research-article

Open access

Using Patient-Generated Data to Support Cardiac Rehabilitation and the Transition to Self-Care

Authors:

Shreya Tadas,

Jane Dickson,

David CoyleAuthors Info & Claims

CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 329, Pages 1 - 16

https://doi.org/10.1145/3544548.3580822

Published: 19 April 2023 Publication History

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Abstract

Patient-generated data from commercially available self-tracking devices has the potential to enhance support for people transitioning from hospitalization to self-care. However, studies have revealed significant barriers to the routine use of such data in clinical settings. This paper explores the use of patient-generated data in the context of cardiac rehabilitation. We describe a two-stage investigation: (1) a co-design study with clinicians to design a data system that combines objective and subjective patient data; and (2) an 18-week field-study where this system was deployed as part of a hospital-based rehabilitation program. Our findings suggest the system is feasible, supported clinicians’ workflow, and helped patients to bridge the gap between supervised and self-managed care. Subjective data contextualized objective data and a structured approach data collection helped generate actionable information. The paper also provides insight on patients' attitudes towards peer data sharing and demonstrates the importance of timing when introducing self-tracking technology.

1 Introduction

Rehabilitation is vital to long-term recovery following an acute cardiac incident and is a key component in the management of cardiovascular disease. Rehabilitation programs are a secondary prevention model, shown to reduce mortality and the risk of recurrent events and improve quality of life (QoL) [70]. The primary objectives are to help patients recover from the initial incident, regain autonomy, understand how to manage their condition, and ultimately reduce the risk of further adverse outcomes [38,72]. There are variations in the provision and organization of cardiac rehabilitation (CR) services from country to country. Broadly however, CR programs follow a consistent structure. CR is typically supported by specialist medical teams and includes clinical assessment, medication review, risk factor modification (e.g., dietary changes), psychological support, and supervised exercise [38]. In the UK, for example, standardized CR is structured across four phases, starting with (Phase 1) acute in-hospital rehabilitation; (Phase 2) outpatient clinic follow-up; (Phase 3) structured exercise and education programs, targeting lifestyle factors, regular exercise habits, and patients understanding of cardiac health; and finally (Phase 4) longer-term maintenance and self-management [10]. CR programs in the United States more often describe three phases [38]. However, these phases are broadly consistent with phases 1-3 of the UK model, with longer-term maintenance and self-management considered separate from the rehabilitation process. This paper focuses on the design of technology to support Phase 3 of both models. This is a critical point in rehabilitation where patients still receive regular, structured support from a medical team, but the emphasis is on supporting the transition to longer term self-management of their health [63,70].

Previous HCI research has described how people face emotional and physical challenges during the transition from hospitalization to self-management [53]. Studies suggest that technology supported CR programs have the potential to support this transition [22,47,61,72], but a recent systematic review [62] also notes significant limitations, arguing for greater use of participatory and iterative approaches in the design of future systems. Key barriers identified include a lack of trust, technology becoming a burden or not addressing needs of both patients and clinicians, and a lack of technical knowledge [6,22,62,63]. In a detailed study of CR, Tadas et al. [63] provide recommendations on key patient needs during the rehabilitation process, emphasising patients’ desire for a normal life, the value of shared experiences or connectedness, the need for physical and emotional safety, and the value of both generalised and in-the-moment knowledge.

In addressing these recommendations we focus on the use of patient-generated data. Many papers have argued that self-tracking data generated through wearable devices has the potential to ‘bridge the gap’ between formal medical settings and day-to-day life, capturing and sharing patients’ activities and symptoms outside medical settings [26]. However, here to a wide range of barriers are observed in the routine and effective use of patient-generated data in clinical contexts [17,69]. Chung et al. [13] found that clinicians, nurses, and specialists found patient-generated data hard to use due to time constraints and a lack of standardized formats. Andersen et al. [3] identify three key challenges in designing eHealth systems. Systems should (1) make sense to both patients and clinicians, (2) be actionable to both clinicians and patients; and (3) be feasible within the organizational and social context.

This paper directly addresses these challenges in the context of patient generated data. It presents the findings of an 18-week field study undertaken in the cardiology unit of a large hospital. The tracking and data sharing system used in the field study was co-designed with clinicians and built on detailed patient engagement research. The co-design process and key design decisions are described. The overall aim was to understand how tracking and sharing both activity (objective) and experience (subjective) data could support CR patients and clinicians during a Phase 3 CR program. The paper provides a detailed description of how patients, physiotherapists, and other clinicians engaged with and used patient-generated data.

The paper makes several contributions. Firstly, we show how a combination of subjective experience data and objective wearable data can help to make patient generated data more meaningful for clinicians. We also show how a structured approach to experience data, when combining with summarisation and filtering, helps clinicians to track trends and makes data actionable. In contrast, an open approach to experience data provided patients with a channel to share experiences with clinicians, when they might be hesitant to do so in-person. We also demonstrate the importance of introducing technology at an early stage in the rehabilitation process. This helps patients to develop self-efficacy, both in regard to technology and through greater understanding of their physical safe zones. Finally, the paper provides insight on data sharing between patients. While patients were open to sharing their own data with peers, they were hesitant about viewing data shared with them. We discuss the reasons for this. Based on our findings the paper provides design recommendations for the use of patient generated date to support rehabilitation and the transition to self-care.

2 Related Work

2.1 Supporting the Transition from Hospitalization to Self-management

Tadas et al. [62] provides a systematic review of literature on the use of technology in CR. They report that research has mainly focused on interventions to increase physical activity [25], enable monitoring [1,24,27,61], medication and diet management [36,48], and virtual rehabilitation [9]. It has also addressed information engagement and support for patient-clinician communication during in-clinic visits [39,40,49]. Some CR programs are moving towards a hybrid structure that involves both in-person and remote support tailored to patient and situational needs – a move accelerated by the COVID-19 pandemic. However, evidence suggests that patients prefer to attend in-person CR programs, which give them an opportunity to talk directly to experts and share their experiences with others going through similar rehabilitation experiences [62,63]. Clinicians have also reported that hybrid approaches create additional demands and require extra resources that are not available given their time constrained workflows [13,17]. Tadas et al. [62] find that prior work in the cardiac domain has made limited use of user-centred and iterative methods. There is thus significant opportunity in drawing on lessons from the broader HCI literature.

Indeed, rehabilitation and self-management have become significant issues in HCI research. Examples include technologies addressing chronic disease management for older adults [8,19,41], chronic obstructive pulmonary disease therapy [60,64], and diabetes [18,55]. Being discharged from hospital involves a transition from a safe environment to an unsupervised environment [52,71]. Pollack et al. [53] provide a detailed understanding of patient experience, describing how people are often unprepared and identifying three important challenges for patients recovering from illness: (1) lack of support for health knowledge, (2) lack of opportunities to access resources, and (3) lack of opportunities to promote self-efficacy. They argue convincingly that research should target–and can help in addressing–these challenges.

