First all-digital clinical trial studies app-driven physical activity interventions

Stanford’s Euan Ashley has conducted an entirely digital clinical trial using the MyHeart Counts app, which is being used for patient recruitment, consent and interventions, and returns data to participants.

 1075 participants completed at least one intervention, and 493 completed the entire trial.  The higher than normal completion rate was attributed to the ease of enrollment and use.

The digital trial “prescribed” one of four simple interventions weekly, including  reminders to walk more or stand up. The team saw a 10% increase in activity compared to baseline.

The study serves as a template for future all-digital randomized clinical trials, which can include more nuanced questions, and an increase in interventions, which are ideally personalized.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche – Nicola Neretti, Brown

Join ApplySci at the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 on Sand Hill Road featuring talks by Zhenan Bao, Stanford – Rudy Tanzi, Harvard – Shahin Farshchi – Lux Capital – Sheng Xu, UCSD – Carla Pugh, Stanford – Nathan Intrator, Tel Aviv University | Neurosteer – Wei Gao, Caltech

AI detects CHF through analysis of one heartbeat

Sebastiano Massaro at the University of Surrey,  Mihaela Porumb and Leandro Pecchia at the University of Warwick, and Ernesto Iadanza at the University of Florence have developed an advanced signal processing and machine learning method to identify congestive heart failure with 100% accuracy through analysis of one raw ECG heartbeat.

REGISTRATION RATES INCREASE SEPTEMBER 20 | Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School featuring talks by Brad Ringeisen, DARPA – Joe Wang, UCSD – Carlos Pena, FDA  – George Church, Harvard – Diane Chan, MIT – Giovanni Traverso, Harvard | Brigham & Womens – Anupam Goel, UnitedHealthcare  – Nathan Intrator, Tel Aviv University | Neurosteer – Arto Nurmikko, Brown – Constance Lehman, Harvard | MGH – Mikael Eliasson, Roche

Atrial fibrillation-detecting ring

Eue-Keun Choi and Seoul National University colleages have developed an atrial fibrillation detecting ring, with similar functionality to AliveCor and other watch-based monitors. The researchers claim that the performance is comparable to medical grade pulse oximeters.

In a study, Soonil Kwon and colleagues analyzed data from 119 patients with AF who underwent simultaneous ECG and photoplethysmography before and after direct-current cardioversion. 27,569 photoplethysmography samples were analyzed by an algorithm developed with a convolutional neural network. Rhythms were then interpreted with the wearable ring.

The accuracy of the convolutional neural network was 99.3% to diagnose AF and 95.9% to diagnose sinus rhythm.  The accuracy of the wearable device was 98.3% for sinus rhythm and 100% for AF after filtering low-quality samples.

Choi believes that: “Deep learning or [artificial intelligence] can overcome formerly important problems of [photoplethysmography]-based arrhythmia diagnosis. It not only improves diagnostic accuracy in great degrees, but also suggests a metric how this diagnosis will be likely true without ECG validation. Combined with wearable technology, this will considerably boost the effectiveness of AF detection.”.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

3D printed, vascularized heart, using patient’s cell, biological materials

Tel Aviv University professor Tal Dvir has printed a 3D vascularized engineered heart, including cells, blood vessels, ventricles and chambers,  using a patient’s own cell and biological materials.

A biopsy of fatty tissue was taken from patients. Cellular and a-cellular materials were separated. While the cells were reprogrammed to become pluripotent stem cells, the extracellular matrix were processed into a personalized hydrogel that served as printing “ink.” After being mixed with the hydrogel, the cells were efficiently differentiated to cardiac or endothelial cells to create patient-specific, immune-compatible cardiac patches with blood vessels and, subsequently, an entire heart.

Dvir believes that this “3D-printed thick, vascularized and perfusable cardiac tissues that completely match the immunological, cellular, biochemical and anatomical properties of the patient” reduces the risk of implant rejection.

The team now plans on culturing the printed hearts and “teaching them to behave” like hearts, then transplanting them in animal models.


Join ApplySci at the 12th Wearable Tech + Digital Health + Neurotech Boston conference on November 14, 2019 at Harvard Medical School and the 13th Wearable Tech + Neurotech + Digital Health Silicon Valley conference on February 11-12, 2020 at Stanford University

Adhesive emergency response sensors

VitalTag by Pacific Northwest National Laboratory is a chest-worn sticker that detects, monitors and transmits blood pressure, heart rate, respiration rate and other vital signs, eliminating the need for multiple medical devices.

