Future Health: DNA is one thing, but 90% of you is not you

One of my pet hates is seeing my wife visit the doctor, getting hunches of what may be afflicting her health, and this leading to a succession of “oh, that didn’t work – try this instead” visits for several weeks. I just wonder how much cost could be squeezed out of the process – and lack of secondary conditions occurring – if the root causes were much easier to identify reliably. I then wonder if there is a process to achieve that, especially in the context of new sensors coming to market and their connectivity to databases via mobile phone handsets – or indeed WiFi enabled, low end Bluetooth sensor hubs aka the Apple Watch.

I’ve personally kept a record of what i’ve eaten, down to fat, protein and carb content (plus my Monday 7am weight and daily calorie intake) every day since June 2002. A precursor to the future where devices can keep track of a wide variety of health signals, feeding a trend (in conjunction with “big data” and “machine learning” analyses) toward self service health. My Apple Watch has a years worth of heart rate data. But what signals would be far more compelling to a wider variety of (lack of) health root cause identification if they were available?

There is currently a lot of focus on Genetics, where the Human Genome can betray many characteristics or pre-dispositions to some health conditions that are inherited. My wife Jane got a complete 23andMe statistical assessment several years ago, and has also been tested for the BRCA2 (pronounced ‘bracca-2’) gene – a marker for inherited pre-disposition to risk of Breast Cancer – which she fortunately did not inherit from her afflicted father.

A lot of effort is underway to collect and sequence the complete Genome sequences from the DNA of hundreds of thousands of people, building them into a significant “Open Data” asset for ongoing research. One gotcha is that such data is being collected by numerous organisations around the world, and the size of each individuals DNA (assuming one byte to each nucleotide component – A/T or C/G combinations) runs to 3GB of base pairs. You can’t do research by throwing an SQL query (let alone thousands of machine learning attempts) over that data when samples are stored in many different organisations databases, hence the existence of an API (courtesy of the GA4GH Data Working Group) to permit distributed queries between co-operating research organisations. Notable that there are Amazon Web Services and Google employees participating in this effort.

However, I wonder if we’re missing a big and potentially just as important data asset; that of the profile of bacteria that everyone is dependent on. We are each home to approx. 10 trillion human cells among the 100 trillion microbial cells in and on our own bodies; you are 90% not you.

While our human DNA is 99.9% identical to any person next to us, the profile of our MicroBiome are typically only 10% similar; our age, diet, genetics, physiology and use of antibiotics are also heavy influencing factors. Our DNA is our blueprint; the profile of the bacteria we carry is an ever changing set of weather conditions that either influence our health – or are leading indicators of something being wrong – or both. Far from being inert passengers, these little organisms play essential roles in the most fundamental processes of our lives, including digestion, immune responses and even behaviour.

Different MicroBiome ecosystems are present in different areas of our body, from our skin, mouth, stomach, intestines and genitals; most promise is currently derived from the analysis of stool samples. Further, our gut is only second to our brain in the number of nerve endings present, many of them able to enact activity independently from decisions upstairs. In other areas, there are very active hotlines between the two nerve cities.

Research is emerging that suggests previously unknown links between our microbes and numerous diseases, including obesity, arthritis, autism, depression and a litany of auto-immune conditions. Everyone knows someone who eats like a horse but is skinny thin; the composition of microbes in their gut is a significant factor.

Meanwhile, costs of DNA sequencing and compute power have dropped to a level where analysis of our microbe ecosystems costs from $100M a decade ago to some $100 today. It should continue on that downward path to a level where personal regular sampling could become available to all – if access to the needed sequencing equipment plus compute resources were more accessible and had much shorter total turnaround times. Not least to provide a rich Open Data corpus of samples that we can use for research purposes (and to feed back discoveries to the folks providing samples). So, what’s stopping us?

Data Corpus for Research Projects

To date, significant resources are being expended on Human DNA Genetics and comparatively little on MicroBiome ecosystems; the largest research projects are custom built and have sampling populations of less than 4000 individuals. This results in insufficient population sizes and sample frequency on which to easily and quickly conduct wholesale analyses; this to understand the components of health afflictions, changes to the mix over time and to isolate root causes.

There are open data efforts underway with the American Gut Project (based out of the Knight Lab in the University of San Diego) plus a feeder “British Gut Project” (involving Tim Spector and staff at University College London). The main gotcha is that the service is one-shot and takes several months to turn around. My own sample, submitted in January, may take up 6 months to work through their sequencing then compute batch process.

In parallel, VC funded company uBiome provide the sampling with a 6-8 week turnaround (at least for the gut samples; slower for the other 4 area samples we’ve submitted), though they are currently not sharing the captured data to the best of my knowledge. That said, the analysis gives an indication of the names, types and quantities of bacteria present (with a league table of those over and under represented compared to all samples they’ve received to date), but do not currently communicate any health related findings.

My own uBiome measures suggest my gut ecosystem is more diverse than 83% of folks they’ve sampled to date, which is an analogue for being more healthy than most; those bacteria that are over represented – one up to 67x more than is usual – are of the type that orally administered probiotics attempt to get to your gut. So a life of avoiding antibiotics whenever possible appears to have helped me.

However, the gut ecosystem can flex quite dramatically. As an example, see what happened when one person contracted Salmonella over a three pay period (the green in the top of this picture; x-axis is days); you can see an aggressive killing spree where 30% of the gut bacteria population are displaced, followed by a gradual fight back to normality:

Salmonella affecting MicroBiome PopulationUnder usual circumstances, the US/UK Gut Projects and indeed uBiome take a single measure and report back many weeks later. The only extra feature that may be deduced is the delta between counts of genome start and end sequences, as this will give an indication to the relative species population growth rates from otherwise static data.

I am not aware of anyone offering a faster turnaround service, nor one that can map several successively time gapped samples, let alone one that can convey health afflictions that can be deduced from the mix – or indeed from progressive weather patterns – based on the profile of bacteria populations found.

