Statistical Innovation Group, AbbVie, North Chicago, IL 60064, USA.
Digital Science, AbbVie, North Chicago, IL 60064, USA.
Sensors (Basel). 2024 May 24;24(11):3364. doi: 10.3390/s24113364.
Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity. Digital health technologies (DHTs), such as wearable sensors, have been widely used to capture behaviors in clinical and real-world settings. In this work, we develop and validate a machine learning algorithm using wrist-wearing actigraphy that could objectively quantify nocturnal scratching events, therefore facilitating accurate assessment of disease progression, treatment effectiveness, and overall quality of life in AD patients. A total of seven subjects were enrolled in a study to generate data overnight in an inpatient setting. Several machine learning models were developed, and their performance was compared. Results demonstrated that the best-performing model achieved the F1 score of 0.45 on the test set, accompanied by a precision of 0.44 and a recall of 0.46. Upon satisfactory performance with an expanded subject pool, our automatic scratch detection algorithm holds the potential for objectively assessing sleep quality and disease state in AD patients. This advancement promises to inform and refine therapeutic strategies for individuals with AD.
夜间搔抓严重影响特应性皮炎（AD）等皮肤状况患者的生活质量。目前对搔抓的临床测量依赖于患者对过去 24 小时内瘙痒的报告结果（PROs）。此类测量缺乏客观性和敏感性。数字健康技术（DHT），如可穿戴传感器，已广泛用于在临床和现实环境中捕捉行为。在这项工作中，我们使用腕戴活动记录仪开发和验证了一种机器学习算法，该算法可以客观地量化夜间搔抓事件，从而能够准确评估 AD 患者的疾病进展、治疗效果和整体生活质量。共有 7 名受试者被纳入一项研究，在住院环境中过夜生成数据。开发了几种机器学习模型，并比较了它们的性能。结果表明，表现最佳的模型在测试集上的 F1 评分为 0.45，同时具有 0.44 的精度和 0.46 的召回率。在对更大的受试者群体进行满意的性能评估后，我们的自动搔抓检测算法具有客观评估 AD 患者睡眠质量和疾病状态的潜力。这一进展有望为 AD 患者提供信息并完善治疗策略。
