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Objective <p>Smoking during pregnancy poses significant health risks, necessitating accurate continuous monitoring of pregnant women’s smoking behaviours. Existing methods relying on self-reporting lack objectivity, while biochemical measures like exhaled carbon monoxide (CO) provide validation but suffer from low participant engagement. We developed <i>myCOtrak</i> to address these limitations by integrating real-time CO monitoring with self-reported smoking, nicotine replacement therapy (NRT), and e-cigarette use.</p> Results <p><i>myCOtrak</i> combines automated CO data from the Bedfont iCO monitor with daily surveys. It demonstrated high feasibility and usability in initial testing with 23 participants, with 75% continuing data submission for ≥ 14 days. Key features include seamless CO integration, cloud-based storage, and longitudinal tracking, offering a validated, scalable tool for smoking cessation research.</p>

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myCOtrak: an app which records smoking, nicotine use and exhaled carbon monoxide concentrations concurrently for use in smoking research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

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  • Yue Huang,
  • Joanne Emery,
  • Lisa McDaid,
  • Felix Naughton,
  • Miranda Clark,
  • Anne Dickinson,
  • Sue Cooper,
  • Tim Coleman

摘要

Objective

Smoking during pregnancy poses significant health risks, necessitating accurate continuous monitoring of pregnant women’s smoking behaviours. Existing methods relying on self-reporting lack objectivity, while biochemical measures like exhaled carbon monoxide (CO) provide validation but suffer from low participant engagement. We developed myCOtrak to address these limitations by integrating real-time CO monitoring with self-reported smoking, nicotine replacement therapy (NRT), and e-cigarette use.

Results

myCOtrak combines automated CO data from the Bedfont iCO monitor with daily surveys. It demonstrated high feasibility and usability in initial testing with 23 participants, with 75% continuing data submission for ≥ 14 days. Key features include seamless CO integration, cloud-based storage, and longitudinal tracking, offering a validated, scalable tool for smoking cessation research.