Introduction
Since the inception of the “Quantified Self” movement approximately 15 years ago
[1], the proliferation of consumer
wearable devices has been remarkable. Today, consumer wearables have become a staple
in many people’s lives. In the United States and Europe, consumer wearable ownership
is between 40–53%, respectively [2]. In
the realm of exercise and health, the adoption of these devices is even more
pronounced, particularly among younger athletes [3].
Consumer wearable devices are increasingly used in competitive sports to quantify
performance and monitor training load [4]
[5]
[6]. For example, in team sports such as
soccer, field hockey, basketball, and American Football, GPS units and heart rate
monitors are commonly used to measure player activity profiles, physiological
demands, and recovery [6]
[7]. Similarly, both elite and
recreational runners have reported finding these devices useful in improving
performance, tracking training loads, and providing real-time feedback on speed and
cadence [8].
Also, in healthcare and public health settings, consumer wearable technologies are
playing an increasing role in transforming approaches to disease prevention,
management, and research. Traditionally, public health surveillance has relied on
self-reported measures such as interviews and questionnaires [9]. Despite their widespread application,
these methods are susceptible to various biases [10]
[11]. The emergence of smartphones as tools for health surveillance marked
an important transition in the field, enabling researchers to investigate patterns
of human movement [12], mood [13], the spread of disease [14], and socioeconomic status [15]. For example, “Our Future Health” in
the UK aims to be the largest research program to understand how diseases can be
prevented by using a variety of data sources, including consumer wearable devices,
to track health data in real-time, thereby enabling early intervention and
personalized healthcare strategies [16].
Similarly, research conducted by the Robert Koch Institute utilizes consumer
wearables to gather large-scale epidemiological data, enhancing the understanding
of
public health trends and contributing to the development of targeted health policies
[17]. The Scripps Research
Translational Institute’s DETECT study leverages consumer wearables to monitor
physiological signals that may indicate viral illnesses, such as COVID-19,
demonstrating how these devices can support early detection and response to
outbreaks [18].
These initiatives underscore the versatility of wearable devices in not only
advancing personal health and fitness but also in contributing to critical research
efforts that aim to improve public health outcomes on a global scale. Through the
integration of consumer wearable technology in diverse settings, from sports to
comprehensive health studies and exercise prescription, we are witnessing a paradigm
shift towards more data-driven and personalized health and fitness solutions. This
review aims to summarize the main challenges and opportunities presented by consumer
wearable technology in exercise, health, and sports contexts. The discussion extends
across five key areas, including the requirements for harnessing wearables to
improve performance and health, the need for data accuracy and reproducibility, user
engagement and adherence, ethical considerations in data harvesting, and future
research directions.
Requirements for the Effective Use of Wearables for Improving Performance and
Health
Recent advancements in wearable technology, driven by innovations in material
science, microchip technology, and the integration of a wider array of sensors, have
significantly enhanced the potential capabilities of these devices. Developments in
flexible electronics, e-textiles, and ultra-low-power microcontrollers have enabled
wearables to become more comfortable, powerful, and capable of sophisticated
real-time data analysis [19]. Presently,
wearable devices not only can track step counts through accelerometers or inertial
measurement units (IMUs) but can also capture a range of physiological signals,
including heart rate, blood oxygen saturation, and blood pressure; they can even
detect atrial fibrillation [20], falls
[21], and car crashes [22].
However, this wealth of information also presents a significant challenge:
translating diverse wearable-derived data into actionable insights for exercise and
health professionals. Effectively analyzing wearable-derived data necessitates
expertise in data science, human physiology, and health behaviors. However, the
scarcity of researchers with the required training diversity in these areas
complicates collaboration and communication between disciplines. Furthermore, the
rapid evolution of wearable technology introduces concerns regarding data privacy,
the validity of results, and the impact of academic partnerships on business models.
These challenges underscore the need for a multidisciplinary approach to harness the
full potential of wearable devices in improving health and performance outcomes,
balancing technological innovation with ethical considerations and data accuracy.