My questions include:

  1. Is there demand for a fast turnaround, wholesale profile of a bacterial population to assist medical professionals isolating a indicators – or the root cause – of ill health with impressive accuracy?
  2. How useful would a large corpus of bacterial “open data” be to research teams, to support their own analysis hunches and indeed to support enough data to make use of machine learning inferences? Could we routinely take samples donated by patients or hospitals to incorporate into this research corpus? Do we need the extensive questionnaires the the various Gut Projects and uBiome issue completed alongside every sample?
  3. What are the steps in the analysis pipeline that are slowing the end to end process? Does increased sample size (beyond a small stain on a cotton bud) remove the need to enhance/copy the sample, with it’s associated need for nitrogen-based lab environments (many types of bacteria are happy as Larry in the Nitrogen of the gut, but perish with exposure to oxygen).
  4. Is there any work active to make the QIIME (pronounced “Chime”) pattern matching code take advantage of cloud spot instances, inc Hadoop or Spark, to speed the turnaround time from Sequencing reads to the resulting species type:volume value pairs?
  5. What’s the most effective delivery mechanism for providing “Open Data” exposure to researchers, while retaining the privacy (protection from financial or reputational prejudice) for those providing samples?
  6. How do we feed research discoveries back (in English) to the folks who’ve provided samples and their associated medical professionals?

New Generation Sequencing works by splitting DNA/RNA strands into relatively short read lengths, which then need to be reassembled against known patterns. Taking a poop sample with contains thousands of different bacteria is akin to throwing the pieces of many thousand puzzles into one pile and then having to reconstruct them back – and count the number of each. As an illustration, a single HiSeq run may generate up to 6 x 10^9 sequences; these then need reassembling and the count of 16S rDNA type:quantity value pairs deduced. I’ve seen estimates of six thousand CPU hours to do the associated analysis to end up with statistically valid type and count pairs. This is a possible use case for otherwise unused spot instance capacity at large cloud vendors if the data volumes could be ingested and processed cost effectively.

Nanopore sequencing is another route, which has much longer read lengths but is much more error prone (1% for NGS, typically up to 30% for portable Nanopore devices), which probably limits their utility for analysing bacteria samples in our use case. Much more useful if you’re testing for particular types of RNA or DNA, rather than the wholesale profiling exercise we need. Hence for the time being, we’re reliant on trying to make an industrial scale, lab based batch process turn around data as fast we are able – but having a network accessible data corpus and research findings feedback process in place if and when sampling technology gets to be low cost and distributed to the point of use.

The elephant in the room is in working out how to fund the build of the service, to map it’s likely cost profile as technology/process improvements feed through, and to know to what extent it’s diagnosis of health root causes will improve it’s commercial attractiveness as a paid service over time. That is what i’m trying to assess while on the bench between work contracts.

Other approaches

Nature has it’s way of providing short cuts. Dogs have been trained to be amazingly prescient at assessing whether someone has Parkinson’s just by smelling their skin. There are other techniques where a pocket sized spectrometer can assess the existence of 23 specific health disorders. There may well be other techniques that come to market that don’t require a thorough picture of a bacterial population profile to give medical professionals the identity of the root causes of someone’s ill health. That said, a thorough analysis may at least be of utility to the research community, even if we get to only eliminate ever rarer edge cases as we go.

Coming full circle

One thing that’s become eerily apparent to date is some of the common terminology between MicroBiome conditions and terms i’ve once heard used by Chinese Herbal Medicine (my wife’s psoriasis was cured after seeing a practitioner in Newbury for several weeks nearly 20 years ago). The concept of “balance” and the existence of “heat” (betraying the inflammation as your bacterial population of different species ebbs and flows in reaction to different conditions). Then consumption or application of specific plant matter that puts the bodies bacterial population back to operating norms.

Lingzhi Mushroom

Wild mushroom “Lingzhi” in China: cultivated in the far east, found to reduce Obesity

We’ve started to discover that some of the plants and herbs used in Chinese Medicine do have symbiotic effects on your bacterial population on conditions they are reckoned to help cure. With that, we are starting to see some statistically valid evidence that Chinese and Western medicine may well meet in the future, and be part of the same process in our future health management.

Until then, still work to do on the business plan.

My Apple Watch: one year on

Apple Watch Clock Face

I have worn my Apple Watch every day for a year now. I still think Apple over complicated the way they sell them, and didn’t focus on the core use cases that make it an asset to its users. For me, the stand outs are as follows:

  1. It tells the time, super accurately. The proof point is lining up several Apple watches next to each other; the second hands are always in perfect sync.
  2. You can see the time in the dark, just looking at your wrist. Stupidly simple, I know.
  3. When a notification comes in, you get tapped on the wrist. Feeling the device do that to you for the first time is one of the things that gives someone their first “oh” moment; when I demo the watch to anyone, putting it on their wrist and invoking the heart Taptic pulse is the key feature to show. Outside of message notifications, having the “Dark Sky” app tell me it’s going to rain outside in 5 or 10 minutes is really helpful.
  4. I pay for stuff using my Nationwide Debit or Credit cards, without having to take them out of my wallet. Outside my regular haunts, this still surprises people to this day; I fear that the take up is not as common as I expected, but useful nonetheless.
  5. I know it’s monitoring some aspects of my health (heart rate, exercise) and keeps egging me on, even though it’s not yet linked into the diet and food intake site I use daily (www.weightlossresources.co.uk).
  6. Being able to see who’s calling my phone and the ability to bat back a “busy, will call you back” reply without having to drag out my phone.

The demo in an Apple Store doesn’t major on any of the above; they tend to focus on aesthetics and in how you can choose different watch faces, an activity most people ever do once. On two occasions while waiting in the queue to be served at an Apple Store, someone has noticed I’m wearing an Apple Watch and has asked what it’s like day to day; I put mine on their wrist and quickly step through the above. Both went from curiosity to buying one as soon as the Apple rep freed up.

I rarely if ever expose the application honeycombe. Simplicity sells, and I’m sure Apple’s own usage stats would show that to them. I also find Siri unusable every time I try to use it on the watch.

