After decades of generic health advice based on broad population studies, a new era of highly personalized insights is emerging thanks to the combination of consumer health devices, data science, and artificial intelligence. In a recent episode of the Augmented Life podcast, I spoke with Dr. Ajay Bhargava, a scientist and Python programmer, who conducted a fascinating sleep experiment with someone who shared their data from an Oura ring, Apple Watch, and continuous glucose monitor, using the Fulcra platform tech.
By passively collecting and combining months of data from these devices into a computational notebook, then exploring it with an open mind, Dr. Bhargava uncovered some surprising factors that significantly influenced sleep quality and quantity. One key finding was that the timing of the subject’s last meal and the resulting glucose spikes had a major impact on his sleep metrics that night. Eating earlier and avoiding late night carbs led to better rest.
Beyond just diet, the analysis also highlighted how environmental factors like wind speed outside the window affected sleep. By starting with a "boil the ocean" approach of collecting as much data as possible, Dr. Bhargava let the numbers guide him to unexpected correlations that became helpful conversation starters with the research subject about their habits and routines.
Dr. Bhargava represents a "data science unicorn" in being able to wrangle multiple wearable device APIs into Python code to extract meaning. But he believes platforms like Fulcra are making this kind of multidimensional personal research accessible to anyone, by unsiloing data and making it easy to combine, visualize and query without needing to be a programmer.
These N=1 individual case studies are hugely valuable, because generic advice based on population averages often fails to account for individual variations. What works for you in your 20s may be disastrous in your 40s. Continuously tracking your own vitals and behaviors and correlating them is the only way to optimize your personal health over time.
Looking ahead, we speculated about a future where this kind of multifactor self-analysis becomes the norm. Social networks may evolve from sharing text and photos to pooling biometric and behavioral data among friends and family. AI could learn to spot patterns across similar individuals to make proactive personalized recommendations.
The combination of consumer tracking devices and accessible data science tools is ushering in a transformative era for individual wellness and medicine. By empowering citizen scientists to ask and answer their own health questions in a data-driven way, we may uncover a myriad of "obvious in retrospect" interventions that are far more potent than generic "eat right and exercise" guidance. The hyper-personal health insights revolution is just beginning.
To dive a little deeper, watch the full episode here:
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