We make research more manageable with easy project setup, real-time adherence tracking, and downloadable data files. The participant experience features effortless data upload and task monitoring via a user-friendly app.
Whether your study needs detailed raw physiological data like ECG RR-interval, or aggregated steps, we have you covered. Include custom surveys and event markers, conveniently timestamped.
Capture a broad range of granular data including sleep staging, steps and heart rate.
Surveys optimized for researchers with built-in scheduling, recurrence options, and reminders that correspond to participant tasks.
Add a new layer of insight to your data by knowing when your participants completed tasks and for how long.
Add participants to your project by distributing a unique invite code generated in Labfront to each one.
See the data that participants are collecting. This allows you to take proactive measures like sending notifications when a participant has forgotten to complete tasks.
Whether you use Matlab or SPSS, Labfront’s files are all timestamped and organized for easy analysis in your research.
From lifestyle data like activity and step count to detailed beat-to-beat interval that helps you derive heart rate variability, we've got you covered.
A robust CSV file offering valuable insights on a number of different metrics over the course of a participant’s day. Each row of statistics represents one day.
Researchers are able to observe high-level data for each day such as average heart rate, maximum/minimum stress levels, total steps taken, calories burned, etc.
View how the daily summary data is organized for you in Labfront.
This file contains activity types- from walking to running to cycling- and the intensities of such activities for each of your participants.
With this data, you can derive ratios such as amount of time spent active versus the amount of time spent sedentary over the course of a specific time period.
Learn how Jenn from Duke University used the activity data to better understand her cystic fibrosis patients’ exercise regime during their rehabilition program.
Calculated through a combination of actigraphy and heart rate data, Garmin’s sleep data files can offer you a column by column break down of sleep duration, sleep staging and sleep quality for each participant.
Gain more insight into overall sleep patterns than just one or two nights in a lab can offer by remotely monitoring sleep in a participant’s natural environment.
See how the Garmin Vivosmart performed in a 2018 sleep validation study.
Learn more about Garmin’s sleep data files here.
Collect heart rate data at the sampling rate of your choice. Garmin’s specially tuned PPG sensors can also pick up each individual heart beat and provide you with heart rate interval data, one of the most unique metrics available in the consumer wearable market.
Heart rate is useful for measuring stress, exercise intensity, arrhythmia detection, and so much more.
1 sec - 60 mins
Learn how Garmin’s Heart Rate data metric performed when compared to an ECG.
Check out our validation study on the HR accuracy of the Garmin vivosmart 4 compared to an EKG.
This numerical output is calculated based on a combination of a participant’s heart rate, activity, and heart rate variability data.
Traditionally evaluated based on self-reported data alone, researchers can now obtain a more objective representation of their participants’ stress levels through Garmin’s stress score.
10 secs - 60 mins
Read more about how this psychotherapy feasibility study looked at stress in seven different case examples.
Learn more about how Garmin derives its stress score
Approximate activity by tracking the amount of steps taken each day by each participant over the course of your study.
Rather than relying on subjective self-reporting, measuring steps can add an objective assessment of physical activity to your research.
1 min - 15 mins
Read a comparison study of three activity trackers and how they performed in a study observing activity in older adults.
Respiration tracks breathing rate throughout the day, during sleep, and during activities such as breathwork and yoga. It is measured in breaths per minute.
Respiratory rate is usually a stable metric, so an increased respiratory rate may be a sign of illness or stress (especially when paired with low activity). Tracking respiration can be useful for sleep staging as well.
10 secs - 60 mins
Respiration is only supported on some Garmin devices, such as the vivosmart 5 and Venu SQ. Check our compatible devices page for the full list.
High resolution electrocardiogram (ECG) data is the gold standard for heart rate rate monitoring, especially useful if you are measuring during exercise.
This data can be used to derive HRV as well as sleep apnea (cardiopulmonary coupling), or to detect heart tremors and atrial fibrillation.
128Hz - 512Hz
Check out this study validating the Movesense Medical sensor against a conventional 12 channel ECG for measuring HRV.
Although most often measured using a finger clip, blood oxygen saturation can also be obtained on the wrist. Select Garmin wearables support wrist-based blood oxymetry.
Fluctuations in blood oxygen levels can help provide insight into a participant’s overall health. Also used to in sleep apnea measurements.
10 secs - 60 mins
View how SpO2 measurements taken via a Garmin fēnix® watch compare to those taken on a standard medical-grade pulse oximeter.
Heart rate interval (also known as RRI/IBI/BBI) is the time between each individual heartbeat. RR-Interval captured via ECG is available from Movesense devices. BBI captured through the pulse can be obtained from Garmin wearables.
From BBI data, researchers are able to derive valuable metrics such as heart rate variability (HRV) at the most granular level available. HRV is an accepted measure to approximate autonomic nervous system (ANS) function.
Find out how Dr. Ann Hsing from Stanford derived HRV from Garmin’s BBI data to measure wellbeing in her participants.
Stanford Well for Life Wearable Pilot Study
Paired with Labfront, Garmin wearables offer the ability to aggregate 3-axis accelerometer data into zero-crossing (ZC) data. This is the number of times the signal crosses the zero axis. Generally, the ZC of an acceleration signal measures movement frequency. The threshold is also configurable.
Zero crossings is useful for sleep research as it can help determine sleep and wake periods (actigraphy is derived from zero crossing data).
60 min, 30 min, 15 min, 3 min, 1 min, 30 sec
With Labfront and Movesense, track any type of motion with high-resolution 9-axis motion tracking. Get IMU data in aggregate or individually select from 3-axis accelerometer data, 3-axis gyroscope data, and 3-axis magnetometer data.
Used to track everything from walking cadence and gait to tremors from Parkinson's.
12.5Hz - 208Hz
Read this study presenting a mobile fall risk assessment solution for daily-life settings that uses Movesense acceleration data.
Our team of data scientists will work with you to understand your needs and help get the most from your data with custom analytics.
Labfront is adheres to ICH-GCP, HIPAA, SOC 1/2/3 and GDPR. We keep up to date with the regulations so you don’t have to.
Unlike big-tech, we believe in data privacy. You control your data. We will never sell your data, ever. Period.
Labfront is built with the best practices in security in mind. Features like secure encryption that protects your data both at rest and in transit are tablestakes.