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Related Concept Videos

Experimental Designs01:16

Experimental Designs

18.2K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

256
Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
256
Group Design02:01

Group Design

10.7K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.7K
Factorial Design02:01

Factorial Design

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Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
14.1K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

210
Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
210
Bioavailability Study Design: Single Versus Multiple Dose Studies01:11

Bioavailability Study Design: Single Versus Multiple Dose Studies

251
Bioavailability studies are essential for understanding how a drug is absorbed, distributed, metabolized, and excreted in the body. These studies assess the extent and rate at which the active pharmaceutical agent becomes available at the site of action. The design of bioavailability studies can involve single-dose or multiple-dose regimens, each with distinct advantages and limitations.Single-dose studies are the preferred approach due to their simplicity and reduced drug exposure for...
251

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An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
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QuantifyMe: An Open-Source Automated Single-Case Experimental Design Platform.

Sara Taylor1, Akane Sano2, Craig Ferguson3

  • 1Affective Computing Group, MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. sataylor@media.mit.edu.

Sensors (Basel, Switzerland)
|April 6, 2018
PubMed
Summary
This summary is machine-generated.

QuantifyMe is a new smartphone platform that helps individuals conduct scientific self-experiments to find personalized health and wellbeing insights. This tool enables automated, proper-methodology single-case experiments for better self-understanding.

Keywords:
mobile healthself-experimentself-trackingsingle-case experimental designwearable sensors

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Area of Science:

  • Digital Health
  • Behavioral Science
  • Personalized Medicine

Background:

  • Wearable sensors and smartphones collect vast user data, but current recommendations lack personalization.
  • Individualized insights are crucial for effective health and wellbeing strategies.
  • Conducting rigorous self-experiments, like single-case experimental designs, is challenging for individuals.

Purpose of the Study:

  • To design, develop, and evaluate QuantifyMe, a novel platform for novice self-experimenters.
  • To enable automated, scientific single-case self-experiments using smartphones.
  • To explore personalized insights into factors affecting happiness, stress, productivity, and sleep efficiency.

Main Methods:

  • Development of the QuantifyMe platform with software for customized single-case studies.
  • Evaluation of the platform through four personalized investigations.
  • A six-week pilot study with 13 participants to assess QuantifyMe's usability and effectiveness.

Main Results:

  • The QuantifyMe platform was successfully developed and evaluated for conducting single-case self-experiments.
  • Pilot study demonstrated the platform's potential for personalized investigations into health and wellbeing variables.
  • Lessons learned during development and pilot testing were documented for platform improvement.

Conclusions:

  • QuantifyMe empowers individuals to conduct scientific self-experiments, generating personalized health insights.
  • The platform has the potential to significantly reduce administrative costs in advancing human health and wellbeing.
  • Further development and broader adoption of QuantifyMe could democratize scientific self-discovery.