You’ve completed 14,000 – well done! But does that actually mean anything? The buzz on your wrist, the on-screen fireworks – we all love hitting our steps target for the day. But what does that figure really indicate, if anything?
By itself, 14k steps is just a number – like knowing your heartrate is 71 – or even a harmless distraction. We’ve all heard stories about how the arbitrary 10k steps a day figure came into being as a marketing ploy. Many would discount the data that comes back from wearables and fitness trackers as pointless information. However, our daily steps count is also an everyday reflection of the fact that the amount of health data available to us has exploded over the last few years.
In fact, far from being a useless number, good application of healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general.
With an estimated 1.7MB of data created every second by every individual throughout 2020 (Source: Domo), this is also where AI and machine learning can come into their own, joining all the dots together and understanding what’s missing in the pattern of our health. It’s possible to bring all of the information together and present it in an easy to digest format for all involved.
The steps count becomes valuable when this data can be mapped and analysed against your medical records, analysing how the steps helped you burn a specific number of calories, change your lifestyle and how increasing those steps will help you maintain your ideal weight.
It becomes even more valuable when you link it with other data sources – there are a multitude – and analyse them as a data set. Has the person been able to maintain or reduce a long-term condition by increasing their movement, for example? Is this sustainable for them? Once we look at the data created by an individual as part of the jigsaw not the final picture, we can start looking for further information – like a health detective – to build this up.
The Data jigsaw could be made up of
Imagine if we could have a “single person view”, bringing together all the information in one place; analysing and spotting trends and predicting future issues. So much of the data that we can make use of to monitor our health and care actually already exists. It’s already being tracked and analysed – just not in a centralised format or system and not always for the reasons you think.
In fact, the majority of the data created about you doesn’t sit with you or under your control. Combining different data points doesn’t have to be a sophisticated process – take the 14k step count – if you’re also consuming 5,000 calories a day, you’d need to be doing many more steps. If we could join up those two data points alone, that would be powerful.
These are the first steps for creating plans for people and ones that are sustainable.
To enhance our “single person view”, we could go one stage further. We know that trauma and adversity in childhood raise the risk of numerous health problems such as diabetes, heart disease, cancer, and mental illness in adulthood. *
There’s physical and biological evidence that toxic stress alters brain development affecting health in later life; research shows that social processes also play a key role. Exposure to trauma affects emotional regulation, impulsivity, and ability to form intimate ties. Another recent study shows exposure to childhood adversity is linked to a higher lifetime cancer risk among women—but not among men.**
Should this data also be looked at when trying to understand a person’s health care needs and how to they deal with health issues and health conditions?
In the past, most patients were satisfied with undergoing a physical once a year and only checking in with their doctors when something went wrong. But in the digital age, patients are focusing on prevention and maintenance and demanding information about their health more frequently.
As a result, healthcare companies are being proactive by investing in wearable technology devices that can provide up-to-date monitoring of high-risk patients to determine the likelihood of a major health event.According to a recent report, the wearable medical device market is expected to reach more than $27 million by 2023: a spectacular jump from almost $8 million in 2017.***
A poll by Ipsos MORI commissioned by The Health Foundation found that more than half of the respondents would be willing to share their data with the NHS via a lifestyle app or fitness tracker. Support was higher among younger age group: half of under 65s were open to the idea. A third of those over 65 said they would be very unwilling to do so. It would be troubling if the people who could benefit most from wearables were unable to or unwilling to access this kind of technology. Should the NHS be putting funding in place to train clinicians and patients for this kind of self-management so that it reaches the largest number of people?
It would be wrong to ignore the very real challenge around data and security, especially when navigating a complicated infrastructure like the NHS. Clinicians and professionals often do not know what data exists, whether it is safe to use and how to make the best use of large amounts of patient information.
Data breaches are no small matter; in 2015 the Royal Free NHS trust failed to comply with data laws by giving 1.6 million patient records to Google’s Deepmind Health app, Streams, without their knowledge. Whilst the NHS wasn’t charged a penalty, the ICO (Information Commissioner’s Office) did rule that the sharing of data was unlawful, the information commissioner, Elizabeth Denham, stated.
Most recently Palantir was contracted in March 2020 by the NHS to help develop the NHS COVID-19 Data Store for a fee of £1. The contract was due to expire in June but was extended for four months at a cost of £1 million. Palantir was awarded an additional £23 million contract to continue working with the NHS data store.
Taking into account the data minefield and the level of fragmented data we have, it is only a matter of time individuals start to ask for greater control of their data under a blockchain enabled centralised health system owned and controlled by the individual. This is a positive step and will actually help us to move to a move connected care model. It will enable us to ‘join the dots’ and use data and digital tools to bring patients and care providers closer for more effective and personalised care.
So, on its own 14k steps doesn’t mean much, beyond the fireworks and kudos from friends. Without context and understanding, this data is a point-in-time snapshot. But it becomes not just usable but incredibly useful when we start to pull in everything we know and understand about a person, and analyse it together. Then we can unlock its power.
This article was originally published in May 20th 2021 on Open access government - https://www.openaccessgovernment.org/data-potential-of-wearable-health-tech-readings/110904/
* [source: Hector Alcalá, A. Janet Tomiyama, and Ondine von Ehrenstein, “Gender Differences in the Association Between Adverse Childhood Experiences and Cancer”.]
** [source: Centers for Disease Control and Prevention, Adverse Childhood Experiences Journal Articles by Topic Area, (https://www.cdc.gov/violenceprevention/childabuseandneglect/acestudy/journal.html).