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It’s not about the capture, it’s about what we can learn”: a qualitative study of experts’ opinions and experiences regarding the use of wearable sensors to measure gait and physical activity



The use of wearable sensor technology to collect patient health data, such as gait and physical activity, offers the potential to transform healthcare research. To maximise the use of wearable devices in practice, it is important that they are usable by, and offer value to, all stakeholders. Although previous research has explored participants’ opinions of devices, to date, limited studies have explored the experiences and opinions of the researchers who use and implement them. Researchers offer a unique insight into wearable devices as they may have access to multiple devices and cohorts, and thus gain a thorough understanding as to how and where this area needs to progress. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices to measure gait and physical activity.


Twenty professionals with experience using wearable devices in research were recruited from academic, industry and clinical backgrounds. Independent, semi-structured interviews were conducted, audio-recorded and transcribed. Transcribed texts were analysed using inductive thematic analysis.


Five themes were identified: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Researchers overwhelmingly supported the use of wearable sensor technology due to the insights that they may provide. Though barriers remain, researchers were pragmatic towards these, believing that there is a paradigm shift happening in this area of research that ultimately requires mistakes and significant volumes of further research to allow it to progress.


Multiple barriers to the use of wearable devices in research and clinical practice remain, including data management and clear clinical utility. However, researchers strongly believe that the potential benefit of these devices to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary research integrating the expertise of both academia, industry and clinicians is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders.


The use of wearable sensor technology to track health behaviours and outcomes of care, using measures of physical activity and gait, has exponentially grown in recent years. Technological advancements have led to the development of smaller, less obtrusive wearable devices that can be used for both recreational and medical purposes, where benefits include the ability to remotely and objectively monitor health behaviours in the context of people’s own lives [1, 2]. In response, the World Health Organisation (WHO) recently created a Digital Health Strategy which recognises the opportunities that exist for digital health, including the ubiquitous presence of wearable devices, to enable improved health coverage and well-being globally [3]. For this to happen, digital health must be accessible, reliable, safe and sustainable [3].

The promise of wearable sensor technology is that it will help transform healthcare by providing access to real-world, objective data [4,5,6], yet to do this, wearable devices need to meet the needs of all stakeholders who will interact with them. Critical to this is that wearable devices are usable, not only for the participants who will wear them, but also for the researchers and clinicians who implement them, and who ultimately will drive their continued use in various contexts. The International Organization for Standardization (ISO) defines usability as “the effectiveness, efficiency, and satisfaction with which specified users achieve specified goals in particular environments” [7]. It has been suggested that for wearable devices to be accepted, they must be easy to wear, easy to use, affordable, contain relevant functionality and be aesthetically pleasing [8,9,10]. However, although a number of studies have evaluated participants’ perceptions of various wearable devices for gait or physical activity [11,12,13,14,15,16], to date, the experiences of researchers who use the data provided by them to address research questions, or assist in clinical decision making, is rarely assessed. Usability should not only be focused on those who wear them, but also those who interact with them to collect and analyse data.

A small number of studies have investigated the use of wearable devices by clinicians [1, 17,18,19], where barriers such as increased workloads, difficulty handling large volumes of data, and an inability to integrate data into health records have been highlighted. However, typically, these studies have focused on either a single clinical cohort or a single device. Furthermore, to date, no study has evaluated wearable sensor technology from the perspective of researchers who use them. Therefore, there is a wealth of potential learnings and experiences that remains untapped. Specifically, it is important to understand how researchers view the use of wearable devices in healthcare, their perceived barriers and facilitators, the learnings they have gained from participants, and the usability factors that researchers perceive to be the most important or influential when selecting a wearable device for a study. Given the rapid evolution of wearable sensor technology, it is necessary to understand the views of all stakeholders, so that clinically relevant, useful, validated and usable wearable devices can be developed for everyone who needs to interact with them. Therefore, the aim of this study was to explore the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable devices that measure gait and physical activity, across a range of clinical cohorts and ages.


Study design

Ethical approval for this cross-sectional, qualitative study was granted by the Human Ethics Board of the local research institution, University College Dublin (LS-E-19-122-Keogh-Caulfield).


