Your DNA – a Self Testing 101

23andMe testing kitYour DNA is a string of protein pairs that encapsulate your “build” instructions, as inherited from your birth parents. While copies of it are packed tightly into every cell in, and being given off, your body, it is of considerable size; a machine representation of it is some 2.6GB in length – the size of a blue-ray DVD.

The total entity – the human genome – is a string of C-G and A-T protein pairs. The exact “reference” structure, given the way in which strands are structured and subsections decoded, was first successfully concluded in 2003. It’s absolute accuracy has gradually improved regularly as more DNA samples have been analysed down the years since.

A sequencing machine will typically read short lengths of DNA chopped up into pieces (in a random pile, like separate pieces of a jigsaw), and by comparison against a known reference genome, gradually piece together which bit fits where; there are known ‘start’ and ‘end’ segment patterns along the way. To add a bit of complexity, the chopped read may get scanned backwards, so a lot of compute effort to piece a DNA sample into what it looks like if we were able to read it uninterrupted from beginning to end.

At the time of writing (July 2017), we’re up to version 38 of the reference human genome. 23andMe currently use version 37 for their data to surface inherited medical traits. Most of the DNA sampling industry trace family history reliably using version 36, and hence most exports to work with common DNA databases automatically “downgrade” to that version for best consistency.

DNA Structure

DNA has 46 sections (known as Chromosomes); 23 of them come from your birth father, 23 from your birth mother. While all humans have over 99% commonality, the 1% difference make every one of us (or a pair of identical twins) statistically unique.

The cost to sample your own DNA – or that of a relative – is these days in the range of £79-£149. The primary one looking for inherited medical traits is 23andMe. The biggest volume for family tree use is AncestryDNA. That said, there are other vendors such as Family Tree DNA (FTDNA) and MyHeritage that also offer low cost testing kits.

The Ancestry DNA database has some 4 million DNA samples to match against, 23andMe over 1 million. The one annoyance is that you can’t export your own data from these two and then insert it in the other for matching purposes (neither have import capabilities). However, all the major vendors do allow exports, so you can upload your data from AncestryDNA or 23andMe into FTDNA, MyHeritage and to the industry leading cross-platform GEDmatch DNA databases very simply.

Exports create a ZIP file. With FTDNA, MyHeritage and GEDmatch, you request an import, and these prompt for the name of that ZIP file itself; you have no need to break it open first at all.

On receipt of the testing kit, register the code on the provided sample bottle on their website. Just avoid eating/drinking for 30 minutes, spit into the provided tube up to the level mark, seal, put back in the box, seal it and pop it in a postbox. Results will follow in your account on their website in 2-4 weeks.

Family Tree matching

Once you receive your results, Ancestry and 23andMe will give you details of any suggested family matches on their own databases. The primary warning here is that matches will be against your birth mother and whoever made her pregnant; given historical unavailability of effective birth control mechanisms and the secrecy of adoption processes, this has been known to surface unexpected home truths. Relatives trace up and down the family tree from those two reference points. A quick gander of self help forums on social media can be entertaining, or a litany of horror stories – alongside others of raw delight. Take care, be ready for the unexpected:

My first social media experience was seeing someone confirm a doctor as her birth father. Her introductory note to him said that he may remember her Mum, as she used to be his nursing assistant.

Another was to a man, who once identified admitted to knowing her birth mother in his earlier years – but said it couldn’t be him “as he’d never make love with someone that ugly”.

Outside of those, fairly frequent outright denials questioning the fallibility of the science behind DNA testing, none of which stand up to any factual scrutiny. But among the stories, there are also stories of delight in all parties when long lost, separated or adopted kids locate, and successfully reconnect, with one or both birth parents and their families.

Loading into other databases, such as GEDmatch

In order to escape the confines of vendor specific DNA databases, you can export data from almost any of the common DNA databases and reload the resulting ZIP file into GEDmatch. Once imported, there’s quite a range of analysis tools sitting behind a fairly clunky user interface.

The key discovery tool is the “one to many” listing, which does a comparison of your DNA against everyone elses in the GEDmatch database – and lists partial matches in order of closeness to your own data. It does this using a unit of measure called “centiMorgans”, abbreviated “cM”. Segments that show long exact matches are totted up, giving a total proportion of DNA you share. If you matched yourself or an identical twin, you’d match a total of circa 6800cM. Half your DNA comes from each birth parent, so they’d show as circa 3400cM. From your grandparents, half again. As your family tree extends both upwards and sideways (to uncles, aunts, cousins, their kids, etc), the numbers will increasingly dilute by half each step; you’ll likely be in the thousands of potential matches 4 or 5 steps away from your own data:

If you want to surface birth parent, child, sibling, half sibling, uncle, aunt, niece, nephew, grandparent and grandchild relationships reliably, then only matches of greater than 1300cM are likely to have statistical significance. Any lower than that is an increasingly difficult struggle to fill out a family tree, usually persued by asking other family members to get their DNA tested; it is fairly common for GEDmatch to give you details (including email addresses) of 1-2,000 closest matches, albeit sorted in descending ‘close-ness’ order for you).

