Abstract
Patients with atopic dermatitis experience increased nocturnal pruritus which leads to scratching and sleep disturbances that significantly contribute to poor quality of life. Objective measurements of nighttime scratching and sleep quantity can help assess the efficacy of an intervention. Wearable sensors can provide novel, objective measures of nighttime scratching and sleep; however, many current approaches were not designed for passive, unsupervised monitoring during daily life. In this work, we present the development and analytical validation of a method that sequentially processes epochs of sample-level accelerometer data from a wrist-worn device to provide continuous digital measures of nighttime scratching and sleep quantity. This approach uses heuristic and machine learning algorithms in a hierarchical paradigm by first determining when the patient intends to sleep, then detecting sleep–wake states along with scratching episodes, and lastly deriving objective measures of both sleep and scratch. Leveraging reference data collected in a sleep laboratory (NCT ID: NCT03490877), results show that sensor-derived measures of total sleep opportunity (TSO; time when patient intends to sleep) and total sleep time (TST) correlate well with reference polysomnography data (TSO: r = 0.72, p < 0.001; TST: r = 0.76, p < 0.001; N = 32). Log transformed sensor derived measures of total scratching duration achieve strong agreement with reference annotated video recordings (r = 0.82, p < 0.001; N = 25). These results support the use of wearable sensors for objective, continuous measurement of nighttime scratching and sleep during daily life.
Highlights
Pruritus is a primary symptom seen in numerous chronic eczematous conditions, especially prevalent in patients with atopic dermatitis (AD)[1]
Traditional assessments of pruritus and sleep are primarily based on clinical outcome assessments (COAs) and patient reported outcome assessments (PROs)
COAs are aimed at assessing total body surface area (BSA) of the lesion[8] as well as lesion severity[9] but these are physician-derived measurements and provide limited insight into the fluctuations of symptoms experienced outside the clinic
Summary
Pruritus (itch) is a primary symptom seen in numerous chronic eczematous conditions, especially prevalent in patients with atopic dermatitis (AD)[1]. To address some of these limitations, Moreau et al.[18] trained Recurrent Neural Networks (RNNs) using annotated scratch events during an overnight clinic visit to classify nighttime scratch directly from sample-level accelerometer data While this approach improves upon previous works by enabling continuous measurement and leveraging data from nonsimulated scratch events, it does not segment data into patient’s sleep periods, which can increase the likelihood of false positives during free-living conditions. Across all available in-clinic participant visits, a total of 753.2 min of scratch and restless (non-scratch hand movements; refer to “Methods” section for further details) data (22.8 ± 26.5 min per participant) obtained from both in-clinic visits were used for assessment of nighttime scratch and sleep based on accelerometer data captured using a wrist-worn wearable device. Leveraging reference data collected in a sleep laboratory with thermal video scoring (NCT ID: NCT03490877), we examine the performance of each module individually as well as the performance of the proposed method as a whole for continuously deriving endpoints of sleep and nighttime scratch
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