{"id":36850,"date":"2018-11-14T10:56:29","date_gmt":"2018-11-14T15:56:29","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/optimizing-athletic-performance-and-recovery-using-machine-learning-at-whoop\/"},"modified":"2018-11-14T10:57:59","modified_gmt":"2018-11-14T15:57:59","slug":"optimizing-athletic-performance-and-recovery-using-machine-learning-at-whoop","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/optimizing-athletic-performance-and-recovery-using-machine-learning-at-whoop\/","title":{"rendered":"Optimizing athletic performance and recovery using machine learning at WHOOP"},"content":{"rendered":"

While the technology in wearable health trackers such as Fitbit, Apple Watch, and Garmin devices are becoming a commodity, a Boston based-startup named WHOOP, founded out of the 性视界 iLab, is changing the game. WHOOP offers a device targeted at athletes to monitor strain, recovery, and sleep using proprietary algorithms based on Heart Rate Variability (HRV), the variation in time between each heartbeat, as well as four other variables tracked 100 times per second [1].<\/p>\n

 <\/p>\n

Improving wrist-based heart rate measurement<\/strong><\/p>\n

While chest straps have been able to provide more accurate HR data for decades, getting this information from a device that people will wear 24\/7 is crucial to answer meaningful questions for optimizing performance. Wrist-based devices have exhibited a less accurate heart rate (HR) during physical activity [2]. However, WHOOP claims to use machine learning with 250 features, including accelerometer and other physiological data, improve upon its measurement of your heart rate [3].<\/p>\n

\"\"<\/a>
Figure 1: Correlation matrix of 250 features to estimate heart rate [3].<\/figcaption><\/figure>\n

 <\/p>\n

HRV and training strategy<\/strong><\/p>\n

Monitoring HRV is a well-known strategy to measure recovery and prescribe high\/low intensity training or rest [4]. WHOOP has implemented supervised learning to create metrics for strain (training load) and recovery (readiness for stress) based on HRV and determined an optimal level of stress to optimize performance given an athlete\u2019s level of recovery [5].<\/p>\n

\"\"<\/a>
Figure 2: Expected change in HRV based on recovery and strain of workout [5].<\/figcaption><\/figure>Delivering insights through machine learning is the main value proposition for WHOOP. A device collects over 100MB of data per day to better understand individuals or teams of athletes. In the short term, WHOOP is focused on these performance optimization questions such as \u201cis it better for me to work out in the morning or before bed?\u201d or \u201chow should I schedule my workouts around athletic events?\u201d [6].<\/p>\n

 <\/p>\n

A foray into healthcare?<\/strong><\/p>\n

To begin to tackle these questions, WHOOP partnered with the Korey Stringer Institute (KSI) to track 40 UCONN athletes over eight months during the 2016-2017 season. In addition to the WHOOP data, KSI was able to collect athlete demographic data, blood-biomarkers, training and competition loads, and fitness and hydration status [7]. WHOOP hopes to demonstrate correlation between its device metrics and athlete performance data to offer better insights to its customers.<\/p>\n

Over the medium term, WHOOP will try to use its data on recovery to improve health outcomes outside of athlete performance. WHOOP is participating in a study with Evidation and Tidepool to explore the linkage between nocturnal hypoglycemia, next-day behavior, sleep patterns, and heart rates [7,8]. Additionally, one of WHOOP\u2019s users is Ryan Reed, a NASCAR Driver with Diabetes, whose diabetes management has improved through the use of WHOOP to encourage proper recovery [9]. If WHOOP continues to collect more demographic and performance data, they can keep feeding this back into their product development process.<\/p>\n

 <\/p>\n

Publish some papers already!<\/strong><\/p>\n

WHOOP shows promise as a standard device that health researchers can utilize to measure stress and recovery in longitudinal studies. However, WHOOP first needs to validate the accuracy and precision of its device and algorithms before it can be trusted by comparing the estimated HR with that measured by an ECG (such as described in [2]).<\/p>\n

Secondly, the problems that WHOOP is trying to solve are complex and have so many variables that they should continue to grow their user base and collect additional data. They have demonstrated that the product is sticky, so I would add surveys and other integrations (such as MyFitnessPal for nutrition data) to collect relevant auxiliary data to improve the models. WHOOP could also open up their platform to allow physiologists to run opt-in studies on users to crowdsource innovation. This may be necessary especially because WHOOP has failed to publish results from their study a couple years ago.<\/p>\n

