Medication Plans on The Blockchain – Building a Decentralised Application in Healthcare

The theme of this post is easily generalised to other use cases and serves as an example of how blockchain technology can shift power and trust in a well-established system, in this case the one of health care.

TL;DR

Medical prescriptions should be unified and digitalised. They should be resilient and controlled by the real owner of the prescription (and thus of the personal data). This can be achieved by a blockchain-based solution. A system of smart contracts in Solidity is proposed which achieves this and furthermore is modular and update-able. Some general advice on designing a blockchain solution is given.

What’s the problem?

How many of you know what iatrogenic illness means? I confess that prior to writing my Master thesis upon which this post is based, I also had no idea. So, to not keep you waiting, here’s the definition from Merriam-Webster:

ioatrogenic: induced inadvertently by a physician or surgeon or by medical treatment or diagnostic procedures

from the Greek word for physician (iatros). Add an illness to that and you have an illness caused by a physician. Now, it sounds like an oxymoron, but it is in fact more common than we would of course like to be. You can divide the causes for iatrogenic illness into so-called Adverse Drug Events (ADE) and, to be completely MECE*, other reasons. Other reasons would include things such as rough examinations, surgical errors (there’s a reason they draw arrows on the limb to be amputated) and so on. ADE includes all injuries or complications caused be medication, be it the wrong medication, drugs interacting in unintended ways and so on. [1] ADE has shown to be the most common cause of injury to hospitalised patients, and furthermore, the most preventable one.

Where is the problem coming from?

In fact, computer-based prescribing systems have been shown to decrease medication errors by 55% to 80% in a study from 2004. [2] It does not, however guarantee that the most severe of those medication errors are prevented by the usage of an IT solution. Among ADE’s, the most common form of avoidable medication errors are prescribing errors (i.e. an error made somewhere in the process of getting a drug to a patient). There is a list of sixteen classes of these prescribing errors, but basically they boil down to:

  • Knowledge deficiencies – among doctors, patients or pharmacist about drugs, other parties, et c.
  • Mistakes or memory lapses – e.g. a patient forgets what medication he/she is already on
  • Name-related errors – complicated-sounding substance gets mistaken for other complicated-sounding substance
  • Transferring errors – information is missing or incorrect once the order arrives at the pharmacist
  • ID checks – patient, doctor or pharmacist ID isn’t properly verified
  • Illegible handwriting (!)
  • Wrong type of document filled out

These errors all illustrate why prescribing errors are so common, but also why they should, to a large extent, be avoidable. [3] The thing is that, considering the current rate of prescribing errors causing damage or danger to patients being relatively low (ca. 2% [2]), its importance is overshadowed by more clinical research in medicine and is thus being overlooked by the research community and public in general. One reason for this could be the wide-ranging competencies required to implement a system for decreasing the rate of prescribing errors to zero. To do such a thing, one would require technical expertise within security and privacy as well as all the various skills for application development, one would also require medical and pharmacological knowledge, and essentially, one would need to have experience within information systems management.

A step in the right (digital) direction

To combat prescribing errors, many public health systems require or recommend that patients with more than three different prescribed medications have a unified medication plan which should theoretically contain all prescriptions. The effectiveness and quality of medication plans was examined in 2015 by a group of German researchers. The results were scary. 6.5% of all medication plans examined did not contain discrepancies! Where discrepancies means differences in drug names, additional or missing drugs, deviations in dosage, et c. In spite of this, or perhaps to improve the quality of medication plans, a law was passed in Germany three months after the publication of the medication plan review, which makes it mandatory for all patients with three or more medications to have a medication plan. In order to cope with the slowness of technology adoption in healthcare, up until January 2018, there is no requirement that the medication plans should be digital. Thereafter they should be available on an electronic health card (eGK). [4]

Considering the different types of prescribing errors we’ve identified, it is not difficult to translate those into some type of requirements for a system to solve those errors. The resulting requirements happen to fit very well to a blockchain system with smart contracts, therefore we’ll propose a design of a system of smart contracts to function as medication plan. Let’s look at the errors one by one and explain which requirements fit to them:

Knowledge deficiencies

To resolve this error, data regarding patients and their medications needs to be unified, available and guaranteed correct. There shouldn’t be multiple versions with equal or uncertain amounts of validity. Additionally, there should be little chance of the data getting lost or not available when it is needed.

Mistakes or memory lapses

It is completely human and expectable that a patient taking many different medication can’t remember the details of complicated names of each substance. This can be solved, however, by the unification of medication plans and assurance that all prescriptions are correct and active.

