Tech Doctor Establishes Activity, Sleep, and Heart Rate Variability Benchmarks from Wearable Data of 2,000 Japanese Workers
Key facts
- Tech Doctor Establishes Activity, Sleep, and Heart Rate Variability Benchmarks from Wearable Data of 2,000 Japanese Workers
- Tech Doctor has analyzed wearable data from approximately 2,000 Japanese workers over a 15-month period to establish age- and gender-specific benchmarks for activity, sleep, and heart rate variability. These findings, presented at the 99th Annual Meeting of the Japan Society for Occupational Health, are expected to improve the precision of health management and disease risk prediction.
- Source: PR Times
- Date: June 2, 2026
Direct answer
Tech Doctor has analyzed wearable data from approximately 2,000 Japanese workers over a 15-month period to establish age- and gender-specific benchmarks for activity, sleep, and heart rate variability. These findings, presented at the 99th Annual Meeting of the Japan Society for Occupational Health, are expected to improve the precision of health management and disease risk prediction.
- Citation
- Tech Doctor Establishes Activity, Sleep, and Heart Rate Variability Benchmarks from Wearable Data of 2,000 Japanese Workers (June 2, 2026), PR Times
- Source
- PR Times
- Date
- June 2, 2026
Tech Doctor has analyzed wearable data from approximately 2,000 Japanese workers over a 15-month period to establish age- and gender-specific benchmarks for activity, sleep, and heart rate variability. These findings, presented at the 99th Annual Meeting of the Japan Society for Occupational Health, are expected to improve the precision of health management and disease risk prediction.
📋 Article Processing Timeline
- 📰 Published: June 2, 2026 at 18:00
- 🔍 Collected: June 2, 2026 at 09:20
- 🤖 AI Analyzed: June 2, 2026 at 09:28 (7 min after Collected)
In this study, using over 500,000 person-days of data obtained from Google Fitbit, benchmarks (median and 95% prediction intervals) were established for 16 metrics related to activity level, sleep, and heart rate variability among the Japanese working population. The study suggested associations not only with aging, gender differences, and temperature changes but also with medical history and metabolic syndrome, demonstrating the potential for personal monitoring and health guidance using wearable data.
■ Research Background
Recently, the use of daily data from wearable devices has progressed, with many reports of links to health status and disease risk. For instance, meta-analysis has shown significant reduction in risk of all-cause mortality and chronic disease through increased steps. However, to utilize wearable data for health management and disease prediction, benchmarks are needed to determine if values are within a 'normal range.' In Japan, large-scale studies on these benchmarks have been limited.
■ Research Methods
Participants: 1,996 employees (1,260 men, 736 women) aged 25 to 65 working in companies.
Devices/Period: Google Fitbit data from Jan 2023 to Nov 2025 (Average duration: 435 days).
■ Research Results
・Heart Rate Variability (SDNN): Males had higher median values at young age (32ms vs. 27ms at age 25). Values decreased with age, with a steeper decline in males, and gender differences narrowed by age 60.
・Activity: Steps were consistently higher in males. Total Physical Activity (TPA) showed an increasing trend with age.
・Sleep: Sleep duration decreased with age in both genders. The proportion of deep sleep also declined.
・Associations: Lower heart rate variability and higher heart rates were confirmed in groups with medical history or metabolic syndrome.
FAQ
What are the benchmark values for wearable data established by TechDoctor?
Based on data from approximately 2,000 Japanese working-age adults (25-65 years), 16 metrics related to activity, sleep, and heart rate variability were defined, including median values and 95% prediction intervals, segmented by age and gender.
What data was used in the study?
Data collected from over 500,000 user-days between January 2023 and November 2025 using Google Fitbit was utilized, covering various biometric indicators such as activity levels, sleep stages, and heart rate variability.
What age-related characteristics were observed in heart rate variability (SDNN)?
In younger age groups, males tend to have higher median values, but these decrease with age for both genders. The decline is more pronounced in males, and by around age 60, the difference between males and females narrows.
What are the potential applications of these benchmark values?
These benchmark values can be used as reference points to determine if measured values fall within normal ranges for individual health monitoring and guidance. They are expected to aid in early disease risk detection and health management.
What was learned about the relationship with health check-up data?
In individuals with a history of health issues or those meeting the criteria for metabolic syndrome, a decrease in heart rate variability and an increase in heart rate were observed compared to the benchmark values.