[Fig. 1] illustrates key areas that
require attention when utilizing consumer wearable technology in exercise and health
settings.
Fig. 1 Navigating the complex landscape of wearable technology – key
considerations.
The challenge extends to using this data to make positive impacts in exercise and
health and to influence individual decision making. In a practical framework that
was established by a collaboration between practitioners and researchers, key
questions for the use of wearable devices were raised in order to facilitate the
decision making for end users [23].
These questions included information related to the value of the collected data; the
necessary trust in the accuracy of the collected data; whether the obtained data can
be integrated and effectively analyzed (or processed); and whether the technology
can actually be implemented in daily practice. In this context, there is an urgent
need to develop sensing technology that is robust, easy-to-wear, and able to measure
bio-signals with increased detection sensitivity and improved signal-to-noise
ratios, specifically at the interface between soft sensing components and rigid
electronics, such as textile-based/fiber-shaped-, multifunctional- and self-powered
sensors [24]
[25]
[26]
[27].
Data accuracy and reproducibility
Data quality remains among the most essential requirements for the sustainable
implementation of consumer wearable technology in exercise and health settings.
In this context, signal quality has also been proposed as a key challenge in the
recent 2023 wearable photoplethysmography roadmap [28]. One of the primary challenges
highlighted in a recent umbrella review that evaluated the validity of wearable
devices is the rapid and ongoing evolution of the consumer wearable technology
landscape [29]. The inherent delay
in academic research and publication processes results in a situation where the
research captured within the review acts as a historical snapshot, capturing the
accuracy and validity of devices that were on the market approximately two years
prior to the review’s execution. This temporal gap is underscored by the
chronology of the studies cited – only one of the identified reviews was
published in 2023, with the majority being from 2022. The authors noted the
ephemeral presence of devices in the market; every device analyzed had either
been phased out or replaced by a newer model by the time of the review – and
fewer than 5% of the devices had been validated for the range of outcomes they
measure [29]. Although newer models
may maintain a semblance of hardware continuity, the introduction of updated
firmware and algorithms likely alters device performance and the accuracy of the
data being produced. This scenario underscores a tension between the slow,
deliberate pace of academic research and the fast-moving, ever-changing nature
of the commercial technology sector. To overcome this, likely a closer
collaboration between the industry and the exercise and health science community
is required. First attempts are currently underway as part of the funding scheme
of the European Commission [30]
[31].
The abundance of factors that affect the signal quality of optical and strain
sensors incorporated in consumer wearable devices have been summarized in two
recent publications by the INTERLIVE-network [32]
[33], an initiative to which the
primary author has significantly contributed. The INTERLIVE-network’s efforts
are aimed at advancing our understanding of the parameters that influence sensor
accuracy, particularly in real-world conditions. This work emphasizes the
importance of interdisciplinary collaboration to address the complexities of
wearable technology and to improve data validity.
In laboratory settings, sensors may provide a very high validity and
reproducibility, however in the real world (i. e. in scenarios representing the
intended use of the wearable device), their accuracy is often low or at least
unknown. For example, it has previously been shown that optical sensors may
provide accurate readings in resting conditions but their accuracy reduces in
exercise settings that are characterized by high-intensity body movements [34]
[35]. This may largely be attributed
to factors related to the human-wearable interface, namely the contact pressure,
skin temperature and motion artefacts [32]. While research on the effects of ambient or skin temperature on
the signal quality of optical sensors is still in early stages [36], contact pressure between the
wearable device and the skin seems to have a profound effect on the readings of
the obtained pulse wave [37]. In a
similar manner, motion artefacts have been shown to influence optical signal
readouts, and may be caused by a number of factors, including displacement of
the sensor over the skin, changes in skin deformation, blood flow dynamics
and/or ambient temperature [38].
Importantly, the individual contribution of each factor may be very dynamic and
prone to changes throughout an exercise bout (i. e. depending on the exercise
intensity and/or duration) or prolonged physical activity, requiring a thorough
validation of these devices and possible adjustments during periods of
continuous data recording.