As for the future, what would make it even more compelling for me? Personally:

  • In the final analysis, an Apple Watch is a Low Energy Bluetooth enabled WiFi hub with a clock face on it. Having more folks stringing other health sensors, in addition to ones on the device itself, to health/diet related apps will align nicely with future “self service” health management trends.
  • Being able to tone down the ‘chatty-ness’ of notifications. I’m conscious that keeping looking at my watch regularly when in a meeting is a big no-no, and having more control on what gets to tap my wrist and what just floats by in a stream for when I look voluntarily would be an asset.
  • When driving (which is when I’m not wearing glasses), knowing who or what is notifying me in a much bigger font would help. Or the ability to easily throw a text to voice of the from and subject lines onto my phone or car speakers, with optional read back of the message body.
  • Siri. I just wish it worked to a usable standard. I hope there are a few Amazon Echos sitting in the dev labs in Cupertino and that someone there is replicating its functionality in a wrist form factor.

So, the future is an eclectic mix between a low energy Bluetooth WiFi hub for health apps, a super accurate watch, selective notification advisor and an Amazon Echo for your wrist – with integrated card payments. Then getting the result easily integrated into health apps and application workflows – which I hope is what their upcoming WorldWide Developers Conference will cover.

Hooked, health markets but the mind is wandering… to pooh and data privacy

Hooked by Nir Eyal

One of the things I learnt many years ago was that there were four fundamental basics to increasing profits in any business. You sell:

  • More Products (or Services)
  • to More People
  • More Often
  • At higher unit profit (which is higher price, lower cost, or both)

and with that, four simple Tableau graphs against a timeline could expose the business fundamentals explaining good growth, or the core reason for declining revenue. It could also expose early warning signs, where a small number of large transactions hid an evolving surprise – like the volume of buying customers trending relentlessly down, while the revenue numbers appeared to be flying okay.

Another dimension is that a Brand equates to trust, and that consistency and predictability of the product or service plays a big part to retain that trust.

Later on,  a more controversial view was that there were two fundamental business models for any business; that of a healer or a dealer. One sells an effective one-shot fix to a customer need, while the other survives by engineering a customers dependency to keep on returning.

With that, I sometimes agonise on what the future of health services delivery is. One the one hand, politicians verbal jousts over funding and trying to punt services over to private enterprise. In several cases to providers of services following the economic rent (dealer) model found in the American market, which, at face value, has a business model needing per capita expense that no sane person would want to replicate compared to the efficiency we have already. On the other hand, a realisation that the market is subject to radical disruption, through a combination of:

  • An ever better informed, educated customer base
  • A realisation that just being overweight is a root cause of many adverse trends
  • Genomics
  • Microbiome Analysis
  • The upcoming ubiquity of sensors that can monitor all our vitals

With that, i’ve started to read “Hooked” by Nir Eyal, which is all about the psychology of engineering habit forming products (and services). The thing in the back of my mind is how to encourage the owner (like me) of a smart watch, fitness device or glucose monitor to fundamentally remove my need to enter my food intake every day – a habit i’ve maintained for 12.5 years so far.

The primary challenge is that, for most people, there is little newsworthy data that comes out of this exercise most of the time. The habit would be difficult to reinforce without useful news or actionable data. Some of the current gadget vendors are trying to encourage use by encouraging steps competition league tables you can have with family and friends (i’ve done this with relatives in West London, Southampton, Tucson Arizona and Melbourne Australia; that challenge finished after a week and has yet to be repeated).

My mind started to wander back to the challenge of disrupting the health market, and how a watch could form a part. Could its sensors measure my fat, protein and carb intake (which is the end result of my food diary data collection, along with weekly weight measures)? Could I build a service that would be a data asset to help disrupt health service delivery? How do I suss Microbiome changes – which normally requires analysis of a stool samples??

With that, I start to think i’m analysing this the wrong way around. I remember an analysis some time back when a researcher assessed the extent drug (mis)use in specific neighbourhoods by monitoring the make-up of chemical flows in networks of sewers. So, rather than put sensors on people’s wrists (and only see a subset of data), is there a place for technology in sewer pipes instead? If Microbiomes and the Genetic makeup of our output survives relatively intact, then sampling at strategic points of the distribution network would give us a pretty good dataset. Not least as DNA sequencing could allow the original owner (source) of output to connect back to any pearls of wisdom that could be analysed or inferred from their contributions, even if the drop-off points happened at home, work or elsewhere.

Hmmm. Water companies and Big Data.

Think i’ll park that and get on with the book.

Grain Brain: modern science kills several fundamental diet myths

Having tracked my own daily food consumption (down to carbs, protein, fat levels, plus nett calories) and weekly weight since June 2002, I probably have an excessive fascination with trying to work out which diets work. All in an effort to spot the root causes of my weight ebbs and flows. I think i’ve sort of worked it out (for me here) and have started making significant progress recently simply by eating much fewer calories than my own Basal Metabolic Rate.

Alongside this has been my curiosity about Microbiomes that outnumber our own cells in our bodies by 10:1, and wondering what damage Antibiotics wreak on them (and their otherwise symbiotic benefits to our own health) – my previous blog post here. I have also been agonising over what my optimum maintenance regime should be when I hit my target weight levels. Above all, thinking a lot about the sort of sensors everyone could employ to improve their own health as mobile based data collection technology radically improves.

I don’t know how I zeroed in on the book “Grain Brain”, but it’s been quite a revelation to me, and largely boots both the claims and motivations of newspapers, the pharmaceutical industry and many vogue diets well into touch. This backed up by voluminous, cited research conducted over the last 30 years.

A full summary would be very too long, didn’t read territory. That said, the main points are:

  1. Little dietary fat and less than 20% of cholesterol consumed makes it into your own storage mechanisms; most cholesterol is manufactured by your liver
  2. There is no scientific basis to support the need for low cholesterol foods; allegations that there is an effect at blocking arteries is over 30 years old and statistically questionable. In fact, brain functions (and defence against Alzheimer’s and other related conditions) directly benefit from high cholesterol and high fat diets.
  3. The chief source of body fat is from consumption of Carbohydrates, not fat at all. So called low fat diets often substitute carbs and sugars, which further exacerbate the very weight problems that consumers try to correct.
  4. Gluten as found in cereals is a poison. Whereas some plants open encourage consumption of seeds by animals to facilitate distribution of their payload, wheat gluten is the other sort of material – designed specifically to discourage consumption. There is material effect on body functions that help distribute nutrition to the brain.
  5. Excessive consumption of carbs, and the resulting effect on weight, is a leading cause of type 2 diabetes. It also has an oxidising effect on cholesterol in the body, reducing it’s ability to carry nutrients to the brain (which is, for what it’s worth, 80% fat).
  6. Ketosis (the body being in a state where it is actively converted stored fats into energy) is a human norm. The human body is designed to be able to manage periods of binge then bust systematically. Hence many religions having occasional fasting regimes carry useful health benefits.
  7. The human genome takes 60-70,000 years to evolve to manage changes in diet, whereas human consumption has had a abrupt charge from heavy fat and protein diets to a diet majoring on cereal and carbs in only the last 10,000 years. Our relatively recent diet changes have put our bodies under siege.