As one example from GEDmatch, the highlighted line shows a match against one of the subjects parents (their screen name and email address cropped off this picture):

GEDmatch parent

There are more advanced techniques to use a Chromosome browser to pinpoint whether a match comes down a male line or not (to help understand which side of the tree relationships a match is more likely to reside on), but these are currently outside my own knowledge (and current personal need).

Future – take care

One of the central tenets of the insurance industry is to scale societal costs equitably across a large base of folks who may, at random, have to take benefits from the funding pool. To specifically not prejudice anyone whose DNA may give indications of inherited risks or pre-conditions that may otherwise jeopardise their inclusion in cost effective health insurance or medical coverage.

Current UK law specifically makes it illegal for any commercial company or healthcare enterprise to solicit data, including DNA samples, where such provision may prejudice the financial cost, or service provision, to the owner of that data. Hence, please exercise due care with your DNA data, and with any entity that can associate that data with you as a uniquely identifiable individual. Wherever possible, only have that data stored in locations in which local laws, and the organisations holding your data, carry due weight or agreed safe harbour provisions.

Country/Federal Law Enforcement DNA records.

The largest DNA databases in many countries are held, and administered, for police and criminal justice use. A combination of crime scene samples, DNA of known convicted individuals, as well as samples to help locate missing people. The big issue at the time of writing is that there’s no ability to volunteer any submission for matching against missing person or police held samples, even though those data sets are fairly huge.

Access to such data assets are jealously guarded, and there is no current capability to volunteer your own readings for potential matches to be exposed to any case officer; intervention is at the discretion of the police, and they usually do their own custom sampling process and custom lab work. Personally, a great shame, particularly for individuals searching for a missing relative and seeking to help enquiries should their data help identify a match at some stage.

I’d personally gladly volunteer if there were appropriate safeguards to keep my physical identity well away from any third party organisation; only to bring the match to the attention of a case officer, and to leave any feedback to interested relatives only at their professional discretion.

I’d propose that any matches over 1300 cM (CentiMorgans) get fed back to both parties where possible, or at least allow cases to get closed. That would surface birth parent, child, sibling, half sibling, uncle, aunt, niece, nephew, grandparent and grandchild relationships reliably.

At the moment, police typically won’t take volunteer samples unless a missing person is vulnerable. Unfortunately not yet for tracing purposes.

Come join in – £99 is all you need to start

Whether for medical traits knowledge, or to help round out your family trees, now is a good time to get involved cost effectively. Ancestry currently add £20 postage to their £79 testing kit, hence £99 total. 23andMe do ancestry matching, Ethnicity and medical analyses too for £149 or so all in. However, Superdrug are currently selling their remaining stock of 23andMe testing kits (bought when the US dollar rate was better than it now is) for £99. So – quick, before stock runs out!

Either will permit you to load the raw data, once analysed, onto FTDNA, MyHeritage and GEDmatch when done too.

Never a better time to join in.

Reinventing Healthcare

Comparison of US and UK healthcare costs per capita

A lot of the political effort in the UK appears to circle around a government justifying and handing off parts of our NHS delivery assets to private enterprises, despite the ultimate model (that of the USA healthcare industry) costing significantly more per capita. Outside of politicians lining their own pockets in the future, it would be easy to conclude that few would benefit by such changes; such moves appear to be both economically farcical and firmly against the public interest. I’ve not yet heard any articulation of a view that indicates otherwise. But less well discussed are the changes that are coming, and where the NHS is uniquely positioned to pivot into the future.

There is significant work to capture DNA of individuals, but these are fairly static over time. It is estimated that there are 10^9 data points per individual, but there are many other data points – which change against a long timeline – that could be even more significant in helping to diagnose unwanted conditions in a timely fashion. To flip the industry to work almost exclusively to preventative and away from symptom based healthcare.

I think I was on the right track with an interest in Microbiome testing services. The gotcha is that commercial services like uBiome, and public research like the American (and British) Gut Project, are one-shot affairs. Taking a stool, skin or other location sample takes circa 6,000 hours of CPU wall time to reconstruct the 16S rRNA gene sequences of a statistically valid population profile. Something I thought I could get to a super fast turnaround using excess capacity (spot instances – excess compute power you can bid to consume when available) at one or more of the large cloud vendors. And then to build a data asset that could use machine learning techniques to spot patterns in people who later get afflicted by an undesirable or life threatening medical condition.

The primary weakness in the plan is that you can’t suss the way a train is travelling by examining a photograph taken looking down at a static railway line. You need to keep the source sample data (not just a summary) and measure at regular intervals; an incidence of salmonella can routinely knock out 30% of your Microbiome population inside 3 days before it recovers. The profile also flexes wildly based on what you eat and other physiological factors.