Twenty researchers were recruited using purposive, convenience sampling from the Mobilise-D consortium [20]. Mobilise-D is a five-year EU-IMI project that aims to produce validated and accepted digital mobility outcomes to monitor daily life gait of people with different mobility problems [20]. The consortium consists of over 150 professionals from universities, hospitals and industry, with various levels of technical and clinical experience. Members of the consortium were contacted through email inviting them to participate in this study. To participate, members needed to be able to speak English, have personal experience of using wearable devices to collect patient data, and consent to have their interview recorded. All interested participants were provided with information regarding the study procedures and were invited to take part in a single semi-structured interview at a suitable time.

Participants came from a variety of backgrounds including clinical, engineering, sports science and computer science (Table 1). Their experience with wearable devices ranged from one year to 20. Researchers had experience with a range of wearables from one to up to 11 devices.

Table 1 Participant details


The interview topic guide was developed using the Technology Acceptance Model (TAM) as a theoretical guide [21]. This model posits that technology is adopted when it is easy to use and is perceived to be useful to participants. Interviews were semi-structured in nature. Three main questions were included which related to participants’ experiences of wearables, including the barriers and facilitators of their use (Additional file 1). If required, a number of potential prompts were included in the topic guide, however the interviewer was instructed to be led by the experiences of each participant independently. Interviews were conducted in English and took place using online video calling software between September and November 2019. Interviews were conducted by AK, a female post-doctoral researcher with a background in physiotherapy and previous publications in qualitative research methods. Interviews lasted between 26 and 64 min, with a median time of 38 min. Recruitment ceased once data saturation was reached. This was determined by AK, based on whether new information was gained from further interviews. All interviews were audio-recorded and transcribed verbatim.


Interviews were coded using an inductive thematic analysis from a realist perspective, whereby it was assumed that the interviews reported the experiences, meanings and reality of the interviewees [22]. No specific theoretical constructs were used to analyse the transcribed texts [22]. However, codes were developed early in the analysis, using the extensive experience of the coders in this area. Thus, a ‘codebook’ method of analysis was used whereby a rich thematic description was sought so that predominant and important themes throughout the full dataset were identified through merging and interpreting the relationship between codes [23].

Interviews were coded by three researchers, AK, BV and KT, following the suggested protocol of Braun and Clarke [22]. BV is a female Professor of Human Movement Science. KT is a female physiotherapist with a PhD in Clinical Medicine. All three coders are members of the Mobilise-D consortium. Coding was completed manually. The interviewer (AK) familiarised herself with the full data set by reading all transcripts twice. An initial coding book was developed and sent to BV and KT for review until agreement was reached (Table 2). Using this developed coding book (Additional file 2) each text was coded by AK (n = 20), while BV and KT coded five texts each, thus 50% (n = 10) of texts were double rated (Table 2). Inter-rater reliability was established using percentage agreement. Agreement was above 80%, indicating excellent reliability of coding. Codes were then collated into potential themes and subthemes. These themes were subsequently refined using mind maps and discussion between all three raters (Fig. 1). Compelling extracts from the interviews were identified to ensure that the themes represented the data set before a final set of themes was agreed upon.

Table 2 Coding process
Fig. 1
figure 1

Representative mind map


Five themes were identified in the analysis: (1) The positives and negatives of using wearable devices in research, (2) The routine implementation of wearable devices into research and clinical practice, (3) The importance of compromise in protocols, (4) Securing good quality data, and (5) A paradigm shift. Supporting quotations are provided in Tables 3, 4, 5, 6, 7.

Table 3 Supporting quotations for Theme 1 ‘the positives and negatives of using wearable devices in research’
Table 4 Supporting quotations for Theme 2 ‘the routine implementation of wearables into research and clinical practice
Table 5 Supporting quotations for Theme 3 ‘the importance of compromise in protocols’
Table 6 Supporting quotations for Theme 4 ‘securing good quality data’
Table 7 Supporting quotations for Theme 5 ‘a paradigm shift’

Theme 1: the positives and negatives of using wearable devices in research

Researchers offered a balanced and pragmatic view regarding the positives and negatives of wearable sensor technology. For the most part there was a belief that the positives outweighed any negatives because of the need to progress this area of research further (detailed further within Theme 5).

Researchers were overwhelmingly in support of wearable sensor technology because of the insights it can offer ahead of traditional questionnaire-based outcomes. Furthermore, remote monitoring in particular has become an area of specific interest as it provides unprecedented access to objective, real-world data, and clinical insights into patient behaviours that were previously inaccessible. For the most part, researchers remarked that wearable devices are generally well accepted by study participants, who typically continue to comply even if some discomfort is present.