Name-related errors

See point Knowledge deficiencies.

Transferring errors

Through the unification of the various systems available currently, the process of transferring prescriptions would be simplified.

ID checks

Through the digitalisation and implementation of a permissions management system patients would only need some type of identification (could be biometric) to collect their medication.

Illegible handwriting

Assuming the doctor enters the prescription into a digital system and doesn’t write with pen and paper, this problem is practically eliminated.

Wrong type of document filled out

Again, through the unification of the different possibilities to prescribe a medication, there would be no such things as the wrong type of document. At least not inside the system.

Design choices in the solution

So what are the technical details one needs to consider when designing a blockchain-based system for a medication plan? I’ll describe the three most important design choices in this blog post. The three questions are:

  • Who needs to participate in the network?

In this case, the only users are doctors, patients and pharmacies. So to not take on additional risk regarding data exposure, only those who are on-boarded and verified through some separate process should be allowed to participate in the network. There are however some negative aspects of choosing a private or permissioned blockchain, one point being that there might not be enough active nodes to keep the consensus building at an acceptable fault-tolerance level at all times. This can be solve by some type of incentive or requirement that for example doctors keep a running node at all times. Another risk of running a private blockchain is that, when the amount of nodes isn’t very large, and the users consists of a specific group of people (such as doctors in Germany), then the risk of collusion becomes considerable. To combat this, the consensus-making should be well-spread geographically and demographically.

  • What data and functions need to be on the blockchain and what should definitely not be there?

In the case of a medication plan, the data which is required to be on the blockchain consists of three parts; user IDs, prescriptions and doctor/pharmacy permissions to prescribe/sell medications. Naturally, we can’t have plaintext information about patients and their prescriptions, even if it is a private network. Therefore, IDs are formed from a public/private key-pair (similar to bitcoin or ethereum), which should be generated by the user, on a user device. Prescriptions are only ever published on the blockchain as hashes, because even though the users theoretically are anonymous, it has been shown that Bitcoin transactions can be traced back to a person. [5] The permissions of doctors and pharmacies also need to be stored on the blockchain, in a smart contract to ensure that they aren’t manipulated or somehow overruled. Including permissions and sensitive data in smart contract means that extreme caution needs to be taken when programming them, to ensure that no syntactic or logical mistakes are made. The functionality needed on the blockchain is basically complimentary to the data pieces, getters and setters. But additionally, permissions needs to be handled on-chain.

    • How should the smart contracts be written?

There are relatively few resources by experienced smart contracts developers on best practices for building smart contracts, but mostly the general advice for writing good code (failing loudly and as early as possible, commenting, etc.) should be followed. There is however, so much to say about specific smart contract programming that it will be more explained in another blog post. Here, I’ll just talk about architecture of the system of smart contracts briefly.

In order to be able to keep an overview of the smart contracts and functionality used in the application, they should be as small and simple as possible, thus facilitating analysis. Ok, so say that you have a fairly complicated (not in a computational way) functionality to begin with, then you separate it into multiple smart contracts and end up with maybe five to ten of them. How are you supposed to keep track of them and increase the modularity of you system? Enter the contract managing contract. [6] It is basically a contract to keep track of (and manage) the different contracts in your system, it logs the addresses and names of each separate contract and provides another contract, the endpoint of the user-facing application, with the possibility to access them.

Conclusion

Designing an application for managing sensitive personal information needs to be resistant to failure, privacy-preserving and provide accountability so that any changes to the information can be traced. A very relevant use case for such an application is a medication plan. A suitable system for building the application back-end, is a blockchain-based system of smart contracts. Smart contracts programming is a fairly new phenomenon and is based on decentralisation, therefore much thought should be given to how such a system should be designed. A possible solution was drafted above.

*MECE stands for Mutually Exclusive, Collectively Exhaustive

References

1. Tierney LM. Iatrogenic Illness. Western Journal of Medicine. 1989;151(5):536-541.
2. The Epidemiology of Prescribing Errors, The Potential Impact of Computerized Prescriber Order Entry. Anne Bobb; Kristine Gleason; Marla Husch; et al, Arch Intern Med. 2004;164(7):785-792. doi:10.1001/archinte.164.7.785
3. Prescription errors in the National Health Services, time to change practice,
Hamid, Harper and Cushley et al., Scottish Medical Journal. Vol 61, issue 1, pp. 1-6. 21.04.2016
4. Full legal text available at: http://www.bgbl.de/xaver/bgbl/start.xav?startbk=Bundesanzeiger_BGBl&jumpTo=bgbl115s2408.pdf
5. Deanonymisation of Clients in Bitcoin P2P Network. Alex Biryukov, Dmitry Khovratovic, Ivan Pustogarov. Proceeding
CCS ’14, Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 15-29, November 03 – 07, 2014
6. Monax – Solidity tutorials, https://monax.io/docs/solidity/solidity_1_the_five_types_model/, Accessed on 15/05/2017.

