STAGES (n=30,000)
https://academic.oup.com/sleep/article/41/suppl_1/A124/4988361
Others somewhat smaller
https://sleepdata.org/datasets/hchs (N=16,000; actigraphy but not genomics)
https://sleepdata.org/datasets/mesa (N=6,800; actigraphy but not genomics; strength is longitudinal following)
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Fitbit wearers can also opt in to be part of the PROTECT Study at Stanford University. There, researchers are using data collected from users of Fitbit, three other smartwatches — including Apple Watch — and one smart ring. Specifically, Dr. Michael Snyder’s laboratory at Stanford is studying data from smartwatch users who have a confirmed or suspected case of coronavirus, have been exposed to someone who has a confirmed or suspected case, or are at a higher risk of exposure, such as health care or grocery store employees.
One of the metrics Snyder and his team are focusing on is how a smartwatch can measure heart rate and body temperature.
Heart rate is the number of times a heart beats in one minute. Though it can vary greatly from person to person, the normal resting heart rate for an adult is between 60 and 100. A lower rate means a person is in peak cardiovascular shape. Unusual numbers on the high or low scale could indicate an underlying illness. The challenge is that a heart rate can spike because of various factors including age, smoking, high cholesterol, diabetes, activity, weight and medications.
When you’re sick, “your heart rate goes up before you’re congested. … So, worst-case scenario, it goes up around the time you’re feeling yucky, but it probably goes up before that, we think,” Snyder explained.
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https://cen.acs.org/environment/pollution/Predicting-asthma-attacks-kids/97/i32
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The Southern California team is building an informatics platform that integrates commercially available air pollution sensors as well as wearable environmental sensors developed by academic researchers. The project is part of the PRISMS initiative established in 2015 by the US National Institutes of Health. Information from the sensors, along with a person’s geolocation, physical activity, and health data, is wirelessly transmitted to the person’s smart watch and smartphone in real time. Participants use the smartphone to self-report symptoms and information related to daily activities. The informatics platform also uses the individual’s location to integrate weather, traffic, and air-quality data into the data stream.
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.@Fitbit’s 150 billion hours of heart data reveal secrets about health, by @Pogue
https://finance.Yahoo.com/news/exclusive-fitbits-150-billion-hours-heart-data-reveals-secrets-human-health-133124215.html One interesting observation: “You see heart rate go up before your family reunions, & then tend to really take a long time to come back after it.”
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“Kind of wild to see how starting to use a treadmill — the first regular cardio workouts I’ve ever really gotten — visibly lowered my entire heart-rate range.
Also, it turns out that having kidney stones is bad for you. My heart rate went through the roof both times.
I was surprised and amused, though, to see the second most stressful events on my graph: holiday get-togethers.
“You see the heart rate go up before your family reunions, and then tend to really take a long time to come back after it,” notes McLean. In other words — who knew?? — holidays with the family are not a guarantee of peace, relaxation, and joy.
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Design for data sharing
http://www.Nature.com/nbt/journal/v34/n4/full/nbt.3516.html Issues in distributing mPower mobile dataset – no DAC, allowing donors to change preferences
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“This March, Sage Bionetworks (Seattle) began sharing curated data collected from >9,000 participants of mPower, a smartphone-enabled health research study for Parkinson’s disease. The mPower study is notable as one of the first observational assessments of human health to rapidly achieve scale as a result of its design and execution purely through a smartphone interface. To support this unique study design, we developed a novel electronic informed consent process that includes participant-determined data-sharing preferences. It is through these preferences that the new data—including self-reported outcomes and quantitative sensor data—are shared broadly for secondary analysis. Our hope is that by sharing these data immediately, prior even to our own complete analysis, we will shorten the time to harnessing any utility that this study’s data may hold to improve the condition of patients who suffer from this disease.
Turbulent times for data sharing
Our release of mPower comes at a turbulent time in data sharing. The power of data for secondary research is top of mind for many these days. Vice President Joe Biden, in heading President Barack Obama’s ambitious cancer ‘moonshot’, describes data sharing as second only to funding to the success of the effort. However, this powerful support for data sharing stands in opposition to the opinions of many within the research establishment. To wit, the august New England Journal of Medicine (NEJM)’s recent editorial suggesting that those who wish to reuse clinical trial data without the direct participation and approval of the original study team are “research parasites”. In the wake of colliding perspectives on data sharing, we must not lose sight of the scientific and societal ends served by such efforts.” “}}
resource that can be used to examine aggregate fitbit data…don’t know if this includes disease status.
https://www.fitabase.com/