Given our focus on rehabilitation and the transition to self-management, a detailed review of literature on longer term self-management is beyond the scope of this paper. However, rehabilitation and self-management are clearly interconnected. Effective rehabilitation provides a foundation for successful self-management. It is thus helpful to identify challenges in long term self-management that rehabilitation technologies might seek to alleviate. Nunes et al. [44] provide a detailed review of technology assisted self-management (or self-care), addressing trends and tensions in this area. They found that diabetes is the most common condition addressed in HCI literature, with few studies addressing cardiac conditions. Of the tensions identified several are particularly relevant in this paper. Firstly, they highlight the potential long-term tension between supporting the autonomy of the patient versus control of chronic condition by clinicians. Whereas some technologies put patients in a rather passive role and emphasize clinicians’ responsibility, others enable patients to take an active role, increasing their participation in self-care activities. They note that the appropriate balance between patient autonomy and clinician control is dependent on the complexity and severity of the chronic condition, and the potential consequences of self-care decisions. No specific approach is appropriate in all cases, and it cannot be assumed that autonomous use of technology is always appropriate. As a general rule however, they suggest that designers focus on providing the most autonomy possible to the patients, rather than over monitoring or over controlling the patient's behaviours. This tension is less of a concern at the rehabilitation stage, as patients are still expected to receive close support and supervision from clinicians. However, the rehabilitation stage should help in addressing Nunes et al. longer-term tension. It can do this by leveraging technology to develop patient autonomy and self-efficacy during a time when they also have clinical support and regular communication with clinicians.

A second tension identified by Nunes et al. [44] regards device choice and the orientation of this choice around a chronic condition or daily life. While it is important to ensure that any self-care technology is safe and reliable, it is also important to consider the stigma people might associate with devices and avoid the over medicalization of chronic conditions. They argue that self-care technologies should prioritise the lived experience of patients and integrate with the routines, activities and setting of the patient's everyday life. This resonates with the finding by Tadas et al. that patients going through CR have a strong desire for a normal life [63]. To assist the transition to self-management, we therefore argue that technologies used during rehabilitation should, in as much as is possible, integrate with and become part of a person's daily life. However, before we discuss this issue further, we first provide a broader perspective on self-tracking and the integration of patient-generated data into clinical practice.

2.2 Self-tracking and the Integration of Patient-generated Data into Clinical Practice

Research has demonstrated the importance of knowledge and personalized contextual data in supporting self-management [19,55,56,60,64]. Enabled by the widespread use of self-tracking technology, patient-generated data is increasingly seen as important in the delivery of personalized health [5,26], having the potential to provide unique insights and help support effective diagnosis and care [32]. Increasing evidence suggests that patient-generated data can be useful for patients themselves and can support healthy behaviour [50]. Examples include increased physical activity and involvement [15], support for reflection on health conditions [7,12], and enhanced patient-provider collaborations [14]. Jacobs et al. argue that data helps patients to make more informed choices by shaping insights about their wellbeing [34]. Andersen et al. [2] describe how user-centred design methods were applied to reintroduce cardiac patients as active diagnostic agents post hospitalization.

One form of patient-generated data is sensor-captured data from consumer wearable devices. Through widely available devices it is possible to automatically track signals such as step count, heart rate, and sleep. The use of commercially available fitness trackers and smartphone health Apps in clinical practice has become increasingly popular, supporting clinical consultations, and enabling remote diagnosis for clinicians [39]. Notwithstanding these potential benefits, the diffusion of consumer wearable data into routine clinical practice has uncovered several practical barriers. These include insufficient clinician time, unfamiliar or non-standardised data formats, and irrelevant information [28]. Research suggests differences between patients’ and clinicians’ expectations of patient-generated data, with Zhu et al. [73] pointing to the difference between clinician-initiated and patient-initiated tracking. Clinician initiated tracking was often for a specific medical reason and therefore seen as high value (from a clinical perspective), as compared to patient-initiated tracking which was less targeted. To make patient-generated data actionable to clinicians, it is important to understand clinician's needs and workflow. In Jacobs et al.’s [34] study, clinicians expressed the need to receive data on both physical and emotional health. There is also growing recognition of the value of combining subjective experiences with objective data points to make data more meaningful [58].

Our research aligns with this perspective on patient-generated data and extends prior work. It exams the use of structured, clinician initiated, and patient captured subjective and objective data.

2.3 Peer Sharing and Shared Experiences

One of the most important motivating factors during in-person, group-based CR programs is the opportunity to share experiences with peers [4]. Experience sharing can help people to better understand their own experience and make sense of their symptoms, triggers, and treatments [45]. Tadas et al. argue that “normal” life is a goal and incentive in and of itself for patients following cardiac surgery, and experience sharing can help in addressing this need [63]. “Normal” refers to a person's acceptance of their own health experience when they discover it to be close to what is experienced by others [23,51]. Previous studies with cardiac patients [62,63] found that patients normalize their new experiences during the CR classes by interacting and sharing experiences with other patients. This echoes efforts by people with diabetes to validate personal experiences of their condition and determine whether these experiences were normal [45,67].

Researchers are moving towards ways to articulate the knowledge that patients develop and use in their daily lives and make it transferable and useful to others. Patient knowledge can be understood as a form of practical knowledge that patients use to make sense of the medical and technical knowledge. Patients then translate this into useful information to help manage their daily life with disease [54]. Hartzler & Pratt [29] provide a detailed study of the differences between information shared by patients and clinician. They note that clinician expertise is often focused on medical facts and closely tied to formal health delivery systems, biomedical research, and facts. In contrast information shared by fellow patients is typically more narrative and practical, and is grounded in lived experience. While it may be less comprehensive, information shared by patients is often more accessible and more personal. It provides valuable emotional support and practical strategies for coping with day-to-day experiences. Technology has the potential to enhance this sharing [34,63]. However, it is also observed that cardiac patients can be hesitant to use online forums and web-based groups to share experiences [63]. This is mainly due to lack of trust and concerns about privacy.

Our research focuses on sharing data in the trusted environment of a CR program. It extends prior literature by investigating the impact of shared patient data on both patients and clinicians in a real-world clinical context. Through this we aim to provide recommendations for future data-sharing applications.

3 Overview of Methodology and Research Questions

As noted in the Related Work section, Andersen et al. [3] have identified three key, unmet challenges regarding the use of patient generated data in clinical settings. To be useful data needs to (1) make sense to both patients and clinicians, (2) be actionable to both clinicians and patients; and (3) be feasible within the organizational and social context. The studies outlined below seek to directly address these challenges. In doing so, we also seek to understand how the rehabilitation stage can help to alleviate the longer-term tensions in technology supported self-management identified by Nunes et al. [44], in particular the tensions regarding patient autonomy and the use of technology that integrates with everyday life. We also seek to building on research demonstrating the value of peer sharing, investigating patients attitudes to sharing data with both clinicians and other patients.

The work in this paper was undertaken in collaboration with a large hospital (the Beacon Hospital Dublin) which includes a specialized cardiology unit. To support patients following hospitalization for an acute cardiac incident, the hospital provides a 6-week Phase 3 CR program based on standard UK CR model [10]. The research was divided into two stages. The first stage involved co-design with a CR team to develop a data system to support the Phase 3 CR program. The data system involved gathering and sharing objective and subjective data through consumer devices and self-report questionnaires. The second stage involved a field study, where the system was deployed as part the CR program. The field study ran across four separate iterations of the CR program. Each individual CR program lasted six weeks and had a separate group of patients. In total the field study lasted for 18 weeks (the individual programs overlapped) and involved 16 patients. Over the course of the field study, the data system was iteratively refined, based on ethnographic observations of its usage, clinicians’ feedback, and patient data. Ethics permission for all studies was granted by Beacon Hospital Research Ethics Committee (BHREC Ref: BEA0159) and the University College Dublin Research Ethics Committee (REERN: LS-E-21-145-Tadas-Coyle).

