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Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Yunzhao Xing1, Bolin Song2,3, Michelle Crouthamel2

  • 1Statistical Innovation Group, AbbVie, North Chicago, IL 60064, USA.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
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This study developed a machine learning algorithm using wearable sensors to objectively measure nocturnal scratching in atopic dermatitis (AD) patients. This technology offers a more accurate way to assess disease progression and treatment effectiveness.

Area of Science:

  • Biomedical Engineering
  • Dermatology
  • Digital Health

Background:

  • Nocturnal scratching significantly impacts the quality of life for atopic dermatitis (AD) patients.
  • Current methods for measuring scratch, primarily patient-reported outcomes (PROs), lack objectivity and sensitivity.
  • Digital health technologies (DHTs) offer potential for objective behavioral monitoring.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for objectively quantifying nocturnal scratching events using wrist-worn actigraphy.
  • To improve the assessment of disease progression, treatment effectiveness, and quality of life in AD patients.

Main Methods:

  • Development and comparison of several machine learning models.
  • Utilizing wrist-worn actigraphy data to capture nocturnal scratching behaviors.
Keywords:
atopic dermatitisdigital health technologymachine learningnocturnal scratchwearable

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  • Validation of the algorithm on data from seven subjects in an inpatient setting.

    • Main Results:

    • The best-performing machine learning model achieved an F1 score of 0.45 on the test set.
    • The model demonstrated a precision of 0.44 and a recall of 0.46 for scratch detection.
    • Preliminary results show promise for objective scratch quantification.

    Conclusions:

    • An automatic scratch detection algorithm using actigraphy has the potential to objectively assess sleep quality and disease state in AD patients.
    • Further validation with a larger subject pool is required.
    • This advancement could refine therapeutic strategies for AD management.