What you can expect from Datarella in 2016

We always take a little time in the very first days of a year to define Datarella’s main goals for this year. This time it was a pleasant task since 2015 went very well for Datarella: we achieved most of our goals and we could start without any legacy issues.

So, what to expect from Datarella in 2016? Beside our growing consulting business with fascinating projects and clients, we will focus on our product Data Trust and our project Data Coach.  Due to our tight schedule in 2015, we haven’t published much about Data Trust and Data Coach, yet.  I’d like to give a brief overview on both in this post.

Data Trust
Generally speaking, Data Trust is a secure data market model for Big Data projects. Sharing data between businesses makes much sense: Both, data processing and analytics scale with the data, and development, quality assurance, as well as support become very efficient. The problem: Many businesses are hesitant to share their data with partners for security reasons, to maintain their competitive advantage, and also obligatory compliance aspects regarding data protection.

Data Trust solves this deadlock: With it we provide a secure sharing solution for corporations. Datarella organizes each client’s original data in separate data buckets.

Data Trust enables businesses to put their data to work together with the data of their business partners with guaranteed data security and control. Without giving away their data, they can now profit from analytics, results, and predictions that are based on the joint data within their network of partners. Thus, Data Trust is a market model – it provides each participant of a market with unparalleled insights into the market.

Datarella Prediction Engine

The Datarella Prediction Engine runs on top of the separated data buckets. The Datarella Prediction Engine has been designed for gathering precise statements regarding future business success in the areas of media & advertising, eCommerce, finance, mobility and health.  Together with the Datarella Prediction Engine, Data Triust provides an absolutely trustful environment for clients to manage and analyze their company’s data.

Of course, the Datarella Data Trust can be audited.

Data Coach
Whereas Data Trust already is a product and is already creating value for our clients, Data Coach is still in an experimental phase. The user interface of Data Coach is an app that provides the user with body activity and environmental data.  The user shares this data with a closed professional graph and receives actionable insights into her health condition, behavior, training, etc. as feedback. Based in this feedback the user can react by changing her behavior.

The core of Data Coach is a blockchain environment that provides three essential elements of a professional network:

  1. Data Security
  2. Data Provenance
  3. Peer-to-Peer Architecture

Cryptographic hash functions and completely historicized data chains make data sharing absolutely secure. The user completely owns her data. And she always knows her data’s whereabouts and defines whi can use it, how and when.

An essential part of our Data Coach project is our partner Ethereum, that provides a decentralized blockchain platform we build Data Coach on.

We are running very early tests of Data Coach in the area of sports and entertainment. We are active,y looking for partners to establish a pilot project in the health sector. So, if you think Data Coach could add value to your business and customers or patients, please don’t hesitate to contact me.

Wearable Data Hack Munich 2015

Today, we would like to announce something special. Something we can’t wait to take place and until mid June it’s going to be tough to sit tight. Please, feel invited to our Wearable Data Hack Munich 2015!

The Wearable Data Hack Munich 2015 is the first hack day on wearable tech applications and data. It will take place right after the launch of Apple Watch – the gadget we expect to rise the tide for all wearables. Withe the Wearable Data Hack Munich 2105, we aim to kick-off app development for the emerging smartwatch and wearable tech market. During this weekend you will have the first occasion to share your views and ideas and jointly gather experience with the new data realm.

Apple calls the Apple Watch “Our most personal device ever”. And with good cause: The data from wearable tech, smartphones and smartwatches are really the most personal data ever. Our mobile devices accompany every step we take, every move we make. A plentitude of sensors on the devices draw a multidimensional picture of our daily lives. Applications of wearable data range from fitness to retail, from automotive to health. There is hardly an industry that cannot make direct use of it. And yet, wearable apps still are in their childhood. The Apple Watch will be hitting the street in April and will get the ball rolling.

The Wearable Data Hack Munich 2015 is jointly organized by Stylight and Datarella.

