Collecting, exploring and sharing personal data: Why, how and where

2.1.Health information systems

Health information systems refer to systems that deal with health data and are used by patients, doctors, and decision-makers. By contrast, we use the term personal health information (ϕ)33 to indicate the interrelation between personal and health data. Personal data augment research studies based on medical and health records with more dimensions on health, quality of life and well-being.

Due to the myriad of technology innovations that provide sources for personal health information, we loosely characterize ϕ (uncountable) dimensions. We hypothesize that leveraging these dimensions can help researchers to get a holistic view of the person’s quality of life unfolding and changing over time, as well as a better understanding of health outcomes. This hypothesis is supported by numerous studies and reviews of the use of technology in health conditions, e.g., mental illness, injuries and disabilities, and cardiovascular diseases [20–25].

Decision making in healthcare systems is mostly based on health outcomes. Health outcomes are measurements of an aspect of health that provide a unique view of a health condition. Health outcomes could be generic or disease-specific [26], and both can be used in combination to gain more insight and compare alternative treatments. Disease-specific outcomes are considered ill-suited for the general population or patients with multiple chronic conditions; instead, the alternative patient-centric models that considered the perspective of the patient to measure health outcomes are promising [27].

Meanwhile, the vast bulk of patient data is collected in clinical studies through clinician-reported (ClinRO), performance-based (PerfO), self-reported (SRO) outcomes and emerging technologies (TechOs) [28]. Professionals assess activities in PerfO, while the participant is required to fill SRO. However, PerfO are momentary and expensive and SRO may be inaccurate due to perception bias.

Patient-centric approaches aim at providing a holistic approach that takes into account the multidimensional aspects of health, QoL and well-being. There is general agreement that QoL and well-being measurements have to be sought from the individual own’s perception, and wearable devices and smartphones facilitate its quantification in daily life activities. Consumers and patients with chronic conditions keep showing interest in these devices. In particular, quantified-self trackers are extreme consumers that wear multiple sensors and may develop their solution to capture data and run analytics if the market fails to satisfy their needs. In a sense, self-quantification and life-logging technologies are invaluable data sources for patient-centric research. These data collections can augment patient-supplied data in healthcare systems even for individuals who are not patients at any given moment.

2.2.Emerging technologies contributing to health outcomes

The spiral shown in Fig. 2 illustrates the ever-growing ecosystem of emerging technologies that create personal health information. Its inner sections correspond to records generated by healthcare/domain experts. Its outer sections incorporate data generated by non-experts and with less discrimination extends to the rich variety of lifelog data sources, examples can be found in the reference [29]. We assume that the integration of non-expert sources contributes to reducing data acquisition costs and increases the possibilities to capture with higher fidelity the “dynamic nature of quality of life” [30]. At least for healthy patients, the innermost section generates records every couple of months or years and frequency increases as we move towards the outer sections. An exception can be found in patients treated with complex medical procedures, where the innermost section becomes a source of high-dimensional high-resolution data too.

Fig. 2.

The main sources for personal health information are electronic medical records, electronic health records, patient-generated health data, wearable devices and mHealth apps. They contribute to fill the ever-growing volume of phi dimensional data about an individual.

In patient-centric models, emerging technologies are likely to improve health outcomes. The time interval between measurements taken by experts can be long (except for patients with critical health conditions who are closely monitored in hospitals). The use of dynamic data is encouraging to understand if interventions are working or not. In other words, laying data out over time is useful to understand trends and improve analytics. Thus, measurements taken at home may unveil relevant patterns even if the measuring device was not designed for medical purposes. Furthermore, in the QoL domain, Evans and other researchers have argued that objective measurements should rely more on the actual manifested behaviour and current circumstances of the person compared with an external baseline [31]. Overall, dynamic data can provide a more comprehensive quantification than static data or self-reports used alone.

