Keywords

Introduction

The importance of lifestyle-related diseases (LRD) such as type 2 diabetes (T2DM), COPD, cardiovascular disease (CVD) [1, 2] and the consequent importance of assessing citizens’ risk for developing LRDs at an early stage is widely acknowledged, but has proven to be notoriously difficult to achieve. The reasons for this are many: Most importantly, health risk behaviors are deeply ingrained in the daily life of people or part of valued habits and social interactions, and hence difficult to change, as well as many individuals are either unable or unwilling to articulate these. In effect, it requires a massive effort to effectively screen a population through conventional means such as health checks and interviews performed at regular intervals by e.g. general practitioners (GPs). Hence, too many citizens remain effectively at a distance from preventive healthcare services such as smoke cessations courses, dietary consultancy, physical exercise or even sleep.

From a public health perspective, decreasing the distance between citizens and preventive care services is essential in the sense that such health services must be available, accessible, affordable, and acceptable for the citizens. Currently, LRDs such as T2DM, asthma, COPD, CVD, and certain types of cancers account for 50–60% of all hospital admissions [3], and have a significant impact on the life expectancy and quality of life of the individual by affecting both the physical well-being and mental health [4,5,6,7]. LRDs are often, although not exclusively, incurred by health-risk behaviors such as smoking, harmful use of alcohol, poor diet, and a sedentary lifestyle [1, 2], and it is predicted that it will lead to a surge in LRDs if rates of obesity and physical inactivity continue to increase [8, 9].

Health-risk behaviors, early symptoms, and socio-demographic conditions can in principle be used for identification of individuals at risk [10,11,12]. This requires a proactive approach in which providers reach out to people at risk while their condition is still revocable, rather than merely react and provide treatment when people eventually reach providers and get diagnosed because the damage is beyond repair [13].

Current initiatives in preventive healthcare in developed countries have primarily focused on improving the ability of healthcare services to take responsibility by reaching out to citizens, for instance through information campaigns and systematic health checks performed by general practitioners (GPs) [14, 15]. Experience, however, shows that these approaches are either ineffective or very resource intensive for the healthcare systems.

While the preventive healthcare system struggles to reach out to citizens, recent trends suggest that digital technologies hold great potential for enabling people to assess and monitor their own health condition autonomously or in collaboration with health professionals. Under the umbrella term quantified-self , this entails various types of devices enabling production of data representations of the behavioral patterns and health condition of an individual, which in turn can support health management and lifestyle change, leading to better life quality in the long term. The purpose of this chapter is to investigate how quantified-self technologies can contribute to decreasing the distance between preventive healthcare services and citizens by addressing the following two research questions: RQ1: In what ways are citizens at a distance from the preventive healthcare system? RQ2: And how can quantified-self technologies enable citizens to decrease this distance?

Based on a qualitative case study of the pilot implementation of a preventive healthcare intervention in Denmark, this chapter first analyses the challenges faced by citizens for engaging in early assessment of risk for LRD and then proceeds to discuss the potential of quantified-self technologies to increase the accessibility to preventive healthcare.

Background

Towards Data-Driven Approaches to Early Detection

In Denmark, the responsibility for supporting citizens (5.8 million) through lifestyle change is shared among municipal health centers and GPs. These health services are tax funded, free to use for all citizens, and developed to support citizens through all stages of lifestyle change. However, while citizens can get referred to lifestyle offers, for instance by their GP, it is their own responsibility to reach out in case they want support. The municipalities (98) are responsible for offering consultancy and support for lifestyle change through services such as smoke cessation courses and dietary consulting. The GPs (app. 3500) are responsible for identifying citizens with risk behaviors and for monitoring their health status over time. Preventive healthcare first and foremost involves assisting citizens in converting to a healthier lifestyle, and in retaining this for a lasting period of time [16,17,18,19,20]. Conversion not only involves supporting behavior change through, for instance, improved diet, physical exercise, or smoking cessation [11], but also generates motivation in the citizens for engaging in behavior change, and ensure that the preventive care offers match the person’s stage of readiness. Likewise , retention is challenging as health-risk behaviors are often deeply ingrained habits and preventive care must therefore extend over sufficient time to allow new habits to form [21,22,23]. Without effective assessment of the lifestyle of citizens at risk of developing LRD, and without the means to attract these to the right lifestyle offers, preventive healthcare will, however, remain at a distance to the citizens.