It’s what that data can tell us is the real positive. It’s not about the capture, it’s about what we can learn. I think we can learn so much more about people, you know, to help us manage them, to help us learn what works and what doesn’t work, to help us make them better sports people, to help them recover.”—Participant 4, Male, Academic

This is still a rapidly evolving area and as such, there remains a number of problems with wearable sensor technology which researchers need to consider. Some issues relate to the study participant (e.g. skin irritation, device loss, incorrect attachments, not wishing to wear a device etc.) while others relate to how the researcher interacts with the wearable device (e.g. is it valid, no integration of multiple data streams or information about environmental factors). The design of a wearable device will impact study participant compliance. Thus, researchers have learned which wearable devices won’t be tolerated by study participants (e.g. ankle-worn devices) and how certain features are considered non-negotiable (e.g. raw data access, battery life of a week, validity). Furthermore, a certain level of competence is required by those using the wearable devices, both by the study participant and the researcher. Typically, when researchers consider these issues as part of wearable device selection, the biggest risk is that the negative issues will result in either data loss, or poor-quality data, which ultimately is what researchers wish to avoid (discussed further in Theme 4).

“It’s not so much the burden of the eh, charging the device, it’s the fact that if the device isn’t charged then you lose those days…..if you’re only collecting seven days of data and you know you miss one day or something, you’re down 14% of data ... It’s like a big deal,….again it’s about the compliance and the continuity within the project.” - Participant 6, Male, Academic

Theme 2: the routine implementation of wearables into research and clinical practice

Although the positives of wearable sensor technology were clear from a research perspective, what was less clear to all involved, was how wearable devices may be routinely and easily implemented into practice. Regardless of whether researchers came from a clinical or technical background, the current iteration of wearable devices does not offer a clearly identifiable, easily understood, clinical utility.

There was agreement among researchers that this is an area at somewhat of a crossroads. Wearable devices are widely available from many avenues, and yet, it is not always clear how they are useful clinically. Researchers who are experienced with using wearable devices, and who can take the time to learn how to set them up, handle, and manage their data, can see their worth. However, that has not yet transferred into explicit clinical usefulness. This is partly because there is a perception that clinicians are not sure about what they want from these wearable devices, either because this type of technology has not been readily available to them, or because the technology cannot currently measure what would be useful, with one researcher remarking that they “cannot understand what they [clinicians] extract out of sensors that is clinically meaningful”.

For me, … it’s a bit more abstract, but we really need to think about how we are going to get people to use these tools in the clinic and how patients are going to use them in the home. What is it that we are going to do that will make it routine practice? What do we have to do to get there? And I think we’re still, there’s a lot of assumptions of course.… That’s still one of my motivating, one of my drivers because I believe that we can improve assessment, I believe we can improve patient feedback, but what is it that will actually get us to that point.” - Participant 12, Female, Academic

Linked inherently to the uncertainty of how wearable devices, and their learnings, may be integrated into practice, is the role of the clinician in this process. Although they are positive about the potential that wearable devices may offer, there is a gap between what is currently possible and what may be promised by them. Typically, the depth of information provided by wearable devices that allow clinicians to access raw data, requires a level of data processing skills that clinicians often do not have, and do not routinely need for their role. While consumer-based devices are both easy to set up and use, typically, what can be learned from them may only be summary-level, basic insights. Furthermore, there were concerns around the validity of consumer-based devices. Nonetheless, some researchers spoke about the value that their study participants gained from these basic insights including increased motivation to wear the device. Therefore, it remains to be seen how this area will progress. Do the insights provided by the data captured from wearable devices outweigh their cost and burden? Can wearable sensor technologies provide meaningful clinical insights instead of simply generating data that does not augment the clinical decision-making process ? While this is still being established, multi-disciplinary teams offer the best method of combining insights from both clinical and technical worlds.

