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Electronic Distance Measuring Instruments01:30

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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over short distances...

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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A Bluetooth-Based Smartphone App for Detecting Peer Proximity: Protocol for Evaluating Functionality and Validity.

Nancy P Barnett1, Alexander W Sokolovsky1, Matthew K Meisel1

  • 1Department of Behavioral and Social Sciences, Brown University, Providence, RI, United States.

JMIR Research Protocols
|April 5, 2024
PubMed
Summary
This summary is machine-generated.

This study evaluates Bluetooth beacons and a smartphone app to passively track peer proximity for real-time health behavior research. The technology shows potential for just-in-time interventions, enhancing ecological momentary assessment (EMA).

Keywords:
Bluetooth technologyalcohol useecological momentary assessmentmobile phonepassive sensingproximity detectionsocial influence

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Area of Science:

  • Digital Health and Mobile Health (mHealth)
  • Behavioral Science and Social Psychology
  • Biomedical Informatics and Data Science

Background:

  • Ecological Momentary Assessment (EMA) commonly studies social influences but relies on self-report, lacking real-time passive data.
  • A novel approach using Bluetooth beacons and smartphone detection can passively collect real-time interaction data.
  • This technology has potential for real-world studies of social contexts and peer influence on health behaviors.

Purpose of the Study:

  • To evaluate the functionality and validity of a Bluetooth beacon and smartphone app system.
  • To accurately identify when two or more individuals are in close physical proximity.
  • To assess the system's utility for real-time data collection in health behavior research.

Main Methods:

  • Recruited 20 participants (18-29 years) with Android smartphones for a 3-week study.
  • Utilized Bluetooth beacons (Kontakt asset tag) carried by influential peers and a smartphone app (MEI Research) for detection.
  • Collected data via passive beacon detection, signal-contingent and random EMA surveys, and post-participation interviews.

Main Results:

  • Participant recruitment occurred from February to December 2023.
  • Full study results, including system functionality and validity, are anticipated in 2025.
  • Analysis will examine app stability, beacon detection rates, and concordance between passive detection and self-reports.

Conclusions:

  • The developed Bluetooth-based proximity sensing technology offers significant potential for health behavior research.
  • It enables passive, real-time data collection on social interactions, overcoming limitations of traditional EMA.
  • This technology supports the development of just-in-time adaptive interventions targeting high-risk behaviors.