The sum effect is guidance err on the side of much greater fat/protein content, and less carbs in the diet, even if it means avoiding the Cereals Aisle at the supermarket at all costs. And for optimum health, to try to derive energy from a diet that is circa 80% fat and protein, 20% carbs (my own historical norm is 50-55% carbs). Alcohol is generally a no-no, albeit a glass of red wine at night does apparently help.

Note that energy derived from each is different; 1g of protein is typically provides 4 kcals, 1g of fat is 9 kcals, and 1g of carbs is 3.75 kcals. Hence there is some arithmetic involved to calculate the “energy derived” mix from your eating (fortunately, the www.weightlossresources.co.uk web site does this automatically for you, converting your food intake detail into a nice pie chart as you go).

There is a lot more detail in the book relating to how various bodily functions work, and what measures are leading indicators of health or potential issues. That’s useful for my sensor thinking – and to see whether widespread regular collection of data would become a useful source for spotting health issues before they become troublesome.

One striking impression i’m left with is how much diet appears to have a direct effect on our health (or lack thereof), and to wonder aloud if changes to the overall carbs/protein/fat mix we consume would fix many of the problems addressed by the NHS and by Pharmaceutical Industries at source. Type 2 Diabetes and ever more common brain ailments in old age appear to be directly attributable to what we consume down the years, and our resulting weight. Overall, a much bigger subject, and expands into a philosophical discussion of whether financial considerations drive healer (fix the root cause) or dealer (encourage a dependency) behaviours.

For me personally, the only effect is what my diet will look like in 2015 after I get to my target weight and get onto maintenance. Most likely all Bread and Cereals out, Carb/Cake treats heavily restricted, Protein and Fat in.

I think this is a great book. Bon Appetite.

Footnote:  I’m also reminded that the only thing that cured my wifes psoriasis on her hands and feet for a considerable time were some fluids to consume prescribed by a Chinese Herbal doctor, and other material applied to the skin surface. He cited excess heat, need for yin/yan balance and prescribed material to attempt to correct things. Before you go off labelling me as a crackpot, this was the only thing that cured her after years of being prescribed steroid creams by her doctor; a nurse at her then doctors surgery suggested she try going to him under a condition of her anonymity, as she thought she’d lose her job if the doctors knew – but suggested he was able to arrest the condition in many people she knew had tried.

I suspect that the change in diet and/or setting conditions right for symbiotic microbiomes in her skin (or killing off the effect of temporarily parasitic ones) helped. Another collection of theories to add to the mix if technology progresses to monitor key statistics over millions of subjects with different genetic or physiological characteristics. Then we’ll have a better understanding, without relying on unfounded claims of those with vested interests.


Another lucid flurry of Apple thinking it through – unlike everyone else

Apple Watch Home Screen

This happens every time Apple announce a new product category. Audience reaction, and the press, rush off to praise or condemn the new product without standing back and joining the dots. The Kevin Lynch presentation at the Keynote also didn’t have a precursor of a short video on-ramp to help people understand the full impact of what they were being told. With that, the full impact is a little hidden. It’s a lot more than having Facebook, Twitter, Email and notifications on your wrist when you have your phone handset in your pocket.

There were a lot of folks focussing on it’s looks and comparisons to the likely future of the Swiss watch industry. For me, the most balanced summary of the luxury esthetics from someone who’s immersed in that industry can be found at:  http://www.hodinkee.com/blog/hodinkee-apple-watch-review

Having re-watched the keynote, and seen all the lame Androidware, Samsung, LG and Moto 360 comparisons, there are three examples that explode almost all of the “meh” reactions in my view. The story is hidden my what’s on that S1 circuit board inside the watch, and the limited number of admissions of what it can already do. Three scenarios:

1. Returning home at the end of a working day (a lot of people do this).

First thing I do after I come indoors is to place my mobile phone on top of the cookery books in our kitchen. Then for the next few hours i’m usually elsewhere in the house or in the garden. Talking around, that behaviour is typical. Not least as it happens in the office too, where if i’m in a meeting, i’d normally leave my handset on silent on my desk.

With every Android or Tizen Smart Watch I know, the watch loses the connection as soon as I go out of Bluetooth range – around 6-10 meters away from the handset. That smart watch is a timepiece from that point on.

Now, who forgot to notice that the Apple Watch has got b/g WiFi integrated on their S1 module? Or that it it can not only tell me of an incoming call, but allow me to answer it, listen and talk – and indeed to hand control back to my phone handset when I return to it’s current proximity?

2. Sensors

There are a plethora of Low Energy Bluetooth sensors around – and being introduced with great regularity – for virtually every bodily function you can think of. Besides putting your own fitness tracking sensors on at home, there are probably many more that can be used in a hospital setting. With that, a person could be quite a walking network of sensors and wander to different wards or labs during their day, or indeed even be released to recuperate at home.

Apple already has some sensors (heart rate, and probably some more capabilities to be announced in time, using the infrared related ones on the skin side of the Apple watch), but can act as a hub to any collection of external bluetooth sensors at the same time. Or in smart pills you can swallow. Low Energy Bluetooth is already there on the Apple Watch. That, in combination with the processing power, storage and b/g WiFi makes the watch a complete devices hub, virtually out of the box.

If your iPhone is on the same WiFi, everything syncs up with the Health app there and the iCloud based database already – which you can (at your option) permit an external third party to have access to. Now, tell me about the equivalent on any other device or service you can think of.

3. Paying for things.

The iPhone 5S, 6 and 6 Plus all have integrated finger print scanners. Apple have put some functionality into iOS 8 where, if you’re within Bluetooth range (6-10 meters of your handset), you can authenticate (with your fingerprint) the fact your watch is already on your wrist. If the sensors on the back have any suspicion that the watch leaves your wrist, it immediately invalidates the authentication.

