Automated algorithms for detecting sleep period time using a multi-sensor pattern-recognition activity monitor from 24 h free-living data in older adults

Abstract

Objectives: The aims of the present study were (i) to develop automated algorithms to identify the sleep period time in 24 h data from the Intelligent Device for Energy Expenditure and Activity (IDEEA) in older adults, and (ii) to analyze the agreement between these algorithms to identify the sleep period time as compared to self-reported data and expert visual analysis of accelerometer raw data. Approach: This study comprised 50 participants, aged 65–85 years. Fourteen automated algorithms were developed. Participants reported their bedtime and waking time on the days on which they wore the device. A well-trained expert reviewed each IDEEA file in order to visually identify bedtime and waking time on each day. To explore the agreement between methods, Pearson correlations, mean differences, mean percentage errors, accuracy, sensitivity and specificity, and the Bland–Altman method were calculated. Main results: With 87 d of valid data, algorithms 6, 7, 11 and 12 achieved higher levels of agreement in determining sleep period time when compared to self-reported data (mean difference = −0.34 to 0.01 h d−1; mean absolute error = 10.66%–11.44%; r = 0.515–0.686; accuracy = 95.0%–95.6%; sensitivity = 93.0%–95.8%; specificity = 95.7%–96.4%) and expert visual analysis (mean difference = −0.04 to 0.31 h d−1; mean absolute error = 5.0%–6.97%; r = 0.620–0.766; accuracy = 97.2%–98.0%; sensitivity = 94.5%–97.6%; specificity = 98.4%–98.8%). Bland–Altman plots showed no systematic biases in these comparisons (all p > 0.05). Differences between methods did not vary significantly by gender, age, obesity, self-rated health, or the presence of chronic conditions. Significance: These four algorithms can be used to identify easily and with adequate accuracy the sleep period time using the IDEEA activity monitor from 24 h free-living data in older adults.