THREADS TO BE PURSUED
Developers, data geeks and artists will pursue one or more of these threads:
– Data-driven business models for wearables
– Data-driven wearables
– Smartphone app (Stand alone / combined with smartphone)
– User Experience
– API
– Open Data
– mHealth / Medical Data

So let’s explore what we can do with this data! Let’s play with the possibilities of our wearable gadgets and mobile sensors.

APPLICATION
To apply for the Wearable Data Hack Munich 2015, please send us an email with
– your name
– your profession
– your take on wearable data
– 3 tags describing yourself best.
Don’t wait for too long – the number of participants is limited.

For more information, please have a look here! See you at Wearable Data Hack Munich 2015!

The Datarella World Map Of Behavior

Every smartphone user produces more than 20 MB of data collected by her phone’s sensors per day. Now, imagine the sensor data of 2 billion smartphone users worldwide, translated into realtime human behavior, shown on a global map. That is the vision of the Datarella World Map of Behavior.

A typical 2015 generation smartphone sports up to 25 sensors, measuring activities as diverse as movements, noise, light, or magnetic flux. Most smartphone users aren’t even aware of the fact that their phone’s camera or microphone never are really „off“ but that they constantly collect data about the noise level or the intensity of light the user is experiencing.

Actions speak louder than words
Actions speak louder than words – if we want to really know a person we have to know how she behaves, and not only what she says. And that’s not only true for politicians. We all form our opinions on others by looking at their actions, more than their words. Many inter-personal problems result from NOT looking at people’s actions, but focusing on other aspects, such as their looks or their words. Behind superficial distinctions such as physical appearances, over time we often realize similarities with other people based on their and our actions.

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Our vision of a World Map of Behavior
At Datarella, our vision is to show the actions of people around the world on a global map. By displaying the actions of people of all continents, we want to tell stories about the differences and similarities of global human behavior – to draw a picture of human co-existence. There already are snapshots of global behavior, provided by data focused companies, such as Jawbone, who map sleep patterns worldwide. From that we know that Russians get up latest, and Japanese get the least sleep in total . And there are different behavior-related maps, showing the world’s most dangerous places, defined by the number and seriousness of crimes or actions of war.

Co-operation & Participation
To create the World Map of Behavior is our ambitious project for 2015 that we won’t complete alone. We need your support: if you are an expert in the field of mobile sensor data or if your company already focuses on collecting and interpreting mobile sensor data in the fields of mobility, finance, health or transport and travel. If you are interested to play a role in this project, please send us an email with a brief description of how you would like to contribute. We are looking forward to hearing from you!

BYOD – Bring your own Data. Self-Tracking for Medical Practice and Research

„Facebook would never change their advertsing relying on a sample size as small as we do medical research on.“
(David Wilbanks)

People want to learn about themselves and get their lives soundly supported by data. Parents record the height of their children. When we feel ill, we measure our temperature. And many people own a bathroom scales. But without context, data is little meaningful. Thus we try to compare owr measurements with those of other people.

Data that we track just for us alone

Self-tracking has been trending for years. Fitness tracker like Fitbit count our steps, training apps like Runtustic deliver to us analysis and benchmark us with others. Since 2008, a movement has been around that has put self-tracking into its center: The Quantified Self.

Self-tracking has been tending for years. In this picture you see a wristband that already made it into a museum and is now on display in the London Science Museum.
Self-tracking has been tending for years. In this picture you see a wristband that already made it into a museum and is now on display in the London Science Museum.

However it is not just self-optimizer and fitness junkies who measure themselves. Essential drive to self-tracking originated from self-caring chronically ill.

Data for the physician, for family members, and for nursing staff

In the US like in many countries lacking strong public health-care, it becomes increasingly common to bring self-measured data to the physician. With many examinations this saves significant consts and speeds up the treatment. With Quantified Self, many people have been able to get good laboratory analytics about their health for the first time ever. One example is kits for blood analysis that sends the measurement via mobile to the lab and then displays the results. Such kits are e.g. widely in use in India.
Also for family members and nursing staff, self-tracked data of the pations is useful. They draw a realistic picture of our conditions to those who care for us. Even automatic emergency calls based on data measured at site are possible today.