The other weakness is that your DNA and your Microbiome samples are not the full story. There are many other potential leading indicators that could determine your propensity to become ill that we’re not even sampling. The questions of which of our 10^18 different data points are significant over time, and how regularly we should be sampled, are open questions

Experience in the USA is that in environments where regular preventative checkups of otherwise healthy individuals take place – that of Dentists – have managed to lower the cost of service delivery by 10% at a time where the rest of the health industry have seen 30-40% cost increases.

So, what are the measures that should be taken, how regularly and how can we keep the source data in a way that allows researchers to employ machine learning techniques to expose the patterns toward future ill-health? There was a good discussion this week on the A16Z Podcast on this very subject with Jeffrey Kaditz of Q Bio. If you have a spare 30 minutes, I thoroughly recommend a listen: https://soundcloud.com/a16z/health-data-feedback-loop-q-bio-kaditz.

That said, my own savings are such that I have to refocus my own efforts elsewhere back in the IT industry, and my MicroBiome testing service Business Plan mothballed. The technology to regularly sample a big enough population regularly is not yet deployable in a cost effective fashion, but will come. When it does, the NHS will be uniquely positioned to pivot into the sampling and preventative future of healthcare unhindered.

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.

Can’t vs Don’t 

 I seem to find good articles in my daily feed from Medium these days. This one about the psychology of habits, and how to build a stronger will when you – like me – come off a long diet. This written by the guy who wrote the book in my reading queue about building habit forming products.

It’s a good read. My friend Annika was right all along; the habit is the key thing to establish. Saying “I don’t” is a much stronger push back than “I can’t”. Further reading Here

Politicians and the NHS: the missing question

 

The inevitable electioneering has begun, with all the political soundbites simplified into headline spend on the NHS. That is probably the most gross injustice of all.

This is an industry lined up for the most fundamental seeds of change. Genomics, Microbiomes, ubiquitous connected sensors and quite a realisation that the human body is already the most sophisticated of survival machines. There is also the realisation that weight and overeating are a root cause of downstream problems, with a food industry getting a free ride to pump unsuitable chemicals into the food chain without suffering financial consequences for the damage caused. Especially at the “low cost” end of the dietary spectrum.

Politicians, pharma and food lobbyists are not our friends. In the final analysis, we’re all being handed a disservice because those leading us are not asking the fundamental question about health service delivery, and to work back from there.

That question is: “What business are we in?”.

As a starter for 10, I recommend this excellent post on Medium: here.

Apple Watch: what makes it special

Edit

Based on what I’ve seen discussed – and alleged – ahead of Monday’s announcement, the following are the differences people will see with this device.

  1. Notifications. Inbound message or piece of useful context? It will let you know by tapping gently on your arm. Early users are already reporting on how their phone – which until now gets reviews whenever a notification arrives – now stays in their pocket most of the time.
  2. Glances. Google Now on Android puts useful contextual information on “cards”. Hence when you pass a bus stop, up pops the associated next bus timetable. Walk close to an airport checkin desk, up pops your boarding pass. Apple guidelines say that a useful app should communicate its raison d’être within 10 seconds – a hence ‘glance’.
  3. Siri. The watch puts a Bluetooth microphone on your wrist, and Apple APIs can feed speech into text based forms straight away. And you may have noticed that iMessage already allows you to send a short burst of audio to a chosen person or group. Dick Tracey’s watch comes to life.
  4. Brevity. Just like Twitter, but even more focussed. There isn’t the screen real estate to hold superfluous information, so developers need to agonise on what is needed and useful, and to zone out unnecessary context. That should give back more time to the wearer.
  5. Car Keys. House Keys. Password Device. There’s one device and probably an app for each of those functions. And can probably start bleating if someone tries to walk off with your mobile handset.
  6. Stand up! There’s already quotes from Apple CEO Tim Cook saying that sitting down for excessively long periods of time is “the new cancer”. To that effect, you can set the device to nag you into moving if you appear to not be doing so regularly enough.
  7. Accuracy. It knows where you are (with your phone) and can set the time. The iPhone adjusts after a long flight based on the identification of the first cell tower it gets a mobile signal from on landing. And day to day, it’ll keep your clock always accurate.
  8. Payments. Watch to card reader, click, paid. We’ll need the roll out of Apple Pay this side of the Atlantic to realise this piece.

It is likely to evolve into a standalone Bluetooth hub of all the sensors around and on you – and that’s where its impact in time will one plus to death.

With the above in mind, I think the Apple Watch will be another big success story. The main question is how they’ll price the expen$ive one when its technology will evolve by leaps and bounds every couple of years. I just wonder if a subscription to possessing a Rolex price watch is a possible business model being considered.

We’ll know this time tomorrow. And my wife has already taken a shine to the expensive model, based purely on its looks with a red leather strap. Better start saving… And in the meantime, a few sample screenshots to pore over:

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.