Recently, the manner of which analysis of big data in healthcare can assist early detection by enabling analysis of more comprehensive, diverse, and timely health data has attracted significant interest. This trend stems from the fact that a substantial amount of relevant data is already accumulated in, for instance, electronic medical records, as well as quantified-self technologies which facilitate the production of new data representations of the citizens. Hence, relevant data can originate from a combination of sources, including clinically-based electronic medical records (EMRs) and other health information systems used to routinely collect patients’ data, as well as from sensors and mobile phone apps, and crowdsourced information [24]. In addition, advancements within artificial intelligence and machine learning provide new opportunities for more efficient analysis of big data and for seeing new patterns in these data [25]. This approach, however, comes with its own set of challenges. First of all, it requires a digital infrastructure that is capable of handling the high volume, velocity, and variety of data [25] and often extracting data from otherwise non-integrated and incompatible health information systems [24]. Furthermore, it requires that the clinical models, workflows and organizational procedures for offering preventive care are adjusted to accommodate this potentially more precise way of detecting risk in a larger population of citizens. Lastly, this trend may raise concern about the risk of mental stress incurred by false positives produced by information systems, privacy of citizens and attention towards the potential mental response that increased awareness of risk may bring [24].

Even more importantly, this may not solve the fundamental challenge of bringing citizens closer to the preventive healthcare services, as conventional data-driven approaches assume an interest and willingness from the citizens to participate. One example is the Danish research and development initiative, TOF—Danish abbreviation of ‘Early Detection and Prevention’.Footnote 1 The purpose of this project was to develop an intervention that enabled detection of health-risk behaviors in the broad population of citizens, and in particular among those not in frequent contact with the healthcare system. The core of the intervention was an information system that automatically stratified citizens into risk groups in order to connect them to a suited lifestyle offer either in municipal healthcare or general practice.

The TOF intervention aimed at ensuring ‘right to health’ [26] by (a) securing availability of prevention care services for all citizens, (b) creating accessibility by inviting citizens through eBoks (a secure, public electronic mailbox), (c) economic affordability, as participation was free for all citizens, (d), acceptability, as the intervention was non-discriminatory and inclusive for all citizens, (e) and quality, by basing the intervention on existing, well-established care services.

The TOF intervention has been under development since 2009, and in 2012 the clinical precision of the stratification algorithm was confirmed through a feasibility study [12]. In addition, the intervention was tested through two full-scale pilot implementation projects in 2016 and 2019, each lasting 3 months, and including a total of 4201 citizens (2016: N = 2661; 2019: N = 1540) [27, 28]. The research evaluations of the pilot implementation projects proved the ability of the TOF intervention to attract a large number of citizens and facilitate automatic stratification based on citizen-reported health information. However, the research evaluations also highlighted that while this type of data-driven approach could in fact identify citizen in moderate to high risk of developing LRD, it mainly succeeded in retaining those with fairly moderate health risk behaviors and with a relatively high level of health literacy [28]. Hence, there is a strong indication that this type of data-driven approach to detection of citizens in need of preventive healthcare alone cannot decrease the distance between preventive healthcare services and the broad population of citizens, and especially those who may need it the most.

