Recently, the term ‘precision medicine’ has been extensively discussed as a means to deliver personalized medical care for those already suffering from disease. Conversely, the discussions surrounding the concept of ‘precision health’ have been scarce, despite the important role precision health has to play in disease prevention.

The metabolic syndrome, defined as a combination of central obesity, dyslipidemia, hyperglycemia, and hypertension, is a major cause of diabetes and cardiovascular disease (together known as cardiometabolic disease), as well as all-cause mortality in the world. The metabolic syndrome is, however, a largely preventable condition given that its root causes are lifestyle-related. For this reason, the Japanese Ministry of Health, Labour, and Welfare initiated a policy in 2008 with the aim of reducing the prevalence of metabolic syndrome in Japan by 25 percent by 2020. The importance of this policy cannot be understated; however, it fails to take into consideration all the latest scientific findings of what constitutes lifestyle-related disease risk factors.

Over the past couple of years, large-scale epidemiologic research has identified, in addition to physical inactivity and nutritional intake, short and long sleep durations as novel and independent risk factors of cardiometabolic disease. Despite the importance of epidemiologic research for policy implementation, it has certain limitations due to its observational nature, use of aggregate data, and reliance on self-reported information. In addition, and of particular importance for sleep behaviour, there are large individual variations due to both biological (e.g., age, baseline health conditions) and environmental factors (e.g., occupation, diet, exercise). This can be overcome by using medical devices which accurately report sleep patterns, however such devices are very costly and integrate poorly with the user.

To address these challenges, we aim to first validate an affordable wearable device which has been used predominantly as a fitness tracker, but which has the possibility to be used in precision health. The second step is to develop an automated engine for combining sleep- and other lifestyle-related individual-level data which offers personalised feedback to the user on how to optimize sleep behaviours. Third, we aim to integrate this engine with our lifestyle intervention program to promote evidence-based disease prevention in accordance with the latest scientific findings. We will explore the possibility to develop an independent sleep advice application to prevent sleep-related disease.