MY WEARABLE ANALOGY 

Think about driving somewhere. You decide on the destination (e.g., a friend’s house, a hotel, the office, a car park) and you use Google Maps or other apps to help navigate the most efficient route there. Even with routes that are familiar and that you drive often, it would be foolish not to check in with these apps as they can highlight diversions, delays and accidents. Well – I see wearable technology through the same lens. You still need to decide on your health and performance goals; technology will not do that for you. And the technology helps provide objective data and feedback. It can suggest the most efficient route there; it can highlight when things are a little off; and it can provide feedback on how you respond to certain behaviours. It would also be foolish to only look at Google Maps – you might crash or walk into something. You still need to use your own judgement if you see something clear and obvious.

Key Point: Use your wearable to help you navigate your health & performance journey – but you yourself have to decide on your goals.

WHAT IS THIS ARTICLE ABOUT?

This article will hopefully raise your awareness around and understanding about wearable technology. This is a follow up article to the first one we did a few years ago. Technology and software evolve extremely quickly , but the principles remain consistent – this article is predominantly focused on the application and use of more commonly used wearables (there are many, many wearables out there1), and how they can help change your behaviour and mindset when it comes to technology.

WHAT CAN WEARABLES BE USED FOR?

For the most part, wearables are used as an easy, non-invasive method of collecting continuous physiological data over longer periods of time. This data can (or should) be used to track long term trends & changes in markers which –  when layered with training output, subjective data, and interpreted correctly –  can help improve health, fitness, behaviour or all of the above. Metrics that are common to collect include (but are certainly not limited to) steps, sleep, resting heart rate, pace, distance, temperature. 

Key Point: Wearables can measure many things – so make sure you use the correct metrics aligned to your goals, and monitor these over long periods of time.

DID YOU KNOW?

A scientific review from Peake et al. 20181 identified that over 50% of the wearables they reviewed had not been independently validated (external testing for accuracy). Something to be mindful of when thinking about investing in some technology!

Key Point: Not all metrics from wearables are as accurate as some companies claim. Stick to valid & reliable measures that are sensitive enough to detect changes.

KEY USES OF WEARABLE TECHNOLOGY

Objective data from wearables can help to challenge your perception. Sometimes feelings are off, or can be clouded with bias. Use both in combination to make the best decisions (see image below). On the occasions where things are in agreement (data & perception) you can be confident in the decision you make from those. Sometimes, if you feel fine but your data shows something is up – it is not time for panic, but it is worth a check in to note why that might be. If you see consecutive negative changes in objective data, I personally think it’s worth making a change in your daily behaviours or exercise to try to get things back to normal.

One of the major benefits of using wearables is illness detection, whereby wearable data (i.e., your body’s physiology) can often start to fight illness & change before symptoms occur.2-6 This has been commonly reported recently, particularly with respiratory illnesses such as Covid-19.

Key Point: Combine the objective data with your perception & feelings to make well-rounded decisions around your health and performance.

THE MIDWEEK WINE! 

Data can be useful to remove your own bias from your health. An example that might resonate with some people. You might try to justify a bottle of wine midweek, and convince yourself that it is not affecting your sleep. Even though you wake up tired and dehydrated, you will convince yourself that you slept well and feel fine, because you want to keep drinking those glasses of wine mid-week. A wearable might provide a contrasting view, such as an increase in your resting heart rate or a poorer sleep on the nights that you drink alcohol. This is where your interpretation of data is important and how you interact with your data and your feel. It takes a strong-minded person to battle their own bias!

The graph below shows the relationship between alcohol intake and resting heart rate overnight. Unfortunately, the more alcohol you consume, the bigger effect it has on your health & physiology. Even at small doses (2 glasses of wine) you can start to see the impact on your health and sleep.7 

Key Point: Objective data can help remove bias from your behaviours.

KNOWING WHICH WEARABLE TO USE

Wearables have many different uses, from smart-tech features to capturing overnight sleep data to fitness feedback. There are some that are “do-it-all” and some that are more specialist. What you get depends on what you need it for, and maybe also what you already might have.