Quantified Self

An issue of existing data-driven approaches to early detection, such as TOF, may be that they maintain a traditional division of responsibility between the citizen and the preventive health providers and hereby assume that the distance between citizens and healthcare services can be decreased by enabling the healthcare system to just reach out to the citizens in improved ways (in the case of TOF via eBoks and the software platform ‘the Health Folder’). This alone will, however, not solve the fundamental challenge that citizens must willingly accept and follow up on the offers provided in order for disease prevention to be successful in the long term. At the other end of the spectrum, a long-standing interest exists in terms of how technologies can make citizens engage autonomously in their own health and well-being. It is well known that digital technologies can assist people in assessing and monitoring their own health condition on their own or in collaboration with health professionals. Under the umbrella term, quantified-self, this entails individuals engaging in self-tracking of biological, physical, behavioral, and environmental information using various types of technologies readily available on the consumer market, including exercise wristbands and mobile phone applications, as well as equipment developed to support specific types of health monitoring [29]. Recent numbers from the US show that 69% of the adult population track key health indicators such as bodyweight, exercise routines or symptoms. While many use pen and paper, digital technologies such as wearables (for instance fitness wristbands or other specialized sensor technologies) or smartphone applications are becoming increasingly common tools to track physical indicators such as fitness, sleep, nutrition, vital signs, as well information mental factors such as mood and stress level, or even interactions with one’s social network [30, 31]. In fact, it has been documented that 27% of all Internet users in the US of age 18 or older track health data such as body weight and exercise routines online, and that 29% of all adults have downloaded health management applications [24].

Quantified-self technologies are primarily aimed at supporting individual, autonomous health management and intended to make citizens more qualified to identify relevant health concerns that require professional assistance and seek healthcare at their own initiative. Therefore, they also hold the potential for individuals to track indicators of their quality of life and consequently, they are increasingly referred to as quality of life technologies [32]. This, however, entails a number of ethical and societal concerns related to the movement towards transferring, not only autonomy, but also responsibility towards potentially vulnerable individuals [33]. However, certain types of care can benefit greatly from using technology to create a stronger link between the patient and the health provider. This is not least the case in treatment of chronic conditions, where telemedicine applications have shown to be valuable, for instance by enabling the patient to take an active role in monitoring his/her condition and detecting exacerbations at an earlier stage than in conventional care [34, 35]. In contrast to telemedicine, which refer to situations where measurements are sent to a healthcare provider as part of a mandated type of care [36], the purpose of quantified-self technologies is to enable citizens to monitor health and well-being autonomously [32, 36], and hence drive individuals to engage in the pre-diagnostic work involved in identifying potential health concerns. This has not yet, however, provided the required evidence of efficacy and effectiveness maturity to become a medical device and reliably support the citizen in important health decisions [37]. Additionally, quantified-self technologies do not by definition bring the citizens any closer to the preventive care providers when needed. Hence, the purpose of this chapter is to explore how quantified-self technologies can help decrease the distance between citizens and care providers in preventive healthcare.

Methods

Setting

The study has been conducted within the preventive healthcare system in the Region of Southern Denmark (RSD). RSD is a rural area of Denmark comprised of 22 municipalities with a total population of approximately 1.2 million citizens. Compared to the four other regions in Denmark, the population of RSD has a relatively low socio-economic standing, and the prevalence of lifestyle-related diseases is relatively high. Like the rest of the country, the responsibility for offering preventive healthcare services is distributed across two sectors in RSD. First, the municipalities (N = 22) are responsible for offering support for lifestyle change, e.g. through dietary consultancy and smoke cessation courses, as well as for reaching out and informing the population, for instance through information campaigns. To enable this, many municipalities have created physical health centers over the past decade where these services are integrated. Second, general practitioners are responsible for responding to citizens they identify as having significant health-risk behaviors as part of their normal routine in the clinic, and offer support for lifestyle change, for instance by examining, advising and monitoring the citizen, or by referring the citizen to one of the municipal offers. Currently, these preventive care services are, however, largely uncoordinated, and, hence, it is of high priority for the Region of Southern Denmark to increase the cohesiveness of the system.

The case of the study is the research and development project TOF, which is a partnership between a research unit on general practice, the RSD, the Organization of General Practitioners, and ten municipalities, and aims to develop and implement a data-driven approach to early detection that integrates all preventive care services in the region.Footnote 2 The TOF intervention has been under development since 2009. Since then, two 3-month pilot implementation projects have been conducted in 2016 and 2019 involving a total of three municipalities and 4201 citizens [27, 28].