The spiral in Fig. 2 follows the distinction between “medical records” and “health records” given by the Office of the National Coordinator for Health Information Technology (ONC), US Department of Health and Human Services (HHS),44 and the taxonomy given in the Montreal accord on patient-reported outcomes [28]. The innermost section includes electronic medical records (EMRs), a digital version of the traditional paper patient’s history written in a 5 × 7 inch card. Electronic health records (EHRs) expand the value of EMRs. They contain information from all the clinicians involved in the patient’s care. Patient-generated health data (PGHD) expand the value of EMRs even more with health-related data created, recorded, or gathered by or from patients, family members or other caregivers. They include but are not restricted to patient-reported outcome measures (PROMs), observer-reported outcomes (ObsRO) and proxy reported outcomes (ProxRO). Examples are self-reported questionnaires, face to face interviews and telephone interviews about physical, mental, and social health. In the Montreal accord, technology-reported outcome (TechO) is vaguely mentioned as part of the clinician-reported outcomes (ClinRO) branch and directly linked with performance-reported outcomes (PerfO), which are assessments done by clinicians in close cooperation with the patient. However, health-related quality of life (HRQoL) measurements done with emerging technologies are gaining preponderance. We propose TechO+ to distinguish and take into consideration the data that is collected by apps, sensors, trackers and other IoT devices either worn by the user or available in the user’s environment, e.g., smart home or a smart car, to quantify different aspects of their life. TechO+ makes the outermost section of the spiral. It comprises raw data and algorithm-driven data that quantifies physical health, psychological health and social relationships, and the environment as described in Table 1. The main difference between Tech and TechO+ is that clinicians do not control data generated by TechO+.

2.3.TechO+ in health information systems

The introduction of emerging technologies in the healthcare sector is not new. A central argument is that PGHD and TechO+ data open the opportunity to develop new clinical decision-making metrics, where these new data sources can be leveraged for better diagnosis, assignment of treatment, prognosis and prediction of future health outcomes. The use of personal devices to create digital biomarkers can help researchers and doctors to diagnose diseases. For example, researchers are experimenting with keystroke and touch-screen tracking to early detect mental disorders [32]. ‘Bring your own device’ and similar policies can provide the means for more active patients, hence increasing the levels of patient engagement. However, the integration of emerging technologies into healthcare systems is not close to becoming a reality. The adoption level of patient engagement oriented technologies is varied, and there is space for improvements [33]. Countries like US and UK are just progressing towards nationwide electronic health systems, but migration is costly and complicated [34]. Healthcare information systems are dominated with inflexible legacy systems that do not integrate ClinRO, PerfO and SRO with external data sources. One main difficulty is the role of security and privacy in medical device software [35]. The WannaCry ransomware put in evidence that hospitals infrastructures are populated with outdated software [36]. Other challenges are the cost and legal implications in the case of devices that are prescribed by doctors. On a growing scale, questions are raised concerning data ownership, access rights and sharing of data collected with wearable medical devices due to intellectual property protections [37]. More questions are raised concerning the validity of measurements taken with consumer-grade devices, for example, the sensor placement plays a vital role [38]. Although techniques to minimize these errors are available, the position of the sensor is not always registered with the measurements. In addition, most vendors do not make their algorithms public; versions and update dates to these algorithms are not well documented. The same measurement can have different meanings across data captured with sensors from different vendors or across the same database during different periods.55 High data resolution may be unreliable if the device is in low power mode (not enough battery), manufacturers recommend using only daily aggregates for those cases.66 Researchers need access to data about battery life and syncing behavior in order to assess participant compliance and data reliability. Open questions remain regarding how to preserve the source of uncertainties, how to verify measurements reliability, and how to communicate uncertainties [39]. Nevertheless, there are research and development efforts that help the incorporation of wearables as medical technologies [40].

3.1.Individual perspective

Lifelogging, as defined by Microsoft MyLifeBits research project (1998–2007) [48], is the storage of selected qualitative and quantitative facets of life, namely professional communications, personal records and family activities. Based on MyLifeBits, it has been estimated that a 10-year personal collection required approximately 100 GB. This space requirement may change from one person to another, but such collections would fit in standard consumer hard disks. However, the big data and cloud storage market promotes a different approach. Lifelogging repositories are fragmented, in the sense that personal collections are stored in remote locations and distributed across different vendors. In this scenario, individuals may give away privacy based on illusions of what the solution claim to provide. A study in the US showed that 58.23% (934/1604) mobile phone users downloaded health-related mobile apps mostly with a focus on fitness and nutrition [49]. Many of these applications are unreliable. There are multiple examples of negligence and/or abuses of individual users’ trust that can lead to vulnerabilities and/or security breaches to gain more information about a subject [50–52]. If human blood is for traditional (analogue) healthcare the equivalent to human data for digital healthcare, we can talk about human data bleeding.

