By Amir Kazemeini
SleepHealth aims to use the PsyLap pipeline to study data gathered by the SleepHealth Mobile App Study.
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| Project Tracking | https://github.com/aid4mh/sleepHealth_dataAnalysis/projects/1 |
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| GDrive | https://drive.google.com/drive/folders/1UE1mlN76LZ55-I7QFB27qZb8luVXLNJI?usp=sharing |
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| Sleep disturbance is increasingly recognized as one of the key transdiagnostic risk factors across psychiatric disorders. The SleepHealth Mobile App Study aimed to understand real-world factors impacting people’s sleep and daytime functioning in naturalistic settings. Study participants completed surveys related to sleep health during onboarding along with sleep diaries documenting factors affecting their daily sleep. |
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| To assess whether the personality traits learned from participants’ sleep diaries are associated with sleep quality and daytime functioning. |
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| How we can improve sleep quality by finding the cross-section analysis of features |
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Each participant in this study is requested to complete two journals per day, one in the early morning and one before sleep. PsyLaP pipeline is used to extract psycholinguistic features from the provided free text. In addition to the journals, individuals fill several self-assessed tasks in two different frequencies: daily and once. These tasks collect information about personalized sleep-related issues and personal information. Once both feature sets are gathered, cross-section analysis is performed to indicate the association between journal and self-assessed features. Experiments are done in two different approaches, single association finding for each person, and longitudinal analysis of each individual over time. So far, some single associations are found and are included and submitted to SBM2022. |
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Real-world longitudinal data collected from the SleepHealth mobile app study |
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NLP features are currently extracted and ready for further analysis. Available features are: |
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https://github.com/aid4mh/sleepHealth_data_analysis
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