So, walk up to a contactless till, see the payment amount appear on the watch display, one press of the watch pays the bill. Done. Now try to do that with any other device you know.

Developers, developers, developers.

There are probably a million other applications that developers will think of, once folks realise there is a full UNIX computer on that SoC (System on a Chip). With WiFi. With Bluetooth. With a Taptic feedback mechanism that feels like someone is tapping your wrist (not loudly vibrating across the table, or flashing LED lights at you). With a GPU driving a high quality, touch sensitive display. Able to not only act as a remote control for your iTunes music collection on another device, but to play it locally when untethered too (you can always add bluetooth earbuds to keep your listening private). I suspect some of the capabilities Apple have shown (like the ability to stream your heartbeat to another Apple Watch user) will evolve into potential remote health visit applications that can work Internet wide.

Meanwhile, the tech press and the discussion boards are full of people lamenting the fact that there is no GPS sensor in the watch itself (like every other Smart Watch I should add – GPS location sensing is something that eats battery power for breakfast; better to rely on what’s in the phone handset, or to wear a dedicated bluetooth GPS band on the other wrist if you really need it).

Don’t be distracted; with the electronics already in the device, the Apple Watch is truly only the beginning. We’re now waiting for the full details of the WatchKit APIs to unleash that ecosystem with full force.

Microbiomes and a glimpse to doctors becoming a small niche

Microbiomes, Gut and Spot the Salmonella

When I get up in the morning, I normally follow a path on my iPad through email, Facebook, LinkedIn, Twitter, Google+, Feedly (for my RSS feeds) and Downcast (to update my Podcasts for later listening). This morning, Kevin Kelly served up a comment on Google+ that piqued my interest, and that led to a long voyage of discovery. Much to my wifes disgust as I quoted gory details about digestive systems at the same time she was trying to eat her breakfast. He said:

There are 2 reasons this great Quantified Self experiment is so great. One, it shows how important your microbial ecosystem is. Two, it shows how significant DAILY genome sequencing will be.

He then gave a pointer to an article about Microbiomes here.

The Diet Journey

I’ve largely built models based on innocent attempts to lose weight, dating back to late 2000 when I tried the Atkins diet. That largely stalled after 3 weeks and one stone loss. Then fairly liberated in 2002 by a regime at my local gym, when I got introduced (as part of a six week program) to the website of Weight Loss Resources. This got me in the habit of recording my food intake and exercise very precisely, which translated branded foods and weights into daily intake of carbs, protein and fat. That gave me my calorie consumption and nutritional balance, and kept track alongside weekly weight readings. I’ve kept that data flowing now for over 12 years, which continues to this day.

Things i’ve learnt along the way are:

  • Weight loss is heavily dependent on me consuming less calories than my Basal Metabolic Rate (BMR), and at the same time keeping energy deduced from carbs, protein and fat at a specific balance (50% from Carbs, 20% Protein, 30% fat)
  • 1g of protein is circa 4.0 Kcals, 1g of carbs around 3.75 Kcals, and fat around 9.0 Kcals.
  • Muscle weighs 2x as much as fat
  • There is a current fixation at gyms with upping your muscle content at first, nominally to increase your energy burn rate (even at rest)
  • The digestive system is largely first in, first out; protein is largely processed in acidic conditions, and carbs later down the path in alkaline equivalents. Fat is used as part of both processes.
  • There are a wide variety of symbiotic (opposite of parasite!) organisms that assist the digestive process from beginning to end
  • Weight loss is both heat and exhaust. Probably other forms of radiation too, given we are all like a light bulb in the infrared spectrum (I always wonder how the SAS manage to deploy small teams in foreign territory and remain, for the most part, undetected)

I’ve always harboured a suspicion that taking antibiotics have an indiscriminate bombing effect on the population of microbiomes there to assist you. Likewise the effect of what used to be my habit of drinking (very acidic) Diet Coke. But never seen anyone classify the variety and numbers of Microbiomes, and to track this over time.

The two subjects had the laboratory resources to examine samples of their own saliva, and their own stool samples, and map things over time. Fascinating to see what happened when one of them suffered Salmonella (the green in the above picture), and the other got “Delhi Belly” during a trip abroad.

The links around the article led to other articles in National Geographic, including one where the author reported much wider analysis of the Microbiomes found in 60 different peoples belly buttons (here) – he had a zoo of 58 different ones in his own. And then to another article where the existence of certain microbiome mutations in the bloodstream were an excellent leading indicator of the presence of cancerous tumours in the individual (here).

Further dips into various Wikipedia articles cited examples of microbiome populations showing up in people suffering from various dilapidating illnesses such as ME, Fibromyalgia and Lyme disease, in some instances having a direct effect on driving imbalances to cause depression. Separately, that what you ate often had quite an effect in altering the relative sizes of parts of the Microbiome population in short order.

There was another article that suggested new research was going to study the Microbiome Zoo present in people’s armpits, but I thought that an appropriate time to do an exit stage left on my reading. Ugh.

Brain starts to wander again

Later on, I reflected for a while on how I could supply some skills i’ve got to build up data resources – at least should suitable sensors be able to measure, sample and sequence microbiomes systematically every day. I have the mobile phone programming, NoSQL database deployment and analytics skills. But what if we had sensors that everyone could have on them 24/7 that could track the microbiome zoo that is you (internally – and I guess externally too)? Load the data resources centrally, and I suspect the Wardley Map of what is currently the NHS would change fundamentally.

I also suspect that age-old Chinese Medicine will demonstrate it’s positive effects on further analysis. It was about the only thing that solved my wifes psoriasis on her hands and feet; she was told about the need to balance yin/yan and remove heat put things back to normal, which was achieved by consumption of various herbs and vegetation. It would have been fascinating to see how the profile of her microbiomes changed during that process.


I guess the missing piece is the ability to have sensors that can help both identify and count types microbiomes on a continuous basis. It looks like a laboratory job at the moment. I wonder if there are other characteristics or conditions that could short cut the process. Health apps about to appear from Apple and Google initiatives tend to be effective at monitoring steps, heart rate. There looks to be provision for sensing blood glucose levels non-invasively by shining infrared light on certain parts of the skin (inner elbow is a favourite); meanwhile Google have patented contact lenses that can measure glucose levels in the blood vessels in the wearers eyes.