There’s no system that fits all the clinical needs. So the system that you use in the clinic is a completely different system than you’re using outside the clinic. And I think the biggest issue that I would say is with clinicians, because often clinicians don’t really understand the outcome of what you actually measure. So the whole data processing part is not very well understood.”—Participant 1, Male, Academic

Theme 3: the importance of compromise in protocols

Beyond the features and possibilities of the wearable devices themselves, it was obvious that their usefulness is only as robust as the protocols which are designed to implement them. However, pragmatism is needed as it is clear that what is considered ideal from a research perspective and what is practical, are often not the same. Frequently, what is desired from a technical perspective may not be acceptable from a clinical perspective. Furthermore, it is often a case of trial and error with protocols, as device specific learnings may only come to light in certain contexts. Ultimately however, although researchers have the ability to collect data in a range of ways, it doesn’t mean that they should. Any data collected should reflect the research question under investigation. Moreover, because of the range of wearable devices that are available, researchers noted that it was often a case of ‘better the devil you know’ in that they frequently chose devices they were familiar with and knew were valid. The result is that studies are limited in their ability to compare results because of a lack of standardisation and harmonisation between devices.

If you want good data, so if you need to do a proper gait analysis study, I would always go for pelvis. Well no, I would always go for two sensors on the ankles or on the foot. Not being able to do that, if you have to choose just one sensor and one location, I would always go for the pelvis. If it were wearability, so if I am more interested just in behaviour, I would probably go for the wrist”—Participant 5, Female, Academic

If you want continuous monitoring you can’t wire them up like Christmas trees, just clinical tests or tests once a week at home and that might be possible.”—Participant 17, Male, Industry

Theme 4: Securing good quality data

The priority of most protocols is to balance the burden on study participants, while simultaneously avoiding data loss. However, a number of participant- and researcher-related challenges remain when it comes to collecting high quality data.

Study participants with chronic health conditions are often motivated to help improve the health of other patients who will come after them, irrespective of whether there is a direct benefit to themselves or not. Thus, these study participants are seen to be a willing and compliant cohort. Researchers were unclear as to whether they should provide study participants with performance-related feedback from wearable devices or not. The value of such feedback was highlighted in terms of motivating study participants and ensuring that they are comfortable that the device is working. Indeed, study participants’ interactions with wearable devices that they thought were broken, often inadvertently produced data loss, as they sought to fix the perceived problem. Therefore, it was noted that the risk of unwanted change in participants’ behaviour with limited feedback, was likely to be lower than the risk of data loss or reduced compliance as a result of no feedback. Nonetheless, ideas were provided as to how feedback could be provided without risking changes to behaviour, such as confirmation of data uploads or wear-time.

For some of the patients it would be good to have it be able to give them some feedback, as in a light, that they know that it is working or that it is doing something. Because they are like do I need to do anything? Do I need to turn it on? Do I need to charge it? And I think sometimes there is a comfort that they are not wearing it for no reason because they can see that it is doing something.”—Participant 7, Female, Academic

Researchers noted the value of providing study participants with clear instructions on how the wearable device works, what data is collected by them, what is needed from them, how to put it on or take it off, and how to charge it. These are essential steps to support remote data collection. However, despite the acknowledged importance of planning for good data collection, researchers often do not undertake formal usability testing prior to implementing a wearable device. Instead, they talk to colleagues and undertake dry-runs using their own research teams in order to help determine what device suits their needs best. Associated with this is the need to conduct researcher training in how to run the protocols appropriately. This is particularly necessary when multiple sites are involved in various locations.

They [participants] might have questions so you need to give them some information before you attach the sensor and we have tried to not say too much about being an activity monitor but saying something like being a sensor that measures movement but it will not capture any pictures and so on. So you need to explain it and it is okay for most of them I think.”–Participant 11, Female, Academic

“ So in principle it’s the same as what I said about the patient. You know if you just tell them [collaborators at other sites] I have to do this and that and follow the protocol and then stick a wearable to the patient, then they may do it but they may not be very enthusiastic about it. If you involve them then that’s a different story then hopefully they’ll maybe transfer that to the patient”—participant 17, Male, Industry

Although wearable sensor technology provides researchers with previously inaccessible information, a significant challenge remains regarding the inability to integrate multiple outputs and confusion surrounding how companies conduct their own analyses. Specifically, researchers frequently described the outputs from wearable devices as being like a ‘black box’. While access to raw data can support researchers in making sense of this output, they remarked that it was often still unclear exactly what wearable devices can measure. Furthermore, classifying activities remains problematic in populations with mobility issues such as slower walking or shuffling gaits.

