Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges

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.

Keywords

• Human factors
• ecological momentary assessments
• sleep quality assessment
• sequential analysis
• machine learning
• mobile computing