The local gym has a Boditrax machine that fires an electrical up one foot and senses the signal received back in the other, and can relate body water, muscle and fat content. Not yet small enough for a mobile phone. And Withings produce scales that can report back weight to the users handset over bluetooth (I sometimes wonder if the jarring of the body as you tread could let a handset sensors deduce approximate weight, but that’s for another day).

So, the mission is to see if anyone can produce sensors (or an edible, communicating pill) that can effectively work, in concert with someones phone and the interwebs, to reliably count and identify biome mixes and to store these for future analysis, research or notification purposes. Current research appears to be in monitoring biome populations in:

  1. Oral Cavity
  2. Nasal
  3. Gastrointestinal Organs
  4. Vaginal
  5. Skin

each with their own challenges for providing a representative sample surface sufficient to be able to provide regular, consistent and accurate readings. If indeed we can miniaturize or simplify the lab process reliably. The real progress will come when we can do this and large populations can be sampled – and cross referenced with any medical conditions that become apparent in the data provider(s). Skin and the large intestine appear to have the most interesting microbiome profiles to look at.

Long term future

The end result – if done thoroughly – is that the skills and error rates of GP provided treatment would become largely relegated, just as it was for farm workers in the 19th century (which went from 98% of the population working the land to less than 2% within 100 years).

With that, I think Kevin Kelly is 100% correct in his assessment – that the article shows how significant DAILY genome sequencing will be. So, what do we need to do to automate the process, and make the fruits of its discoveries available to everyone 24/7?

Footnote: there look to be many people attempting to automate subsets of the DNA/RNA identification process. One example highlighted by MIT Review today being this.

Apple iWatch: Watch, Fashion, Sensors or all three?

iWatch Concept Guess Late last year there was an excellent 60 minute episode of the Cubed.fm Podcast by Benedict Evans and Ben Bajarin, with guest Bill Geiser, CEO of Metawatch. Bill had been working on Smart watches for over 20 years, starting with wearables to measure his swimming activity, working for over 8 years as running Fossil‘s Watch Technology Division, before buying out that division to start Metawatch. He has also consulted for Sony in the design and manufacture of their Smart watches, for Microsoft SPOT technology and for Palm on their watch efforts. The Podcast is a really fascinating background on the history and likely future directions of this (widely believed to be) nascent industry: listen here.

Following that podcast, i’ve always listened carefully to the ebbs and flows of likely smart watch releases from Google, and from Apple (largely to see how they’ve built further than the great work by Pebble). Apple duly started registering the iWatch trademark in several countries (nominally in class 9 and 14, representative of Jewelry, precious metal and watch devices). There was a flurry of patent applications from Apple in January 2014 of Liquid Metal and Sapphire materials, which included references to potential wrist-based devices.

There have also been a steady stream of rumours that an Apple watch product would likely include sensors that could pair with health related applications (over low energy bluetooth) to the users iPhone.

Apple duly recruited Angela Ahrendts, previously CEO of Burberry, to head up Apple’s Retail Operations. Shortly followed by Nike Fuelband Consultant Jay Blahnik and several Medical technology hires. Nike (where Apple CEO Tim Cook is a Director) laid off it’s Fuelband hardware team, citing a future focus on software only. And just this weekend, it was announced that Apple had recruited the Tag Heuer Watches VP of Sales (here).

That article on the Verge had a video of an interview from CNBC with Jean-Claude Biver, who is Head of Watch brands for LVMH – including Louis Vuitton, Hennessey and TAG Heuer. The bizarre thing (to me) he mentioned was that his employee who’d just left for a contract at Apple was not going to a Direct Competitor, and that he wished him well. He also cited a “Made in Switzerland” marketing asset as being something Apple could then leverage. I sincerely think he’s not naive, as Apple may well impact his market quite significantly if there was a significant product overlap. I sort of suspect that his reaction was that of someone partnering Apple in the near future, not of someone waiting for an inbound tidal wave from an foreign competitor.

Google, at their I/O Developers Conference last week, duly announced Android Wear, among which was support for Smart Watches from Samsung, LG and Motorola. Besides normal time and date use, include the ability to receive the excellent “Google Now” notifications from the users phone handset, plus process email. The core hope is that application developers will start to write their own applications to use this new set of hardware devices.

Two thoughts come to mind.

A couple of weeks back, my wife needed a new battery in one of her Swatch watches. With that, we visited the Swatch Shop outside the Arndale Centre in Manchester. While her battery was being replaced, I looked at all the displays, and indeed at least three range catalogues. Beautiful fashionable devices that convey status and personal expression. Jane duly decided to buy another Swatch that matched an evening outfit likely to be worn to an upcoming family Wedding Anniversary. A watch battery replacement turned into an £85 new sale!

Thought #1 is that the Samsung and LG watches are, not to put a finer point on it, far from fashion items (I nearly said “ugly”). Available in around 5 variations, which map to the same base unit shape and different colour wrist bands. LG likewise. The Moto 360 is better looking (bulky and circular). That said, it’s typically Fashion/Status industry suicide with an offer like this. Bill Geiser related that “one size fits all” is a dangerous strategy; suppliers typically build a common “watch movement” platform, but wrap this in an assortment of enclosures to appeal to a broad audience.

My brain sort of locks on to a possibility, given a complete absence of conventional watch manufacturers involved with Google’s work, to wonder if Apple are OEM’ing (or licensing) a “watch guts” platform usable by Watch manufacturers to use in their own enclosures.

Thought #2 relates to sensors. There are often cited assumptions that Apple’s iWatch will provide a series of sensors to feed user activity and vital signs into their iPhone based Health application. On that assumption, i’ve been noting the sort of sensors required to feed the measures maintained “out of the box” by their iPhone health app, and agonising as to if these would fit on a single wrist based device.

The main one that has been bugging me – and which would solve a need for millions of users – is that of measuring glucose levels in the bloodstream of people with Diabetes. This is usually collected today with invasive blood sampling; I suspect little demand for a watch that vampire bites the users wrist. I found today that there are devices that can measure blood glucose levels by shining Infrared Light at a skin surface using near-infrared absorption spectroscopy. One such article here.