Furthermore, if not bleeding, the data may become inaccessible and completely lost when companies go out of business. Namely, nowadays, the app stores, as well as consumer electronics stores, host a variety of mobile health solutions. Smartphone users can choose from tens of hundreds of applications, designed to assess the behaviors (e.g., physical activity) and states (e.g., mood) and enable to prevent or manage certain diseases, or induce behavior change to improve health and life quality in general. There are also a variety of affordable, miniaturized and fashionable wearable devices that are designed to assess various behaviors (e.g., sleep, physical activity) unobtrusively to the wearer. In addition, there are patient-focused social networks that create an ecosystem to discuss health conditions with family, friends, informal caregivers, and health professionals. However, it is also observed that these apps, devices, and social networks get discontinued due to company acquisitions, business shifting focus, companies shutdown, and when research project supporting the wearable/apps has been completed. The disappearing networks, apps and dead wearables often imply that their data collections get lost, in most cases – without offering data migration solutions to the users.

To illustrate the wasted resources we discuss the state of the “lost data” evidence via a semi-systematic review of providers, as follows. We review 438 latest available personal wearable technologies enabling self-monitoring, as previously surveyed by Wac [53]. With respect to the wearables, 265 or 438 wearables surveyed by Wac, do not exist anymore. Many of the mobile apps and wearables that disappeared do not even have a corresponding website with information to their users anymore (e.g., web link returns an error or the domain is on sale). For the devices, we estimate the amount of data being lost assuming three (3) months of use, having different data channels (e.g., accelerometer, GPS) collected via a device. For each data channel, based on our past and ongoing experience with body area networks and wearable psycho-physiological devices [54,55] we firstly assume a continuous data stream ranging from high frequency/high resolution, for example, ECG (assumed 128 leads, each 256 Hz frequency, 16 bits per sample) to low frequency, for example, button interaction (assumed 1 event/minute on average, 8 bits per sample). We assume that the psycho-physiological data stream is collected 24/7, and an audio/video stream is collected on average an hour a day. Based on that, we calculate the total data loss incurred in three months for all discontinued devices [56]. We only compile it for the wearables, because we were able to archive the wearables database entries used at that time.77 We are unable to compile that for the mobile apps because we have not archived the details about the mobile apps themselves. The estimate of the data lost due to discontinuity of the wearables (and their corresponding apps) is as follows. For 265 discontinued devices, the overall data loss is estimated to be in the range of 6 TB (out of 9.85 TB for all the devices), with an average of 22.71 GB per a discontinued device providing on average four different data channels. Data waste is substantial.

Other complications appear with patient- and consumer-generated data from wearables and other sensors used for life tracking. The common practice is that devices synchronize their data to cloud-based storage systems directly or via smartphone applications. It becomes challenging to keep track all together with the data from multiple sensors all along the life span of the person. We conjecture that the lack of a reliable storage platform to keep personal health and health-related records may have a repercussion in the user engagement with mHealth technologies and health self-management.

3.2.QoL researcher perspective

From the perspective of researchers, there is no clear line between data ownership and data stewardship. In practice, the researcher takes the role of data owner, and study participants donate data for particular studies. Despite the tacit donation “to science”, databases are kept in silos to the detriment of large scale studies, cross-validation and data reuse. In some countries, study participants have the right to request their data, but that may require complex procedures. The archives of universities, institutions, and research labs are not meant as a backup for study participants.

Undoubtedly, access to data is essential to conduct data-driven research [57]. However, academic labs often lack resources to gather data on a large geographical scale. A remedy could be to conduct research in association with an industry partner to gain access to big data repositories. But, that introduces the risks of statistical bias since data collected by users of a single product brand, e.g. Apple Watch or Fitbit trackers, may not represent the general population. Another problem is that research projects that involve personal health data require ethical approvals. From our own experience, and that of others, the application process can be complex, time-consuming and not necessarily successful.

Clinical studies usually involved large cohorts (in the order of thousands) and some studies extend across 10–30 years. Conversely, the studies in the QoL Technologies Lab extend on average 6 months, involve small cohorts (20–30, sometimes up to 100 participants) and involve analytic systems for high-dimensional, high-resolution data (the outer layers of the ϕ spiral). Similarly, the studies based on smartwatches for health and wellness applications report promising solutions but most of them are only based on small sample sizes [58].

