Sahha Research
The most important sleep biomarkers to track.
Sahha scores, biomarkers, and insights are grounded in scientific research. To comply with medical app guidelines, apps must provide users with accessible citations and sources for the health scores presented. We recommend displaying the following links within your app for transparency and compliance:
Smartphones and wearables can collect a variety of data that are useful for monitoring and improving sleep quality. The raw data, such as a count of hours, can be turned into an “insight” about the user and can then inform them of how to adjust their sleep if needed.
A list of the most important sleep data to track:
Total Sleep Time (TST)
The amount of actual sleep time during the night. It’s crucial for determining if a user is getting enough rest.
Resting Heart Rate (RHR)
Lower RHR during sleep indicates a relaxed state and is associated with better sleep quality.
Heart Rate Variability (HRV)
Higher HRV during sleep suggests a well-rested body and is linked to lower stress levels.
Bedroom Temperature
An optimal temperature range can promote better sleep quality.
Total Sleep Time (TST)
Consistent RR during sleep can indicate good sleep quality. Significant changes may signal stress or sleep disturbances.
Sleep Stages
Different stages of sleep, such as REM and deep sleep, are important for various restorative processes.
Movement and Actigraphy
Physical movement during sleep can indicate restlessness and poor sleep quality.
Ambient Noise and Light
These can affect the ability to fall and stay asleep, impacting overall sleep quality.
This data is calculated from raw smartphone and wearable device data and there is a lot of existing science to back up their important to sleep. A study by Bloomfield and colleagues (1) found that sleep measures from wearables (i.e. TST, RHR, HRV and RR) were significantly associated with perceived stress in college students. In fact wearables have been extensively studied (2,3) and a recent review of these studies (2) highlighted that wearables provide sleep information that is on par with traditional actigraphy - a gold standard method for measuring sleep. The same review also suggested that longitudinal sleep and sleep variability data from wearables is key to understanding health outcomes. This means that consistently tracking sleep over a long period of time can provide important insights that can help improve one’s overall health. Therefore, you can think of sleep scores as a proxy for understanding and predicting health factors, such as stress levels, in individuals.
For Developers/Product Teams: Creating Healthier Sleeping Users
Developers and product teams should focus on the following to promote healthier sleep among users:
Personalised Feedback: Use collected data to provide personalised insights and recommendations for improving sleep.
Sleep Education: Offer information on sleep hygiene and the importance of different sleep stages.
Intervention Strategies: Develop features that suggest lifestyle changes or bedtime routines based on user data.
User Engagement: Create interactive elements that keep users informed and engaged with their sleep patterns.
Data Privacy: Ensure user data is securely handled and privacy is maintained.
By leveraging these data points, and providing users with insights through user-centric features, developers can create products that not only track sleep but also contribute to the overall well-being of the users by promoting healthier sleep habits.
References
Predicting stress in first-year college students using sleep data from wearable devices https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000473
Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies DOI: https://doi.org/10.1007/s40675-023-00272-7 https://link.springer.com/article/10.1007/s40675-023-00272-7#citeas
State of the science and recommendations for using wearable technology in sleep and circadian research DOI: ****https://doi.org/10.1093/sleep/zsad325 https://academic.oup.com/sleep/article/47/4/zsad325/7501518?login=false
Sahha Research
The most important sleep biomarkers to track.
Sahha scores, biomarkers, and insights are grounded in scientific research. To comply with medical app guidelines, apps must provide users with accessible citations and sources for the health scores presented. We recommend displaying the following links within your app for transparency and compliance:
Smartphones and wearables can collect a variety of data that are useful for monitoring and improving sleep quality. The raw data, such as a count of hours, can be turned into an “insight” about the user and can then inform them of how to adjust their sleep if needed.
A list of the most important sleep data to track:
Total Sleep Time (TST)
The amount of actual sleep time during the night. It’s crucial for determining if a user is getting enough rest.
Resting Heart Rate (RHR)
Lower RHR during sleep indicates a relaxed state and is associated with better sleep quality.
Heart Rate Variability (HRV)
Higher HRV during sleep suggests a well-rested body and is linked to lower stress levels.
Bedroom Temperature
An optimal temperature range can promote better sleep quality.
Total Sleep Time (TST)
Consistent RR during sleep can indicate good sleep quality. Significant changes may signal stress or sleep disturbances.
Sleep Stages
Different stages of sleep, such as REM and deep sleep, are important for various restorative processes.
Movement and Actigraphy
Physical movement during sleep can indicate restlessness and poor sleep quality.
Ambient Noise and Light
These can affect the ability to fall and stay asleep, impacting overall sleep quality.
This data is calculated from raw smartphone and wearable device data and there is a lot of existing science to back up their important to sleep. A study by Bloomfield and colleagues (1) found that sleep measures from wearables (i.e. TST, RHR, HRV and RR) were significantly associated with perceived stress in college students. In fact wearables have been extensively studied (2,3) and a recent review of these studies (2) highlighted that wearables provide sleep information that is on par with traditional actigraphy - a gold standard method for measuring sleep. The same review also suggested that longitudinal sleep and sleep variability data from wearables is key to understanding health outcomes. This means that consistently tracking sleep over a long period of time can provide important insights that can help improve one’s overall health. Therefore, you can think of sleep scores as a proxy for understanding and predicting health factors, such as stress levels, in individuals.
For Developers/Product Teams: Creating Healthier Sleeping Users
Developers and product teams should focus on the following to promote healthier sleep among users:
Personalised Feedback: Use collected data to provide personalised insights and recommendations for improving sleep.
Sleep Education: Offer information on sleep hygiene and the importance of different sleep stages.
Intervention Strategies: Develop features that suggest lifestyle changes or bedtime routines based on user data.
User Engagement: Create interactive elements that keep users informed and engaged with their sleep patterns.
Data Privacy: Ensure user data is securely handled and privacy is maintained.
By leveraging these data points, and providing users with insights through user-centric features, developers can create products that not only track sleep but also contribute to the overall well-being of the users by promoting healthier sleep habits.
References
Predicting stress in first-year college students using sleep data from wearable devices https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000473
Using New Technologies and Wearables for Characterizing Sleep in Population-based Studies DOI: https://doi.org/10.1007/s40675-023-00272-7 https://link.springer.com/article/10.1007/s40675-023-00272-7#citeas
State of the science and recommendations for using wearable technology in sleep and circadian research DOI: ****https://doi.org/10.1093/sleep/zsad325 https://academic.oup.com/sleep/article/47/4/zsad325/7501518?login=false