The main gotcha is that the primary areas where such readings a best taken are on the ear drum or on the inside of an arm’s elbow joint. Neither the ideal position for a watch, but well within the reach of earbuds or a separate sensor. Both could communicate with the Health App directly wired to an iPhone or over a low energy bluetooth connection.

Blood pressure may also need such an external sensor. There are, of course, plenty of sensors that may find their way into a watch style form factor, and indeed there are Apple patents that discuss some typical ones they can sense from a wrist-attached device. That said, you’re working against limited real estate for the devices electronics, display and indeed the size of battery needed to power it’s operation.

In summary, I wonder aloud if Apple are providing an OEM watch movement for use by conventional Watch suppliers, and whether the Health sensor characteristics are better served by a raft of third party, low energy bluetooth devices rather than an iWatch itself.

About the only sure thing is that when Apple do finally announce their iWatch, that my wife will expect me to be early in the queue to buy hers. And that I won’t disappoint her. Until then, iWatch rumours updated here.

The Internet of Things withers – while HealthKit ratchets along

FDA Approved Logo

I sometimes shudder at the estimates, as once outlined by executives at Cisco, that reckons the market for “Internet of Things” – communicating sensors embedded everywhere – would be likely be a $19 trillion market. A market is normally people willing to invest to make money, save money, to improve convenience or reduce waste. Or a mix. I then look at various analysts reports where they size both the future – and the current market size. I really can’t work out how they arrive at today’s estimated monetary amounts, let alone do the leap of faith into the future stellar revenue numbers. Just like IBM with their alleged ‘Cloud’ volumes, it’s difficult to make out what current products are stuffed inside the current alleged volumes.

One of my sons friends is a Sales Director for a distributor of sensors. There appear good use cases in Utility networks, such as monitoring water or gas flow and to estimate where leaks are appearing, and their loss dimensions. This is apparently already well served. As are industrial applications, based on pneumatics, fluid flow and hook ups to SCADA equipment. A bit of RFID so stock movements can be automatically checked through their distribution process. Outside of these, there are the 3 usual consumer areas; that of cars, health and home equipment control – the very three areas that both Apple and Google appear to be focussed on.

To which you can probably add Low Power Bluetooth Beacons, which will allow a phone handset to know it’s precise location, even where GPS co-ordinates are not available (inside shopping centres as an example). If you’re in an open field with sight of the horizon around you in all directions, circa 14 GPS satellites should be “visible”; if your handset sees two of them, it can suss your x and y co-ordinates to a meter or so. If it sees 3 satellites, that’s normally enough to calculate your x, y and z co-ordinates – ie: geographic location and height above sea level. If it can only see 1 or none, it needs another clue. Hence a super secret rollout where vendors are offering these LEB beacons and can trade the translation from their individual identifiers to their exact location.

In Apple’s case, Apple Passbook Loyalty Cards and Boarding Passes are already getting triggered with an icon on the iOS 8 home screen when you’re adjacent to a Starbucks outlet or Virgin Atlantic Check-in desk; one icon press, and your payment card or boarding pass is there for you already. I dare say the same functionality is appearing in Google Now on Android; it can already suss when I get out of my car and start to walk, and keeps a note of my parking location – so I can ask it to navigate me back precisely. It’s also started to tell me what web sites people look at when they are in the same restaurant that i’m sitting in (normally the web site or menu of the restaurant itself).

We’re in a lull between Apple’s Worldwide Developer Conference, and next weeks equivalent Google I/O developer event, where Googles version of Health and HomeKit may well appear. Maybe further developments to link your cars Engine Control Unit to the Internet as well (currently better engaged by Phil Windley’s FUSE project). Apple appear to have done a stick and twist on connecting an iPhone to a cars audio system only, where the cars electronics use Blackberry’s QNX embedded Linux software; Android implementations from Google are more ambitious but (given long car model cycle times) likely to take longer to hit volume deployments. Unless we get an unexpected announcement at Google I/O next week.

My one surprise is that my previous blog post on Apples HomeKit got an order of magnitude more readers than my two posts on the Health app and the HealthKit API (posts here and here). I’d never expected that using your iPhone as a universal, voice controlled home lock/light/door remote would be so interesting to people. I also hear that Nest (now a Google subsidiary) are about to formally announce shipment of their 500,000th room temperature control. Not sure about their Smoke Alarm volumes to date though.

That apart, I noticed today that the US Food and Drug Administration had, in March, issued some clarifications on what type of mobile connected devices would not warrant regulatory classification as a medical device in the USA. They were:

  1. Mobile apps for providers that help track or manage patient immunizations by assessing the need for immunization, consent form, and immunization lot number

  2. Mobile apps that provide drug-drug interactions and relevant safety information (side effects, drug interactions, active ingredient) as a report based on demographic data (age, gender), clinical information (current diagnosis), and current medications

  3. Mobile apps that enable, during an encounter, a health care provider to access their patient’s personal health record (health information) that is either hosted on a web-based or other platform

So, it looks like Apple Health application and their HealthKit API have already skipped past the need for regulatory approvals there already. The only thing i’ve not managed to suss is how they measure blood pressure and glucose levels on a wearable device without being invasive. I’ve seen someone mention that a hi res camera is normally sufficient to detect pulse rates by seeing image changes on a picture of a patients wrist. I’ve also seen an inference that suitably equipped glasses can suss basic blood composition looking at what is exposed visibly in the iris of an eye. But if Apple’s iWatch – as commonly rumoured – can detect Glucose levels for Diabetes patients, i’m still agonising how they’d do it. Short of eating or attaching another (probably disposable) Low Energy Bluetooth sensor for the phone handset to collect data from.

That looks like it’ll be Q4 before we’ll all know the story. All I know right now is that Apple produce an iWatch, and indeed return the iPhone design to being more rounded like the 3S was, that my wife will expect me to be in the queue on release date to buy them both for her.

An initial dive into Apples new Health App (and HealthKit API)

Apple HealthKit Icon

Apple announced their new Health application (previously known during rumours as HealthBook) and the associated HealthKit Application Programming Interface (API) at their Worldwide Developers Conference earlier this week. A video of the associated conference presentation that focussed exclusively on it at WWDC was put up yesterday, and another that preceded it – showing how you interface low energy Bluetooth sensors to an iPhone and hence to feed it – should be up shortly.

