DOI: 10.18710/vp5dmu
title: Replication Data for: Automatic time in bed detection from hip-worn accelerometers for large epidemiological studies: The Tromsø Study

description: &lt;p&gt;&lt;b&gt;Dataset description&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;16 participants wore an Actigraph w-GT3X on their right hip together with two consumer-based wearables, a Polar Vantage (Polar Electro Oy, Kempele, Finland) watch and an Oura (Oura Health Oy, Oulo, Finland]) ring. This data, as summarized in this repository, has been used to validate an algorithm to detect time in bed. For more information, see the publication's abstract below.&lt;/p&gt;

&lt;p&gt;This dataset consists of 20 files: "00_ReadMe.txt", "calibration_error.txt", "data_protection_impact_assessment.pdf", "wearable_data.csv" and 16 raw acceleration data files ("participant00.csv to participant15.csv").&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;"00_ReadMe.txt": Contains a detailed information about the dataset&lt;/li&gt;
&lt;li&gt;"calibration_error.txt": Contains the estimated calibration errors, correction coefficients, and success information for the 16 raw acceleration data files.&lt;/li&gt;
&lt;li&gt;"data_protection_impact_assessment.pdf": sets out the legal and ethical grounds for open publication of the dataset.&lt;/li&gt;
&lt;li&gt;"wearable_data.csv": Contains daily sleep duration values from a Polar watch and the Oura ring for the 16 participants&lt;/li&gt;
&lt;li&gt;Each of the 16 raw acceleration data files (participantXX.csv") contains the triaxial raw acceleration data at a 100hz sampling frequency.&lt;/li&gt;
&lt;/ul&gt;
description: &lt;p&gt;&lt;b&gt;Article abstract&lt;/b&gt;&lt;/p&gt;
&lt;p&gt;Accelerometers are frequently used to assess physical activity in large epidemiological studies. They can monitor movement patterns and cycles over several days under free-living conditions and are usually either worn on the wrist or the hip. While wrist-worn accelerometers have been frequently used to additionally assess sleep and time in bed behavior, hip-worn accelerometers have been widely neglected for this task due to their primary focus on physical activity. Here, we present a new method with the objective to identify the time in bed to enable further analysis options for large-scale studies using hip-placement like time in bed or sedentary time analyses. We introduced new and accelerometer specific data augmentation methods, such as mimicking a wrongly worn accelerometer, additional noise, and random croping, to improve training and generalization performance. Subsequently, we trained a neural network model on a sample from the population-based Tromsø Study and evaluated it on two additional datasets. Our algorithm achieved an accuracy of 94% on the training data, 92% on unseen data from the same population and comparable results to consumer-wearable data obtained from a demographically different population. Generalization performance was overall good, however, we found that on a few particular days or participants, the trained model fundamentally over- or underestimated time in bed (e.g., predicted all or nothing as time in bed). Despite these limitations, we anticipate our approach to be a starting point for more sophisticated methods to identify time in bed or at some point even sleep from hip-worn acceleration signals. This can enable the re-use of already collected data, for example, for longitudinal analyses where sleep-related research questions only recently got into focus or sedentary time needs to be estimated in 24h wear protocols. &lt;/p&gt;
description: Polar M430, 3.2.10
description: Oura Model 2P, 1.91.1
description: ActiGraph wGT3X-BT, 1.9.2
description: ActiLife, 6.13.3