We address the following research questions: How did clinicians and patients use the data in and between rehabilitation classes? Is the combination of objective activity data and subjective experience data meaningful and actionable for clinicians? What were the patients’ attitudes towards self-tracking and sharing their data with both clinicians and peers? And finally, is there evidence that our approach is feasible and aided the transition to self-management?

4 Co-design of the Data Gathering and Sharing System

The co-design stage involved working collaboratively with a CR team to develop a data gathering and sharing system. Reflecting the recommendations of Andersen et al. [3], we sought to design a system that would be (1) meaningful to both patients and clinicians, (2) actionable for both clinicians and patients, and (3) feasible within the organizational and social context. Understanding the CR team's experiences in running a Phase 3 CR program, including the activities involved, how they are run, the different clinicians involved in the program and their concerns and needs, was key to achieving these aims.

While patients were not directly involved in the co-design activities reported here, it is important to emphasize that this work built directly on prior research by our team, which did involve working directly with cardiac patients and focused on understanding their needs [62,63]. Throughout the co-design process a member of the research team acted to represent the views of patients based on this direct experience and related patient-centred literature. While the co-design reported here was undertaken in collaboration with clinicians, the emphasis was on addressing the needs of both patients and clinicians. Potential limitations in this approach are also addressed in the Limitations section of the paper.

4.1 Participants

The co-design process began with regular meetings between the first author and the chief physiotherapist of the CR program. This was important in building trust and understanding the workflow, organizational, and social context of the CR program. The full CR team then participated in the design process. This included two physiotherapists, including the chief physiotherapist, and a range of clinical specialists, including one dietitian, one pharmacist, one occupational therapist, and two CR nurses. Within this paper, the word ‘clinician’ is used to describe a range of roles that involve direct contact with patients.

4.2 Methods

The co-design process involved a combination of focus groups, small group meetings, regular discussion of design decisions, and shared prototypes. At the beginning of the first focus group, and to emphasise a patient perspective, findings from previous studies with cardiac patients in CR programs were presented. These included: patients need for physical and emotional safety; the importance of normalizing new experiences through social interactions and peer-experience sharing; the importance of both general knowledge of cardiovascular health and personalized insights; and a focus on capability rather than limitations [62,63]. Clinicians were informed that the aim was to understand their clinical needs and workflows and to develop a data system that would support the CR program, addressing both their needs and the needs of their patients. As the focus groups took place while COVID-19 restrictions were in force, they were held remotely over Zoom. The focus groups and meetings were moderated by the first author and notes were taken.

4.3 Key Design Decisions

This section highlights key design decisions that led to the data system used in the later field study. These design decisions were made on an iterative basis, through on-going discussions and focus groups with the clinical team. Regular discussions within the design team also took place to help ensure decisions also considered the needs of patients.

4.3.1 Organizational Context.

Figure 1 illustrates the structure of the CR program run by the Beacon Hospital Dublin. It is a 6-week program, based on standard UK CR model [10] and consists of a one-hour supervised exercise class twice a week (Tuesdays and Thursdays) conducted by physiotherapists, and a 30-minute educational talk once a week after one of the exercise classes. Patients are encouraged to walk regularly and engage in physical activity of their choice between classes. Each educational talk is given by a different clinical specialist, including a physiotherapist, dietician, occupational therapist, pharmacist, and CR nurse specialists. The hospital did not have a dedicated full-time CR team. CR programs were managed mainly by two physiotherapists, who were also responsible for attending to other treatments provided by the hospital. Other clinicians were involved as required for educational talks. Due to the COVID-19 pandemic and the restriction on the number of people in indoor areas, the CR program had moved to a hybrid format. Patients had the option to take classes online through zoom video calls or in-person at the hospital. As this was a new format adopted by the hospital and due to the COVID restrictions on the number of patients in a class, the team had to increase the number of CR programs. Before the pandemic, the hospital ran one CR program every 6 weeks with 3 to 4 physiotherapists; now, the hospital had to run 2 concurrent CR programs during the same 6 weeks, with 1 to 3 physiotherapists per program. There was also a lack of staff for the administrative needs of the program. This had significantly increased physiotherapists' workload. Prior to our study the use of technology in classes was limited. Physiotherapists used blood pressure monitors, medical chest bands and watches to monitor patient's heart rate (HR) and blood pressure during the classes. They used a computer to connect to online patients through Zoom video conferencing software and to show presentation slides during the educational talks.

Figure 1:

In initial discussions, the chief physiotherapist emphasized the importance of monitoring patients’ physical activity during exercise. This allowed physiotherapists to ensure the patient's HR stayed within safe limits, teach patients about their safe HR zones when exercising, and monitor improvements over the course of the program. They now wanted a way to monitor patients’ activity level and HR outside of the class, to see if they were complying with recommended activity levels and identify patients who needed immediate attention. They felt this would help reduce their workload, allowing them to check patients remotely and follow up with patients who needed further and immediate attention. With this aim in mind, the CR team had previously purchased a number of Fitbits. However, the Fitbits had not actually been used with patients. This was largely because no structured system was in place to support their use. Therefore, the initial focus of our data system was on developing a structured approach to collecting objective data related to patients’ physical activity between CR classes. Given their availability and familiarity to the CR team, and thus feasibility, we decided to use the available Fitbits. While more feature rich tracking devices are available (including research prototypes) Fitbit met clinicians’ core requirement for HR and activity data. From a patients’ perspective it was also important that Fitbits are a widely used consumer device. We felt they would more easily integrate with daily life, help to avoid over-medicalization of tracking and support patients’ preference for normalcy.

4.3.2 Meaningful Data.

In the first focus group the use of Fitbits was discussed with the full CR team. Each clinician shared their data collection needs, past experiences, and their role within the overall structure of the CR program. Physiotherapists directly involved in supervised exercise classes again expressed the need for more information about patients’ activity and rehabilitation progress between classes. In contrast, clinical specialists were mainly concerned with ensuring their educational talks were tailored to the audience. They wanted information about the mindset of patients, their medical condition, and their level of knowledge on the topic covered by their talk. For example, the pharmacist was interested in patients’ medical history, their attitude towards medication, whether they know how and when to take their medication, and who would they approach if they had a question regarding medication. While some of this information was available in their existing patient record system, the system did not provide information on patients’ mindset and understanding of their medications. The focus group led to an extension of the data gathering to include both Fitbit data and weekly patient questionnaires focused on their mindset and knowledge.

4.3.3 Actionable Data.

Subsequent meetings and prototyping focused on actionable data. Prototypes took the form of lo-fidelity paper-based sketches and computer mock-ups, which were shared in a second focus group and discussed on an on-going basis in small group meetings and via email. Several key decisions were taken in relation to Fitbit usage. Fitbits would be given to patients and set it up for them on the first day of the CR program. A clinician dashboard would allow clinicians to see HR, steps, calories, time spent performing intense activity, and sleep. It would provide an overview of all patients’ data on a single screen and then allow them to see hourly data ranging up to the last 7 days for individual patients.

