Medical Devices and Healthcare IT

App Can Predict Your General Health and Biological Age Based on Health Data Gathered By Wearables

29 March 2018

This is a screenshot of the Gero Lifespan app. Source: ©Gero LLCThis is a screenshot of the Gero Lifespan app. Source: ©Gero LLC

GERO, a longevity biotech company, and the Moscow Institute of Physics and Technology (MIPT) researchers have proven that the physical activity data from wearables like a Fitbit or iPhone can be used to calculate digital biomarkers that tell people how they are going to age. This breakthrough proves the potential of combining wearable sensors and AI to provide humans real-time health risk monitoring. This information can be provided to life and health insurers so they can calculate individualized insurance rates. Users can give this information to their doctors so the doctors can help patients receive accurate diagnoses and treatments.

There are many biomarkers of age, such as DNA methylation, that can be used to create accurate biological clocks for a person and provide them with information on how they are going to age. This can help prepare a person for any health risks they may have going forward in life. Large-scale biochemical profiling is currently too expensive to be used beyond academic research, and this new development could be key to making this tech more accessible.

Peter Fedichev, Ph.D., GERO Science Director and head of MIPT lab, says, “Artificial Intelligence is a powerful tool in pattern recognition and has demonstrated outstanding performance in visual object identification, speech recognition, and other fields. Recent promising examples in the field of medicine include neural networks showing cardiologist-level performance in detection of arrhythmia in ECG data, deriving biomarkers of age from clinical blood biochemistry, and predicting mortality based on electronic medical records. Inspired by these examples, we explored AI potential for Health Risks Assessment based on human physical activity.”

To create this new tech, the team analyzed physical activity records and clinical data from a 2003-2006 US National Health and Nutrition Examination Survey (NHANES) and trained a neural network to predict a person’s biological age from these activity measurements. The Convolutional Neural Network unraveled biologically relevant motion patterns to figure out a person’s general health and lifespan. The AI-based algorithm was created by the GERO scientists. This algorithm outperformed all of the other models that found biological age and mortality risks from that data.

Peter Fedichev said, “Life and health insurance programs have already begun to provide discounts to their users based on physical activity monitored by fitness wristbands. We report that AI can be used to further refine the risks models. Combination of aging theory with the most powerful modern machine learning tools will produce even better health risks models to mitigate longevity risks in insurance, help in pension planning, and contribute to upcoming clinical trials and future deployment of anti-aging therapies.”

You can access the first version of this iPhone app here. The paper on the app was published in Scientific Reports.



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