Data Collection and Analysis

The study presented has been conducted as an ethnographic study of the practical use of the TOF intervention, which has been followed by the author since 2013. In order to identify the challenges for citizens in engaging in preventive healthcare, a qualitative field study was conducted during two 3-month pilot implementation projects in 2016 and 2019. During each round of data collection, citizens and health professionals (GPs, nurses, and municipal health professionals) were interviewed about their experiences. All study participants have given informed consent for the study. The TOF pilot project, part of which this study was conducted, has been approved by the Research & Innovation Organization, University of Southern Denmark (SDU RIO). The registration numbers of the approvals are 15/60562 (2016) and 18/32742 (2019). The main focus has been on the experience of the 26 citizens included in the study (12 males, 14 females, aged 32–60). The citizens were followed during their one to three-month participation in either 2016 (N:13) or 2019 (N:13). 13 of the interviewees opted out of the TOF intervention before the health interview with the GP. 21 of the respondents were interviewed over the phone and five interviews were conducted either at the care facilities or in the citizens’ homes. Ten respondents were interviewed twice during the pilot implementation in 2016. In total, 36 interviews (duration of 15–45 min) have been included in this analysis. The interview guide revolved around (a) the interviewees’ general perception of health and previous experiences with lifestyle change, (b) their general experience of participation in TOF, and (c) their experience in using a digital system to prepare and assess their own risk profile and connect to relevant preventive healthcare providers. All interviews were audio recorded.

For the purpose of the analysis, the interviews were partially transcribed by the author. That is, the author listened through all interviews and transcribed the parts of specific relevance to the analysis. In line with the ethnographic approach applied in this study, the analysis of the empirical data was developed through three stages of writing with the aim of building theory through an inductive and interpretative process [38]. This explorative process allows for patterns, occurrences, phenomena and theorising to emerge and connections between themes that otherwise seem disparate and later generalised into a coherent argument [39].

The first stage consisted of writing the field notes (notes taken during field visits and after interviews). The second stage consisted of writing out data (listening through interviews, exploring emerging themes in these and sorting them into categories). From this analytical process, the general theme of ‘distance’ gradually emerged as a concept encompassing both the physical and psychological challenges involved in connecting citizens to preventive healthcare services through the data-driven approach. The third stage consisted of writing up the findings in order to capture the multiple facets of distance experienced by the citizens [38].

Assessing Risk at a Distance

A Data-Driven Approach to Creating Risk Awareness

The fundamental goal of the TOF intervention was to design a data-based workflow that would relieve health professionals from the labor-intensive tasks of detection and enrolment of citizens, and hereby allow them to focus on supporting the conversion and retention of improved lifestyles among citizens. To achieve this, the task of early detection was distributed to a stratification algorithm design to automatically assess the risk profile of potentially all citizens in the region. More specifically, this algorithm would assess the risk of developing hypertension, hyperlipidemia, and COPD (chronic obstructive pulmonary disease), and, based on this, refer the citizen to a relevant preventive healthcare service in either municipal healthcare or general practice. To ensure clinical validity, the algorithm was based on four guidelines: The Swedish National Guidelines for Disease Prevention [40], COPD-PS screener [41], the Danish Diabetes Risk Model [42], and the Heartscore BMI score [43]. To enable the stratification, two sources of data were needed. The first was pre-existing clinical data extracted from the GPs’ electronic medical records. These consisted of structured data, including prescription codes, National Health Service disbursement codes, and International Classification of Primary Care (ICPC-2) codes. The second source was citizen-reported information on risk behaviors. To facilitate this, a 15-item web-based questionnaire was developed covering behaviors such as alcohol consumption, smoking, amount of physical exercise, diet (intake of sweets, fruit, vegetables, and fish), observable symptoms (shortness of breath, coughing), and own experience of general health.

Hereby, the TOF intervention took a great deal of responsibility for decreasing the distance between the preventive healthcare services and the citizens with moderate to high risk of developing LRD, by providing an intentionally efficient channel to convey the required data and the result of the risk assessment.