We don't necessarily know what is happening in that black box of the analysis. They just give you the reports. We have got the raw data, we will probably be analysing it but for the most part it is probably here is a report, this the percentage. But I kind of want to check that.”—Participant 7, Female, Academic

Theme 5: a paradigm shift

Overwhelmingly, experienced researchers who have been working in this field for over a decade or more, consistently noted that they felt this area of research represents a paradigm shift in terms of understanding people’s movements. There was a clear feeling that, although wearable sensor technology does not currently provide the depth of insights that might be desired, that this is a rapidly changing area and one that has developed enormously in a short space of time. Consequently, researchers were pragmatic in their acceptance of some of the current challenges. For instance, they acknowledged that the promise of ‘simple’ behaviour change, whereby an individual simply puts on a wearable device and will change their behaviour based on its feedback, cannot be realised. Furthermore, they also recognise that problems and uncertainty are part of the process given the development stage this research finds itself in. As a result, wearable sensor technology is not currently the ‘game-changer’ that it was originally purported to be.

It is a technical development which may be relevant for learning more about human behaviour within the next decade or two and then I am convinced that the new technology will substitute this technology. This is how it always happens during evolution and even scientific evolution, now we are in a phase where we can collect data with such types of technology.”—Participant 16, Male, Academic

I think there’s a kind of fundamental belief that this is the way that we will measure people in the future. It offers us … a completely new paradigm for trying to understand human behaviour and understand how that changes with different clinical conditions, and how it can change with different treatment interventions.”—Participant 4, Male, Academic

Nonetheless, researchers highlighted the areas that they felt needed to change, or could be improved on, in order to further progress this paradigm shift. Specifically, there was acknowledgement that researchers must continue to collect large datasets, in a variety of cohorts, using a variety of devices and a variety of attachment sites, in order to understand the context of movement, or behaviour, and create digital biomarkers of health. It is only through gathering and understanding this data, that the area will be able to produce standards for what works best for what research question. Furthermore, multi-disciplinary teams were considered vital in the development of wearables. Clinicians, academics, engineers and wearable manufacturers need to continue to build their relationships and interact in order to best understand the needs of all stakeholders in all aspects of wearable device design and implementation. There was no firm consensus regarding what specifically needs to change, reflecting the complexity of the area, the research priorities of various patient populations, and the different experiences of researchers in this area. Nonetheless, a number of ideas were provided, some of which focused on the design of the wearable devices, and some which sought to plot an ‘ideal’ pathway in which the research could be undertaken. Examples included producing smaller devices, integrating devices into shoes and clothing more, being able to capture contextual factors of movements within a wearable device, measuring people only as much as you need to, and developing manufacturing standards that will allow an integration of data streams or ‘bring your own device’ studies in the future. Within this, however, one researcher remarked that despite all the improvements in this area, and the future capabilities of wearable sensor technology, it will remain necessary to ‘keep the human in the loop’ in order to best understand wearable device outputs and to derive clinical meaning from them. Ultimately, for all its promise, the advance of technology cannot come without the expertise of the people driving it.

And if you had a standard, theoretically if a company was producing you know, if they were measuring acceleration and gyro, and it’s on the same part of the body, now going from wrist to waist now is probably a really difficult one but you know if there’s a standard around the sensor characteristics or the measurement capabilities, then theoretically you should be able to go from manufacturer to manufacturer and learn the same things.”—Participant 4, Male, Academic

I just think it’s really important for us to get the conceptual framework of what you’re measuring, what does it mean, and you know what interventions are available. So it’s going to be different for different devices, monitoring outputs, different populations.”—Participant 19, Male, Academic


This qualitative study explored the experiences and opinions of researchers from academic, industry and clinical contexts, in the use of wearable sensor technology to collect gait and physical activity data. The results provide insights into where this area of research is going and what needs to change to allow it to deliver its promise of revolutionising healthcare. Specifically, although the benefits of wearable devices are well known, it is not yet clear how they can be integrated routinely into practice. This is because analysing raw data is a specific skill in itself that is not easily undertaken by those in clinical contexts, while integrating and comparing the data streams from multiple wearable devices is not yet possible. Essentially, the use of wearable sensor technology represents a paradigm shift in how we understand human behaviour, and as with any shift, significant learnings and process changes are needed to maximise the capability of these devices.