Here is a plot to help visualise what I mean.

Key Point: Decide on what you want to use your wearable for, then decide what you need. Sometimes a specialist product might be better for your use case.

For example, when it comes to total sleep time – most wearables perform as well as each other. The data correlates nearly perfectly between devices. The graph below shows a comparison of total sleep time for 3 commonly used devices.

Key Point: Most devices perform well at sleep time – it is more important what you do with the data and that you take action.

However, some other variables do not perform as well – such as duration spent in deep sleep stages. Often, people take these as gospel, and try to adjust behaviours when the data is just not good enough. I would also recommend avoiding comparing some of these data between devices and people.

Key Point: ignore some of the noise with wearables. Sometimes this data can only be analysed in longer term trends.

EXAMPLE USE-CASES*

*Please consider that this table contains examples – and are not complete recommendations. It is very difficult for me to recommend specific products without fully understanding your goals & situation. Many of these products would suffice for all of the people in all of the categories. Often the more important things to consider are not what you use (e.g., which wearable) but how you interpret the data and what you do with the information.

KEY TAKEAWAYS

• If you are considering using (or are using) technology, make sure you clearly define what you are looking to track and why. Connect the data to your overall goals and make sure you are measuring the correct metrics.

• Make sure the wearables and the data don’t completely overtake your ability to reflect and be in-tune with your body. The best approach is to use your perception & the data combined to make decisions for your health and performance. Before you open the wearable app in the morning, make sure you take a moment to ask yourself how you feel and how you slept. 

• Zoom out. There will always be day-to-day variation in numbers, and that’s normal. Don’t let one bad night of sleep throw you off track. If your wearable data looks a little off for once, that’s probably fine. Only start to make changes if you see things are deviating for 2-3 days consecutively. 

• Seek advice. Many of the data points & metrics that are derived from wearables are simple in measurement, but complex in interpretation & application. If you are confused, annoyed or a little lost in the numbers and what to do – reach out for some help.

REFERENCES:

1. Peake JM, Kerr G, Sullivan JP. A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations. Front Physiol. 2018 Jun 28;9:743. doi: 10.3389/fphys.2018.00743. PMID: 30002629; PMCID: PMC6031746. https://www.frontiersin.org/articles/10.3389/fphys.2018.00743/full
2. Miller DJ, Capodilupo JV, Lastella M, Sargent C, Roach GD, Lee VH, Capodilupo ER. Analyzing changes in respiratory rate to predict the risk of COVID-19 infection. PLoS One. 2020 Dec 10;15(12):e0243693. doi: 10.1371/journal.pone.0243693. PMID: 33301493; PMCID: PMC7728254. https://pubmed.ncbi.nlm.nih.gov/33301493/ 
3. Smarr BL, Aschbacher K, Fisher SM, Chowdhary A, Dilchert S, Puldon K, Rao A, Hecht FM, Mason AE. Feasibility of continuous fever monitoring using wearable devices. Sci Rep. 2020 Dec 14;10(1):21640. doi: 10.1038/s41598-020-78355-6. PMID: 33318528; PMCID: PMC7736301. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7736301/ 
4. https://ouraring.com/blog/ucsf-tempredict-study/ 
5. Quer, G., Radin, J.M., Gadaleta, M. et al. Wearable sensor data and self-reported symptoms for COVID-19 detection. Nat Med 27, 73–77 (2021). https://doi.org/10.1038/s41591-020-1123-x. https://www.nature.com/articles/s41591-020-1123-x#citeas
6. Natarajan A, Su HW, Heneghan C. Assessment of physiological signs associated with COVID-19 measured using wearable devices. NPJ Digit Med. 2020 Nov 30;3(1):156. doi: 10.1038/s41746-020-00363-7. PMID: 33299095; PMCID: PMC7705652. https://pubmed.ncbi.nlm.nih.gov/33299095/ 
7. https://www.linkedin.com/pulse/how-alcohol-can-affect-sleep-overnight-health-peter-tierney/