To sustain innovation in the QoL domain, we need data to conduct measurements covering physical health, psychological, social relationships and the environment. To the best of our knowledge, there are no such large vendor-independent data banks for data gathered with wearable devices, i.e., wearable data. Large biobanks have gather wearable data from large cohorts, but only for short periods, e.g., UK Biobank includes a repository of 1-week wearable data used by more than 100K participants. Other sources for research studies are community-based projects and crowdsourcing data. In this direction, we collaborate with the Open Humans (OH) Foundation. This non-profit organization built and support a community-based platform that collects personal data for personal exploration and research study participation [59]. OH has, as 30 of May of 2019, 6,976 members and 42% of them contribute or have contributed to a myriad of private and/or public databases. The most successful projects are related to genetic data. Through this collaboration, we could obtain data from approximately 400 activity trackers (OH’s Fitbit database). Some self-trackers have data from 2008; a pitfall is that OH’s Fitbit database only kept a single summary record per day. Crowdsourcing data is being tested in many areas, e.g., epidemiology studies, infection diseases, nutrition, and diabetes [60–63]. We hope that crowdsourcing mHealth systems may speed research and innovation with high-fidelity and reliable data.

To sum up, we identify two critical aspects for bringing data-driven research and innovation in the QoL domain: (1) increasing the user engagement to quantify and track QoL measurements; (2) facilitating data gathering and data sharing. In this paper, we provide some design guidelines for a system addressing these two key aspects.

Fig. 3.

The hierarchical value of health and health-related data.

4.1.Technological issues

A system that collects data from many sources relies on the definition of data standards to implement interoperability solutions. The caveat is that the definition of data standards is affected by competition, overlapping goals, and poor coordination at national and international levels [68]. Particular to health standards, the Health Level Seven (HL7) specifications are widely used across the globe. HL7 specifications are designed for semantic interoperability, i.e., “the ability for data shared by systems to be understood at the level of fully defined domain concepts” [69]. Fast Healthcare Interoperability Resources (FHIR) is a draft HL7 standard describing data formats and elements and an application programming interface for exchanging electronic health records.

Consumer applications can use FHIR to import patient health data directly to mobile phones. There is a large ecosystem of apps that using free, open healthcare application programming interfaces (APIs) can connect to any EHR [70]. For instance, the Health app88 from Apple is a hub that integrates data from multiple devices and offers limited functionality for accessing electronic health record databases from partner providers. Researchers can make use of open development resources, e.g., Apple ResearchKit99 and Google Fit SDK,1010 to create data connectors to access EHR and TechO+ data from study participants. Hubs are single-points of data integration that include multiple aspects of life and facilitate data exploration.

Data exploration that goes beyond the default tools and features is available in the market. Knowledge sharing via community-driven and research-driven software seeks to reduce the burden of building ad hoc solutions. As examples, we can mention PACO1111 and QuantifyMe [71], both are open-source research frameworks that allow single-case self-experiments.

The development of personalised solutions requires expertise to implement data connectors that integrate data sources in own research hub. The creation of connectors is time consuming due to the myriad of consumer devices that are ingesting human data. Some of the data management platforms that provide data connectors to facilitate development are vendor-agnostic while others only provide connectors for a particular vendor, e.g., Fitabase.1212 A system that ingests data from multiple sources may need from ten to hundreds or thousands of connectors to provide real value to the large diversity of products and individual choice. Data connectors can be leased by paying a subscription fee to third-party vendors. Such fees could be expensive for non-profit projects or for individuals trying to keep a long-term personal data archive. Finally, data connectors may move data to a location that is not selected by the data owner.

Almost any data aggregation solution moves data to cloud-based infrastructure without giving the option to choose the storage location. The use of public cloud storage means that the provider has some control over the data. A claim to sovereignty is a claim of domain under control. Hence, embracing claims to sovereignty is crucial to assure data privacy in a trustless system. Blockchain, often in combination with off-chain data, has gained much attention to address self-sovereignty and manage healthcare data [72–74]. With the freedom to choose the storage location, individuals could opt for personal data vaults (PDV) [75], their proprietary storage media (such available space in local disk), a hybrid storage solution [76] or other communal/cooperative in-house storage systems. For example, a hybrid storage solution may combine media owned by the user and remote resources owned by peers organised in a decentralised architecture. Distribute medical data in different places would be a safer way to protect privacy and information; in fact, many countries have implemented decentralised systems that include data sharing and informed consents [77,78] many years before the boom of blockchain-based solutions. Communal/cooperative in-house storage systems require an upfront investment, but the cost could be afforded with a registration fee. On-premise solutions can keep the data where it should be, in the hands of the cooperative and, preferable, in the hands of the data subject only.