Participants shared the beliefs of others that the data gained from wearables will help the management of various chronic conditions [1, 3]. However, identified challenges were also similar to that of previous research focusing on patient and clinician opinions [1, 2, 11, 15, 17, 18, 24]. Data management and analysis in particular was listed as a major problem irrespective of the background of the researcher. Essentially, researchers do not trust ‘black box’ processing from any wearable device and prefer to either validate it themselves or have access to its raw data. This is unsurprising given that study results rely on reliable, accurate data, and indeed this has been the focus of much of the research in this area up until now [25]. The need for digital solutions to be adopted and scaled into health services is considered a key objective for this area going forward [3]. However, the reliance on raw data brings with it the need for a certain level of competency in data management, a skill which remains daunting to those from clinical backgrounds [8]. It has been suggested that the creation of perfect research conditions limits clinical transference of protocols [26] and it seems that this is still a barrier for many researchers in this area. While multi-disciplinary research teams are used to balance the technical and clinical needs of a project, until data handling becomes manageable by all, regardless of their background, wearable devices risk remaining as ‘nice to have’ rather than ‘need to have’ products.

The relative modernity of these devices means that they are often tested in simulated or highly-controlled environments, thus, learnings from remote contexts are only recently emerging [8]. For the most part, these are gained through trial and error or through the use of ‘friendly’ in-house trials. However, it was notable how few researchers conduct formal usability evaluations as part of their data collection protocols. Measuring the attitudes to, and engagement with, digital health is considered to be an integral part of its progression, and the WHO is working closely with the ISO to ensure that person-centred care is a focus of any developments [3]. Thus, the finding that this concept is not formally evaluated by many in this area of research is disappointing. Researchers are strongly advised to document their experiences and learnings in a more structured manner in the future. The use of fidelity guidelines may help to ensure that they maximise the efficiency and cost of their trials by designing protocols, training staff, and evaluating the delivery of a protocol from the perspective of both researchers and study participants [27]. Combined, these would offer researchers a more robust method of capturing barriers to wearable sensor technology and their procedures, thus allowing them to formally compare multiple wearable devices across various cohorts. Furthermore, without this, researchers are currently relying on using the same wearable devices because they know their faults and won’t be surprised by them. The cost of new devices and testing their validity is also a potential barrier to trying different systems, and so researchers felt it was ‘easier’ and more cost-effective to not stray too far from the familiar. However, the result of this is that device progress is potentially being restricted as researchers are not readily trialling new devices for fear of what they may lose because of it.

To the author’s knowledge, this is the first study to explore the experiences of researchers in their use of wearable sensor technology. Valuable insights have been gained into understanding the challenges that continue to exist in this critical area of research, from both a technical and clinical perspective. It is clear that we are far from the original idea that wearable devices would simply need to be deployed to change healthcare [4]. However, critically, this was not seen as a negative. Rather, there was acceptance that wearable sensor technology, the data it produces, and implementing them, is far more complex than first envisaged. Complex problems are often said to require complex solutions, and this seems to be the case with wearable devices. Nonetheless, the complexity almost adds to their potential, in that researchers who have witnessed their progression, are passionate and convinced that this will be the game changer that was planned, but simply acknowledge that it will take longer than initially intended. Indeed, one of the most important findings was the certainty in which researchers spoke about this being a paradigm shift in which research attempts to not only better understand human movement and behaviours, but understand how it can be best captured and, most importantly, what can subsequently be done with this information.

However, this study is limited by its use of researchers from a single consortium and its associated focus on wearable devices that measure gait and physical activity. Specifically, it is likely that the researchers from this consortium, as a result of their work in this area, are positively biased towards the use of wearable devices. Nonetheless, the Mobilise-D consortium is multi-disciplinary in nature and the participants in this study represent a broad mix of backgrounds and experiences across multiple sites in nine different countries across Europe. Therefore, it is likely that the experiences of these researchers are representative of the field overall. However, it is worth noting that many of the researchers expressed a bias against the use of consumer-based devices for reasons of validity. Additionally, researchers were not followed up with additional interviews or collective discussion in focus groups, which may have provided further insights into specific findings such as the direction and changes needed for research in this area. Finally, two of the three coders used to analyse the data were from physiotherapy backgrounds, therefore it is possible that a classification bias may exist in how codes were generated.