Important decisions were also taken regarding the weekly questionnaires and several trade-offs were agreed. The first weekly questionnaire would collect general information focusing on patients’ confidence and concerns about engaging in physical activity, and their current health status. Clinicians emphasized that patients already receive many emails and questionnaires from the hospital after their surgery and that asking them too many questions could overwhelm them and affect engagement. A maximum of 12 questions per week was agreed. The format of questions was also considered. Clinicians emphasized the need for information which was structured and could be quickly interpreted. They emphasized the need to easily monitor changes in patients’ data through baseline and follow-up questions. Ultimately it was agreed that weekly questionnaires would be based on the language and content of standardised quality of life (QoL) questionnaires, including the SF-12 [66], MacNew health-related QoL [31], the HeartQoL questionnaire [46], and the Dartmouth quality of life index [42]. These standard questionnaires assess the impact of health on people's everyday life. While they had not previously been used as part of the CR program, they are widely used as QoL measures in medical studies. On the recommendation of the design team patients were given an option to elaborate their answers through open responses. It was felt that open responses would give patients greater scope to share emotional and practical concerns that lay outside the scope of standardized questionnaires.

Some questions were asked every week, with others included once or twice during the program. For example, questions on patients’ level of motivation and how informed they felt were asked every week. Questions relating to the educational talk were asked the week before the talk, with follow up questions asked during the week and after the talk is given. Questions relating to emotions, mental health, and social issues were are not included during the first few weeks as clinicians felt they were not a priority at this point. They felt such data was more useful during the later weeks of the program corresponding to the occupational therapist's educational talk (week 5). Questions that repeated every week were scaled questions ranging from 1 to 10 to allow easy tracking of weekly changes.

4.3.4 The Data System.

The final data system was implemented using off-the-shelf wearable technology (a Fitbit) and commonly used web design patterns. It included of a web application for patients, with functionality to answer weekly questionnaires and store their responses (Figure 2). Patients were provided with a Fitbit and the standard Fitbit app was installed on their phone. Alerts through SMS and email were sent to patients at the end of each week with a reminder to fill out the weekly questionnaire. Patients were given guidance on exercise and safe zones for their HR in rehabilitations classes and encouraged to monitor these zones between classes. Patient's Fitbit data was consolidated and presented as a dashboard on a clinician facing web application. Weekly questionnaire responses were consolidated in Excel spreadsheets and shared through emails. This decision was based on discussions where clinicians said email and Microsoft Excel were part of their weekly workflow.

Figure 2:

The system was designed and implemented in such a way that it could be adjusted over the course of the field study. In many respects our approach mirrored the Wizard-of-Oz technique used in HCI studies. Traditional lab-based Wizard-of-Oz studies enable the observation of a user interacting with an interface without knowing that the responses are being generated by a human rather than a computer [68]. This allows researchers to gain an early understanding of user experience and uncover limitations in the technology prior to developing a fully operational system. We applied this approach in our study. Core functionality of the system, including the weekly reminders sent to the patients, presentation of patient's weekly questionnaire responses, data extraction and sharing of patient's Fitbit data was handled manually by the first author. However, it was done in a way that replicated an automated system and from the patient and clinician perspective it was an automated system.

Not implementing a fully automated system allowed flexibility for the system to be easily modified and iterated throughout the CR programs. The aim was to investigate how patients and clinicians used the system and make ad-hoc changes based on their experiences. In practice, physiotherapy classes and educational talks were usually conducted on Tuesdays and Thursdays of each week. The first author, as the human working behind-the-scenes, extracted the data from patients’ Fitbit devices and weekly questionnaire responses and shared them in a consolidated format to the CR clinicians on Mondays.

4.3.5 Allowing Patients to Share and See Other Patients’ Data.

As noted in the Related Work section, prior research, and our own work with patients going through CR, suggests that shared experiences can be very helpful during the rehabilitation process. During the co-design process the possibility of allowing patients within CR programs to share their own data and view the data of other patients was openly discussed. The first author described the results of prior work and the potential benefits of shared experiences in helping patients to both normalize their own experience and learn from the experience of others. Clinicians expressed a strong opinion that it could be harmful to share Fitbit data between patients and felt it could have a negative impact. They explained that exercises in CR classes are tailored to each patient and progress of each patient could be misinterpreted if shared without detailed explanation. Given this concern it was agreed that patient data sharing should not be included in this study. Instead, we used the post field study interviews to develop a more detailed understanding of patients’ attitudes towards sharing data with their peers. While prior research suggests that clinicians’ concerns around peer-sharing can be overstated [33], we believe this was a balanced overall approach, respecting the concerns of clinicians, but also giving us the opportunity to further explore patient attitudes towards peer data sharing.

5 Field Study

In the field study the data system was used as part of a Phase 3 CR program. The overall structure of CR programs and data collection are outlined in Figure 3. The field study involved direct observations of the CR program, together with feedback from CR team and semi-structured interviews with patients. Research ethics approval was granted by the Beacon Hospital Research Ethics Committee (BHREC Ref: BEA0159) and the University College Dublin Research Ethics Committee (REERN: LS-E-21-145-Tadas-Coyle). The study ran from Aug-Dec 2021 at the Beacon Hospital Dublin.

Figure 3:

5.1 Recruitment and Participants

During the field study the group size for CR classes was limited to 4 people exercising together in-class due to COVID-19 restrictions. Some programs had patients who opted for online attendance in classes via Zoom video calls. With the help of the chief physiotherapist, we recruited participants through purposive sampling. All patients attending CR programs during the field study were potentially eligible to participate. The final decision on whether patients were invited to participate was based on the judgement of the chief physiotherapist, giving consideration to whether participation could be detrimental to their rehabilitation. Ultimately all patients were invited to participate. The study ran across 4 separate CR programs. A total of 16 patients participated in the study, with a mean age of 59.25 years, and including 13 males (Table 1).

Table 1.

Participants in rehabilitation program 1-4
	Participant	Gender	Age	Attendance
Program 1	P1	M	70	Online
	P2	M	54	Online
	P3	M	57	Online
	P4	M	56	In class
	P5	M	72	In class
Program 2	P6	M	67	In class
	P7	M	58	In class
	P8	M	66	In class
Program 3	P9	F	65	Online
	P10	F	71	In class
	P11	F	59	In class
	P12	M	62	In class
Program 4	P13	M	52	In class
	P14	M	36	In class
	P15	M	62	In class
	P16	M	41	In class

Table 1. The field study involved four separate iterations of the CR program, with different patients in each program.

5.2 Data Collection

After consent was received, patients completed a pre-study questionnaire to collect demographic information, current smartphone and smartwatch usage, a reflection on their current health condition, including physical and emotional health, and finally their attitudes towards sharing Fitbit and weekly experience data with their peers and CR clinicians. Thereafter, a Fitbit Charge 2 and web App was set up for each participant. The Fitbit was used to collect their physical activity and HR data. The web App was used to collect weekly CR experience data through questionnaires. The standard Fitbit app was also installed on patients’ phones.

It is important to note that the Fitbit and questionnaire data was shared only with the CR team. The authors did not have ethics permission to analyse this data or directly share it with other patients in the CR program. This data was thus not subject to analysis by the research team.

5.3 Ethnographic Observations

The field study also involved ethnographic observations of the CR programs. The first author attended CR programs and observed in-person and online classes. Observations were collected by taking notes, which were guided by a checklist (Supplement file). The aim was to understand the actual usage of collected data during classes. The author did not interfere in any proceedings of the classes during these observations. Feedback was also collected from the CR team in debriefing meetings after each CR program.