4.2.Financial and budgetary issues

Government incentives can help to speed up the adoption of new technologies. For instance, EHR adoption among US hospitals gained eight percentage points in the five years after the implementation of the Health Information Technology for Economic and Clinical Health Act (HITECH Act) [79]. The All of Us Research Program plans to build a database of 1 million US citizen profiles that incorporate lifestyle, environment and biologic factors. The program received $130 million from the US National Health Institute to accelerate precision medicine [80].

Worldwide, independent programs are run and financed cooperatively. A health cooperative model can be self-sustainable by reinvesting earnings from data but challenges remain [81]. Pilot studies based on the cooperative model have been built in different places, Swiss1313 and Dutch1414 initiatives promote new forms of citizen-driven data collection, and cooperatives at the local level and a federation of cooperatives at a global level [82]. Healthcare professionals and many individuals may, nonetheless, restrain from participation. In the late summer of 2010, an organization tried to build a health record bank (HRB) for a community in Phoenix, Arizona (USA). During the process, a national market research firm found that 20% of the survey respondents (1044 participants) were likely to pay $100–150 as an annual fee for a health record bank [83]. The initial aim was to engage 200K users (5% of the population), but the project was abandoned in April 2011 due to insufficient registrations.

Concerning data storage, a cost-effective model is needed, whether it is done by the individual or by a steward organization. The economics of long-term archives are different from models develop to store data that has a lifespan shorter than the life of the storage device [84]. Long-term data preservation has maintenance costs to avoid physical and logical obsolescence. Carefully chosen data types and formats can reduce migrations due to logical obsolescence. The selection of the storage medium, e.g., hard disk (HDD), solid-state device (SDD), tape, dictates physical replacement cycles. Tape storage [85] is a winner technology for archived data due to cost per Gigabyte and duration. However, tape is more susceptible to environmental damage; consequently, it may not be the best solution for individuals who wish to keep personal data at home.

For steward organizations, three main models for long-term preservation systems are: (a) rented storage in a public cloud, (b) monetised storage, and (c) endowed storage. In addition, developing markets for rented storage proliferate in P2P networks. Individuals may keep storage at home and share some resources with other individuals to build a P2P system with higher performance and reliability. Shared resources can be used to increase the performance and reliability of the system by storing redundant encrypted data. Sharing quotas can help to balance the equation costs for each user. But more research is needed to assess the cost-effectiveness of P2P-based solutions and their ability to provide long-term storage services. In a separate project, one of us is conducting experiments to further investigate P2P-based solutions. The monetised model is not a valid option neither for open data nor private data. While we do not suggest that individuals should not monetised data, this model is not compatible with our ultimate aim at designing a system that promotes open data for research and innovation. However, monetary incentives could be introduced for data generated during participatory studies as there is evidence that incentives are effective to improve the response rate in a survey [86]. In the endowment model, individuals pay endowed money that should cover the entire lifetime of data with some part used to pay the initial setup costs such as buying media and the rest is invested. Under the hypothesis that a system designed for long-term archiving of personal data will attract many users, endowed storage seems to be cost-effective in comparison with rented storage. Researchers showed that own private cloud is cost-effective with large clusters and that the cost of ownership of raw storage is almost 100× cheaper than the costs cloud storage providers service [87]. Other study highlighted that over the long term owning the infrastructure can save money [88]. Finally, researchers that studied the endowment model over a 100 years simulated archive founded that SSDs are a better choice media to keep the costs low [89].

4.3.Legal and policy issues

Regulations give a tool to public officers to control what companies are doing with data. Controls are only a first step, as stated by the Open Data Institute (ODI) in its guidance [90]: “Data-related activity can be unethical but still lawful.” Besides, regulations serve to accelerate innovation by providing government incentives, e.g. the case of personalised medicine in the US [91]. Although there are multiple cases of users showing autonomy and “wilfully sharing data”, there is limited protection to open data initiatives [92].

In the US, the Health Insurance Portability and Accountability Act (HIPAA) is the legal framework to protect patient’s privacy and guarantee that patients can have their data on request. It should be noticed that companies that provide services using health data may not be healthcare providers under the provisions of HIPAA; hence, HIPAA is not mandatory to those companies. For example, the website of Lydia, the suggested replacement for the Microsoft Health Vault service,1515 (last accessed 27 April 2019) says “The Service is not subject to HIPAA because our organization is not a healthcare provider”. Additionally, the US Affordable Care Act seeks to improve healthcare and encourage the use of technologies to increase patient engagement.

In Europe, individuals, so-called “data subjects”, are protected through the General Data Protection Regulation (GDPR) – (EU) 2016/679. GDPR imposes obligations to data controllers and data processors, among other protections, to defend the individual’s right to access, move and forget data.