To overcome this, incorporating measures that assess whether a signal is actually
caused by the intended physiological or chemical changes may facilitate the
implementation of feedback mechanisms to the end user. For example, strain
sensors could be utilized to assess the contact pressure or high-sensitivity
motion sensors may be used for the detection of motion artefacts (e. g. the
shifting of the sensor over the skin), thereby distinguishing between artefacts
and actual movement. The data from these sensors could be fed back to the user,
for example, prompting them to adjust their device to improve accuracy.
Additionally, this data could be incorporated at the backend during export or
processing, allowing for the labeling of data quality and suggesting whether the
data might be of lower or higher quality. This could be especially important
when wearables provide clinical readouts, such as the detection of atrial or
even ventricular fibrillation [39]
[40]. However, to the
best of our knowledge, very few manufacturers currently implement
high-sensitivity motion sensors for data quality assurance in a commercially
available device [41].
Data transparency
Ensuring data accuracy in wearable devices also necessitates transparency
regarding data quality to allow for individual decision making and to bolster
the confidence of consumer wearable technology in exercise and health settings.
Often, the data displayed by these devices are estimates, resulting from low
signal quality or decreased sampling frequency to save battery life. This is
particularly common in lower-cost wearables, which adjust sampling rates based
on the user’s activity. For instance, the sampling rate may decrease during
steady-state activities and increase for activities with higher intensity or
dynamic movements. This approach is logical for slowly-changing variables, such
as heart rate, but may not suit dynamic or pulsatile measures like blood
pressure. Regardless of the data source, it is crucial to provide feedback on
the quality of the data retrieved and to determine whether adjustments (such as
to sensor placement) are needed to enhance signal quality. We do acknowledge
that there are multiple ways to make end users aware of the confidence of the
obtained values (e. g. through coloring or displaying values as±to highlight
that this is an estimate rather than an actual measure). In fact, Fitbit
provides confidence values on a scale of 0–2 (with 0 being the lowest and 2
being the highest confidence) for each HR value obtained, but this information
is only accessible once the data is exported retrospectively. Moreover, the
calculation is based on algorithms that remain unknown to the user.
Unfortunately, scientific work on the efficacy of such measures remains in its
infancy and should be encouraged to further support efforts aimed at enhancing
data quality.
The transparency issue is evident in the scarcity of white papers from
manufacturers that explain the calculations underpinning their algorithms and
the conditions required for these algorithms to yield accurate results. For
example, many manufacturers strive to develop algorithms capable of estimating
maximal oxygen consumption (VO2max) using linear associations between
heart rate, workload, and VO2, thereby offering real-time feedback on
an individual’s fitness and health status. A recent meta-analysis uncovered
significant random errors in VO2max estimations across various
studies, with limits of agreement ranging
from±15.24 ml·kg-1·min-1
to±9.83 ml·kg-1·min-1 in resting and exercise
settings, respectively [42]. Since
the systematic bias was very low in either condition (i. e. resting vs.
exercise), these findings may support the use of consumer wearable devices to
estimate VO2max at a population level. However, since the random
error was high, these findings also underscore the difficulties of using these
devices for tailored and personalized exercise prescription. Yet again this is
exaggerated by the lack of reporting the algorithm, making it impossible to
identify potential sources of errors in the provided VO2 estimations.
This lack of transparency is a significant barrier to the trust and reliability
of wearable devices. Users and healthcare professionals are left without a clear
understanding of how data is processed and interpreted, leading to potential
misapplications of health and fitness metrics. This gap highlights the need for
manufacturers to disclose their methodologies and for independent research to
validate these devices comprehensively. By addressing these transparency issues,
the industry can enhance the credibility and effectiveness of wearables in
delivering accurate health insights, thereby fostering greater user confidence
and adherence [43].