Even though the application won’t be here until iOS 8 releases (sometime in the Autumn), the marketing folks have already started citing the already frequent use of iPhones in Health and Fitness applications here (the campaign title is “Strength” and the video lasts for exactly one minute).

Initial discoveries:

  1. The application is iPhone only. No iPad version at first release (if ever).
  2. A lot of the set-up work for an application provider relates to the measures taken, and the associated weight/volume metrics used. These can be complex (like mg/DL, calories, steps, temperature, blood pressure readings, etc) and are stored with corresponding timestamps.
  3. The API provides a rich set of unit conversion functions (all known count, Imperial and Metric measure combinations), so these shouldn’t be needed in your application code.
  4. Access to the data is authorised by class (measure type). Apple have been really thorough on the security model; users get fine grained control on which data can be accessed by each application on the handset. Even to the extent that no-one can ask “Is this user sampling blood pressure on this device”? Apps can only ask “Are there blood pressure readings that my application has permission to access please?”. The effect is that  apps can’t tell the difference between “what isn’t sampled” or “what is sampled but denied access” to them; hence inferences that the user may have diabetes is impossible to deduce from the yes/no answer given. Well thought out security.
  5. There is provision for several devices taking duplicated readings (eg: having a FitBit step counter and the handset deducing step count itself from it’s own sensors). The API queries can be told which is the default device, so that when stats are mapped out, secondary device data can be used if and where there are gaps in the primary sensors data. I guess the use case is wearing your Fitbit out running when leaving your phone handset at home (or vice versa); if both are operating simultaneously, the data samples reported in the time slots mapped come only from the primary device.
  6. Readings are stored in one locally held Object orientated database for all measures taken, by all monitoring devices you use. All health applications on your handset use this single database, and need to be individually authorised for each class of data readings you permit them to be exposed to. No permission, no access. This is the sort of detail tabloid newspapers choose to overlook in order to get clickbait headlines; don’t believe scare stories that all your data is immediately available to health professionals or other institutions – it is patently not the case.

The end result is that you consolidate all your health related data in one place, and can selectively give access to subsets of it to other applications on your iPhone handset (and to revoke permissions at any time). The API contains a statistics functions library and the ability to graph readings against a timeline, as demonstrated by the Health Application that will be present on every iPhone running iOS 8. The side effect of this is that the iPhone is merely acting as a data collection device, and is not dishing out advice – something that would otherwise need regulatory approvals.

Vendors/users of all manner of sensors, weighing scales, Boditrax machines, monitors, etc can add support for their devices to feed data into the users Health database on the users handset. I’m just waiting for the video of the WWDC session that shows how to do this to be made available on my local copy of the WWDC app. More insights may come once I have the opportunity to hear that through.

In the meantime, Mayo Clinic have developed an application that can message a health professional if certain readings go outside safe bounds that they have set for their patient (with the patients permission!). One provider in the USA is giving the ability to feed data – with the patients permission – directly into their doctors patient database. I suspect there are a myriad of use cases that applications can be developed for; there is already quite a list of institutions piloting related applications:

Apple HealthKit Pilot Users

The one point to leave with is probably the most important of all. Health data is a valuable asset, and must be protected to avoid any exposure of the user to potential personal or financial prejudice. Apple have done a thorough piece of work to ensure that for the users of their handsets.

The reward is likely to be that an iPhone will cement itself even further into the daily lives of it’s owners just as they have to date – and without unwanted surprises.

Footnote: now i’ve listened to the associated Health App Devices Presentation from WWDC, i’ve added an extra blog post with more advanced information on the Health Apps capabilities and device support here.

12 years of data recording leads to dose of the obvious

Ian Waring Weight Loss Trend Scatter Graph

As mentioned yesterday, I finally got Tableau Desktop Professional (my favourite Analytics software) running on Amazon Workspaces – deployed for all of $35 for the month instead of having to buy my own Windows PC. With that, a final set of trends to establish what I do right when I consistently lose 2lbs/week, based on an analysis of my intake (Cals, Protein, Carbs and Fat) and Exercise since June 2002.

I marked out a custom field that reflected the date ranges on my historical weight graph where I appeared to consistently lose, gain or flatline. I then threw all sorts of scatter plots (like the one above, plotting my intake in long periods where I had consistent weight losses) to ascertain what factors drove the weight changes i’ve seen in the past. This to nominally to settle on a strategy going forward to drop to my target weight as fast as I could in a sustainable fashion. Historically, this has been 2lbs/week.

My protein intake had zero effect. Carbs and Fat did, albeit they tracked the effect of my overall Calorie intake (whether in weight or in the number of Calories present in each – 1g of Carbs = 3.75 Kcals, and 1g of Fat = 9 Kcals; 1g of Protein is circa 4 Kcals). The WeightLossResources recommended split of Kcals from the mix to give an optimum balance in their view (they give a daily pie-chart of Kcals from each) is 50% Carbs, 30% Fat and 20% Protein.

So, what are the take-homes having done all the analysis?

Breathtakingly simple. If I keep my food intake, less exercise calories, at circa 2300-2350 calories per day, I will lose a consistent 2lbs. The exact balance between carbs, protein and fat intake doesn’t matter too materially, as long as the total is close, though my best every long term loss had me running things close to the recommended balance. All eyes on that pie chart on the WLR web site as I enter my food then!

The stupid thing is that my current BMR (Basal Metabolic Rate is the minimum level of energy your body needs when at rest to function effectively including your respiratory and circulatory organs, neural system, liver, kidneys, and other organs) is 2,364, and before the last 12 week Boditrax competition at my gym, it was circa 2,334 or so. Increased muscle through lifting some weights put this up a little.

So, the basic message is to keep what I eat down to the same calorie value, less the calories from any exercise, down to the same level as my BMR, which in turn will track down as weight goes. That sort of guarantees that any exercise I take over and above what I log – which is only long walks with Jane and gym exercise – will come off my fat reserves.

Simple. So, all else being equal, put less food in my mouth, and i’ll lose weight. The main benefit of 12 years of logging my intake is I can say authoritatively – for me – the levels at which this is demonstrably true. And that should speed my arrival at my optimum weight.