Dealing with Risk at a Distance

A general theme in the interviews was that citizens found the TOF intervention to be an interesting approach that facilitated renewed awareness of the potential consequences of their current lifestyle. While three citizens missed the opportunity to report the health information directly to a health professional and get direct feedback, a more general theme was that it was more meaningful for the citizens to complete the initial risk assessment in the comfort of their own home. The analysis shows that the citizens generally felt reflective and honest when answering the questionnaire, although one citizen stated that a health professional would be judgmental about the person’s way of living, indicating a possible social acceptability bias. Thus, to a certain extent, the format of the TOF intervention resulted in some citizens feeling more comfortable with relating to their lifestyle, and illustratively speaking, TOF brought them closer to the possible risk of this. Yet, the quantitative evaluation of the TOF pilot implementation in 2019 showed a relatively high drop-out rate of citizens. Specifically, 134 out of 358 citizens identified with moderate risk completed the health interview with a municipal health professional, and 144 out of 321 identified with high risk completed the health check at their GP [28].

As observed during the pilot implementation of the TOF intervention, the digital system was useful for the citizens as a tool for reporting and receiving information. Hereby, this system supported the overall intentions of improving the connection between citizens and preventive healthcare services by developing an efficient method of informing citizens about the potential risks of their lifestyle, assuming that this would incite many of them to engage in behavior change, either supported by a GP or other healthcare services, or on their own. As expressed by the notion of health literacy, this, however, presumes not only the ability of the citizen to obtain this information, but also to understand and act upon it [44].

The information provided through the automatic stratification did not come as a surprise to any of the citizens participating in the study indicating a good pre-existing understanding of their health condition, the degree of risk, and the causes of this:

I am fully aware of what my issue is and that is smoking. To my knowledge I have no diseases, so it was not unpleasant for me to participate (in the intervention). (Citizen 5)

In spite of knowing about the correlation between lifestyle and risk, approximately half of the citizens participating in the study did not complete the TOF intervention by attending either the health interview with a municipal health professional or the health check at their GP. Some citizens altered their lifestyle at their own initiative, for instance by introducing moderate changes in their diet or exercise habits (citizens 18, 20, 22, and 23). Among those who did not change their lifestyle following their participation in TOF, the dominant theme emerging from the analysis was that they found it difficult to relate to the risk assessment performed by the stratification algorithm, or in other words; they felt distanced to their own risk of developing LRD rather than to the preventive healthcare services.

First, the analysis showed that that citizens often found the assessment performed by the algorithm to be un-nuanced and not fully reflect their lifestyle the way they perceived it, or not respectful of their priorities in life. Part of the reason for this was that the questionnaire reflected a very structured clinical logic for assessing health, which inhibited their ability to report what they found to be relevant nuances about their behaviors. For instance, the questionnaire asked “do you eat sweets every day” without specifying the amount. Some citizens who had a small but steady consumption of, e.g., dark chocolate experienced that they, by answering yes to this question, would be placed in the same category as people with a significantly less healthy lifestyle, yet faced the dilemma that they had to answer yes in order to respond honestly to the questionnaire:

There were a lot of questions where I felt that I couldn’t be precise in my answers, so I had to choose the lesser of two evils. (Citizen 2).

From a clinical logic, this question was meaningful, as people who respond positively to this statistically have a higher risk of developing LRD without considering the actual nutritional value of the diet. To the citizens, however, this made it difficult to relate to the assessment, in some cases causing them to become frustrated about the assessment or even reject it all together. Adding to this, the system was based purely on one-way communication, hereby relying on the ability of the citizens to ask questions to a health professional at a later stage of the intervention:

Actually, I was slightly appalled by the answer. Of course, the system should do what it does, but the way it was communicated was like, well okay, is it really that bad? With a human I could have asked questions about how this conclusion was reached (Citizen 3)

Second, the long temporal span through which lifestyle may cause LRD obscured the importance of changing behavior in the short term. In contrast to acute disease that typically manifests itself through symptoms that are immediately present (e.g. the flu), the consequence of LRDs appears over a much longer timespan, often years or even decades. Coupled with the fact that risk behaviors such as diet or alcohol consumption are often deeply ingrained in social habits and cultures and hence also contribute positively to the quality of life for many people, a common theme of the interviews was that long-term risk had lower priority than short-term benefit and the pleasure, it would be to maintain the current lifestyle. Out of context of the specific data-driven format of the TOF intervention, citizens therefore showed a temporal distance to the concept of risk, which formed a barrier for acting upon this knowledge.