User engagement and adherence
The integration of wearable technology in the realm of exercise and health has
brought about a paradigm shift in how users monitor and understand their
training, performance, and overall health. However, user engagement is highly
heterogenous. Studies have shown that many users discontinue use after just a
few weeks or months [44]
[45], with only approximately 40%
maintaining regular use for 24 months [46]. The reasons for this lack of long-term adherence among the
majority of wearable users are varied and not yet fully understood [47].
To effectively promote long-term engagement and healthier lifestyles, wearables
must be designed to enhance motivation and adherence [48]. Personalized feedback and goal
setting can help users achieve realistic health goals by providing tailored
recommendations and progress tracking [49]. Incorporating gamification elements like challenges and rewards
can increase motivation and make physical activity more enjoyable [50]. Social features that connect
users with support networks and communities can foster accountability and
encouragement [51]
[52]. Behavioral nudges and reminders
tailored to individual schedules can prompt users to engage in healthy behaviors
[53]. Integrating wearables with
healthcare systems can enhance adherence by involving providers in monitoring
and encouraging patient behaviors. User-centered design, involving patients in
the development process, can help to ensure that wearables are user-friendly and
meet the needs of the target population [48].
Despite issues with long-term adherence, over-reliance on wearables presents a
significant challenge: the paradox of wearable dependency. The core of this
problem is not just the use or adherence to these devices, but the extent to
which users become dependent on them for measuring their training success,
defining their athletic identity, or assessing their health status.
To extract the maximum benefits from wearables, they need to be worn
consistently, so that they can provide a comprehensive picture of sleep, stress,
physical activity, energy expenditure, aerobic capacity, and other markers of
health. However, this high level of attachment has several downsides. Younger
generations in particular have been shown to exhibit a strong attachment to
self-quantification, often using wearable devices as the yardstick for their
success [3]
[54]. This over-reliance can diminish
the intrinsic joy and motivation derived from the activity itself, undermining
primary goals such as fitness, strength, or skill improvement. This
over-reliance seems to be especially pronounced in athletic settings where for
many athletes, especially in endurance sports, wearables and fitness trackers
have become more than tools – they are integral to their identity and daily
routines. Research has shown that endurance athletes in particular feel “naked”
without their wearables, underscoring the deep-seated reliance on these devices
[55]. This dependence, while
beneficial in tracking and improving performance, raises concerns about the
psychological impact and the potential for reduced enjoyment in sports.
Digitizing every aspect of health and training can paradoxically lead to
deteriorations in the very outcomes these technologies aim to improve. A case in
point is the development of orthosomnia, a condition where athletes obsess about
achieving optimal sleep, driven by data from sleep trackers. Ironically, this
obsession can lead to poorer sleep quality and recovery, defeating the purpose
of using the technology [56].
The challenge therefore lies in balancing the benefits of wearable technology
with the need to preserve individual autonomy and enjoyment in physical activity
and health monitoring. It is crucial to educate users on the appropriate use of
these devices, ensuring that they complement rather than dictate training and
lifestyle choices. Exercise and health professionals have a pivotal role in
guiding users to interpret and use data in a way that enhances, rather than
overshadows, their natural instincts and enjoyment.
Ethical and Legal Considerations in Wearable Technology
The integration of wearable technologies in everyday life and healthcare systems
marks a significant shift towards more personalized and preventative health
strategies. These devices offer unprecedented opportunities for large-scale
health research and public health surveillance [57]. However, this evolution
necessitates a considered discourse concerning the ethical implications of data
collection, user consent, privacy, and long-term utilization [57]
[58]
[59].
