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Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
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Development of an alcohol biosensor non-wear algorithm: laboratory-based machine learning and field-based deployment.

Nathan A Didier1, Rachel L Gunn2, Andrea C King3

  • 1Center for Alcohol and Addiction Studies, Brown University School of Public Health, 121 South Main Street, Providence, RI, 02903, USA. nathan_didier@brown.edu.

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A new algorithm accurately detects when wrist-worn alcohol biosensors are removed, improving data reliability for alcohol consumption studies. This method surpasses traditional temperature checks for precise non-wear detection.

Keywords:
AdherenceAlcohol biosensorsLaboratory ground truthMachine learningNon-wear

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Area of Science:

  • Biomedical Engineering
  • Data Science
  • Alcohol Research

Background:

  • Continuous alcohol monitoring using wrist-worn biosensors is valuable but challenged by device removal, leading to inaccurate data.
  • Existing methods, like temperature cutoffs, are insufficient for precise detection of non-wear periods.
  • Unaddressed non-wear data compromises alcohol use observations and intoxication predictions.

Purpose of the Study:

  • To develop and validate a novel algorithm for accurately detecting non-wear intervals from wrist-worn alcohol biosensors.
  • To improve upon existing temperature-based methods for identifying device removal.
  • To enhance the reliability of continuous alcohol monitoring data for research and clinical applications.

Main Methods:

  • A random forest algorithm was trained using laboratory data, incorporating temperature, motion, and their time-series quadratic coefficients.
  • Study One (N=36) involved controlled non-wear periods to generate ground truth data for algorithm training and validation.
  • Study Two (N=114) deployed the algorithm in a four-week field study to assess biosensor adherence against self-reports.

Main Results:

  • The algorithm demonstrated high sensitivity (0.96) for detecting non-wear and specificity (0.99) for confirming wear in laboratory settings.
  • It significantly outperformed univariable temperature cutoffs (25-30°C) in device-based cross-validation.
  • In the field study, the algorithm detected 1.6 hours of daily non-wear per participant, showing better agreement with self-reports than temperature cutoffs.

Conclusions:

  • The developed random forest algorithm effectively and accurately identifies non-wear periods for wrist-worn alcohol biosensors.
  • This method offers a significant improvement over traditional temperature-based approaches for assessing biosensor adherence.
  • The algorithm can enhance data imputation accuracy, leading to more objective models of alcohol-related outcomes in research.