It is meant for emergency responders to quickly assess a person’s state.

Additional sensors are worn on the finger, and in the ear.

Data is displayed in an app, allowing responders to see patients’ location and receive alerts when status changes or they are moved.  Multiple patients can be monitored simultaneously.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

Small ultrasound patch detects heart disease early

Sheng Xu, Brady Huang, and UCSD colleagues have developed a small, wearable ultrasound patch that  monitors blood pressure in arteries up to 4 centimeters under the skin.  It is meant to detect cardiovascular problems earlier, with greater accuracy

Applications include continuous blood pressure monitoring in heart and lung disease, the critically ill, and those undergoing surgery.  It could be used to measure other vital signs, but this was not studied.

The wearable measures central blood pressure, considered more accurate and better at predicting disease than peripheral blood pressure. Central blood pressure is not routinely measured, and involves a catheter inserted into a blood vessel in the arm, groin or neck, and guiding to the heart. A non-invasive method exists, but it does not produce consistently accurate readings.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

Apple watch detects falls, diagnoses heart rhythm, bp irregularities

The Apple Watch has become a serious medical monitor.  It will now be able to detect falls, contact emergency responders, and diagnose  irregularities in heart rhythm and blood pressure.  Its ECG app has been granted a De Novo classification by the FDA.

ECG readings are taken from the wrist, using electrodes built into the Digital Crown and an electrical heart rate sensor in the back crystal. Users touch the Digital Crown and receive a heart rhythm classification in 30 seconds. It can classify if the heart is beating in a normal pattern or whether there are signs of Atrial Fibrillation . All recordings, their associated classifications and any noted symptoms are stored and can be shared with physicians.

The watch intermittently analyzes heart rhythms in the background and sends a notification if an irregular heart rhythm such as AFib is detected.  It can also alert the user if the heart rate exceeds or falls below a specified threshold.

Fall detection is via a built in accelerometer and gyroscope, which measures forces, and an algorithm to identify hard falls. Wrist trajectory and impact acceleration are analyzed to detect falls.  Users are then sent an alert, which can be dismissed or used to call emergency services.  If  immobility  is sensed for 60 seconds,  emergency services will automatically be called, and emergency contacts will be notified.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

David Axelrod: VR in healthcare & the Stanford Virtual Heart | ApplySci @ Stanford

David Axelrod discussed VR-based learning in healthcare, and the Stanford Virtual Heart, at ApplySci’s recent Wearable Tech + Digital Health + Neurotech conference at Stanford;


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson

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Heart attack, stroke, predicted via retinal images

Google’s Lily Peng has developed an algorithm that can predict heart attacks and strokes by analyzing images of the retina.

The system also shows which eye areas lead to successful predictions, which can provide insight into the causes of cardiovascular disease.

The dataset consisted of 48,101 patients from the UK Biobank database and 236,234 patients from EyePACS database.  A study of  12,026 and 999 patients showed a high level of accuracy:

-Retinal images of a smoker from a non-smoker 71 percent of the time, compared to a ~50 percent human  accuracy.

-While doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, Google AI’s algorithm predicts the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.

-According to the company the algorithm predicted direct cardiovascular events “fairly accurately, ” statin that “given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”

According to Peng: “Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70 percent of the time, This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Shreyas Shah– Walter Greenleaf – Jacobo Penide  – Peter Fischer – Ed Boyden

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Contact-free blood pressure, heart and breath rate monitoring

Cornell’s Edwin Kan has developed a contact-free vital sign monitor  using radio-frequency signals and microchip tags. Blood pressure, heart rate and breath rate  are measured when radio waves bounce off the body and internal organs, and are detected by an electronic reader from a location anywhere in the room.  200 people can be monitored simultaneously.

According to Kan, the signal is as accurate as an ECG or blood-pressure cuff.  He believes that the technology could be used to measure bowel movement, eye movement and other internal mechanical motions.


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Michael Eggleston – Walter Greenleaf – Jacobo Penide

Registration rates increase today, Friday, December 22nd.