The increasing use of consumer wearables in exercise and health settings has
highlighted significant data security and privacy challenges. These devices
collect extensive data, often beyond the explicit knowledge of users [60]. This data can be used for a
variety of purposes, which are not always aligned with the user’s intentions
[60]
[61]. A notable example of the risks
involved was a recent security breach that exposed over 61 million fitness
tracker records, compromising the privacy of user data [62]. Such incidents underscore the
complexities of managing wearable device data, which involves navigating
intricate technological protocols and maintaining continuous user consent, often
leading to incomplete datasets that compromise research integrity and findings
[63]. Moreover, the opaque
processing of data by third parties [64]
[65], coupled with the
strategic location of data servers in jurisdictions with less stringent data
protection laws [66], exacerbates
these security risks, suggesting a need for greater regulatory oversight beyond
what is currently afforded by Institutional Review Boards (IRBs) [67]. Different regulatory
environments further complicate these issues; for example, the General Data
Protection Regulation (GDPR) in the EU imposes stringent requirements on data
privacy and user consent, while the Health Insurance Portability and
Accountability Act (HIPAA) in the US focuses more on protecting health
information within healthcare settings. These variations impact the development
and global use of wearable technologies, as companies must navigate a complex
landscape of compliance requirements. Often, companies may skirt the regulatory
grey areas of different legislation to maximize their operational flexibility
and data utilization [68].
At the same time, users need to have significant expertise to properly understand
“appropriate use” and how it differs across regions, placing an unrealistic
burden on individuals to stay informed about the legal nuances that affect their
data privacy and security. The surrender of data begins when the user agrees to
the terms of service and end user license agreement. The complexity and length
of these documents are such that a vast majority of users – up to 97% – agree to
the policies without fully understanding them, simply skimming through documents
that would require considerable time to read thoroughly [60]
[69]
[70]. Moreover, these terms are often
only presented to users after purchasing the device and during the setup
process, leaving them with little choice but to agree or return the device. This
lack of transparency and accessibility in the terms of service agreements
undermines informed consent and highlights the need for clearer, more concise
information upfront. Then, as users begin to use their devices and capture
health data, these data are often sent to the manufacturer’s servers in a
separate location. This situation allows data to become more accessible legally
once stored on overseas servers, thereby increasing data security risks and
allowing private companies to extract the maximum value from their users’ data
[68].
This issue is compounded by the phenomenon of choice legacy, where users become
increasingly tethered to a device manufacturer’s software and service ecosystem
to continue extracting value from the data they generate. The current revenue
model locks users – and their data – into a specific device manufacturer, making
migration between different app ecosystems challenging. For instance, to
maximize the utility of a Garmin, Fitbit, Oura, or Whoop device, users must use
the corresponding app. These apps often require a paid subscription; at the time
of writing, the yearly costs are €79.99 for Fitbit Premium, €69.99 for Oura
membership, and €264.44 for Whoop membership, in addition to the initial
purchase price of the device. If users decide to switch devices or stop using a
wearable altogether, they may lose access to their historical data. Although
companies like Apple and Google offer aggregator apps (Google Fit, Google Health
Connect, and Apple Health) that can fetch data from individual apps with user
permission, they often do not access the full granular data (such as HRV values,
sleep quality, or readiness metrics like “body battery”), which many users find
beneficial [71]. Furthermore, using these aggregators ties the user to the
aggregator app and its core operating system, whether iOS or Android,
propagating the problem.
These issues have significant implications for the ethics of data ownership, the
right to be forgotten, user privacy, and legislative control. There is a
pressing need for more transparent data policies and greater regulatory
oversight to protect user rights and ensure that the value derived from wearable
device data benefits both users and the broader health community. Currently, the
power dynamics often leave users at a disadvantage, constrained by their limited
influence over the data security practices of large tech companies. As the use
of wearable technologies expands, it is essential to adapt how we manage and
utilize the data they produce.
Here, we propose a series of recommendations for the ethical capture and
utilization of wearable device data in sports and healthcare settings: Users
should 1) read privacy policies carefully, use strong, unique passwords, and
enable two-factor authentication on their accounts; 2) regularly audit app
permissions, limit data sharing, and keep software updated; 3) opt out of data
collection, if possible, disable data sharing where feasible, and turn off
Bluetooth when the device is not in use; 4) use encrypted connections, be
cautious with third-party apps, and back up data securely; 5) understand
regional privacy laws and delete unnecessary data to minimize digital
footprints. Some companies also allow users to delete data within the app or by
email request.