The image at the top is taken from the blog of Sara Riggere, who suffers from Parkinson. Sara tracks her medication and the syptoms of her Parkinson’s desease with her smartphone. Her story is worth reading in any case, and it shows all facettes that make the topic „own data“ so fascinating:
http://www.riggare.se/ and
http://quantifiedself.com


Mood-tracking – a mood diary. People suffering from bipolar disorder try to help themselves by recording their mood and other influences of their lives. By doing so, they are able to counteract, when they approach a depression, and they are able to finetune their medication much better, than it would be possible by the rare visits to their psychiatrist. (Shown here is soundfeelings.com)

Data for research

Self-recorded data for the first time maps people’s actions and condition into an uninterupted image. For research, these data are significantly richer than the snap-shots made by classic clinical research – regarding case numbers as well as by making possible for the first time to include the multivariate influences of all kinds of behavior and environment. Even if only a small fraction of self-trackers is willing to share their data with researchers, it is hardly to imagine the huge value the findings will have for medicine, enabled by this.

Privacy

The difficulty with these data: they are so rich and so personal, that it is always possible to get down on the single individual. Anonymization, e.g. by deleting the user id or the IP adress is not possible. Like fingerprints, the trace we leave in the data can always identify us. This problem cannot be solved by even more privacy regulation. Already today, the mandatory committment to informed consent and to data avoidance impede research with medical data to such extent, it is hardly worthwhile to work with it, at all. The only remedy would be comprehensive legal protection. Every person sharing their data with research has to be sure that no disadvantages will come from their cooperation. Insurance companies and employers must not take advantage from the openness of people. This could be shaped similar to anti-discrimination laws. Today, e.g. insurance companies are not allowed to differenciate their rates by the insurant’s gender.

Algorithm ethics

Another issue lies within the data itself. First, arbitrary, technical differences like hardware defects, compression algorithms, or samling rates make the data hard to match. Second, it is hardly the raw data itself, but rather mathematical abstractions derived from the data, that gets further processed. Fitbit or Jawbone UP don’t store the three-dimensional measurements of the gyroscope, but the steps, calculated from it. However, what would be regarded as a step, and what would be another kind of movement, is an arbitrary decision of the author of the algorithm programmed for this task. Here it is important to open the black boxes of the algorithms. As the EU commission demands Google to open its search algorithms, because they suspect (probably with good reasons) that Google would discriminate against obnoxious content in a clandistine way, we have to demand to see behind the tracking-devices from their makers.
Data is generated by the users. The users have to be heared what is made from it.

Sharing Goods And Sharing Data: Both Is Fun, Big Business And A Social Responsibility

Around 2010, Lisa Gansky coined the term Sharing Economy, or Mesh companies, offering their customers efficient shared access to their products instead of selling their products to them. Recently, it’s being called Collaborative Consumption or Collaborative Economy. It’s all about finding ways to make better use of valuable resources that have remained unused. Convenient access is being made affordable to people who can’t afford different products, or simply don’t need to own those products since they would only use them infrequently.

Typical mesh businesses like AirBnB, LendingClub or Cookening, demonstrate the power of sharing in very different ways: AirBnB is on the way to pass Hilton as the world’s largest hotelier in 2015, that is 7 years after its inception. The US peer-to-peer lending company Lending Club has originated over 4 billion USD in loans – it was originally founded as a Facebook app in 2006. The typical Mesh business runs a stylish app with a high usability. It’s service is new, easy to use and affordable. But all that does not fully explain the tremendous speed they conquer one market after the other. Who is the driver behind the Sharing Economy and it’s success?

It’s the user.
It’s the user. The user offers and asks for private overnight stays on AirBnB, the user provides and lends money on LendingClub. Even with services like Zipcar, when the product is provided by a company, the user „uses“ a product instead of buying and owning it. He has to rely on other users‘ good maintenance of Zipcars, since if there were too many ‚abusers‘ the company had to raise rates and the product would become unaffordable for most people. The same is true for AirBnB and others: users have to be sure that landlords don’t sell cubbyholes to them, whereas – vice versa – landlords have to trust their guests not to steal the TV or destroy the flat. So, the user has to use the service and she has to behave in an orderly manner – this is the foundation for a properly working and successful Sharing Economy.

The Sharing Economy

Image: The Sharing Economy, Latitude

Now let’s adopt the principles of the Sharing Economy to the individual who shares her statuses with her social graph on Facebook, discusses the latest news on Twitter and shares her preferred fashion designs on Pinterest. She dos it because she wants to express herself and she wants to communicate with a wider circle of friends than she can meet in person. She communicates in both, synchronous and asynchronous ways. She has learnt that the more she adds to discussions, the more she gets in return. In Social Media, she experiences the Pay-it-Forward principle in action at its best.