The level of data protection legislation around the globe may differ from country to country.1616 Transborder data flow, sometimes cross-border data flow, is meant to protect data sovereignty and to avoid transferring data to countries that do not offer the same level of guarantees. For instance, taking in consideration that countries may take different approaches to protect data privacy, personal data transfers between the US, EU countries and Switzerland are protected by the Privacy Shield Framework.1717 GDPR is gaining global impact, and its core principles are evolving in a de-facto globalised standard as companies perceive that the adoption of a global compliant standard can save costs [93].

Compliance to regulations often means the obligation to obtain informed consent from users. But decades of raised concerns about the reading level of those privacy statements, which may be intelligible only to a minority or inadequate to vulnerable populations [94–96], suggest room for improvement. Individuals may give consent to gather data based on the expectations to address health concerns. In this context, tech/health giants keep exploiting business opportunities with closed data, although it may affect individuals who become exposed to privacy and security tensions in an undisclosed algorithmic decision-making world [15,97].

Data subjects are powerless with respect to data control and data process. The mere term “data subject” imposes a passive role to the individual that actually generates data. Data subjects are expected to give consent to others to act on their behalf. An active role requires participatory engagement. For that, we need systems that empower users and regulatory frameworks that act accordingly.

4.4.Institutional and managerial issues

In the governance of health data, trust in developers and regulators is a key condition to generate innovations in digital health [98]. Trust may be only generated with secure technologies and individuals with control [99]. Solutions that gather personal data without empowering the individual to take control may leave the data subject at the mercy of technology providers, researchers, healthcare providers, doctors, wellness centers, and others. All of them may have in place their systems to manage and store data from their patients/consumers, hence multiplying silos and points of attacks and vulnerabilities. Even if systems must comply with the law, there is still room for arbitrarily decisions in privacy and security policies, and for ambiguities that can lead to different interpretations affecting data subjects. For that reason, interpretative guidance and compliance with standards could become useful aids to organizations.

Transparency is closely related to trust. It helps the data subject to know how the data is processed, supporting the principle of accountability. A transparent system, which is now a GDPR requirement, needs to provide the data subject with the means to verify that the usage policies are being followed and that the business process complies with the policies consented by the user and the applicable data protection regulations [100]. Transparency suggests a socio-technical concept of global interest. Transparency Enhancing Tools (TETs) can help to accomplish transparency, at least as a technical principle [101].

The FAIR principles were designed to ensure transparency, reproducibility and reusability of scientific data [102]. FAIR stands for findability, accessibility, interoperability, and reusability. These principles are described as guidance to support proper data management and stewardship As an example, in the Netherlands, the personal health train initiative promotes the use of FAIR principles and citizen-controlled health data lockers [103]. Trains are a metaphor for data workflows, each responding to specific research questions, built and maintained by a researcher.

4.5.Cultural and behavioural factors

The OECD working group conclude in their study [57] that reward structures for those who produce and those who manage research data are a necessary component for promoting data access and sharing practices. In the context of our study, self-tracking cultures [104] are sustained by selfhood, i.e., the motto “self-knowledge through numbers”, and communities. Online communities of practice promote the engagement of healthy individuals and patients in their health care [105]. They are vehicles for sharing their personal self-tracking experiences which other members. The Quantified Self community1818 is a global movement of individuals and developers who share an interest in self-tracking tools. The Open Humans community1919 is a diverse community of people who want to learn more about themselves by forging meaning from personal data. It is governed democratically, and its board of directors include a seat elected by the community. Both communities have intersections in the private and communal self-tracking mode. But other cultures have emerged, these are pushed, imposed and exploited self-tracking [106]. Another issue is that online communities may disappear, altogether with the knowledge database and the networks created. We have surveyed the communities listed by Swan [9] in 2008, and almost half of the links do not work. Some of the disappeared communities had info about hundreds of health conditions and hundreds of groups.

Self-tracking practices may bring into effect the values of autonomy, solidarity, and authenticity. In practice, these values are enacted differently, and consequently, researchers have begun to ask what type of practices will guarantee these values in meaningful ways to individuals and communities [107].

5.1.OHA: Preliminary specifications

At this stage of the research, our specifications are necessarily speculative since OHA is not yet implemented. However, given the crescent attention to health data governance and stewardship, this subsection presents some of the novel aspects of our design. We propose and examine the following OHA’s core features; an overview diagram is shown in Fig. 4.