national survey lacks data on women before age 35 years, women in our model could not develop hypertension, type 2 diabetes mellitus, or MI before age 35 years; 2) because longitudinal survey data were only available for a 5 - year interval, we assumed that transition probabilities were stable within the 5 - year intervals and converted these probabilities from 5 - year to 1 - year intervals; 3) because the survey data were too few to provide stable estimates by year of age, we used transition probabilities for women in three age groups: aged 50 years and younger, 51 — 65 years, and 65 years and older.

Of note, our models may underestimate the true maternal costs of suboptimal breastfeeding; we modeled the effects of lactation on only five maternal health conditions despite data linking lactation with other maternal health outcomes.46 In addition, women in our model could not develop type 2 diabetes mellitus, hypertension, or MI before age 35 years, although these conditions are becoming increasingly prevalent among young adults.47 Although some studies have found an association between lactation and rates of postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and MOf note, our models may underestimate the true maternal costs of suboptimal breastfeeding; we modeled the effects of lactation on only five maternal health conditions despite data linking lactation with other maternal health outcomes.46 In addition, women in our model could not develop type 2 diabetes mellitus, hypertension, or MI before age 35 years, although these conditions are becoming increasingly prevalent among young adults.47 Although some studies have found an association between lactation and rates of postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and Mof suboptimal breastfeeding; we modeled the effects of lactation on only five maternal health conditions despite data linking lactation with other maternal health outcomes.46 In addition, women in our model could not develop type 2 diabetes mellitus, hypertension, or MI before age 35 years, although these conditions are becoming increasingly prevalent among young adults.47 Although some studies have found an association between lactation and rates of postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and Mof lactation on only five maternal health conditions despite data linking lactation with other maternal health outcomes.46 In addition, women in our model could not develop type 2 diabetes mellitus, hypertension, or MI before age 35 years, although these conditions are becoming increasingly prevalent among young adults.47 Although some studies have found an association between lactation and rates of postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and Mof postmenopausal diabetes22, 23 and cardiovascular disease, 10 we conservatively limited the duration of lactation's effect on both diabetes and Mof lactation's effect on both diabetes and MI.

In a study that included data from more than three million children and adolescents from diverse geographic regions of the United States, researchers found that the prevalence of both type 1 and type 2 diabetes increased significantly between 2001 and 2009, according to the study in the May 7 issue of JAMA, a theme issue on child health.

But scientists like Snyder, who diagnosed himself with type 2 diabetes, think that someday people could regularly collect these sorts of data to track changes in their health over time.

«This type of data can inform public health interventions,» said Schwartz, who is also an assistant professor of Computer Science at Stony Brook University.

• More prescriptions are dispensed for antidepressants — 232.7 million nationwide in 2007 — than for drugs of any other type, according to the data firm, IMS Health.

«Local media is the bedrock of internet surveillance — the kind of work that we do in terms of scouring the web looking for early signs of something taking place in a community,» explained Brownstein, chief innovation officer at Boston Children's and a pioneer in the field of using sources other than public health data to do this type of work.

Researchers examined the data of 314 overweight and obese Latino children who were between 8 and 15 years old when they enrolled in the National Institutes of Health - funded Study of Latino Adolescents at Risk of Type 2 Diabetes (SOLAR) study, a 12 - year undertaking.

The data gathered for the QUALITY study (family study on the prevention of cardiovascular disease and Type 2 diabetes in kids and teens) was ideal for verifying children's dental health.

«It's particularly encouraging that these analyses use the types of data available to any large health care system,» said NIMH Director Thomas Insel, M.D.» These methods could help us prevent civilian as well as veteran suicides.»

A study led by Leif Groop of Lund University in Sweden, using data from two long - term health studies, confirmed that result and also found that people carrying the high - blood - sugar gene variant have an increased risk of developing type 2 diabetes.

Many types of aircraft transmit technical data about things like engine health to the airline.

The study analyzed data from The Cancer Genome Atlas (TCGA), a research program supported by the National Cancer Institute and National Human Genome Research Institute within the National Institutes of Health that is looking at genomic changes in more than 20 different types of cancer.