Future Areas of Research
The future of wearable technology in exercise and health is poised for further
innovation, particularly through the integration of AI and machine learning. These
advancements promise to revolutionize how we approach predictive analytics in these
fields. Here, we provide few examples of areas bearing an enormous potential for the
further utilization of wearable technology.
Real time data streaming
In the context of sports performance, new developments are underway to
comprehensively assess physiological, biochemical, and biomechanical variables
in real competition situations, which is considered one of the Holy Grails of
modern sports science. Traditionally, due to a lack of available technology and
regulations outlined by sports federations, these assessments have been
conducted mainly through simulations in laboratory settings or during training.
Advances in material science and the integration of AI and machine learning
algorithms could enable wearables to provide immediate, actionable insights
during competitions, enhancing both performance and safety. Real-time tracking
in sporting environments has so far been possible only in pilot projects, such
as one carried out at the Tokyo Olympics 2020 in 2021 [72]. However, such developments may
ignite discussions about fairness, as not all athletes may have equal access to
these technologies. Once established, these ecosystems could also be utilized in
health-related settings, such as remote exercise therapy for patients suffering
from chronic diseases. Current attempts are underway, opening a new era for the
use of wearable technology at the interface between exercise and health [73].
Intelligent training guidance
Ultimately, the abundance of physiological and biomechanical data gathered by
wearable technology may be used for intelligent (automated) exercise
prescription. Indeed, nearly every commercially available smart device
incorporates some form of training load estimation or recovery prediction.
However, assessing training load and recovery is an enormously complex
phenomenon involving literally all bodily systems and understanding recovery
dynamics has been a matter of debate for many decades. In 2018, a consensus
statement on recovery and performance outlined that single physiological or
psychological parameters will only represent very isolated aspects of recovery
[74], making it also challenging
to define protocols for the validation of recovery predictions.
In conjunction with this, most algorithms currently incorporated in wearable
devices are mainly based on surrogates of cardiovascular function (i. e. heart
rate, heart rate variability, or excess post-exercise oxygen consumption
[EPOC]). For example, EPOC represents the increased rates of oxygen intake
following strenuous activities and is based on the restoration of resting
states, including factors such as replenishment of fuel stores, restoring
hormone balance, and cellular repair [75]. However, wearable sensors typically estimate EPOC solely from
the calculated (or directly assessed) heart rate (or in some cases heart rate
variability) and thus the estimates provided are not based on actual
physiological or chemical alterations. Due to the complexity of recovery
dynamics, it becomes obvious that multivariate approaches are needed to provide
automated guidance on exercise and recovery, including, for example, additional
biomarkers obtained from saliva or sweat in a non-invasive and continuous
manner. Developing sensors that can continuously track changes in biomarkers,
for example, lactate, electrolytes, or even hormonal concentrations will open
new avenues for a better understanding of the physiological processes during
exercise and recovery. AI algorithms could analyze these diverse data streams to
provide more accurate predictions of recovery and readiness.
Safety aspects of exercise training
Wearable technology may also be used to improve the safety of competitive sports.
For example, it has recently been debated whether headers should be banned from
soccer in order to reduce the risks of concussion and subsequent brain injuries
[76]. In fact, only recent
advances in wearable technology (e. g. strain sensors such as accelerometers)
have helped to quantify the frequency and intensity of headers in amateur and
professional soccer [77]. As a
consequence of this, the English Football Association has very recently
introduced new guidance on the use of headers during training and matches,
reducing the regular exposure of head impacts [78]. However, while guidelines on the
diagnosis, treatment and return-to-sport decisions after concussion currently do
not include sensor-derived data, it is plausible that further advancements in
sensor size and accuracy may also enable wearable technology to aid important
medical decisions.