Participants differed in their opinions regarding the next steps for this area of research. However, all agreed this field will not improve without a multi-disciplinary approach, and most critically, an ability to compare data sets [3]. This requires significant work from, and between, wearable device manufacturers regarding the availability of their processing algorithms [3, 28]. Rapid, scalable processing solutions that work across multiple wearable devices are also required [3, 8], while platforms need to communicate between each other to break down the barriers and uncertainty regarding data management [3, 28]. Academically, multi-disciplinary work that collaborates with industry, patients, researchers, and clinicians is required to ensure that the needs of all stakeholders are met. Indeed, projects such as Mobilise-D [20] have recognised this and are implementing and optimising existing algorithms in real-world data. However, these need to become the norm and not the exception if the promise of the paradigm shift is to be realised over the coming years.


Significant changes have emerged in the measurement of human movement in recent years with the evolution of wearable sensor technology. This development is the result of substantial work from academics, clinicians and industry, the outcome of which is a new paradigm for how research in this area is conducted. However, despite the advances, multiple barriers to the use of wearable devices in research and clinical practice remain. In particular the following barriers are worth noting:

  • A clear, clinical utility for the output derived from wearable sensor technology has not yet been identified and implemented into practice.

  • The management of data derived from wearable sensor technology still requires a set of skills that many clinicians do not have. The result of this is a continued reliance on proprietary algorithms or a need for multi-disciplinary teams to handle data processing and analysis.

  • Capturing good quality data in the ‘home’ environment of any clinical cohort is a complex matter that requires clear planning, well-defined protocols that consider the challenges and barriers of remote monitoring (i.e., interactions with daily living, battery life, patient abilities, clinical versus technical needs).

Nonetheless, researchers strongly believe that the potential benefit of wearable sensor technology to support and create new clinical insights for patient care, is greater than any current barrier. Multi-disciplinary, publicly available research integrating the expertise of both academia, healthcare and industry is a fundamental necessity to further develop wearable devices and protocols that match the varied needs of all stakeholders. Future research utilising wearable sensor technology to capture gait and physical activity is advised to consider the following:

  • Conduct structured and formal usability assessments as standard when implementing wearable sensor technology.

  • Continue to capture rich data, at scale, in multiple cohorts and with a variety of devices in order to increase our understanding of human behaviours, including how it can be best captured and what can be learned from this information.

  • Conduct collaborations between academic, clinical and industry-based partners in order to maximise the learnings and expertise from each group and to build protocols that consider the needs of all stakeholders.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to the potential for identifying researchers through their responses within the full transcripts but are available from the corresponding author on reasonable request.



International Organization for Standardization


World Health Organisation


  1. Bruno E, Simblett S, Lang A, Biondi A, Odoi C, Schulze-Bonhage A, et al. Wearable technology in epilepsy: the views of patients, caregivers, and healthcare professionals. Epilepsy Behav. 2018;85:141–9.

    Article  Google Scholar 

  2. Izmailova ES, Wagner JA, Perakslis ED. Wearable devices in clinical trials: hype and hypothesis. Clin Pharmacol Ther. 2018;104(1):42–52.

    Article  Google Scholar 

  3. WHO. Draft global strategy on digital health. World Health Organisation; 2020.

  4. Appelboom G, Yang AH, Christophe BR, Bruce EM, Slomian J, Bruyere O, et al. The promise of wearable activity sensors to define patient recovery. J Clin Neurosci. 2014;21(7):1089–93.

    Article  Google Scholar 

  5. Lee J, Kim D, Ryoo H-Y, Shin B-S. Sustainable wearables: wearable technology for enhancing the quality of human life. Sustainability. 2016;8(5):466.

    Article  Google Scholar 

  6. Motti VG, Caine K. Human factors considerations in the design of wearable devices. Proc Hum Factors Ergon Soc Annu Meet. 2014;58(1):1820–4.

    Article  Google Scholar 

  7. ISO. ISO 9241–11:2018(en) Ergonomics of human-system interaction—part 11: Usability: Definitions and concepts: International Organization for Standardization; 2018.

  8. Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Linden M. A systematic review of wearable patient monitoring systems—current challenges and opportunities for clinical adoption. J Med Syst. 2017;41(7):115.

    Article  Google Scholar 

  9. Dvorak J. Moving wearables into the mainstream: taming the Borg. New York: USA Springer; 2008.

    Google Scholar 

  10. Park E. User acceptance of smart wearable devices: an expectation-confirmation model approach. Telemat Inform. 2020;47:101318.