Sharing Economy has attained full age in 2014
Let’s assume we can all agree on that: communication openly and actively, sharing ideas, opinions, homes, cars, money and much more with others is not an extravagant imagination of Utopia, Inc., but a multi-billion dollar business eclipsing traditional business models around the world. Furthermore, it’s not just a gigantic business but a sympathetic and friendly way of matching supply and demand of individuals. Who wouldn’t prefer an individually furnished private home over a standard hotel room?

If we agree on the power of sharing the above mentioned martial and immaterial goods, can we also agree on the power of sharing data? Our data? Our own body’s data? Can we agree on the tremendously positive and socially relevant effects of sharing the data we produce ourselves, day by day? If you own a smartphone (you most probably will), you produce about 20 MB of smartphone data (i.e. data racked with your smartphone’s sensors) each day. Perhaps you haven’t been aware of that fact, or you just didn’t know how relevant this data could be for yourself, and for your social graph, respectively. Do you know how much you move each day? The U.S. Surgeon General wants you to move at least 10,000 steps a day to prevent and decrease overweight and obesity. (It’s very easy to know your steps: just get yourself one of those fitness trackers.)

And, did you know that your Vitamin D level is one of the key drivers of your well-being? Most North Americans, North and Central Europeans suffer from a Vitamin D deficiency. Do you actually know your Vitamin D level? Do you know that you can find it out yourself?

Let’s get more complex regarding data: microbes in the human body are responsible for how we digest food and synthesize vitamins, our overall health and metabolic disorders. The aggregate of microbes is called microbiome. Do you have any idea about your individual microbiome? Do you know that you can find out about your microbiome yourself, by using a simple kit?

Sharing Data still in its infancy, but…
So far, we have talked about the relevance and value of our data for ourselves. But – weren’t you interested in preventing your first stroke because thousands of other men at your age have provided their heart and respiratory rates anonymously and based on the analysis of this data you had been warned early enough to take appropriate action?

Wouldn’t you agree that the Pay-it-Forward principle works perfectly in the field of personal body data? The difference to the AirBnB model is that you provide your body data anonymously. It will be aggregated and used in a way that nobody knows that’s you behind your data. Since data analysis and respective actions or recommendations rely on big data, it’s necessary that many people participate and share their own data.

… will become a Social Responsibility in 2017
Today, in late summer of 2014, many people are sceptical and hesitate to provide their data. We think that personal body data sharing will be regarded as quite normal within a few years. If this movement takes up the same speed as the Sharing (Goods) Economy, it will be accepted as „normal“ within 2-3 years. We believe, that data sharing will become a social responsibility, comparable to fasten one’s seat belt or wearing a bike helmet. It probably won’t be called data sharing since this is a B2B term. There already is a very good term  – the Quantified Self, or QS. The term itself does not include the sharing element. But for every active members of the QS movement sharing is a relevant part of the quantification process because the value of an individual’s data is even bigger if used for general purposes.

We regard data sharing – our Quantified Self – as one of the most important movements of modern times and we would love to know how you think about it: please comment, provide us with your feedback: do you already share your data? How do you do it? Or, are you still sceptical?

Feature Image: Max Gotzler of Biotrakr, presenting findings of a Testosterone study at #QSEU14

Global Sleep Patterns

Sleep is one of the most interesting aspects of life: during sleep we don’t act consciously (apart from a few natural processes inside our body) and therefore some people try to minimize sleep to get most out of their lives. Others maximize their sleep: for them sleep simply is the greatest activity they could think of. The Quantified Self folks try to optimize their sleep; i.e. to maximize their sound sleep phases and minimize light sleep and times of being awake.

The guys from Jawbone looked at their UP band user’s sleep data and could provide us with this interesting global sleep pattern. Since Jawbone’s data are more detailed and accurate than the American Time Use Survey, this view on the different sleep patterns provides great insights in how inhabitants of cities behave, or how active a city is, seen as a whole. The average hours of sleep shown in the feature visual above do not include time awake in bed.

Science tells us that we should sleep between 7 to 8 hours per night and we should sleep during the same cycles in order to maximize recovery and relaxation from our daily routines. Now look at people living in Tokyo: with 5h 44min they sleep least, whereas Melburnians (yes, without the „o“) sleep most with 6h 58min – which is just the bottom end of the recommended length. Again Australians, this time the folks in Brisbane, go to bead earliest, at 10.57pm. The night-owls in Moscow hit the pillow almost 2 hours later, at 12:46am. But Muscovites (rise latest at 8:08am) sleep only 29 min less than the Brisbanian who raise at 6:29am.

sleep cycle

Image: The author’s sleep pattern on August, 19 – provided by the Jawbone UP app

How about you? Do you know how long you sleep? Do you know about the quality of your sleep?

