Abstract
Sleep is one of the most important factors in maintaining both physical and mental health. There are many causes of sleep problems, it is generally necessary to maintain a healthy lifestyle to avoid them. In the medical field, information related to sleep problems including lifestyle information is obtained through interviews, but this approach is limited because it is dependent on the patient’s memory. Thus, there are many studies adopting ecological momentary assessments (EMAs) to collect patient’s lifestyles. Some of them also use smart devices to collect data effectively. However, these studies focused on specific factors such as smoking, exercising so that they have limits to reflect complex narrative of lifestyle patterns. Therefore, we proposed indicators consist of EMAs data for assessing everyday sleep quality and these indicators contain the complex lifestyle contexts in a quantitative manner. First, we collected real-life data using a smartphone through a 4-week data collection experiment. Second, we develop a method of generating daily indexes reflecting geospatial and social habits, social condition, activity level, and emotional condition using self-report data. Third, we evaluated daily indexes whether could use to supplement indicators comprising features using EMAs from conventional sleep questionnaires. The goal of analysis consists of five metrics of sleep quality that explain perceived sleep quality. The result of analysis indicates that features using both daily indexes and sleep questionnaires lead to better prediction of sleep quality. Additionally, it also shows the potential to generate indicators identifying complex human behaviors with the help of mobile devices and EMAs. Further research on user-friendly data acquisition methods and more diverse lifestyle information should be useful to support behavior decisions for better sleep in well-being services and in specialized medical fields.
Highlights
Health and maintaining a quality of life are essential to human well-being
EXPERIMENTAL RESULT OF SLEEP QUALITY PREDICTION We explore which model and classification are proper for sleep quality, the logistic regression (LR), decision tree (DT), and random forest classifier (RF) are applied for each proposed model
This study proposed indicators consist of ecological momentary assessments (EMAs) data for assessing everyday sleep quality and these indicators contain the complex lifestyle contexts in a quantitative manner
Summary
Health and maintaining a quality of life are essential to human well-being. Sleep is the most important factor affecting health and quality of life because it is closely related to physical and psychological functions such as energy conservation, food intake, brain functioning, metabolism, and psychiatric health [1]. Sleep disorders have become a serious public health concern in Korea [2]. Sleep disorder represents a serious public health concern, which is known to have a negative effect on physical health, memory, work performance, social relationships, business agility and efficiency, concentration, and daytime alertness [3]. In order to improve the quality of sleep, it is necessary to accurately determine the sleep quality by analyzing the complex elements of daily life. Existing method using sensors and wearable devices focused on analyzing fragmentary factors (exercise, habit, etc.) that
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