Certain factors, however, provided a push towards engaging in lifestyle changes. Most notably, it created awareness when citizens looked at themselves and considered the consequences of LRDs in the light of someone they held dear, for instance family or friends, who suffered or had passed away due to avoidable disease:

It made me look at myself in a new perspective. There were questions (in the TOF questionnaire) about my social network and family. I realized that there have been heart conditions on my father’s side. Compared to his age when he developed his heart disease, that will be me in 7 years. So perhaps I should start to eat healthier (Citizen 12).

An even more significant trigger was the prospect of losing the ability to fulfill their moral social responsibility, for instance by being a good parent, spouse or friend in the years ahead. However, this was not directly supported by the TOF intervention and, hence, it remained a challenge for the citizens in the study to make long-term risk a present, short-term concern.

Summary of Findings

In response to RQ1 “In what ways are citizens at a distance from the preventive healthcare system?”, this study has found that citizens can both be physically and psychologically distanced from preventive healthcare services. While the physical distance did not impose a barrier for any of the citizens participating in this study, the psychological distance was prominent. More specifically, these citizens felt distanced to their own risk of developing LRD. This was either because they did not recognize themselves in the assessment performed by the algorithm, or because the concept of risk to many was difficult to grasp because of the long time span until it affects one’s health and well-being.

Decreasing Distance with Quantified Self

Design Implications

As argued so far , data-driven approaches to early detection have the potential to decrease the burden of the preventive healthcare services when performing stratification of a large population. Furthermore, it creates a space where the citizens in the comfort of their own home can engage in their own health without feeling exposed to moral scrutiny of health professionals. While such an approach in one sense decreases the distance between preventive healthcare services and the citizens by enabling healthcare professionals to actively reach out to a larger number of individuals, it does not solve the fundamental challenge of making the concept of risk more palpable for the citizens. Rather, as the analysis showed, the citizens often felt distanced to their own risk of developing LRD, either because they did not recognize themselves in the assessment performed by the algorithm, or because the concept of risk to many was difficult to grasp because of the long time span until it affects one’s health and well-being.

In response to RQ2 “How can quantified-self technologies enable citizens to decrease this distance?”, two implications can be elicited to guide design of systems for data-driven early detection aimed at decreasing the distance between citizens and healthcare providers in preventive healthcare:

  • Design implication 1: The risk assessment provided by the system must be experienced as relevant by the citizen by accounting for individual variances with regard to physical, psychological, and social characteristics.

  • Design implication 2: To generate motivation for lifestyle change, a system must not only ensure that citizens realize the consequences of the lifestyle for their physical well-being and lifespan, but also for their psychological well-being and ability to fulfill social roles and moral obligations for family and friends.

Discussion

The findings of this study provide insights into what requirements quantified-self technologies must fulfill in order to contribute to decreasing distance between preventive healthcare services and the citizens in need of these. The most important implication is that design should not only focus on creating a connection between existing preventive healthcare services and the citizens in order to facilitate efficient early detection, but also create fertile ground for citizens to gain an interest in and motivation for engaging in the activities related to first detecting potential risk and later for converting to a healthier lifestyle. Quantified-self technologies first off hold the potential for creating risk profiles that are accurate, relevant, timely and relatable to the citizens. This can be achieved by combining multiple sources of data, for instance different types of wearables, such as activity trackers, and self-reported behavioral and health information, which is collected over time. While this approach may require technical skills and interest from the citizens as well as alignment of the underlying risk assessment model with clinical guidelines, it will address the most important issue related to existing data-driven detection models, namely that citizens gain influence on shaping a representation of their risk profile that takes the specific details and conditions of their lifestyle into account.