Above, you see a snapshot of my own sleep: On August, 19, I had 5h 22min of sound sleep and 1h 52min of light sleep – which is a pretty good ratio. I reached my goal of 7 hours of sleep as well, which is partially due to the fact that we are in the middle of school vacation and there is no need to mange kids in the mornings. So, don’t worry if your sleep looks less sound – I wake 1 time every 3-4 nights.

The most relevant criteria defining my personal sleep are
– duration
– cycle; i.e do I go to bed and rise at roughly the same times?
– alcohol input
– time without staring at a display before going to bed
– general family mood during the evening

I’d love to know about your experiences with your sleep. Do you track? What makes you sleep sound or light, short or long?

The social relevance of the explore app guides – The Datarella Interview

Today, we speak with Michael Reuter (KMR), Co-founder of Datarella, about the social relevance of the explore app guides.

Q
At Datarella, you offer different programs your users can participate in. Can you elaborate on the meaning behind these programs?

KMR
With our explore app, we provide a useful free tool for smartphone users to optimize their lives. There is a broad range of specific life situations in which the explore programs provide valuable and sustainable benefits. From lifestyle oriented programs as SMILE!, our guide to learn how to smile in 5 days, to specific health programs as our OsteoGuide which supports users suffering from Osteoporosis – we provide a broad range of programs. The most important aspect for Datarella is to always provide real benefits to our users: it’s not about technology, it’s about the social relevance of technology, its immediate impact on the user.

Q
Could you describe one of those programs and its impacts on your users in more detail?

KMR
Sure! Let’s take the OsteoGuide: in countries with populations with median ages of 45 and older, Osteoporosis has become a widespread disease. People suffer from Vitamin D shortage, move less and less during the day and, as a result, their bone structure becomes more fragile. If Osteoporosis is analyzed at an early stage it’s curable in most cases. To cure a patient from Osteoporosis you have to help her to regulate her Vitamin D level and to move more; i.e. to change her behavior: the patient should use the staircase instead of the escalator, or walk or go by bike instead of using the car or a taxi.

A change of human behavior is one of the toughest challenges you can think of. Ask yourself: how easy is it for you to quit smoking, stop taking the extra bar of chocolate, etc. The best method to support people in changing their behaviors is to provide them with instant feedback of their behavior and to give regular counsel in terms of notifications and recommendations. With the explore app and our programs, we cover these aspects perfectly. We accompany our users during a certain period of time and help them to change their behavior to the better, step by step, day by day. In case of the OsteoGuide, we cooperate with Prof. Dr. med. Reiner Bartl of the Bayerisches Osteoporosezentrum, an acknowleged expert in the field of Osteoporosis.

Q
That sounds fascinating: you say that people in need of medical care can get rid of their diseases by using the explore app?

KMR
To be very clear: the explore app cannot fully compensate a medical treatment. And Datarella is not a team of health professionals. We have to join forces with experts like Prof. Bartl to provide our share of a solution for a patient. But, in many cases, medication can only applied successfully if the patient herself contributes to her well-being. And, in most cases, this means that she has to change her behavior. We have string evidence that the explore app programs are perfect tools to achieve this goal.

Q
You mentioned that the explore programs are free. Where is your business model?

KMR
Yes, every smartphone user can download the explore app and apply for any of the explore programs. It’s free to participate on the basic program level which includes, tasks, notifications and recommendations during the complete program. If a user wants more, e.g. if she is looking for a personalized individual coaching, she would have to subscribe to the premium version of the corresponding program. With the premium version she would also get tasks, notifications and recommendations, but on an personalized level, customized to her individual needs. This coaching approach is mostly sought-after by users who must change their behavior in order to achieve a satisfying level of personal well-being. And if behavior change is a must, then you’ll look for the easiest way to reach your goal. The explore app programs fit very well into that requirement since the user will be coached in a soft, but equally demanding and rewarding way.

Q
Thank you very much for these insights!

Boost your wellbeing and happiness with the explore app program SMILE!

Too much workload, stress and ultimately the burnout – that’s how many people see their everyday life. One way to handle the negative aspects of daily routines is to make it to the weekend (TGIF), another is to go on vacation. Whereas the first tactic is easy to realize but only helpful to a certain degree, the latter is possible once or twice a year for most of us. But there is another, more easy way to calm down and to boost your wellbeing and happiness: create and repeat small positive experiences – and you will see an immediate effect on your overall awareness of life.