5.4 Semi-structured Interviews

After each of the four CR programs, semi-structured interviews were conducted with patients. All the patients from the field study participated in the interviews. They were asked about their experience of wearing the Fitbit and answering weekly questionnaires for the entire duration of their CR program and the impact it had on them, including reflection on their physical and emotional health condition. The full interview guide is included in supplemental files. The interview also included questions to understand their attitudes towards a future patient-to-patient (peer) experience sharing application and their willingness to share their experiences with peers via such an app. While patients were not shown actual peer data, they were given illustrative examples of such data during the interviews.

Audio recordings of the semi-structure interviews were transcribed verbatim. The initial coding framework was jointly agreed by the first and third author and was derived from prior literature and our prior work with patients [62,63]. Initial codes included “normalizing new experiences”, “personalized insights”, “shared experiences”, “safe zone for physical exercise”, “emotional support”, and “focusing on capabilities”. A combination of inductive and deductive thematic analysis was applied to this data [11]. This analysis was led by the first author who reviewed and coded all transcripts. Regular meetings were held with the third author, who also reviewed and directly coded a smaller portion of the transcripts. New codes were discussed and refined based on joint agreement to create a final set of codes, which were applied to all transcripts. Following this the data was iterated to produce high-level themes. Again this work was conducted jointly by the first and third author. Finally these themes were abstracted out to three main themes presented in this paper.

6 Findings

Our findings are reported in two sections. The first reports the results of ethnographic observations and debriefing sessions with the CR team. This includes examples of problems encountered and changes made to the data system over the course of the field study. The second section reports the thematic analysis of patient interviews.

6.1 Findings From Ethnographic Observations and Debriefing Meetings

6.1.1 Initial Configuration and Subsequent Use.

During the first class of the CR program, physiotherapists conducted a step test with patients to assess their capacity to perform exercises. They tracked patient's real-time HR using a medical chest band and watch while the patients stepped up and down a step-up board. Patients wore both the watch connected to the chest band and the Fitbit during the class. Between classes they just wore the Fitbit. The classes started with warm up exercises followed by cardio and strength building exercises and ended with cool down exercises and stretches. Each exercise was guided and monitored by the physiotherapist and patient's HR reading was noted after each exercise. This HR record determined if the intensity of their exercise was within safe limits.

It was observed that sometimes patients used the Fitbit HR measurement in class when their chest band failed to work. The were also many occassions when physiotherapists noted the HR measurement from patient's Fitbit device. This initiated conversations between patients and physiotherapists about how to measure other activities on Fitbit and any usage difficulties faced with the device.

Based on the decisions made during the co-design stage, the clinician facing website showed hourly patient HR data. After the deployment of the system in the first CR program, clinicians felt hourly data was unnecessary and the dashboard was changed to show daily HR data. Clinicians' interaction and engagement with patient data changed across to the phases of CR. For example, physiotherapists reported that consultations are more frequent for patients initially and that it is essential to monitor patient's vitals frequently. As patients progress through the CR program, frequent monitoring and consultations were no longer necessary. During the 6 weeks of the CR program, a pattern was observed in the use of Fitbit and questionnaire data. During the first week, clinicians were interested in patient's baseline physical test. Thereafter, trends in their patient's HR and activity levels across a timeframe are considered more valuable. The Fitbit data gathered by the system was thus shown across a 7-day timeframe and monitored once or twice a week. Weekly questionnaire responses were viewed at a day before the next class.

6.1.2 Data Supported Preparation and Greater Sharing.

Data collected through the weekly questionnaire responses gave clinicians an understanding of the needs and attitudes of their patients, what limited their patients’ physical activity, why their recommended HR levels were not achieved outside of class, and their motivation levels to maintain physical activity outside of class. Physiotherapists stated that “Tracking the experiences each week was useful, it gave an understanding of their mindset before the class next week.” At the beginning of each class, before starting exercises, physiotherapists checked-in with their patients and casually asked questions about their health. The responses from the questionnaire helped them personalize and tailor their questions during these check-ins. Based on these check-ins they made changes to the exercises if necessary. Although clincians had access to patient's past 7-day Fitbit data during classes, they did not collaboratively look at the data during classes. Instead, if they felt it necessary, they indirectly asked questions related to patient's weekly activity during the check-ins. For the other CR clinicians, questionnaire responses before their talk gave them an understanding of the patient's current knowledge and their mindset. With this information, they tailored their talk if necessary. The questionnaire responses post-talk provided them with feedback to consider for talks in future sessions.

Interestingly, it was observed many times that patient's shared problems through the questionnaire that they did not share in class. Patients did not openly discuss their health condition or emotions. Thus, the questionnaire became a medium to share issues that patients would perhaps hesitate to share in the face-to-face classes. A physiotherapist highlighted an example of a participant using the questionnaire to share feelings they did not share during the class. In the questionnaire that patients had stated: “Feeling a little downhearted as I have to attend for blood tests to monitor the INR in my blood quite often.”

It was also observed that patients did not have any conversations with other patients about the questions in the weekly questionnaire or their responses. They mainly chatted about topics related to Fitbit usage (e.g. HR readings and its other features), casual happenings in their life, and other news topics. Furthermore, conversations were generally observed less frequently between patients then between patients and clinicians. The chief physiotherapist commented from her past observations that the amount conversations and social interactions during the classes often varied and tended to depend on the composition of the group.

6.1.3 Supporting Clinicians’ Workflow.

The Wizard-of-Oz technique allowed us to experiment with data presentation. During the initial weeks the web application provided patient's objective data to clinicians via a dashboard, but many clinicians did not log into it. Discussions with the physiotherapists after the first and second CR program revealed that some clinicians felt they had to be in front of a computer to access the dashboard. Although the website was made responsive to access on smaller screens, they preferred viewing it on a computer. Due to the nature of their work, most clinicians, especially the physiotherapists, spent limited time in front of a computer. Similarly, during the first CR program, weekly questionnaire responses were shared through an Excel file attachment and responses that felt important were included separately as points in the email. Although clinicians reported that they could easily make sense of the subjective data as it was based on a structured questionnaire, they found the presentation of the response data in an Excel format overwhelming and needed a better way to glace the data. To address both these issues, the data extraction and presentation process was iterated to include this feedback and was applied in the next CR programs. This involved the first author summarizing and highlighting important information from the weekly responses before sharing with the clinicians (Figure 4). To address the issue of clinicians not using the dashboard to view Fitbit data, screenshots of Fitbit data were shared through email along with the website link (Figure 4). This proved more effective as clinicians got an overview of the data on a single screen and in their email, which was part of their existing workflow.

Figure 4:

6.2 Themes from the Semi-structured Interview

This section presents the themes identified in the semi-structured interviews with patients following the CR program. They are: a connection between in-class and between-class activity; engagement with self-tracking and the transition to self-care; and attitudes towards data sharing.

6.2.1 A Connection Between In-class and Between Class Activity.

For patients wearing a Fitbit provided a connection between the supervised activities in rehabilitations classes and their experiences outside class. Nudging features of Fitbit like vibration if sitting down too long and reminders to be physically active was related to the experience of being monitored in during classes. Fitbit gave them a sense of motivation and confidence to keep themselves active outside of the class even on low days and helped them to see if they were achieving a target HR zone for exercise.