In line with this, attempts have been made to assess core temperature during
competitions. Tracking of the core temperature was implemented during the 2016
Road Cycling World Championships as well as the 2019 World Athletic
Championships in Doha, Qatar [79]
[80]. In fact, these
systems would also have merits for global health monitoring, e. g. in countries
with high temperatures. However, despite issues related to the accuracy of such
systems, implementing this also requires further guidance on possible ethical
dilemmas. This concerns especially decisions that are to be made when critical
core temperatures indicative of exertional heat illness are observed. A summary
on the possible issues related to this can be found elsewhere [81]. In this context, attempts have
been made to allow sensors to assess skin temperature and induce cooling when a
certain temperature has been exceeded. The primary aim of such devices is to
improve the perceived thermal comfort of individuals, e. g. in the presence of
hot flashes. However, the scientific evidence for the efficacy of the device
remains contradictory. While initial data showed beneficial effects on the
perceived temperature [82] or sleep
in women experiencing sleeping problems due to hot flashes [83], others did find improved distal
skin temperature with no beneficial effects on temperature perception in adults
not separated by sex [84]. So far,
the use of these devices in exercise related settings seems unexplored but it is
obvious that there is potential especially when performing in hot
environments.
Discussion and Conclusion
The landscape of wearable technology in exercise, health, and sports settings is rich
with opportunities but also fraught with challenges. A confluence of factors – the
rapid advancements of wearable technologies, the accumulation of large datasets
offering novel insights into health and performance, and the nascent role of new
commercially available AI technologies – mean that we stand on the brink of a
technological revolution that promises to redefine our approach to personal health,
fitness, and athletic performance. However, realizing this potential requires us to
navigate the complex interplay between technological innovation, data accuracy, user
engagement, privacy, and ethics.
To harness the full power of wearable technology, a concerted effort among various
disciplines is essential. Engineers working with healthcare professionals and
patients to design devices that meet user needs; data scientists developing
algorithms to accurately interpret complex data from wearables; and ethicists who
ensure the ethical use of data, safeguarding user privacy and consent. We posit that
this multidisciplinary approach will be essential to address the complexities of
designing, implementing, and utilizing wearable devices – and such an approach will
need to take into account a number of key priorities.
First, this interdisciplinary community needs to develop agile validation frameworks
that adhere to accepted methodologies to keep up with the commercial ecosystem. When
validation studies are conducted according to best practice protocols, they should
no longer be subjected to the traditional journal-peer-review system. Alternative,
fast-track platforms are required that allow for a rapid peer-review process that
is
primarily based on the protocol used, with the results being published with no delay
– so that key stakeholders have access to the most relevant validation research for
the latest crop of wearable devices.
Second, the fragmented digital health landscape limits the utility of collected data.
A multidisciplinary approach can address this by standardizing data formats and
terminologies across various systems. Engineers can contribute by designing
interoperable technologies that comply with standards such as Health Level Seven
International (HL7) and Fast Healthcare Interoperability Resources (FHIR) [85]. These standards facilitate seamless
data exchange between different health IT systems. Data scientists can develop
algorithms that not only integrate data from multiple sources but also ensure it is
comparable and actionable. This includes using standardized terminologies such as
SNOMED CT and LOINC to harmonize data [86]. Healthcare professionals can collaborate to ensure these systems
support clinical workflows and meet the needs of end users. Together, these efforts
enable comprehensive data integration and analysis, enhancing the overall impact of
wearable technology.
Third, incorporating end users as partners in the development process is vital. While
different fields of expertise are needed, true value is attained by focusing on end
user needs. Engaging patients in user-centered design ensures that wearables meet
real-world needs and are user-friendly. This partnership leads to solutions that are
technically advanced, widely accepted, and effective in improving health
outcomes.
Together, we must strive for advancements that not only push the boundaries of what
is technically possible and affordable but also prioritize data accuracy and
integrity, ethical standards, and user well-being. A multidisciplinary approach will
be essential to ensure that wearable technology serves as a force for good,
empowering individuals to achieve their health and performance goals while
safeguarding their privacy and autonomy, and at the same time providing new insights
into human physiology to further advance exercise and health sciences.
