According to GamesBeat, companies now measure and on-the-fly adjust to a player many parameters in a game, like onboarding techniques, the time to rich a specific level, the rate at which players can pick up goodies, etc.
“Gaming companies are now manipulating all of these variables as needed to ensure gamers onboard, get engaged, and keep playing over the long haul. Because, of course, it’s all about retention. If you can retain players, you can monetize. If you can’t, you won’t make money.”
But in the long run, collection of gaming data is not only about retention. It is at much extent about monitoring the people’s gaming abilities and brain activities to design games for treating psychiatric and mood disorders, elevating pain, learning new skills and manipulating robots, sensors and nanobots (for example, inside the body). Gaming data are needed for learning robots and manipulating them. And gaming data will be eventually used for searching for individuals with unusual brains and motor functions — to learn more about human neurology, brain physiology and anatomy to design new medical treatments and to advance the human brain. What else can you imagine?
Google announced a prototype of a contact lens that measures glucose levels in tears “using a tiny wireless chip and miniaturized glucose sensor embedded between two layers of soft contact lens material.” It can read glucose once per second. Integrated with tiny LED lights, the lens would light up to indicate the glucose levels above or below certain thresholds. iHealthBeats provides info on clinical research trials of the lens.
What does this lens have to do with Big Data? Imagine if only a half of diabetics in the US alone — that is about 15 Million now — start wearing such lenses, and data, read every second, will be collected in some central database on Cloud — to be accessible for monitoring and providing services for patients, doctors and researchers via mobile apps — that will be 15 Million data points (plus meta-data) collected every second, or 54 Billion every day, 20 Trillion every year.
Now, imagine that all diabetics in the world (381 Million now and 592 Million projected by 2035) and pre-diabetics (35% of the US adults over 20, i.e., 110 Million in the US, or at least 1 Billion in the world) — more than a Billion people now — monitor glucose level with such lenses. Then we talk about real Big Data: 1 Billion data points per second, 3.6 Trillion per day, 1.3 Quadrillions (1.3E+15) data points per year, which is 2.6E+15 bytes, or 2.6 petabytes per year (if to use 2 bytes per each measurement, and not considering meta-data. Glucose in blood is measured in the range from 10 to 2,350 mg/dL; the latter number is from the Guinness book of World Records.) In practice, it might no need to report data each second; data averaged for each 10-30 minutes will be enough. While, still meta-data, at least time and a person ID will be needed, assuming that all other personal data — geo-location, type of a person activity, and other measurements useful for searching patterns — will come from the cell phone and other sensors.
The technology used in this lens is not provided, while from the picture it looks like RFID chip, which power might be assisted by a mobile phone.
iHealthBeat asked a variety of stakeholders to weigh in on health IT progress in 2013. Several experts reflected on the increased use of mobility (apps, sensors, wearable tech devices) and the significance of FDA guidance on mobile apps. At the same time an access to and utilization of EHR (Electronic Health Record) data disappoint: the case with 23andMe was mentioned. I applaud FDA for stopping the company of offering personal genome services, which to me always looked unproven and misleading: just think about how much damage has been done to people getting prognoses on potential diseases.
Also, there was mentioned a need in widespread use of personal Blue Button two-page sheets providing data on immunization, hospitalizations, medication, etc. — some kind of a fast accessible by any medical provider health web identifier (like SSN) for each person — which is very feasible in the nearest future. In any case, policies on ethics and use of medical data are becoming as important as never before: to develop health apps and analysis tools for medical, genomics, proteomics, and other omics data developers and researchers need first to have data, and as many as possible.