Another potential of quantified-self technologies is that they often include a social dimension by connecting the user to a community of other users in comparable situations and life conditions, often for the purpose of creating an incentive to perform well, e.g. in relation to physical exercise and weight loss. In the context of preventive healthcare, this can enable the citizens to compare their own lifestyles, not only to population statistics, but also to how different lifestyles affect the abilities and quality of life of other people, especially other individuals like them (for instance with regard to age, gender, socio-demographics, family context, etc.). While this approach requires conscientious considerations towards the potential ethical and privacy-related issues that it may entail to induce citizens to expose very private information about their lifestyle to others, it will address an important barrier for engaging in early detection and lifestyle change, that is not breached by existing data-driven approaches to early health assessment: as found in this study, it is very challenging for citizens to relate to the very long-term effects of an unhealthy lifestyle. Also, many citizens are not as concerned about how an unhealthy lifestyle will affect their own health and wellbeing as they are about how LRDs will influence their ability to fulfill their social roles and obligations for people they hold dear. Connecting citizens to a social network that can expose them to knowledge about how the consequences of an unhealthy lifestyle can also lead to the loss of ability to fulfill these obligations is therefore likely to incite more to engage in lifestyle change.

The study hereby shows that the matter of distance in early health assessment in preventive healthcare goes much beyond the challenge of connecting preventive healthcare services to relevant citizens, and assisting citizens in reaching out to relevant health care services. From the perspective of the citizens, the issue is not as much where to go but why to go, and the barrier for creating motivation for this is a psychological distance between the citizens and their own risk.

In a recent study, Wulfovich et al. [37] found that, in order to offer improved support for health self-management, quantified-self technologies must be further refined by addressing various human aspects of their use. These include the usability of devices and applications, personalization and context-awareness towards the user’s life conditions, routines, and lifestyle, as well as the lifestyle advise provided by the devices must be timely and non-judgmental. In line with this, the results of this study suggest that data-driven approaches in preventive healthcare must be further developed to decrease the distance towards the citizens in order to ensure their ‘right to health’ [26]. While the physical distance, as demonstrated by the TOF intervention, can in fact successfully be bridged by existing digital means, and hereby ensure availability and accessibility for citizens, the psychological distance remains a greater challenge. From the perspective of the citizens, this first and foremost will require that the early health assessment provided by data-driven preventive healthcare interventions becomes increasingly nuanced to be immediately recognizable and reliable for the citizens and hereby become mentally and cognitively affordable for the user. Furthermore, the early assessments provided must be adaptable to personal differences between citizens to accommodate for the varying physiological preconditions as well as circumstances in life that characterize the heterogeneous populations to whom such services are offered. Lastly and most fundamentally, the health assessments must take into account that citizens do not only experience risk as personal loss of health and capability, but also as the possible loss of ability to fulfill social roles and moral responsibilities, in order to produce the intended motivation for lifestyle change. This speaks for, to a greater extent, focusing on such factors that evidently increase quality of life in the design of such technologies, rather than on developing increasingly sophisticated ways to quantify health.

Conclusion

As lifestyle-related diseases continue to have a significant impact on the life expectancy and quality of life of individuals, it is becoming increasingly critical to develop efficient approaches to assist citizens through lifestyle change. An essential aspect of preventive healthcare is to detect citizens in need of preventive care services at an early stage, when the risk is still reversible. Current approaches to early detection tend to focus on how the large number of citizens as well as the physical distance between these and the preventive care services prevent health professionals in screening for risk at a population level. Hence, development of data-driven approaches that support citizens in receiving a risk assessment and support for conversion of their lifestyle in the comfort of their own home is attracting significant interest. Based on a qualitative study of the practical use of data-driven early detection in Denmark, this chapter concludes that this approach, while showing great promise, does not succeed in decreasing the distance between citizens and preventive healthcare services. This is that many citizens feel distanced from this type of risk assessment due to a lack of personalization as well as they find it difficult to relate to how an unhealthy lifestyle will affect their health and quality of life in the long term. Quantified-self technologies may help address these barriers by providing an opportunity for producing a more personalized representation of users’ lifestyles and helping them gain autonomy and influence in creating life changes and a better understanding of how their quality of life is actually affected by an unhealthy lifestyle and what are actual behavior changes that would fit their social context. This will entail the psychological distance between citizens and preventive healthcare being shortened from a citizen perspective, rather than a system perspective, leveraging the human intrinsic motivation.