“It vibrates to let me know that you are sitting down too long and to walk about. I like that because, well, what happens is when you go to the CR class, you will do what you're told to do because you're there. But when you're at home, when you are alone, you would say, I'm not going to do it, there's no one pushing. So you think to yourself, I won't do it, I'll do it tomorrow. But when this thing goes off, it's like telling me and then I am like, yeah right I will get up, I will get up and walk around. So I think it's a good thing. I really do, you know.” (P7)

“I found it very helpful, found it motivational. Once I got this one up and running I looked at it many times a day, mostly in the evenings and just got into the Fitbit app on the phone…there were some days I would have no active minutes and there'd be other days I might be busy…I need to get these active minutes going. So whether that was a walk or whether it was a swim or whether it was the help of the two classes a week were very good for getting into that sort of routine to get your heart rate up.” (P2)

For participants using a smartwatch for the first time, wearing a Fitbit during the CR program changed their attitude towards smartwatches. It had a positive impact on their motivation to be active. For example, “Well I'd never had a Fitbit before and it was a first for me but I was able to manage it no problem and the information was very useful in the sense that you know, for heart rate. I didn't freak out when I went over a hundred [laugh] because I knew it was in my safe zone.” (P1) Online participants reported that Fitbit was like a companion during the program.

One of the main uses of Fitbit was to match the HR achieved in class outside of class. It enabled them to stay in a safe HR zone. “That particular system that you had with the monitoring with the Fitbit, helped alleviate all my worries, you know, I'm not worried any more about doing some activity.” (P1). Participants observed the HR and calories they were achieving while performing exercises during the class and used that as a reference while performing activities on their own outside of the classes. Physiotherapist explained the importance of purposeful exercise within a HR zone, corresponding to moderate exercise and guided patients individually on their target HR zones.

“We were told the maximum figures that we were supposed to be at, and then the target figures we were supposed to be at, and then the resting figures. And then depending upon the exercise I was doing, I found that when I was exercising at the start of it, I was at my maximum, but then that started reducing down. Likewise, the exercise heart-rate was reducing, and the resting heart rate was reduced so you can actually see the trend, yeah.” (P4)

Answering weekly questions was found useful as a way of revising the educational and physical guidance received in class. One participant stated: “Well it just made you reflect on what you were doing. As I say most of mine, I focused the time actually trying to get through the actual physio but the questionnaires just made you look back and how you're getting on with it, how you're enjoying it, all that, you know how you're feeling.” (P3).

6.2.2 Engagement with Self-tracking and the Transition to Self-care.

Most patients engaged with their Fitbit data daily. The most popular Fitbit features were the step count and HR monitoring, as almost all patients chose walking as their daily exercise. They also liked to monitor their sleep. Fitbit App visualizations that represented their progress and trends were valued. Some reported having explored features on the device which they might not have considered without the classes and educational talks. For example, several patients started using the guided breathing feature of Fitbit after they attended the educational talk by the occupational therapist.

“After the education talk by the occupational therapist, I explored the breath feature. When you tap it shows you how to breathe in. I use that a lot and now it probably is not giving me any benefit because I've just been watching it but it's helped me breathe properly, if you know what I'm saying. Yeah, I am actually using that as well. I showed my daughter as well on how to use the breathing feature. Oh yeah. It's been a feature on it which is good. I think it's good.” (P7)

When asked about their attitude towards using Fitbit most patients said that they might not have used it had it been introduced at the end of the program. This shows the timing of introducing Fitbit was important. Introducing a Fitbit at the beginning of the CR program enabled patients to engage with it during the program and made them more confident about using it after the program as part of self-care.

“If I had been given after I probably wouldn't realise what it was doing, you know, and because in fact it was given to me at the start of it and made me realize, OK, how does this work and what do I do now? And then when I realised I could do so much with it, I was like oh brilliant. If I was given after the class I might have felt I actually do not need it. When you actually realise now what it's actually doing, you think, oh, well, hold on a minute, this is a good thing to have. OK, so I actually think yeah, yeah. Give it up to somebody after could be a problem. I think when you when you get them using it it's great.” (P7)

When asked about other information they would like to see on the web app, many participants suggested including the information received during the educational talks and resource recommendations by clinicians. For example, some participants wanted more information and resources related to relaxation and improving mental health: “We were given handouts after the educational talks, but I would like to have access to more relaxation videos recommendations” (P11)

6.2.3 Attitudes towards Data Sharing.

Somewhat surprisingly, participants expressed little reluctance towards sharing their own Fitbit and questionnaire data with others in their class. However, there were interesting and important differences in their attitudes to seeing other patients’ data. When asked if they would like to see other patients’ Fitbit data, many expressed hesitancies. They felt CR was a personal journey and seeing others’ data could potentially have a negative impact on them. Largely this was because they felt that some patients in their class did not represent their condition, so they did not think it useful to compare with them.

“A guy could be doing half the active minutes that I do and half the steps, he could be doing a much better job than I am with the heart that he has and the stage that he's at you know so it's trying to – the information that is being provided it would have to be very well explained that everybody is different. You know don't look at other people's data and say I have to mimic that. If that fella's doing 30,000 steps a day and doing 300 active minutes, you know that's completely, you know, alien to most people.” (P2)

Patients were more interested in seeing other patients’ questionnaire responses, to learn from their experiences, especially about changes in diet, relaxation, and lifestyle. However, they wanted to be able to see such information a week or more after they had responded and not immediately. This was because they did not want to be overly influenced by others’ experiences. They would prefer to try out things on their own first and then look at what others are doing.

“I do not want to get influenced. Do the survey, answer the questions, and then let it be seen. I can see them all afterwards, not during. The next week you see the response of the previous week.” (P5)

When asked about staying connected and sharing experiences with others in the class many were interested in a more passive digital way of sharing experiences, rather than a real time or active social messaging app like WhatsApp. Moreover, they preferred face-to-face interaction to share and connect with each other. Online participants suggested opening the zoom call 15-20 minutes before the start of classes for social interaction.

“Certainly, a WhatsApp like App can be intrusive and it ends up being a conversation if you know what I mean. It can be over and back and over and back whereas reading all their experiences on the screen later would be more formal and I can read it at any time.” (P23)

7 Discussion

The field study allowed us to investigate the use of patient-generated data as part of a CR program, which itself is designed to support the transition from hospitalization to self-management. In the Related Work section, we noted the recommendations by Andersen et al. [3] that systems should (1) make sense to both patients and clinicians, (2) be actionable to both clinicians and patients; and (3) be feasible within the organizational and social context. Here we reflect on whether our approach supported these recommendations. We also return to our key research questions: How did clinicians and patients use the data in and between rehabilitation classes? Is the combination of objective activity data and subjective experience data meaningful and actionable for clinicians? What were the patients’ attitudes towards self-tracking and sharing their data with both clinicians and peers in their CR program? And finally, is there evidence that our approach is feasible and aided the transition to self-management or self-care?

Throughout this section we highlight key recommendations for the design and use of patient generated data to support rehabilitation and the transition to self-care.

