JMIR Publications

ABSTRACT

Background:

Over the past few decades, there has been a rapid increase in the number of wearable sleep-tracking devices and mobile sleep-tracking apps in the consumer market. Consumer sleep-tracking technologies allow users to track their sleep quality in naturalistic environments. In addition to tracking sleep per se, some sleep-tracking technologies also support users in collecting information on their daily habits and sleep environments and reflecting on how those factors may contribute to sleep quality. However, the relationship between sleep and contextual factors may be too complex to spot through visual inspection and reflection. Advanced analytical methods are needed to discover new knowledge and insights into the rapidly growing volumes of personal sleep-tracking data.

Objective:

This review aims to summarize and analyze existing literature that applies formal analytical methods to discover personal knowledge and insights in the context of personal informatics. Guided by the problem-constraints-system (PCS) framework for literature review in computer science, we framed four main questions regarding the general research trend, sleep quality metrics, contextual factors considered, knowledge discovery methods, significant findings, challenges, and opportunities of the interested topic.

Methods:

Web of Science, Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Fitbit Research Library, and Fitabase were searched to identify publications that meet the inclusion criteria. After full-text screening, 14 publications were included in the final analysis.

Results:

Research on knowledge discovery in sleep-tracking is scarce. More than half (n=8, 57%) of the studies were conducted in the USA, followed by Japan (n=3, 21%). Only a few (n=5, 36%) publications are journal articles, the rest are conference proceeding papers. The most used sleep metrics are subjective sleep quality (n=4, 29%), sleep efficiency (n=4, 29%), sleep onset latency (n=4, 29%), and time at lights off (n=3, 21%). Ratio parameters such as deep sleep ratio and REM ratio were not used in any reviewed studies. A dominant number of the studies applied simple correlation analysis (n=3, 21%), regression analysis (n=3, 21%), and statistical tests or inferences (n=3, 21%) to discover the links between sleep and other aspects of life. Only a few studies used machine learning and data mining for sleep quality prediction (n=1, 7%) or anomaly detection (n=2, 14%). Exercise, digital device usage, caffeine, alcohol, places visited before sleep, and sleep environments were important contextual factors significantly correlated to various dimensions of sleep quality.

Conclusions:

This scoping review shows that knowledge discovery methods have great potential in extracting hidden insights from a flux of self-tracking data and are considered more effective than simple visual inspection. Future research should address the challenges related to collecting high-quality data, extracting hidden knowledge from data while accommodating within-individual and between-individual variations, and translating the discovered knowledge into actionable insights.