As Sonya Lyubomirsky and Kristin Layous show in their paper, based on research by Ed Diener and others, it’s the small and regularly repeated positive experiences which influence your wellbeing and happiness to a great extent. According to the Positive-Activity Model, features of positive activities, including their dosage, variety, sequence, and built-in social support, all influence their success in that process.

Positive-Activity Model

Positive-Activity Model

For our editorial team at Datarella, this model was a challenge: how could we use the explore app to get this model work in an optimal way? As always, the team decided not to head for the optimal – but for a good solution, to invite volunteers to participate  in a special program and ultimately to optimize the program together with the explore users. This program, SMILE!, should be designed very lean, with just a minimum number of interactions, and with an active participation for just a few days,  in order not to interfere with the model’s cause-and-effect relationships.

After the 5-day-program, our data team analyzed the results. In short: our findings completely back the findings of Ed Diener et al.:

  1. participants of the SMILE! program experienced a significant increase of their happiness with each additional day during the program
  2. participants of the SMILE! program experienced an increase of their happiness compared with a test group of non-participants whose happiness level remained constant
  3. small, regular and well-portioned challenges triggered a change of the participant’s behavior resulting in an increased happiness level

The two charts below demonstrate the SMILE! effect:

Hast Du heute viel gelacht?hast Du nach dem Programm mehr gelacht?

(For non-german speaking users: Translation Chart 1:  „Did you laugh a lot, today?“, Translation Chart 2:“Do you think that you laughed more often at the end of the program?“, Translation Feature Visual:“How do you feel at the moment?)

The Datarella team itself participated in SMILE!, too. For me personally, it was a great experience. Being an optimistic guy and smiling often, the SMILE! challenges opened my eyes: in reality I have been smiling much less than I had thought. And triggered by the SMILE! challenges I was forced to become much more friendly.

For Datarella, the SMILE! program was a first test. We are planning to roll out several programs of this kind, all of them aiming to boost personal wellbeing and happiness. Since our editorial team is still in the process of creating these programs we’d like to invite you to participate and add your ideas, proposals and thoughts! We’d love to hear from you!

Vitamin D – the happiness factor you are in control of

Do you know your Vitamin D level? Did you know that your Vitamin D Level has a great influence on your wellbeing? Living in northern Europe or Canada, you are at risk for vitamin D deficiency. Vitamin D is known as the sunshine vitamin, but it also occurs in some foods, including fish, eggs and dairy products.

As an adult, you should ingest at least 20 micrograms of vitamin D per day. To prevent diseases, such as multiple sclerosis or cancer, even significant higher doses are recommended. If you go for walks or exercise outdoor on a daily basis, you already do a lot for a healthy vitamin D level. If you are more the manager type, sitting indoor all day and use a car or other „indoor“ means of transportation instead of riding your bike, you probably should supply your body with an extra portion of vitamin D.

Starting by the end of June, we will run a field study with our explore app and we would like to invite you to participate. Together with our partner biotrakr, a Berlin-based health startup, we will check your vitamin D level: you will get our blood test kit with which you can take a sample of your own blood. Additionally, you should answer a special vitamin D interaction in the explore app. A few days after having finished the interaction and having sent the kit back to our laboratory, you will be provided with your lab results in a personal area of the biotrakr website. The results of the vitamin D interaction will, as usual, be presented in explore itself.

In this short video the blood test is explained (German):

The vitamin D check-up will be supervised by Dr. med. Astrid Vidal, who runs doctor’s offices in Weilheim and Rosenheim, Germany.

How can you participate in the vitamin D check?
On Wednesday, June 11, you can answer our interaction „Vitamine“ in the explore app. If you answer the last question with „Yes“ and you are one of the very first 25 applicants, you are in! You will be informed via a personalized „Tipp“ in explore. This first vitamin D check will be in German language only. If you prefer to participate in English, you’ll have to wait for the next check, wich will be offered in both languages.

The vitamin D check:
Delivery of blood test kits: between June, 16 – 20
Return of blood test kits to laboratory: latest June, 27
Answering of vitamin D interaction in explore app: until June, 27
Results in explore app: immediately after having finished the interaction
Results of laboratory blood test: July, 4
Participants: max. 25
Terms & conditions: You may participate in the vitamin D check if you are one of the first 25 explore app users who apply for the check in the published interaction „Vitamine“.

We are looking forward to run this vitamin D check together with you and to compare the results of the laboratory blood test with the interaction in explore.