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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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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Using big data from long-form recordings to study development and optimize societal impact.

Margaret Cychosz1, Alejandrina Cristia2

  • 1Department of Hearing and Speech Sciences, University of Maryland, College Park, MD, United States; Center for Comparative and Evolutionary Biology of Hearing, University of Maryland, College Park, MD, United States.

Advances in Child Development and Behavior
|March 7, 2022
PubMed
Summary
This summary is machine-generated.

Wearable technology captures long-form, child-centered recordings for breakthroughs in clinical treatment, interventions, and language documentation. This big data approach promises more equitable patient care and less biased scientific insights.

Keywords:
Algorithm biasAudio recordingAutomatic measurementBig dataChildrenLanguageWearable technology

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Area of Science:

  • Child Development
  • Wearable Technology
  • Big Data Analytics

Background:

  • Big data is pervasive, with long-form, child-centered recordings from wearable devices offering a novel, unobtrusive data source.
  • These recordings capture rich, real-world behavioral data, presenting a significant opportunity for diverse research fields.

Purpose of the Study:

  • To explore the potential of long-form, child-centered recordings from wearable technologies in clinical treatment, large-scale interventions, and language documentation.
  • To advocate for the integration of these recordings into research for improved patient equity, reliable intervention measurement, and reduced observational bias.

Main Methods:

  • Utilizing unobtrusive, encompassing long-form recordings from wearable technologies focused on children.
  • Demonstrating applications in clinical treatment, large-scale interventions, and language documentation.

Main Results:

  • Potential for breakthrough applications in child-centered research and clinical practice.
  • Anticipated improvements in equitable patient treatment and unbiased measurement of real-world behavior.

Conclusions:

  • Incorporating wearable-based recordings enhances research reliability and scientific insight.
  • Proposing a platform for hosting recordings and training less biased algorithms to advance big data in child research.