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.

AI News NQ Analysis

Tech Doctor Establishes Activity, Sleep, and Heart Rate Variability Benchmarks from Wearable Data of 2,000 Japanese Workers

NQ Score 83/100

N1 Content Completeness 9

AI Summary (NQ-processed)

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.

AI Analysis

Frequently Asked Questions

Q: What are the benchmark values for wearable data established by TechDoctor?: A: 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.
Q: What data was used in the study?: A: 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.
Q: What age-related characteristics were observed in heart rate variability (SDNN)?: A: 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.
Q: What are the potential applications of these benchmark values?: A: 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.
Q: What was learned about the relationship with health check-up data?: A: 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.