7.1 Use of Data by Patients and Clinicians

Table 3 summarises how activity and self-report data were used by patients and clinicians. It also captures the frequency at which each group engaged with data. Important to note here is a contrast in the use of the data. Clinicians viewed the data less frequently and took a more task orientated approach, with an emphasis on supporting pre-existing tasks in their workflow, e.g., checking compliance with exercise. This is perhaps unsurprising given the time constrains they face. In contrast patients viewed their Fitbit data more frequently and took a more exploratory approach, investigating the functionality of Fitbit app and asking questions in classes about different ways in which it might be used (e.g., exploring guided breathing exercises and Fitbit visualisations and goal setting).

Table 3:

	Fitbit data		Questionnaire responses
Patients		Interaction with data: Highly important Frequency: Daily Used tracking of activity and sleep Used to monitor HR during exercise – monitor safe zones Motivated them to stay active Guided breathing was found useful It initiated conversations in class	Interaction with data: Important Frequency: Once a week Encouraged reflection on the information they received during the classes Encouraged reflection of their current health Provided a medium to share physical and emotional issues with clinicians
Physiotherapists		Interaction with data: Important Frequency: 1-2 times a week Used to monitor if patients continued recommended activity levels outside class Used to monitor HR during exercise Used as a secondary device to check HR after exercises in class	Interaction with data: Important Frequency: Once a week Provided insight on patient's physical health and mindset Used to check compliance with the information received during classes Used to plan and tailor conversations with patients
Other CR Clinicians		Interaction with data: not important Frequency: 0 Not used	Interaction with data: before and after their talk Frequency: Twice a week Provided insight into patients’ knowledge and mindset before educational talks Used to check compliance with the information received during talks. Enabled tailoring of educational talks.

Table 3: Use of and engagement with data grouped by patients and clinicians.

Design recommendation:

•

Support both task oriented and exploratory use of patient generated data and the associated self-tracking systems.

7.2 Making Patient-Generated Data Meaningful and Actionable in a Clinical Context

Pantzar and Ruckenstein suggest that due to the automated and standardised collection, data from tracking devices can be perceived as essentially objective [48]. They argue that the meaning of tracking data is in fact deeply tied to the particular contexts in which it was collected. A study by Ada et al. on the use of Fitbits to support the treatment of patients with post-traumatic stress disorder supports this view, again arguing that context was crucial in supporting patient-clinicnan interactions around data [43]. Pantzar and Ruckenstein propose the concept of “situated objectivity”, which combines mechanical objectivity with the important role of context in knowledge formation.

Findings of this study suggest that, in the context of CR, experience data collected through weekly questionnaires played a vital role in supporting “situated objectivity” and in making data meaningful. As an example from our findings, Fitbit data might indicate low activity levels by a patient in a certain timeframe. However, there could be multiple reasons for this: the patient's ability to function could be impaired due to anxiety and stress; low motivation due to external factors; or it could reveal a fault with the Fitbit, or the patient simply not wearing the device. Collecting experience data along with physical activity data on a regular basis provided context to the objective data that was collected automatically using Fitbit. It allowed clinicians to get a better understanding of what limited their patients’ physical activity, why their recommended HR levels were not achieved outside of class, and their motivation levels to maintain physical activity outside of class.

Of course, to be clinically useful data must also be actionable and fit with a clinical workflow. The research in this paper was motived by studies such West et al. [69] that identified barriers to the use of patient-generated data in clinical setting. Alongside data lacking context, barriers included: incomplete data, insufficient time, unfamiliar structure, and misaligned objectives. They report that clinicians want patient-generated data to be interpretable ‘at a glance’ in a short amount of time. This is possible if the data is presented to them in a familiar representation [69]. In our study we addressed these needs in two ways: through structured self-report, and using summarisation. The structured approach to subjective data collection was grounded in clinicians’ workflow and based on standardised medical questionnaires. These questionnaires were adapted and shortened to reduce the demand on patients. The release of questionnaires was timed to concide with relevant points in the CR program and made use of repeat measures to allow clincinans to track progress.

Importantly, the inclusion of more flexibly or open approach to data collection also served a valuable purpose. In our case an open approach was supported through open questions around patients’ experiences. This gave patients greater flexibility to describe their experiences and provided a channel share experiences they might have been reluctant to discuss face-to-face. In future studies this open approach could be extended beyond simple open questions, considering for exampe a wider range of expressive media (e.g. audio, image, drawing or video). In exploring this more open approach it will however be important to recognise and balance the time demands and workflow constaints of clinicians. In our study patient data was filtered and summarised for clinicians. This was enabled through a Wizard of Oz approach, but could be automated in future systems. For example, it was possible to filter out the important information present it to the clinicians in the form of a summary or highlights. This made the captured experience data quicker to interpret and glanceable for the clinicians. After experiencing the system in this study, clinicians proposed for a future App that could show dashboard for Fitbit data along with the corresponding experience data highlights with an ability to select timeframe and an option to see more information of a specific experience data highlight if needed. There is also potential for making the highlighting process automated through natural language processing [16,30].

Design recommendations:

•

Collect both subjective experience data and objective tracking data. Subjective data can help to make the objective data more meaningful and supports ‘situated objectivity’.

•

Structured experience data, combined with filtering and summarisation, can help to make patient generated data actionable for clinicians.

•

An open approach to patient experience data is also helpful. It gives patients an additional channel to communicate issues they might be reluctant to discuss in-person with clinicians.

7.3 Designing for Peer Sharing

Our intial (and ultimately naïve) assumption was that patients would welcome the opportunity to learn from the data of other patients, but might be reluctant to share their own data. This was not the case. Instead participants were open to sharing their data, but they had reservations about seeing peer data. Key to this finding is that rehabilitation is highly personalized journey. Patients felt that viewing shared Fitbit data would only be useful if their peers were closely match in terms of physical fittness and medical condition. In this, patients were echoing the doubts expressed by clinicians during our initial co-design process. Clinicians felt strongly that sharing Fitbit data between patients could have a negative impact. They have the expertise to carefully tailor exercise programs to each patient and progress is understood in the context of personal capability. Without careful and potentially time-consuming explanation, shared patient data could be misinterpreted or could cause other people to become demotivated.

We now believe that level of detail is critical in peer sharing. Tracking technologies enable the sharing of detailed information. This is not what rehabilitation classes actually facilitate. Classes provide a space for participants to give and receive general social support and encouragement, based on a broadly common overall experience. Our ethnographic observations support this view. Patients did not have detailed conversations about their health or emotions. Nor did they have detailed conversations about their Fitbit data or the weekly self-report questionnaire. Aside from general conversations about Fitbit usage, patients mainly talked about casual happenings in their life and general news topics. Ultimately, from a peer support perspective, it appears that broad high-level support is beneficial, but detailed sharing is less desirable, or only becomes useful if it can be clearly contextualised or matched against individual cases. As described in the Related Work section, Hartzler & Pratt [29] investigated of the differences between information shared by patients and clinician. They found that while it is less comprehensive, information shared by patients is often more narrative, practical, and emotionally supportive, in comparison to the more formal information shared by clinicians. This was mirrored in the informal chats we observed during rehabilitation classes.

