Résumé
Background: Prolonged periods of sedentary behaviour, for instance, engendered by home confinement in Shenzhen city, has led to negative mental health consequences, especially in adolescents. Previous research suggests, in general, that sedentary behavior can increase negative emotions. However, the specific mechanism driving the relationship between sedentary behavior and negative emotions is still relatively unclear. Social support and sleep quality might partly explain the effect of sedentary behavior on negative emotions. Thus, the current study aimed to examine the associations between sedentary behavior and negative emotions, and to investigate if social support and sleep quality mediate such a relationship. Method: During home confinement due to the COVID-19 Omicron variant outbreak, 1179 middle and high school students in Shenzhen were invited to voluntarily complete an e-questionnaire, including the 21-item Depression Anxiety Stress Scale (DASS-21), the short form of the International Physical Activity Questionnaire (IPAQ-SF), the Social Support Rating Scale (SSRS) and the Pittsburgh Sleep Quality Index (PSQI). Data from 1065 participants were included in the analysis. Results: We observed significant sex-related and demografic-related differences in emotional (e.g., anxiety, stress and social support) and other outcome variables (e.g., sitting duration and PSQI score). Furthermore, sedentary behavior, social support, and sleep quality were associated with negative emotions (p < .01), even after controlling for sex, age, only-child case, body mass index, and metabolic equivalent level. In addition, social support and sleep quality partially mediated the association between sedentary behavior and negative emotions. Conclusion: The findings of the current study suggest that social support and sleep quality partially mediate the relationship between sedentary behavior and negative emotions in middle and high school students during home confinement in Shenzhen city. © 2022 The Author(s)
Résumé
Coughing is a common symptom across different clinical conditions and has gained further relevance in the past years due to the COVID-19 pandemic. An automated cough detection for continuous health monitoring could be developed using Earbud, a wearable sensor platform with audio and inertial measurement unit (IMU) sensors. Though several previous works have investigated audio-based automated cough detection, audio-based methods can be highly power-consuming for wearable sensor applications and raise privacy concerns. In this work, we develop IMU-based cough detection using a template matching-based algorithm. IMU provides a low-power privacy-preserving solution to complement audio-based algorithms. Similarly, template matching has low computational and memory needs, suitable for on-device implementations. The proposed method uses feature transformation of IMU signal and unsupervised representative template selection to improve upon our previous work. We obtained an AUC (AUC-ROC) of 0.85 and 0.83 for cough detection in a lab-based dataset with 45 participants and a controlled free-living dataset with 15 participants, respectively. These represent an AUC improvement of 0.08 and 0.10 compared to the previous work. Additionally, we conducted an uncontrolled free-living study with 7 participants where continuous measurements over a week were obtained from each participant. Our cough detection method achieved an AUC of 0.85 in the study, indicating that the proposed IMU-based cough detection translates well to the varied challenging scenarios present in free-living conditions. © 2022 IEEE.
Résumé
Persistent coughs are a major symptom of respiratory-related diseases. Increasing research attention has been paid to detecting coughs using wearables, especially during the COVID-19 pandemic. Microphone is most widely used sensor to detect coughs. However, the intense power consumption needed to process audio hinders continuous audio-based cough detection on battery-limited commercial wearables, such as earbuds. We present CoughTrigger, which utilizes a lower-power sensor, inertial measurement unit (IMU), in earbuds as a cough detection activator to trigger a higher-power sensor for audio processing and classification. It runs all-the-time as a standby service with minimal battery consumption and triggers the audio-based cough detection when a candidate cough is detected from IMU. Besides, the use of IMU brings the benefit of improved specificity of cough detection. Experiments are conducted on 45 subjects and CoughTrigger achieved 0.77 AUC score. We also validated its effectiveness on free-living data and through on-device implementation. © 2022 IEEE
Résumé
Tracking the type and frequency of cough events is critical for monitoring respiratory diseases. Coughs are one of the most common symptoms of respiratory and infectious diseases like COVID-19, and a cough monitoring system could have been vital in remote monitoring during a pandemic like COVID-19. While the existing solutions for cough monitoring use unimodal (e.g., audio) approaches for detecting coughs, a fusion of multimodal sensors (e.g., audio and accelerometer) from multiple devices (e.g., phone and watch) are likely to discover additional insights and can help to track the exacerbation of the respiratory conditions. However, such multimodal and multidevice fusion requires accurate time synchronization, which could be challenging for coughs as coughs are usually concise events (0.3-0.7 seconds). In this paper, we first demonstrate the time synchronization challenges of cough synchronization based on the cough data collected from two studies. Then we highlight the performance of a cross-correlation based time synchronization algorithm on the alignment of cough events. Our algorithm can synchronize 98.9% of cough events with an average synchronization error of 0.046s from two devices. © 2020 ACM.