The global market of wearables is booming, and it includes wristbands and smartwatches capable of monitoring physical activity and selected health parameters. In 2019, its value was USD 28 billion and, according to forecasts, it will grow by 15% year-on-year between 2020 and 2027. In 2020 alone, Apple sold approx. 32 million Apple Watches (approx. 55% market share). Samsung, Garmin, and Fitbit are also leading manufacturers.
While smartwatches have a relatively wide range of features, including some functions of smartphones and traditional watches, fitness bands have a narrowly defined purpose: tracking physical activity and motivating the user to maintain or increase the intensity of training or change their lifestyle to improve their physical condition and health. In many cases, this promise tempts the consumers. Therefore, to accelerate market growth, the wearables manufacturers promote a new trend of technology-driven healthy lifestyles.
But is there any causal correlation between wearables and health at all?
Data, numbers, statistics and charts. As much and as little
The most recent Forrester Research report on wearables in healthcare is not the first document to question the benefits of such devices. Technologies measure a wide range of health parameters with increasing accuracy, but it is not enough. Consumers receive pure data without any information or guidance on what to do with it or how to interpret it. Is it good or bad when the band shows a pulse of 120 after 20 minutes of cycling? Is waking up two times during the night still the norm? Are 150 calories burned per day enough to lose 2 kilograms a month?
Another problem is the selectivity of measurements: the fitness band will praise us for regular workouts. Still, it will not check whether we perform the exercises correctly and whether the intense running does not harm the joints. Also, according to the Forrester Research report, doctors are reluctant to interpret the data provided by the patient for fear of its reliability. No wonder – they are responsible for a diagnosis so that they want to be sure they make decisions based on trusted data sources.
Finding evidence for the preconceived notion
Let’s have a look at the devices designed to help you lose weight. Many scientific studies either question their effectiveness or draw attention to the bias of their analyses. A randomized clinical trial, “Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss”, performed on 471 adults and published in the JAMA Network, proved that “devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioural weight loss approaches.”
Furthermore, the study “Long-Term Weight Management Using Wearable Technology in Overweight and Obese Adults: Systematic Review” (JMIR Publications) demonstrates that in the case of the five analysed studies, there were no reliable results due to their bias, differences in the objectives and methods of the study.
Scientific evidence of the health benefits of wearables is hard to identifyThe aforementioned research bias may have many sources. One of them is so-called confirmation bias. It occurs when the researcher has a biased preference for information that confirms prior expectations and hypotheses while ignoring facts that contradict the assumptions. For example, let’s say we want to demonstrate that wearing a fitness band that measures sleep quality positively affects mood. As you can guess, better sleep quality means better mental condition. To prove this thesis, a study can be conducted among people who wear or do not wear a fitness band. In this case, an inconvenient fact that may interfere with the reliability of the study is the influence of simply wearing the wristband on the subjective perception of one’s own well-being. However, this can be omitted to prove the initial thesis. A similar confirmation error can occur when surveying owners of fitness trackers and people who do not use such devices. When we turn a blind eye to the fact that the owners of wearables are statistically wealthier, more committed to taking care of their health or living in better districts, then the study will certainly confirm that users of wearables are actually happier. The correlation between two phenomena is a causal relationship yet. It is a problem not only in research but also in industry reports on some med tech – the authors see what they want to see or simply pick out evidence to confirm their point of view. Unfavourable results are left out of the picture Demonstrating the benefits of digital health technology is much more complex than proving the effectiveness of a drug. Many psychological factors come into play, and it is impossible to use a placebo in follow-up studies and to measure the effects objectively. Furthermore, clinical trials are costly and only large pharmaceutical corporations can afford them. Anyway, most wearables are not medical but lifestyle devices. However, the line between a wellness device and a medical device is not clear for everyone. The research so far shows that wearables either have little or no effect on improving health results. Changes in behaviour are determined by many factors other than just wearing an electronic wristband. Lastly, it is worth mentioning another study published in The American Journal of Medicine: “Is There a Benefit to Patients Using Wearable Devices Such as Fitbit or Health Apps on Mobiles? A Systematic Review”. Three researchers evaluated selected publications (550 publications extracted, six studies met the final criteria) on wearable technology. Conclusion: There was little indication that wearable devices provide a benefit for health outcomes, including for chronic diseases. They play a role as facilitators in motivating and accelerating physical activity, but current data do not suggest other consistent health benefits. Research on the effectiveness of wearables has a short history, as the technology itself has become available relatively recently. Although clinical trials are the most reliable validation path, they are often financially unattainable and complex in terms of organization. Do we need a new research model for digital health interventions?
