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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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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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Dimensions of Health and Illness01:21

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The factors influencing the health-illness continuum can be internal or external and may or may not be under conscious control. They are related to the following eight human dimensions, and each dimension is interrelated to one other.
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Factors Affecting Illness01:18

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When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
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Functional Classification of Joints01:09

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
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A multidimensional longitudinal dataset on functional somatic syndromes.

Peyman Adibi1, Simindokht Kalani2, Alireza Ani3

  • 1Isfahan Gastroenterology and Hepatology Research Center, Isfahan University of medical sciences, Isfahan, Iran.

Data in Brief
|June 29, 2023
PubMed
Summary
This summary is machine-generated.

This study provides a comprehensive dataset on functional somatic syndromes, exploring factors like lifestyle and psychological health in adults. The data aids researchers in understanding these prevalent conditions.

Keywords:
Cohort profileFunctional somatic syndromesPhenomics

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Area of Science:

  • Multidisciplinary research
  • Health sciences
  • Data science

Background:

  • Functional disorders are common and impact individuals and healthcare systems.
  • Understanding the multifactorial nature of functional somatic syndromes is crucial.
  • Existing research requires comprehensive, longitudinal data for deeper insights.

Purpose of the Study:

  • To present a multidisciplinary dataset for studying functional somatic syndromes.
  • To facilitate research on the interplay of factors contributing to these conditions.
  • To provide a valuable resource for researchers, policymakers, and clinicians.

Main Methods:

  • Longitudinal study design with four years of follow-up.
  • Inclusion of diverse data types: symptoms, psychological, lifestyle, demographics, labs, clinical, and historical.
  • Data collected from a large cohort of seemingly healthy adults (aged 18-65) in Isfahan, Iran.
  • Random selection and monitoring of participants.

Main Results:

  • A dataset comprising 1930 participants at baseline (2017).
  • Follow-up data collected from 1697 (2018), 1616 (2019), and 1176 (2020) participants.
  • Comprehensive data covering multiple domains relevant to functional disorders.

Conclusions:

  • The dataset offers a robust foundation for analyzing functional somatic syndromes.
  • Enables exploration of complex interactions influencing these prevalent health issues.
  • Supports evidence-based strategies for healthcare policy and clinical practice.