More than one in 10 Americans over the age of 12 use antidepressants, making it the second most prescribed type of drug in the U.S., according to data from the Centers for Disease Control and Prevention (CDC) and IMS Health.

Using data from a United Kingdom database known as The Health Improvement Network (THIN), researchers extracted health records of more than 12,000 patients ages 55 and older who had Type 2 diabetes and heart failure but no prior history of heart attack, stroke, peripheral artery disease or atrial fibrillHealth Improvement Network (THIN), researchers extracted health records of more than 12,000 patients ages 55 and older who had Type 2 diabetes and heart failure but no prior history of heart attack, stroke, peripheral artery disease or atrial fibrillhealth records of more than 12,000 patients ages 55 and older who had Type 2 diabetes and heart failure but no prior history of heart attack, stroke, peripheral artery disease or atrial fibrillation.

Gina will discuss the rise of new data sources and technology and how the field is working to use patient data to improve health outcomes for type 1 diabetes.

New CSAIL research employs many types of medical data, including electronic health records, to predict outcomes in hospitals.

There is growing recognition that the amalgamation of different data types including data produced by individuals through internet services, social media, and mobile phones could reveal valuable insights into a person's health

A dedicated development team, including several members of the MRC Centre for Genomics and Global Health (CGGH), helped MalariaGEN to build an innovative new web application that connects various types of genetic data, empowering the broader scientific community to make use of the rich data resource generated by the MalariaGEN P. falciparum Community Project.

Abecasis leads the University's Biostatistics department, which is training a new generation of scientists and making contributions to the statistical and computational machinery for the analysis of diverse types of biomedical data — including not only genomic data but also electronic health records, registry data and health surveys, a variety of imaging data types, and environmental exposures, among others.

Food is information, and you're either feeding your body the type of information that encourages health and healing or the kind of data that causes disease and imbalances.»

Data from the International Food Information Council (IFIC) Foundation 2015 Food & Health Survey revealed that 30 percent of Americans report limiting or avoiding mono - and polyunsaturated fatty acids, the healthful types people should be consuming more of, not abstaining from.

In a recent prospective analysis of National Health and Nutrition Examination Survey data, decaffeinated coffee consumption was associated with a lower risk of type 2 diabetes only in younger participants (aged ≤ 60 years)(3).

In this 8 - year trial, involving 41,186 particpants of the Black Women's Health Study, research data confirmed inverse associations between magnesium, calcium and major food sources in relation to type 2 diabetes that had already been reported in predominantly white populations.

The richness of the Florida data — especially given that the Florida Departments of Education and Health merged birth and school records for the purpose of the research agenda in which this study is a part — allows us to investigate the effects of Haitian earthquake migrants on many different types of incumbent students.

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With sufficient participation, CHIC will allow breed health committees to monitor disease prevalence at any given point in time and to track changes in frequency over time, types of data that can also be invaluable to research.

703-772-2530, [email protected] Capital Bikeshare Releases Two New Survey ReportsSurveys Provide Detailed Data on Bikeshare Usage and Regional Impact (Washington, D.C.) Today Capital Bikeshare released two new reports that provide detailed information on how members are utilizing the system, the types of trips they make and the overall impact of the program on the members» health.

Dr. Walter Willett, chairman of the nutrition department at the Harvard T. H. Chan School of Public Health, says: «Given the data that we have today, we have shown the refined carbohydrates and especially sugar - sweetened beverages are risk factors for cardiovascular disease, but that the type of dietary fat is also very important.»

Microsoft is banking on Health to be the service everyone uses to track fitness — that way, the company can have access to a plethora of fitness data from many types of people.

