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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Data Collection by Survey01:07

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Longitudinal Studies

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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Data Collection III01:05

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The physical assessment examines the patient for objective data that defines the patient's condition, and aids in formulating the nursing care plan. The purpose of physical assessment is a health status appraisal, which includes identifying health problems, and establishing a database for nursing intervention.
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Bioavailability Study Design: Healthy Subjects Versus Patients01:15

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Bioavailability studies are essential for evaluating a drug's therapeutic efficacy and understanding its absorption patterns under various physiological conditions. Conducting such studies on target patient populations provides more relevant data by simulating real-world disease states. However, practical challenges often necessitate the use of young, healthy adult volunteers as study subjects.Patients may exhibit altered drug absorption patterns due to the effects of the disease itself,...
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Related Experiment Video

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An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Nathan D Price1,2, Andrew T Magis2, John C Earls2

  • 1Institute for Systems Biology, Seattle, Washington, USA.

Nature Biotechnology
|July 18, 2017
PubMed
Summary

This study integrated personal health data, including genomics and metabolomics, to identify disease biomarkers and correlates of genetic risk. Personalized coaching using this data improved participant health outcomes, demonstrating the value of longitudinal personal data clouds for understanding health and disease.

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Area of Science:

  • Multi-omics and personalized health
  • Biomarker discovery and validation
  • Integrative data analysis

Background:

  • Longitudinal personal data integration is crucial for understanding complex health and disease states.
  • Identifying novel biomarkers and understanding genetic risk correlates can advance precision medicine.
  • Personalized interventions informed by comprehensive data can improve clinical outcomes.

Purpose of the Study:

  • To develop a comprehensive personal data cloud integrating multi-omics, clinical, and activity data.
  • To identify novel biomarkers and molecular correlates of polygenic risk for various diseases.
  • To assess the impact of data-informed behavioral coaching on participant health.

Main Methods:

  • Collected whole genome sequences, metabolomes, proteomes, microbiomes, clinical tests, and activity data from 108 individuals over 9 months.
  • Generated correlation networks to identify communities of related analytes and potential biomarkers.
  • Calculated polygenic risk scores and analyzed their correlation with molecular data.

Main Results:

  • Discovered interconnected analyte communities associated with physiology and disease, identifying candidate biomarkers like gamma-glutamyltyrosine for cardiometabolic disease.
  • Found molecular correlates of polygenic risk, such as a negative correlation between inflammatory bowel disease genetic risk and plasma cystine.
  • Demonstrated that data-informed behavioral coaching improved participants' clinical biomarkers.

Conclusions:

  • Longitudinal measurement of personal data clouds enhances understanding of health, disease, and early disease transitions.
  • Integrated multi-omics data analysis can reveal novel biomarkers and genetic risk associations.
  • Personalized health insights derived from comprehensive data can drive measurable improvements in clinical outcomes.