Lastly, WHOOP should invest more in unsupervised learning. If WHOOP is truly measuring valuable signals, this could allow for some less intuitive and more interesting insights. For example, WHOOP may be able to predict when a user is beginning to get sick and what exercise or sleep regimen he or she could implement to heal quickest. Currently these findings are coming through intentional studies such as when the CEO discovered that if he did a really short workout before a flight that involves jetlag, he would have a better recovery the next day.<\/p>\n

 <\/p>\n

The future of WHOOP<\/strong><\/p>\n

Given the recent shift from a standalone unit cost to a subscription model, WHOOP\u2019s core competency seems to be less about their patented device than the analytics platform. Despite this, should WHOOP open up its platform and data and if so, to who? Also, what do you think about WHOOP\u2019s choice to use supervised vs. unsupervised learning.<\/p>\n

 <\/p>\n

(787 words)<\/p>\n

———<\/p>\n

References<\/strong><\/p>\n

[1] Goode, L., Goode, L., Ceres, P., Barrett, B., Ceres, P., Perlmutter, K., Ceres, P. and Staff, W. (2018).\u00a0Here’s One Way to Keep Wearables on Wrists: Subscriptions<\/em>. [online] WIRED. Available at: https:\/\/www.wired.com\/story\/whoop-wearable-subscription\/ [Accessed 13 Nov. 2018].<\/p>\n

[2] Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P. and Gillinov, M. (2017). Accuracy of Wrist-Worn Heart Rate Monitors.\u00a0JAMA Cardiology<\/em>, 2(1), p.104.<\/p>\n

[3] WHOOP. (2018).\u00a0Improving Heart Rate Accuracy: Your WHOOP is Getting Smarter! – WHOOP<\/em>. [online] Available at: https:\/\/www.whoop.com\/the-locker\/improving-heart-rate-accuracy-whoop-getting-smarter\/ [Accessed 13 Nov. 2018].<\/p>\n

[4] KIVINIEMI, A., HAUTALA, A., KINNUNEN, H., NISSIL\u00c4, J., VIRTANEN, P., KARJALAINEN, J. and TULPPO, M. (2010). Daily Exercise Prescription on the Basis of HR Variability among Men and Women.\u00a0Medicine & Science in Sports & Exercise<\/em>, 42(7), pp.1355-1363.<\/p>\n

[5] Capodilupo, E. and Lee, T. (2018).\u00a0TRAINING WITH WHOOP: USING RECOVERY AND STRAIN TO UNLOCK YOUR POTENTIAL<\/em>. [online] Whoop.com. Available at: https:\/\/www.whoop.com\/wp-content\/uploads\/2018\/08\/180806_whoop_training_with_whoop.pdf [Accessed 13 Nov. 2018].<\/p>\n

[6] CAPODILUPO, J. (2018).\u00a0The CTO and the Analysis of the Human Body – WHOOP<\/em>. [online] WHOOP. Available at: https:\/\/www.whoop.com\/the-locker\/the-cto\/ [Accessed 13 Nov. 2018].<\/p>\n

[7] Businesswire.com. (2018).\u00a0Business Wire<\/em>. [online] Available at: https:\/\/www.businesswire.com\/news\/home\/20170731005519\/en\/WHOOP-Korey-Stringer-Institute-Conduct-Largest-Athlete [Accessed 13 Nov. 2018].<\/p>\n

[8] Draper, S. (2018).\u00a0Evidation Health Teams Up with Tidepool to Use Connected Devices for Studying Sleep, Type 1 diabetes<\/em>. [online] Wearable Technologies. Available at: https:\/\/www.wearable-technologies.com\/2018\/08\/evidation-health-teams-up-with-tidepool-to-use-connected-devices-for-studying-sleep-type-1-diabetes\/ [Accessed 13 Nov. 2018].<\/p>\n

[9] Evidation. (2018).\u00a0Evidation Health, Tidepool Partner to Use the Data Generated Every Day by People with Diabetes to Improve Clinical Research – Evidation<\/em>. [online] Available at: https:\/\/evidation.com\/news\/evidation-health-tidepool-partner\/ [Accessed 13 Nov. 2018].<\/p>\n

[10] Van Deusen, M. (2018).\u00a0WHOOP helps NACSAR driver Ryan Reed train, and manage his diabetes<\/em>. [online] WHOOP. Available at: https:\/\/www.whoop.com\/the-locker\/9-questions-ryan-reed-nascar-driver-diabetes\/ [Accessed 13 Nov. 2018].<\/p>\n

[11] PATRICK PULLEN, J. (2018).\u00a0Why Professional Athletes Love This Fitness Band<\/em>. [online] Available at: http:\/\/time.com\/4744459\/whoop-strap-fitness-tracker-band\/ [Accessed 13 Nov. 2018].<\/p>\n","protected":false},"excerpt":{"rendered":"

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