Current and historical details of heart rate data in the Health app on iPhone, including new Heart Rate Variability (HRV) and VO2 Max data types

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• Organized and processed paperwork, reports and all kinds of claims documentation • Entered, recorded and reviewed claims into claims information management system • Performed verification checks on the customer / claimant loss - claims following company's standard policies and procedures • Attended to clients, claimants, field appraisers and management queries, regarding claims using the claims MIS • Forwarded appropriate claims for new losses verifying data for accuracy • Performed billing and payment processes • Processed routine claims transactions related to reserves and issued required checks or receipts • Resolved all kinds of issues / problems regarding claims and payments • Regularly run and generated claims reports for management • Gave formal presentations regarding all claims activities to the senior management at the bimonthly • Utilizing outstanding communication and interpersonal skills maintained strong and positive relationships with the providers, the claimants, and the clients • Provided company with necessary clerical support like handling fax, attending and making telephone calls as directed, filing and photocopying, matching checks with receipts etc. • Prepared, updated and organized customer and client's files • Managed all types of correspondence preparing, reviewing and sending memos, letters, emails, reports, applications, and forms • Provided effective CSR to providers, field appraisers, agents, insurance agencies, clients and customers • Matched incoming emails, mails, and faxes with the claims records • Arranged and set up medical appointments for health claims • Kept department's office supplies stocked • Maintained confidential claims information including correspondence with sensitive information • Accelerated claims correspondences as well as updated claims diaries • Worked in a team on several pilot claim projects • Reviewed and kept the record of closed files

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Today, there is a demand for coding specialists for accurately coding data from the medical record in all types of health care institutions.

Community Care Health Centers / Huntington Beach Community Clinic, Huntington Beach • CA 2004 — 2006 Administrative Assistant to the Director of Clinical Services Answered calls, entered data, faxed, typed, and made outbound follow - up calls.

Limitations: (1) cross-sectional data, limiting scope for causal inference, (2) lack of data by tobacco type or volume of use and (3) data from a reproductive health survey may suffer from social desirability bias.

Data were obtained on the location and numbers of female contraceptive clients served at all sites according to the type of center providing care (FQHC, health department, hospital, Planned Parenthood or other independent site).

The work of this project will include: • Assisting hundreds of professionals from these various disciplines in assessing their level of competence in core areas for the purposes of professional development, • Developing preferred competence profiles for various types of service providers, • Providing data on NH's progress in supporting a more competent early childhood and mental health workforce and • Beginning to lay the foundation for a voluntary credentialing system in early childhood mental health in NH.

Area - level explanatory variables will include: accessibility and remoteness, as measured by the Accessibility / Remoteness Index of Australia Plus (ARIA +); 54 socioeconomic disadvantage, as measured by the Australian Bureau of Statistics (ABS) Socioeconomic Indexes for Areas (SEIFA); 55 presence of Aboriginal Medical Services; presence of an AMIHS; proportion of Aboriginal pregnancies / births in an area managed by an AMIHS; numbers of Aboriginal and non-Aboriginal children attending preschool; numbers of full - time equivalent health workers (including general medical practitioners, nurses, midwives and Aboriginal health workers) per 10 000 population; measures of social capital from the NSW Population Health Survey; 56 features of local communities (derived from ABS Census data), such as information on median personal and household income, mortgage repayment and rent; average number of persons per bedroom and household size; employment; non-school qualifications and housing type for Aboriginal residents in each ahealth workers (including general medical practitioners, nurses, midwives and Aboriginal health workers) per 10 000 population; measures of social capital from the NSW Population Health Survey; 56 features of local communities (derived from ABS Census data), such as information on median personal and household income, mortgage repayment and rent; average number of persons per bedroom and household size; employment; non-school qualifications and housing type for Aboriginal residents in each ahealth workers) per 10 000 population; measures of social capital from the NSW Population Health Survey; 56 features of local communities (derived from ABS Census data), such as information on median personal and household income, mortgage repayment and rent; average number of persons per bedroom and household size; employment; non-school qualifications and housing type for Aboriginal residents in each aHealth Survey; 56 features of local communities (derived from ABS Census data), such as information on median personal and household income, mortgage repayment and rent; average number of persons per bedroom and household size; employment; non-school qualifications and housing type for Aboriginal residents in each area.57

State laws typically cover specific types of data, such as personal health or financial information, and spell out what businesses must do in the event of a security breach.