    Article  Google Scholar 

  11. Keogh A, Dorn JF, Walsh L, Calvo F, Caulfield B. Comparing the usability and acceptability of wearable sensors among older irish adults in a real-world context: observational study. JMIR Mhealth Uhealth. 2020;8(4):e15704.

    Article  Google Scholar 

  12. Preusse KC, Mitzner TL, Fausset CB, Rogers WA. Older adults’ acceptance of activity trackers. J Appl Gerontol. 2017;36(2):127–55.

    Article  Google Scholar 

  13. Rupp MA, Michaelis JR, McConnell DS, Smither JA. The role of individual differences on perceptions of wearable fitness device trust, usability, and motivational impact. Appl Ergon. 2018;70:77–87.

    Article  Google Scholar 

  14. Liang J, Xian D, Liu X, Fu J, Zhang X, Tang B, et al. Usability study of mainstream wearable fitness devices: feature analysis and system usability scale evaluation. JMIR mHealth uHealth. 2018;6(11):e11066-e.

    Article  Google Scholar 

  15. Mercer K, Giangregorio L, Schneider E, Chilana P, Li M, Grindrod K. Acceptance of commercially available wearable activity trackers among adults aged over 50 and with chronic illness: a mixed-methods evaluation. JMIR mHealth uHealth. 2016;4(1):7.

    Article  Google Scholar 

  16. Yang H, Yu J, Zo H, Choi M. User acceptance of wearable devices: an extended perspective of perceived value. Telemat Inform. 2016;33(2):256–69.

    Article  Google Scholar 

  17. Dixon WG, Michaud K. Using technology to support clinical care and research in rheumatoid arthritis. Curr Opin Rheumatol. 2018;30(3):276–81.

    Article  Google Scholar 

  18. Papi E, Murtagh GM, McGregor AH. Wearable technologies in osteoarthritis: a qualitative study of clinicians’ preferences. BMJ Open. 2016;6(1):e009544.

    Article  Google Scholar 

  19. West P, Van Kleek M, Giordano R, Weal MJ, Shadbolt N. Common Barriers to the Use of Patient-Generated Data Across Clinical Settings. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems—CHI '182018. p. 1–13.

  20. Mobilise-D. Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement 2020.

  21. Lee Y, Kozar KA, Larsen KRT. the technology acceptance model: past, present, and future. Commun Assoc Inform Syst. 2003.

    Article  Google Scholar 

  22. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

    Article  Google Scholar 

  23. Braun V, Clarke V. One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qual Res Psychol. 2020.

    Article  Google Scholar 

  24. Byrom B, Watson C, Doll H, Coons SJ, Eremenco S, Ballinger R, et al. Selection of and evidentiary considerations for wearable devices and their measurements for use in regulatory decision making: recommendations from the ePRO consortium. Value Health. 2018;21(6):631–9.

    Article  Google Scholar 

  25. Shin G, Jarrahi MH, Fei Y, Karami A, Gafinowitz N, Byun A, et al. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform. 2019;93:103153.

    Article  Google Scholar 

  26. Mombers C, Legako K, Gilchrist A. Identifying medical wearables and sensor technologies that deliver data on clinical endpoints. Br J Clin Pharmacol. 2016;81(2):196–8.

    Article  Google Scholar 

  27. Borrelli B. The assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials. J Public Health Dent. 2011;71(s1):S52–63.

    Article  Google Scholar 

  28. Baig MM, Afifi S, GholamHosseini H, Mirza F. A systematic review of wearable sensors and IoT-based monitoring applications for older adults—a focus on ageing population and independent living. J Med Syst. 2019;43(8):233.

    Article  Google Scholar 

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This project received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 820820. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This publication reflects the authors’ view and neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

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BC conceptualised the study and research question. AK completed the interviews. AK, KT and BV analysed the data and drafted the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alison Keogh.

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This study was approved by the University College Dublin research institute’s committee on human research (LS-E-19-122-Keogh-Caulfield). All participants provided informed consent prior to participating in this study.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1.

Expert learnings regarding the use of wearables in healthcare research to measure PA in older adults.

Additional file 2.

Expert interviews for Mobilise-D—Coding book.

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Keogh, A., Taraldsen, K., Caulfield, B. et al. It’s not about the capture, it’s about what we can learn”: a qualitative study of experts’ opinions and experiences regarding the use of wearable sensors to measure gait and physical activity. J NeuroEngineering Rehabil 18, 78 (2021).

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