We do not want to dismiss the potential value of more detailed peer sharing, certainly not based on this one study. It is possible that our findings reflect the limitations of our system. Prior literature provides strong support for the value of peer sharing [45,67]. It is also interesting that patients were willing – and often enthusiastic – about sharing their own data. It is possible the act of sharing is valuable in and of itself, e.g., as an opportunity to validate their experience. Timing was also important in patients’ attitude to sharing data. While patients felt immediate sharing could have a negative impact, delayed sharing was viewed more positively. Following this study clinicians expressed an interest in understanding the impact of making their patients’ Fitbit data more visible during the CR class, so that patients can see each other's in-the-moment data, but in a structured and supervised manner. Such work could draw on HCI research that highlights visual alternatives to the numeric presentation of data. This has been found to emphasize multiple possible meanings of data, potentially reducing the emphasis with quantified achievements [35,59,65].

We also see an opportunity to build on research in recovery narratives. Recovery narratives are structured, first-person accounts of recovery [37]. They are rich in personal and contextual detail, and often make use of text and multimedia content (e.g., photos and audio recordings). The patient generated data in our study was not rich in this manner, focusing instead on Fitbit data and brief experience questionnaires. Recovery narratives have been explored in detail in mental health literature, with evidence for the benefits of both creating personal narratives and viewing other people's narratives [37,57]. They have also been explored in stroke rehabilitation [21]. We believe they are worthy of further research in the CR space, as they could provide a structured, narrative, and practical approach to peer sharing.

Design recommendations:

•

Carefully consider the level of detail and timing when designing peer sharing features.

•

Sharing detailed information may not be beneficial and should be approached with caution.

•

Delayed sharing, use of a narrative format, and a focus on lived experience may be beneficial, but again careful consideration is required.

7.4 Supporting the Transition from Clinical care to Self-care

The final research question in this paper asked if our approach to tracking and sharing data is feasible and aids the transition to self-management? Based on our findings we believe the approach is feasible. It made use of a widely available tracking device and patients responded positively. The approach also integrated well with the workflow of the CR program. Whether the approach aided to the transition to self-management is more difficult to address. Strong evidence would require a longer-term study, with direct measurements of improvements (or a reduction) in self-management. This was beyond the scope of our study, but the initial evidence is promising.

Pollack et al. [53] identified three important challenges for patients recovering from illness: (1) lack of support for health knowledge, (2) lack of opportunities to access resources, and (3) lack of opportunities to promote self-efficacy. Tadas et al. expanded on the need for health knowledge [63] highlighting CR patients initial need for general knowledge, shifting towards a need for detailed personalized knowledge as they transition to self-care. Traditional CR programs are well suited to supporting general health knowledge. Self-tracking data enhanced the potential for detailed personal insight. Sharing patient data with clinicians also enhanced patient-clinician communication and supported clinicians’ preparation. Self-report questionnaires provided channel for patients to share experiences they might be reluctant to discuss in-person.

A recent article on what it means to empower a patient identifies self-efficacy as one of the essential components of empowerment [20]. Greater self-efficacy is ultimately dependent on patients’ independent activity between classes. Our study suggests that self-efficacy was supported in several ways. Both the timing and context in which tracking was introduced were important. The Fitbit provided a connection between in-class and independent exercise, enabling patients to mimic the safe exercise zones identified in class and build their confidence. Even those who were new to self-tracking technology became confident by the end of the CR program and could continue achieving the recommended HR and activity levels the help of their smartwatch. Introducing the technology at the beginning of the program was important in this regard.

Design recommendations:

•

Use self-tracking and patient generated data to provide a connection between clinical and non-clinical settings. This can help to bridge the gap between supervised and self-managed care.

•

Self-tracking technology should be introduced at an early point in the rehabilitation process.

8 Limitations

As mentioned above, patients were not directly involved in the co-design process of the data system. Although a member of the research team acted to represent patient views throughout the design process, based on our own direct experience of working with patients, we acknowledge the possibility of bias towards clinicians’ priorities in the design. We accept the design of the system might have been different if patients were directly actively involved in the co-design process described in the paper.

We further acknowledge that, as the study took place in a single hospital in Europe, all our participants were of a western European background. All participants were classified as middle-income. We acknowledge that the future work should look at participants with more diverse backgrounds. However, we also note that the participant demographics reflect the real-world situation in the Beacon Hospital Dublin.

Finally, our findings were over representative of patients who took the CR program in-person compared to online. We recommend that future work balances this representation to get a richer perspective and possibly compare perspectives of remote and in-person rehabilitation.

9 Conclusion

This paper contributes to our understanding of the use of patient-generated data in a clinical context, derived from co-design research and a field study conducted as part of a CR program. Our findings suggest that subjective data can help to make objective data more meaningful. A structured approach to collecting subjective data helped to make data actionable, providing information clinicians could quickly understand and act on, e.g., through conversations with patients and tailoring educational talks. Findings suggest the approach is feasibly and supported clinicians’ workflow. Modifications to the clinician dashboard, increased use of email and data summaries were also important in supporting this workflow. The use of a commercial activity tracker was well received by patients. It provided a connection between in-class and independent activity. This helped patients to stay motivated, understand their cardiac health, and build confidence in their ability to exercise safely. Both activity data and self-report subjective data further bridged the divide between clinical and independent use by helping clinicians to prepare for classes, gain an insight into patients’ mindset, and by supporting in-class conversations.

Acknowledgments

This research is part of the Eastern Corridor Medical Engineering project, which has been funded by the European Union's INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB). Supported was also provided by Science Foundation Ireland through the Insight Centre for Data Analytics (12/RC/2289_P2).

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References

[1]

Rikke Aarhus and Stinne Aalokke Ballegaard. 2010. Negotiating Boundaries: Managing Disease at Home. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), Association for Computing Machinery, New York, NY, USA, 1223–1232.

Abstract

1 Introduction

2 Related Work

2.1 Supporting the Transition from Hospitalization to Self-management

2.2 Self-tracking and the Integration of Patient-generated Data into Clinical Practice

2.3 Peer Sharing and Shared Experiences

3 Overview of Methodology and Research Questions

4 Co-design of the Data Gathering and Sharing System

4.1 Participants

4.2 Methods

4.3 Key Design Decisions

4.3.1 Organizational Context.

4.3.2 Meaningful Data.

4.3.3 Actionable Data.

4.3.4 The Data System.

4.3.5 Allowing Patients to Share and See Other Patients’ Data.

5 Field Study

5.1 Recruitment and Participants

5.2 Data Collection

5.3 Ethnographic Observations

5.4 Semi-structured Interviews

6 Findings

6.1 Findings From Ethnographic Observations and Debriefing Meetings

6.1.1 Initial Configuration and Subsequent Use.

6.1.2 Data Supported Preparation and Greater Sharing.

6.1.3 Supporting Clinicians’ Workflow.

6.2 Themes from the Semi-structured Interview

6.2.1 A Connection Between In-class and Between Class Activity.

6.2.2 Engagement with Self-tracking and the Transition to Self-care.

6.2.3 Attitudes towards Data Sharing.

7 Discussion

7.1 Use of Data by Patients and Clinicians

7.2 Making Patient-Generated Data Meaningful and Actionable in a Clinical Context

7.3 Designing for Peer Sharing

7.4 Supporting the Transition from Clinical care to Self-care

8 Limitations

9 Conclusion

Acknowledgments

Supplementary Material

References

Cited By

Index Terms

Recommendations

Changing Categorical Work in Healthcare: the Use of Patient-Generated Health Data in Cancer Rehabilitation

Patient-oriented support roles of self-management systems

Overview of Transition Care Clinics and Patient No-Shows

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